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href="/sponsored-content/scaling-up-mab-production-with-a-platform-medium-and-feed-system">Scaling Up mAb Production with a Platform Medium and Feed System</a><a class="TwoColumnHighlight-ItemTitle TwoColumnHighlight-ItemTitle_displayOption_mobile TwoColumnHighlight-ItemTitle_itemType_primary" data-testid="column-item-mobile-title" data-discover="true" href="/sponsored-content/scaling-up-mab-production-with-a-platform-medium-and-feed-system">Scaling Up mAb Production with a Platform Medium and Feed System</a></div><p data-testid="column-item-summary" class="TwoColumnHighlight-ItemSummary TwoColumnHighlight-ItemSummary_itemType_primary">As the monoclonal-antibody (mAb) market continues to grow, it is becoming increasingly important for pharmaceutical and biotech companies to accelerate process development and establish a scalable manufacturing process capable of achieving high titers and consistent product quality. Central to this is a cell culture media and feed system that can provide the required nutrients for optimal cell growth, viability, and function, as well as facilitate a seamless transition from development to clinical and commercial manufacturing. However, choosing an appropriate system can be a complex and time-consuming process, often requiring the evaluation of multiple medium and feed options. Platform systems, consisting of a medium and complementary feed developed for a specific cell line and application, have emerged as a valuable option for developers to help simplify selection and increase scale-up efficiency. 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This special report discusses how accurate volume measurement affects patient safety, product quality, and regulatory compliance. Sartorius emphasizes the roles that infusion pumps, laboratory pipettes, and quality control (QC) measures play in ensuring reliable and reproducible results. Register or Login and hit Download Now to get the full Special Report. </p></div></div><div class="TwoColumnHighlight-ColumnButtonWrapper"><a data-component="button" class="Button Button_size_medium Button_variant_textLink TwoColumnHighlight-ColumnButton" rel="" data-discover="true" href="/publications/special-reports/special-reports" target="_self"><span class="Button-ContentWrapper">See all</span><span data-component="icon" data-name="ChevronRight" class="Button-Icon Button-Icon_position_right" style="mask-image:url(/build/_assets/ChevronRight-W5LPP5NG.svg);-webkit-mask-image:url(/build/_assets/ChevronRight-W5LPP5NG.svg);mask-repeat:no-repeat;-webkit-mask-repeat:no-repeat;-webkit-mask-position:center;-webkit-mask-size:contain"></span></a></div></div><div class="TwoColumnHighlight-Column" data-testid="two-column-highlight-column"><p class="TwoColumnHighlight-ColumnTitle" data-testid="column-title">Ask the Expert Webcasts</p><div class="TwoColumnHighlight-ColumnItem TwoColumnHighlight-ColumnItem_itemType_primary" data-testid="two-column-highlight-column-item"><div class="TwoColumnHighlight-ImageContainer"><div class="TwoColumnHighlight-ImageAspectWrapper"><a data-discover="true" href="/sponsored-content/navigating-biopharma-commercialization-the-interplay-of-regulatory-affairs-process-development-and-manufacturing-to-ensure-drug-quality"><img data-component="image" class="TwoColumnHighlight-Image" data-src="https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltcc8e38b7538dbf25/6740f228fbc6d82586601064/11-21-2024-Lonza-ATE-slide1.png?width=700&auto=webp&quality=80&disable=upscale" src="https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltcc8e38b7538dbf25/6740f228fbc6d82586601064/11-21-2024-Lonza-ATE-slide1.png?width=700&auto=webp&quality=80&disable=upscale" loading="lazy" alt="Lonza Ask the Expert webcast" title="Lonza Ask the Expert webcast"/></a></div><span data-component="keyword" class="Keyword Keyword_variant_standard Keyword_title_sponsoredContent Keyword_disabled TwoColumnHighlight-PrimaryItemCategory">Sponsored Content</span><a class="TwoColumnHighlight-IconWrapper" title="Link to all webinar" aria-label="Redirect to All page" data-testid="content-preview-icon-link" data-discover="true" href="/sponsored-content/navigating-biopharma-commercialization-the-interplay-of-regulatory-affairs-process-development-and-manufacturing-to-ensure-drug-quality"><span data-component="icon" data-name="ChalkBoard" class="TwoColumnHighlight-Icon" style="mask-image:url(/build/_assets/ChalkBoard-7VYJPH3F.svg);-webkit-mask-image:url(/build/_assets/ChalkBoard-7VYJPH3F.svg);mask-repeat:no-repeat;-webkit-mask-repeat:no-repeat;-webkit-mask-position:center;-webkit-mask-size:contain"></span></a></div><div class="TwoColumnHighlight-SupplementalItemTitleWrapper"><a class="TwoColumnHighlight-ItemTitle TwoColumnHighlight-ItemTitle_displayOption_default TwoColumnHighlight-ItemTitle_itemType_primary" data-testid="column-item-default-title" data-discover="true" href="/sponsored-content/navigating-biopharma-commercialization-the-interplay-of-regulatory-affairs-process-development-and-manufacturing-to-ensure-drug-quality">Navigating Biopharma Commercialization: The Interplay of Regulatory Affairs, Process Development, and Manufacturing to Ensure Drug Quality</a><a class="TwoColumnHighlight-ItemTitle TwoColumnHighlight-ItemTitle_displayOption_mobile TwoColumnHighlight-ItemTitle_itemType_primary" data-testid="column-item-mobile-title" data-discover="true" href="/sponsored-content/navigating-biopharma-commercialization-the-interplay-of-regulatory-affairs-process-development-and-manufacturing-to-ensure-drug-quality">Navigating Biopharma Commercialization: The Interplay of Regulatory Affairs, Process Development, and Manufacturing to Ensure Drug Quality</a></div><p data-testid="column-item-summary" class="TwoColumnHighlight-ItemSummary TwoColumnHighlight-ItemSummary_itemType_primary">This webcast features: Yves Scheurer, manager, regulatory affairs, Lonza & Robin Schumacher, late-stage expert, Lonza. In this webinar, join Lonza to explore the essential alignment between regulatory affairs, process development, and manufacturing in biopharmaceutical commercialization. Their expert speakers will discuss how integrating these functions ensures drug quality, safety, efficacy, and enables navigation of the complex regulatory landscape. This webinar draws attention to the commercialization of microbial bioprocesses, though the playbook that is presented can also be applied to other technologies in Lonza’s portfolio. First, gain insight into regulatory guidelines, with a focus on chemistry, manufacturing, and controls (CMC) documentation. Then, learn how effective regulatory affairs support can expedite global compliance and accelerate the commercial filing process. Finally, discover how regulatory guidelines shape manufacturing processes, with practical insights on applying quality ris...</p></div><div class="TwoColumnHighlight-SupplementalItemsList"><div class="TwoColumnHighlight-ColumnItem TwoColumnHighlight-ColumnItem_itemType_supplemental" data-testid="two-column-highlight-column-item"><span data-component="keyword" class="Keyword Keyword_variant_alternate Keyword_title_sponsoredContent Keyword_disabled TwoColumnHighlight-SupplementalCategory">Sponsored Content</span><div class="TwoColumnHighlight-SupplementalItemTitleWrapper"><a class="TwoColumnHighlight-ItemTitle TwoColumnHighlight-ItemTitle_displayOption_default TwoColumnHighlight-ItemTitle_itemType_supplemental" data-testid="column-item-default-title" data-discover="true" href="/sponsored-content/linking-mab-product-quality-profile-changes-to-your-bioprocess-case-study-of-dynamic-glucose-level-control-in-cho-process-improves-product-quality">Linking mAb Product Quality Profile Changes to Your Bioprocess: Case Study of Dynamic Glucose Level Control in CHO Process Improves Product Quality</a><a class="TwoColumnHighlight-ItemTitle TwoColumnHighlight-ItemTitle_displayOption_mobile TwoColumnHighlight-ItemTitle_itemType_supplemental" data-testid="column-item-mobile-title" data-discover="true" href="/sponsored-content/linking-mab-product-quality-profile-changes-to-your-bioprocess-case-study-of-dynamic-glucose-level-control-in-cho-process-improves-product-quality">Linking mAb Product Quality Profile Changes to Your Bioprocess: Case Study of Dynamic Glucose Level Control in CHO Process Improves Product Quality</a></div><p data-testid="column-item-summary" class="TwoColumnHighlight-ItemSummary TwoColumnHighlight-ItemSummary_itemType_supplemental">This webcast features: Erin Redman, PhD, principal scientist, & Graziella Piras, PhD , senior director of strategic marketing, life science, 908 Devices. Changes made to the bioprocess can be linked to product quality attributes, but only if there are frequent data points to show the development throughout the bioprocess. Access to good product quality data at frequent timepoints from a full bioprocess can be a tall order for the analytical department. In this webinar, Erin Redman, PhD, principal scientist at 908 Devices, guides us through an easy solution to acquire mAb quality attribute profiling (charge variant profile, sequence identity, and glycosylation profile). Presented here is a simple dilute-and-shoot analysis using ZipChip capillary electrophoresis separation coupled to a high-resolution mass spectrometer. Then, Graziella Piras, PhD, senior director of strategic marketing, life science, describes a case study where a comparison of CHO fed-batch process runs with the usual daily bolus gluc...</p></div></div><div class="TwoColumnHighlight-ColumnButtonWrapper"><a data-component="button" class="Button Button_size_medium Button_variant_textLink TwoColumnHighlight-ColumnButton" rel="" data-discover="true" href="/webinars" target="_self"><span class="Button-ContentWrapper">See all</span><span data-component="icon" data-name="ChevronRight" class="Button-Icon Button-Icon_position_right" style="mask-image:url(/build/_assets/ChevronRight-W5LPP5NG.svg);-webkit-mask-image:url(/build/_assets/ChevronRight-W5LPP5NG.svg);mask-repeat:no-repeat;-webkit-mask-repeat:no-repeat;-webkit-mask-position:center;-webkit-mask-size:contain"></span></a></div></div></div><div data-module="highlight-contents" class="HighlightContents"><div class="HighlightContents-Wrapper"><p class="HighlightContents-Title" 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chief commercial officer says his firm and other Indian CDMOs are “a very viable alternative” if major Chinese manufacturers are removed from the US biopharma supply chain.","keyword":{"title":"Facilities \u0026 Capacity","url":"/bioprocess-insider/facilities-capacity"},"paidGating":{"paidContent":false,"paidProductTaxonomy":[]}},{"title":"How and why AstraZeneca’s oncology eyes turned to cell therapies","publishDate":"2024-10-18T12:24:51.000Z","url":"/therapeutic-class/how-and-why-astrazeneca-s-oncology-eyes-turned-to-cell-therapies","image":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt70e4887d0cc69ac6/6712540a51fffeda6299d6b4/Mark_CObbold_SVP_AZ_BWB_2024.JPG","alt":""},"summary":"Cell therapies will play an increasingly important position in AstraZeneca’s broad oncology pipeline, an SVP told delegates at Biotech Week Boston (BWB) last month.","keyword":{"title":"Therapeutic Class","url":"/bioprocess-insider/therapeutic-class"},"paidGating":{"paidContent":false,"paidProductTaxonomy":[]}}],"button":{"text":"Read More","url":"/program/bioprocess-international-biotech-week-boston-insider-daily"}}},{"type":"module_three_column_highlight","uid":"blt612199b15cc2d2fc","data":{"title":"Three Column Upstream Downstream Manufacturing","moduleLocation":"homepage","dateDisplay":"show","data":[{"contents":[{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt8c599472e2caa3c3/66b535dc9cb293d8dab78784/expression.png","alt":"BPI Theater panelists"},"articleName":"Improving Expression and Manufacturing of Novel Biologics","articleSummary":"","articleBody":[{"type":"figure","attrs":{"figcaption":"Moderator Millie Nelson with panelists Steve McCloskey (left), Larry Forman (center), and Nathan Lewis (right)"},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt04cbe588074bd77e/66be2f0d8737030c41b3c70b/22-7-8-Theater-Panel-Expression-P1.jpg","alt":"22-7-8-Theater-Panel-Expression-P1.jpg","title":null,"style":{"float":"center"}}},{"type":"text","text":"Moderator Millie Nelson with panelists Steve McCloskey (left), Larry Forman (center), and Nathan Lewis (right)"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Although expression titers have increased significantly since the 1990s, biopharmaceutical scientists still have much to learn about engineering cell lines for biologics manufacturing, especially as therapeutic proteins grow in diversity and complexity. At the BPI Theater during the 2024 BIO International Convention, "},{"type":"text","marks":[{"type":"italic"}],"text":"BioProcess Insider"},{"type":"text","text":" editor Millie Nelson moderated a "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.bioprocessintl.com/event-videos/improving-expression-and-manufacturing-of-novel-biologics","target":"_blank","rel":null,"class":null}}],"text":"panel discussion"},{"type":"text","text":" about strategies for improving cell-line development (CLD). Conversation focused especially on how advanced computational tools, including some based on artificial intelligence/machine learning (AI/ML), could enhance host-cell genome engineering and protein design. Joining Nelson were Larry Forman (chief executive officer (CEO) of cell-line engineering company CHO Plus), Nathan Lewis (professor of pediatrics and bioengineering at the University of California in San Diego, UCSD), and Steve McCloskey (CEO of Nanome)."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Engineering for Predictable Protein Expression"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Nelson asked first about designing cell lines for maximum yield and quality. Lewis noted that CLD scientists historically have focused on engineering cell-culture components and conditions to enhance protein expression. Thus, the industry still needs technologies for engineering cells themselves. Lewis’s laboratory at UCSD investigates methods for targeted genome engineering, especially for cell lines with complex phenotypes."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Another possibility, Forman explained, is to take a broad, whole-genome approach to cell-line engineering. That entails disruption of the Chinese hamster ovary (CHO) cell genome, random amplification of chromosomes, and selection of advantageous phenotypes. The approach might seem like applying “a sledgehammer,” but CHO Plus has had much success with it, and screening processes can provide nuance."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"McCloskey added that synthetic biology and AI/ML are raising opportunities to optimize protein sequences for manufacturing. In some cases, it makes sense to start with sequence optimization and then to “work your way back” to host-cell genome editing for additional adjustments to protein expression."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Such tools, he continued, can be applied to increase understanding of "},{"type":"text","marks":[{"type":"bold"}],"text":"posttranslational modifications"},{"type":"text","text":" (PTMs). Although the AlphaFold 3 model (Google DeepMind and Isomorphic Labs) has visualized hundreds of thousands of protein structures, biological factors still make PTMs difficult to anticipate. “We can’t reproduce the cellular environment"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"in silico right now,” McCloskey reported. But as industry feeds biomanufacturing data into ML models of increasing sophistication, scientists will begin to understand and control PTMs."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Lewis recalled that PTMs can have negative or positive effects on a given protein. Some changes can compromise product quality — e.g., by initiating proteolysis. The industry has addressed some such problems by engineering them out of host-cell genomes. But some PTMs provide proteins with features that are necessary for their biological activity. “We really don’t know much about what controls the addition of such PTMs,” Lewis explained. “There is no amino-acid sequence that you can just encode; there’s something more inherent.” Some of that adjustment comes from host cells, which will express specific machinery to add or remove given modifications."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Researchers are making headway in understanding necessary PTMs. Lewis’s group has investigated protein codes for glycan structures and has developed a “template” to help with encoding glycosylation patterns. Echoing McCloskey, Lewis said that generating more data and enhancing algorithms will enable researchers to modulate PTMs."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Forman noted that although scientists’ understanding of PTMs must improve, the industry knows enough about protein expression to forge ahead with important CLD work. He noted that researchers could investigate the influences of cellular Golgi bodies on protein quality. Scientists already know how to engineer cells to modulate Golgi number and quality, so that could be a fruitful entry point for research into PTMs. Other changes — e.g., to intracellular pH and oxidation–reduction (redox) reactions — can be engineered into cells to influence protein assembly. “Although we don’t understand [PTMs] perfectly yet . . . the cells know what they are doing, and we are guiding the cells,” Forman said."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"When asked whether cell lines could be designed for complete predictability, the panelists agreed that some randomness is inherent to working with living cells. Forman compared the ideal of complete predictability to developers’ enthusiasm for "},{"type":"text","marks":[{"type":"bold"}],"text":"platform production approaches"},{"type":"text","text":". For decades, companies investigated the possibility of using the same expression vector, cell line, culture medium, and manufacturing process for an array of recombinant antibodies. As it turns out, each antibody requires a tailored expression system. “We would like to think that we can engineer cells for predictability, but the best thing that we can do is to have a constellation of cells with different phenotypes.” The same can be said for production of adenoassociated virus (AAV) vectors: Slowly, the industry is realizing the impossibility of creating a single human embryonic kidney (HEK) cell line for all AAV serotypes."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Considering the difficulty of establishing platform approaches, Lewis emphasized the importance of "},{"type":"text","marks":[{"type":"bold"}],"text":"libraries "},{"type":"text","text":"for cell lines, vectors, and related raw materials. A panel of cell lines could be engineered to perform specific PTMs for most recombinant proteins. As needed, AI/ML could be applied to identify ideal expression systems. Such an approach could facilitate early screening activities."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"McCloskey highlighted related efforts in the small-molecule pharmaceutical industry, particularly a movement toward open science. Chemical libraries with billions of compounds are already available worldwide. Similar initiatives could work in the biopharmaceutical industry, with companies releasing data related to commonly used cell lines."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Making Space for Innovation"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Nelson asked the panelists about what role"},{"type":"text","marks":[{"type":"bold"}],"text":" AI/ML"},{"type":"text","text":" could play in the industry. McCloskey observed that current conversations about AI/ML center on data analytics — e.g., for identifying patterns in genomic data. But some companies are exploring computer vision and other ML methods for continuous bioprocess monitoring. Computer vision could develop deep insights into cellular processes, enabling dynamic process control. McCloskey added that AI-based analytics already are linked inextricably with biopharmaceutical research and computational biology. Companies that delay implementing such technologies could fall behind the rest of the industry."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Lewis responded that AI will take considerable time and resources to implement effectively. AI utility depends on the quality of the data fed into it. And even with excellent algorithms and training data, an AI-based system will be unhelpful if it cannot respond adequately to user queries. Data scientists must engineer prompts carefully. Adopting an AI system also will require significant investment to establish data warehouses and automated systems for data generation, transfer, and storage."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Forman countered McCloskey, saying that generation of a sufficiently comprehensive model — e.g., about the productivity of cells with different phenotypes — requires time and labor. A business case could be made for developing algorithms for complicated analyses. “In the meantime, we are not going to wait” for AI to be developed. It might be more fruitful to hone the predictive value of existing tools."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Conversation turned to the"},{"type":"text","marks":[{"type":"bold"}],"text":" rate of innovation "},{"type":"text","text":"in the biopharmaceutical industry. Forman remarked that only a small part of a large company has sufficient bandwidth and resources to explore new processes and technologies. Innovation occurs incrementally. “But big changes are going to come from [taking] small stabs at a big idea.”"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"McCloskey emphasized the value of industry consortia in accelerating innovation. Citing the COVID-19 pandemic, he said that research and development (R\u0026D) can occur rapidly amid a willingness to share data. He advocated for a “distributed” innovation ecosystem in which networks of companies share intellectual property (IP) as well as costs, risks, results, and rewards from R\u0026D activities."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Lewis added that emerging technologies could facilitate consortia. Contributors could pool data into a secure system that leverages AI to mask proprietary information while enabling analysis of anonymized data. Setting up consortia with such capabilities could foster innovation through collaboration."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Plotting the Path Ahead"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Nelson concluded the discussion by asking how biomanufacturing might look in five years. McCloskey expressed excitement about the number of companies that would be applying AI/ML capabilities to transition products from idea to proof of concept. Lewis said that ML developments are encouraging, but drug makers should expect some resistance from investors, regulatory agencies, and other stakeholders. Historically, the industry has adopted technologies methodically. Regulators will expect drug companies and technology developers to demonstrate that emerging tools will provide high-quality insights, especially as companies adopt AI to accelerate development activities — sometimes hastily so. Forman ended the session with a nod to cell and gene therapies, predicting that many such products will reach the market soon and that their costs will begin to decline. He welcomed advances, AI included, that would increase advanced-therapy availability."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Brian Gazaille"},{"type":"text","marks":[{"type":"italic"}],"text":", PhD, is managing editor of BioProcess International; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"brian.gazaille@informa.com","target":"_blank","rel":null,"class":null}},{"type":"italic"}],"text":"brian.gazaille@informa.com"},{"type":"text","marks":[{"type":"italic"}],"text":"; 1-212-600-3594."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Watch Online"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"See all panel discussions online from "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://bioprocessintl.com/category/bpi-theater/bpi-theater-bio-2024","target":"_blank","rel":null,"class":null}},{"type":"italic"}],"text":"BPI Theater at BIO 2024"},{"type":"text","marks":[{"type":"italic"}],"text":"."}]}],"mobileHeadline":"Improving Expression and Manufacturing of Novel Biologics","articleUrl":"/expression-platforms/improving-expression-and-manufacturing-of-novel-biologics","listPageUrl":"/expression-platforms/improving-expression-and-manufacturing-of-novel-biologics","linkAttrs":{},"date":"Jul 31, 2024","timeRead":0,"categoryName":"Expression Platforms","categoryUrl":"/upstream-processing/expression-platforms","contributors":[{"name":"Brian Gazaille","avatar":"","link":"/author/brian-gazaille"}],"paidGating":null}},{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltd88eec4897923bae/6671f407aa136b6e14f52b5e/22-6-tech-Chaundhry-F1.jpg","alt":"turbulent flow image"},"articleName":"Lessons in Bioreactor Scale-Up, Part 2: A Refresher on Fluid Flow and Mixing","articleSummary":"","articleBody":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Commercial-scale production of recombinant therapeutic proteins routinely involves suspension cultures of mammalian cells in bioreactors with up to 10,000 L capacity. With advances made over the past 30 years in cellular engineering, basal and feed media development, and bioprocess engineering, expression titers of ~10 g/L, viable-cell densities of \u003e3 × 10"},{"type":"text","marks":[{"type":"superscript"}],"text":"7"},{"type":"text","text":" cells/mL, and specific productivities of \u003e20 pg/cell/day are now common. Such high cell densities (with the potential to go even higher) increase mixing and aeration demands and can subject cells to aggressive environments such that they experience high hydrodynamic stresses. This installment of my bioreactor scale-up series should refresh your knowledge of fluid flow, mixing, and mass transfer in bioreactors, including how the interplay of those parameters creates the environment that cells experience. That in turn influences their growth, metabolism, and protein production."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Fluid Flow in Bioreactors"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Fluids — either liquids or gases — are substances that undergo continuous "},{"type":"text","marks":[{"type":"italic"}],"text":"deformation "},{"type":"text","text":"when subjected to shear force. Shearing causes deformation, a change in the relative positions of fluid components. Such force must be applied to produce fluid flow ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":"). Scientists categorize fluids according to their density and viscosity as"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• compressible (or incompressible) — whether their density changes (or not) in response to pressure, with gases generally being compressible and liquids being incompressible"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• viscous (all fluids have a finite viscosity, which is responsible for “internal friction” during flow, otherwise expressed as a fluid’s resistance to motion)"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• Newtonian or non-Newtonian, depending on whether they obey Newton’s law of viscosity."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Shear stress is calculated by dividing the shear force applied to a fluid by the cross-sectional area to which it is applied. Strictly speaking, shear stress is one of two types of fundamental hydrodynamic forces, the other being “normal stress.” Both are determined commonly as viscosity multiplied by the velocity gradient, with flow direction being perpendicular for shear stress and parallel for normal stress. Hence, a velocity gradient for shear stress is written as "},{"type":"text","marks":[{"type":"italic"}],"text":"dU"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"x"},{"type":"text","marks":[{"type":"italic"}],"text":"/dy"},{"type":"text","text":" and for normal stress as "},{"type":"text","marks":[{"type":"italic"}],"text":"dU"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"x"},{"type":"text","marks":[{"type":"italic"}],"text":"/dx,"},{"type":"text","text":" where "},{"type":"text","marks":[{"type":"italic"}],"text":"U "},{"type":"text","text":"is the velocity of fluid and "},{"type":"text","marks":[{"type":"italic"}],"text":"x "},{"type":"text","text":"and "},{"type":"text","marks":[{"type":"italic"}],"text":"y "},{"type":"text","text":"represent the direction of the force/stress."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"When fluid flows through a pipe or over a solid object, the velocity of that fluid changes depending on position. The fluid layer in contact with the wall of a pipe, for example, is assumed to have the same velocity as the wall or solid object (zero velocity). The next fluid layer inward has a bit higher velocity because of the overall fluid motion, the next layer inward has more, and so on. Within every fluid is a continuous exchange of particles among those layers, and that influences the velocity of their flow. Particles moving from a slow-moving layer to a faster layer will act to reduce its speed; those coming from a fast layer to a slower one will have an accelerating effect. Thus are shear stresses induced by fluid flow as a consequence of velocity differences."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Depending on fluid velocity, flow can be characterized as laminar or turbulent (Figure 1). When the overall fluid motion is slow, adjacent fluid layers move in an orderly (more or less parallel) motion called"},{"type":"text","marks":[{"type":"italic"}],"text":" laminar flow"},{"type":"text","text":". In a fast-moving fluid, particles and parcels of fluid frequently cross layers randomly and chaotically in a "},{"type":"text","marks":[{"type":"italic"}],"text":"turbulent flow"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 1: Familiar examples of laminar and turbulent flow."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt71a2aa8640d66acf/6671f01baca37b5d34441a04/22-6-tech-Chaundhry-F1.jpg","alt":"22-6-tech-Chaundhry-F1.jpg","title":null,"style":{"height":"auto","text-align":"center"}}},{"type":"text","text":"Figure 1: Familiar examples of laminar and turbulent flow."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Reynolds number"},{"type":"text","text":" ("},{"type":"text","marks":[{"type":"italic"}],"text":"Re"},{"type":"text","text":") is a dimensionless variable for characterizing fluid motion as laminar or turbulent. Equation 1 calculates "},{"type":"text","marks":[{"type":"italic"}],"text":"Re "},{"type":"text","text":"for fluid flow in pipes with circular cross-section:"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Re = DUρ/μ"},{"type":"text","text":" where "},{"type":"text","marks":[{"type":"italic"}],"text":"D "},{"type":"text","text":"is the pipe diameter, "},{"type":"text","marks":[{"type":"italic"}],"text":"U "},{"type":"text","text":"is fluid velocity, and "},{"type":"text","marks":[{"type":"italic"}],"text":"ρ "},{"type":"text","text":"and "},{"type":"text","marks":[{"type":"italic"}],"text":"μ "},{"type":"text","text":"are density and viscosity of the fluid, respectively."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Because the biopharmaceutical industry is concerned primarily with fluid flow in stirred-tank bioreactors, the relevant Reynolds number is defined by Equation 2:"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Re"},{"type":"text","marks":[{"type":"subscript"}],"text":"i "},{"type":"text","text":"= "},{"type":"text","marks":[{"type":"italic"}],"text":"N"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","marks":[{"type":"italic"}],"text":"D"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","marks":[{"type":"superscript"}],"text":"2"},{"type":"text","marks":[{"type":"italic"}],"text":"ρ/μ"},{"type":"text","text":" where "},{"type":"text","marks":[{"type":"italic"}],"text":"N"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","text":" is the stirrer speed, "},{"type":"text","marks":[{"type":"italic"}],"text":"D"},{"type":"text","marks":[{"type":"subscript"}],"text":"i "},{"type":"text","text":"is the impeller diameter, and"},{"type":"text","marks":[{"type":"italic"}],"text":" ρ"},{"type":"text","text":" and"},{"type":"text","marks":[{"type":"italic"}],"text":" μ"},{"type":"text","text":" are density and viscosity of the fluid, respectively."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Conceptually, the Reynolds number expresses the ratio of inertial to viscous forces. During laminar flow (low Reynolds number), viscous forces dominate, and fluid motion is slow. However, with increasing Reynolds numbers, inertial forces become more dominant, and flow transitions from laminar to turbulent — then becomes fully turbulent at sufficiently high "},{"type":"text","marks":[{"type":"italic"}],"text":"Re "},{"type":"text","text":"values."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Pipe flow is laminar if "},{"type":"text","marks":[{"type":"italic"}],"text":"Re "},{"type":"text","text":"\u003c 2100. For "},{"type":"text","marks":[{"type":"italic"}],"text":"Re "},{"type":"text","text":"values of 2000–4000, the flow is considered to be in a transition state from laminar to turbulent. It is fully turbulent once "},{"type":"text","marks":[{"type":"italic"}],"text":"Re "},{"type":"text","text":"\u003e 4000. In stirred tanks, the value of "},{"type":"text","marks":[{"type":"italic"}],"text":"Re "},{"type":"text","text":"that marks the transition from laminar to turbulent flow depends on tank and impeller geometry."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Turbulent Flow in Bioreactors"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Turbulent flow is the flow regime that cells encounter in bioreactors. Turbulence is essential for effective mixing (mass and heat transfer in fluids), so achieving turbulent-flow conditions in bioreactors is vital to culture success. As noted above, the Reynolds number indicates when turbulent flow is generated. As that number increases, inertial forces come to dominate viscous forces in the fluid, overcoming the tendency of viscous effects to dampen flow instability. Thus, turbulence can be regarded as a highly disordered fluid motion resulting from instabilities growing in an initially laminar flow field ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Turbulent flow is very complex by nature, so our understanding of it is far from complete. The flow has an overall direction, but superimposed on that are secondary chaotic movements of fluid particles causing flow deviations and generating vortices of varying shape, size, speed, and rotational direction. Those irregular secondary motions in turbulent flow have profound consequences for mixing efficiency, energy loss, and shear intensity. Turbulent flows develop spinning or swirling fluid structures called "},{"type":"text","marks":[{"type":"italic"}],"text":"eddies"},{"type":"text","text":", which can stretch, coalesce, and divide. Fluid velocity within an eddy undergoes great change in magnitude and direction within a relatively short distance and time."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Eddies of different sizes appear in turbulent flow. The size of the largest eddies is limited by the boundary of the flow system. Hence, the diameter of the largest eddies in a bioreactor is similar to (but somewhat smaller than) the diameter of the reactor vessel. Large eddies are unstable and give rise to smaller ones, which in turn produce even smaller eddies, and so on. In a stirred bioreactor, large eddies derive energy from the bulk flow generated by the impeller, so they contain most of the turbulent kinetic energy. The smallest eddies cannot sustain rotational motion and lose their energy as heat through the effects of viscosity and fluid friction. Maintaining turbulent flow thus requires continuous supply of energy to make up for what is lost as heat ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Dispersion "},{"type":"text","text":"is the process of breaking up the bulk flow into smaller and smaller eddies to facilitate rapid transfer of material throughout a tank. The degree of homogeneity possible as a result of dispersion is limited by the size of the smallest eddies that form. Assuming that isotropic turbulence is prevailing in a stirred tank and that flow is turbulent ("},{"type":"text","marks":[{"type":"italic"}],"text":"N"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"Rei"},{"type":"text","text":" \u003e 10"},{"type":"text","marks":[{"type":"superscript"}],"text":"4"},{"type":"text","text":"), the size of the smallest eddies is given approximately as the Kolmogorov scale of mixing ("},{"type":"text","marks":[{"type":"italic"}],"text":"λ"},{"type":"text","text":") or scale of turbulence, defined by Equation 3:"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"λ"},{"type":"text","text":" = ("},{"type":"text","marks":[{"type":"italic"}],"text":"ν"},{"type":"text","marks":[{"type":"superscript"}],"text":"3"},{"type":"text","text":"/"},{"type":"text","marks":[{"type":"italic"}],"text":"ε"},{"type":"text","text":")"},{"type":"text","marks":[{"type":"superscript"}],"text":"1/4"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"where"},{"type":"text","marks":[{"type":"italic"}],"text":" λ"},{"type":"text","text":" is the characteristic length of the smallest eddies, "},{"type":"text","marks":[{"type":"italic"}],"text":"ν "},{"type":"text","text":"is the kinematic viscosity of the fluid, and "},{"type":"text","marks":[{"type":"italic"}],"text":"ε "},{"type":"text","text":"is the local rate of turbulent energy dissipation per unit mass of the fluid."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"At a steady state, the average rate of turbulent energy dissipation throughout a bioreactor is equal to the power input by the impeller to the fluid within the tank. Hence, the greater the power input to the impeller, the smaller the eddies will be. Because "},{"type":"text","marks":[{"type":"italic"}],"text":"λ "},{"type":"text","text":"depends on viscosity, smaller eddies are produced in low-viscosity media for a given power input. An energy input of 0.1 W/kg, which currently is considered to be high for animal-cell cultures, yields an estimate for the smallest eddy diameter "},{"type":"text","marks":[{"type":"italic"}],"text":"λ "},{"type":"text","text":"of about 0.06 mm (60 μm). That represents the smallest scale of mixing achievable by dispersion under such conditions."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The characteristic length of the smallest eddies ("},{"type":"text","marks":[{"type":"italic"}],"text":"λ"},{"type":"text","text":") often is an indicator of the potential for cell damage caused by fluid shear. Experiments have shown that if biological entities (e.g., mammalian cells) are smaller than "},{"type":"text","marks":[{"type":"italic"}],"text":"λ "},{"type":"text","text":"in a bioreactor, then shear damage to such entities will not occur. Minow et al. presented a comparative study of mixing and mass transfer in two different 1000‑L single-use bioreactors. The authors calculated the Kolmogorov length of the smallest eddies prevailing in each bioreactor type ("},{"type":"text","marks":[{"type":"bold"}],"text":"2"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"As Table 1 shows, the calculated values of "},{"type":"text","marks":[{"type":"italic"}],"text":"λ "},{"type":"text","text":"for each bioreactor type were close to 80 μm — far larger than the 15-μm size of Chinese hamster ovary (CHO) cells used in the study. Similarly, Godoy-Silva et al. described CHO cells that could withstand power inputs ≤6000 W/kg ("},{"type":"text","marks":[{"type":"bold"}],"text":"3"},{"type":"text","text":"). At power dissipation rates \u003e300 W/kg, which is still much higher than the power densities in commercial cell-culture reactors, the only impact observed was in the glycosylation pattern of expressed proteins, but the cells were not affected ("},{"type":"text","marks":[{"type":"bold"}],"text":"7"},{"type":"text","text":"). Even if such results are specific to the system studied, they still show that shear damaging has been overestimated in recent decades. Nienow arrived at the same conclusion in two different papers ("},{"type":"text","marks":[{"type":"bold"}],"text":"4, 5"},{"type":"text","text":"). Hence, it is safe to say that eddies \u003c10–20 μm (smaller than the typical mammalian cell size) possess very little turbulent kinetic energy, whereas larger eddies merely carry cells along convectively ("},{"type":"text","marks":[{"type":"bold"}],"text":"8–12"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":null},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltb1c087227f2585d6/6671f34a61be5300d80146bf/22-6-Chaudry-T1.PNG","alt":"22-6-Chaudry-T1.PNG","title":null,"style":{"max-width":"500px","width":"500px","height":"auto","text-align":"center"}}}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"Table 1: Comparison of parameters used for calculating hydrodynamic stress in two"}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"different 1000-L single-use bioreactors (adapted from Reference 2)"}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"Mixing in Bioreactors."}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","marks":[{"type":"superscript"}],"text":"1"},{"type":"text","text":" from Cytiva"},{"type":"text","marks":[{"type":"superscript"}],"text":" 2"},{"type":"text","text":" from Thermo Fisher Scientific"}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"* Maximum values during a process at the current bioreactor size ** Calculated from EDR mean"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Mixing is a physical operation that reduces nonuniformities in fluid by eliminating gradients of temperature, concentration, and other properties ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":"). During mixing, material interchanging among different locations of a vessel (such as a mixing container or a bioreactor) causes components to “mingle.” Mixing is widely used in bioprocessing for"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• blending soluble components of powder media in water to prepare for cell culture"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• dispersing gases such as air and oxygen through liquids in the form of bubbles"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• maintaining suspension of solid particles such as cells or cellular aggregates (e.g., on microcarriers)"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• promoting heat transfer to or from liquids."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Mixing is one of the most important unit operations in bioprocessing. It is not enough to just fill a bioreactor with nutrient-rich media and cells. Unless the contents of that bioreactor are mixed, cells will settle, and zones of nutrient depletion will develop as they rapidly consume the material they need within their local environment. Mixing controls their access to dissolved nutrients and oxygen while playing a critical role in controlling culture temperature. Mixing equipment has a significant effect on agitation efficiency, directly affecting power requirements and operating costs. Development of hydrodynamic forces in media and other fluids drives aeration processes such as gas-bubble break-up, gas hold-up, and uniform dispersion of bubbles. Thus, mixing can determine the success a bioprocess."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Bioreactor Geometry:"},{"type":"text","text":" Most stainless-steel (or reusable) large-scale bioreactors are cylindrical, with a ratio of tank height to diameter ("},{"type":"text","marks":[{"type":"italic"}],"text":"H/T"},{"type":"text","text":") \u003e1 (Figure 2). "},{"type":"text","marks":[{"type":"italic"}],"text":"Baffles "},{"type":"text","text":"— cylindrical metal strips — are mounted against a bioreactor wall to reduce gross vortexing and swirling in the liquid. Mixing is achieved by means of an impeller mounted on a centrally located stirrer shaft driven by a motor. The rotating impeller pumps and forces liquid away from it, creating a regular flow pattern that circulates through the vessel and periodically returns to the impeller region. For aeration, gas (usually air and/or oxygen) is introduced into the vessel by means of a sparger located beneath the impeller. A headspace typically falls in the range of 20–30% of total vessel volume for cylindrical bioreactors."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 2: Dimensions of mixing-related components in a bioreactor."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltd8d34e4b3ee290b6/6671f041bd78122f44ab0cbf/22-6-tech-Chaundhry-F2.jpg","alt":"22-6-tech-Chaundhry-F2.jpg","title":null,"style":{"max-width":"227px","width":"227px","height":"auto","text-align":"center"}}},{"type":"text","text":"Figure 2: Dimensions of mixing-related components in a bioreactor."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The shape of the base of a stirred tank affects mixing efficiency. Most stirred bioreactors have a rounded rather than a flat base, preventing sharp corners and pockets into which fluid currents could penetrate, and thus discouraging the formation of stagnant zones. The energy required to keep solids (such as cells) suspended in a stirred tank also depends on the shape of the vessel base. For efficient mixing with a single impeller, the impeller diameter ("},{"type":"text","marks":[{"type":"italic"}],"text":"D"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","text":") should fall between 0.25 and 0.50 of the tank diameter ("},{"type":"text","marks":[{"type":"italic"}],"text":"T"},{"type":"text","text":"), and the liquid height ("},{"type":"text","marks":[{"type":"italic"}],"text":"H"},{"type":"text","marks":[{"type":"subscript"}],"text":"L"},{"type":"text","text":") should fall within 1.0 and 1.25 of "},{"type":"text","marks":[{"type":"italic"}],"text":"T"},{"type":"text","text":". That’s because mixing intensity decreases rapidly as fluid moves away from the impeller zone, so large liquid volumes in the upper parts of a vessel (away from the impeller) are difficult to mix — and thus should be prevented. Another aspect of vessel geometry that influences mixing efficiency is the clearance ("},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","text":") between the impeller and the tank bottom, which affects solids suspension, gas dispersion, and hydrodynamic stability. For most stirred bioreactor systems, the ratio of clearance to fluid height ("},{"type":"text","marks":[{"type":"italic"}],"text":"C/H"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"L"},{"type":"text","text":") typically falls into the range of 0.33–0.66 ("},{"type":"text","marks":[{"type":"bold"}],"text":"3"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Impeller type is critical to bioreactor design. An impeller system must be able to impart enough power into the cell culture medium for enabling good mixing efficiency, homogeneity, and sufficient mass transfer to satisfy cellular oxygen demand. Figure 3 shows two of the most common impeller designs used in both reusable and single-use bioreactors: Rushton and pitched-blade impellers. When the blade (or paddle) of an impeller rotates, it pushes the adjacent fluid. That action creates a high-pressure region on the front face and a low-pressure region immediately behind the blade (or paddle). Those regions are responsible for fluid motion inside a bioreactor and eventually for fluid mixing. Fluid located away from the impeller generally remains at a constant pressure."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 3: Pitched-blade and paddle (Rushton) impellers commonly used in bioreactors (adapted from Reference 3)."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blte0a30ba01a55b6fc/6671f08961e23c492d9735d2/22-6-tech-Chaundhry-F3.jpg","alt":"22-6-tech-Chaundhry-F3.jpg","title":null,"style":{"height":"auto","text-align":"center"}}},{"type":"text","text":"Figure 3: Pitched-blade and paddle (Rushton) impellers commonly used in bioreactors (adapted from Reference 3)."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Fluid velocity is highest at the tip of a rotating blade (or paddle), and it rapidly decreases away from the impeller. As it rotates, the impeller induces rotational velocity in the surrounding fluid, which can be observed by the high velocity region next to the impeller. Rushton impeller paddles also induce radially outward flow in nearby regions. It pulls liquid from top and bottom regions of a bioreactor, which creates a circular motion inside the vessel. That motion enhances fluid mixing. By contrast, pitched-blade impellers create mainly an axial flow (from top to bottom)."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The preferred impeller design for cell-culture bioreactors uses pitched blades (with optimal blade diameter and agitation speed) because paddles are more likely to cause shear damage. Pitched-blade impellers primarily generate axial flow, but with appropriate blade configuration they also can balance both axial and radial flow patterns ("},{"type":"text","marks":[{"type":"bold"}],"text":"4, 5"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Axial flow is necessary for top-to-bottom mixing in a stirred-tank bioreactor, and pitched-blade impellers (with blades angled \u003c90° to the plane of rotation) can generate such flow efficiently. As Figure 4 shows, fluid leaving an impeller is driven downwards until it is deflected from the bottom of the vessel. Then the fluid spreads out and flows up along the vessel walls before it is drawn back to the impeller. The rotational flow makes baffles necessary to break down vortex formation along the vessel walls."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 4: The axial flow pattern induced by pitched blade impellers (left) and the turbulent kinetic energy distribution profile of a pitched-blade impeller (right) generated by computational fluid dynamics (CFD) simulation (adapted from Reference 1)."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt07f261174c918e8b/6671f0c2f826a3e42c359c6b/22-6-tech-Chaundhry-F4.jpg","alt":"22-6-tech-Chaundhry-F4.jpg","title":null,"style":{"height":"auto","text-align":"center"}}},{"type":"text","text":"Figure 4: The axial flow pattern induced by pitched blade impellers (left) and the turbulent kinetic energy distribution profile of a pitched-blade impeller (right) generated by computational fluid dynamics (CFD) simulation (adapted from Reference 1)."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"A pitched-blade impeller agitating at sufficiently high velocity in a low-viscosity medium (e.g., water and cell culture media) generates a turbulent region of high shear and rapid mixing near the impeller. That high-shear region is responsible for bubble break-up in sparged bioreactors. As Figure 4 shows, the distribution of turbulent kinetic energy is not uniform throughout the tank, with the highest values mostly occurring in a region close to the impeller. Levels of turbulent kinetic energy in the remainder of the tank away from the impeller are up to an order of magnitude lower than the maximum values measured ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Axial flow is useful when strong vertical currents are required — e.g., to suspend solids or cells in fluid. The ability to operate a pitched-blade impeller in either up-flow or down-flow mode is an important requirement that can be provided by a bidirectional motor. For most animal cell-culture systems, these impellers are programmed for up-flow pumping mode. However, the down-flow option can be useful for enabling suspension of adherent-cell cultures on microcarriers at low speeds with low specific power input to prevent shearing the cells from their carriers ("},{"type":"text","marks":[{"type":"bold"}],"text":"3"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Disc-mounted impellers with six flat blades, commonly known as Rushton turbines, generate radial flow (Figure 5). They are used frequently in microbial fermentation, for which they can generate very high shear and thus provide effective gas dispersion. A Rushton turbine generates a jet of high-speed flow radially outward from the impeller toward the walls of the tank."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 5: The radial flow pattern induced by Rushton impellers (left) and their turbulent kinetic energy distribution profile (right) generated by computational fluid dynamics (CFD) simulation (adapted from Reference 1)."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltd52fe80803f3ad5d/6671f0ef24c3b3a277c8bdb6/22-6-tech-Chaundhry-F5.jpg","alt":"22-6-tech-Chaundhry-F5.jpg","title":null,"style":{"height":"auto","text-align":"center"}}},{"type":"text","text":"Figure 5: The radial flow pattern induced by Rushton impellers (left) and their turbulent kinetic energy distribution profile (right) generated by computational fluid dynamics (CFD) simulation (adapted from Reference 1)."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"That fluid stream gets divided in half, with one stream flowing up and the other down toward the tank bottom. Bulk flow in the vessel thus comprises two large ring vortices, one above and the other below the impeller."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Another characteristic of Rushton turbines is their generation of trailing vortices in fluid behind the horizontal edges of each flat impeller blade (Figure 5). The vortices play a critical role to determine mixing performance because most mixing takes place near the vortices issuing from the impeller blades. These trailing vortices also provide gas dispersion in sparged systems. The associated steep velocity gradients provide a major source of turbulence. One reason for widespread use of Rushton turbines in microbial fermentors is their ability to handle high gas-flow rates and provide efficient gas dispersal."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The power required to achieve a given stirrer speed depends on frictional forces and"},{"type":"text","marks":[{"type":"italic"}],"text":" form drag"},{"type":"text","text":" (caused by the pressure difference between the front and rear of the impeller blades) that resists impeller rotation. Friction and form drag create torque on the stirrer shaft. The power input can be determined experimentally by measuring the induced torque (Equation 4):"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"P "},{"type":"text","text":"= 2π"},{"type":"text","marks":[{"type":"italic"}],"text":"N"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","marks":[{"type":"italic"}],"text":"M"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"where "},{"type":"text","marks":[{"type":"italic"}],"text":"P "},{"type":"text","text":"is the power, "},{"type":"text","marks":[{"type":"italic"}],"text":"N"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","text":" is the stirrer speed, and "},{"type":"text","marks":[{"type":"italic"}],"text":"M "},{"type":"text","text":"is the torque."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The power required to mix nongassed liquids depends on stirrer speed, impeller shape and size, tank geometry, and liquid density and viscosity. The relationships among those variables generally is represented by the dimensionless power number (Equation 5):"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"N"},{"type":"text","marks":[{"type":"subscript"}],"text":"P"},{"type":"text","text":" = "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","text":"/ρ"},{"type":"text","marks":[{"type":"italic"}],"text":"N"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","marks":[{"type":"superscript"}],"text":"3"},{"type":"text","marks":[{"type":"italic"}],"text":"D"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","marks":[{"type":"superscript"}],"text":"5"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"where "},{"type":"text","marks":[{"type":"italic"}],"text":"N"},{"type":"text","marks":[{"type":"subscript"}],"text":"P "},{"type":"text","text":"is the power number, "},{"type":"text","marks":[{"type":"italic"}],"text":"P "},{"type":"text","text":"is the power, "},{"type":"text","marks":[{"type":"italic"}],"text":"N"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","text":" is the stirrer speed, and "},{"type":"text","marks":[{"type":"italic"}],"text":"D"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","text":" is the impeller diameter."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Consider the power number as analogous to a drag coefficient for a stirrer system. The relationship between "},{"type":"text","marks":[{"type":"italic"}],"text":"N"},{"type":"text","marks":[{"type":"subscript"}],"text":"P"},{"type":"text","text":" and "},{"type":"text","marks":[{"type":"italic"}],"text":"Re "},{"type":"text","text":"generally is determined experimentally for a range of impeller and tank configurations. The power number for different impeller designs becomes constant once flow is turbulent ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":"). Under turbulent flow conditions in a stirred tank, the power number for a Rushton turbine generally remains constant at 5, whereas that of an axial pitched-blade impeller is about 1. Thus, a Rushton turbine has strong form drag, generates high levels of torque, and transmits more power at the same operating speed than does a pitched blade impeller ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Fluid Flow: "},{"type":"text","text":"Rotating impeller blades pump fluid in a bioreactor vessel. The volumetric flow rate of fluid leaving a blade is a characteristic of the impeller design and varies with operating parameters such as stirrer speed and blade size. A dimensionless flow number represents the effectiveness of a pumping process (Equation 6):"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"N"},{"type":"text","marks":[{"type":"subscript"}],"text":"flow"},{"type":"text","text":" = "},{"type":"text","marks":[{"type":"italic"}],"text":"Q"},{"type":"text","text":"/"},{"type":"text","marks":[{"type":"italic"}],"text":"N"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","marks":[{"type":"italic"}],"text":"D"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","marks":[{"type":"superscript"}],"text":"3"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"where "},{"type":"text","marks":[{"type":"italic"}],"text":"Q "},{"type":"text","text":"is the volumetric flow rate of fluid leaving the impeller blade, "},{"type":"text","marks":[{"type":"italic"}],"text":"N"},{"type":"text","marks":[{"type":"subscript"}],"text":"i "},{"type":"text","text":"is the stirrer speed, and "},{"type":"text","marks":[{"type":"italic"}],"text":"D"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","text":" is the impeller diameter."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"A pitched-blade impeller produces nearly three to four times the flow of a Rushton turbine for the same power input. This is why the former design is recognized for high pumping capacity and effectiveness of blending operations, whereas the latter are considered to have low pumping efficiency ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"For mixing to be effective in stirred vessels, the velocity of fluid leaving an impeller must be sufficient to carry material into the most remote regions of a tank. Circulating fluid also must sweep the entire vessel in a reasonable time. Fluid flow must be turbulent for good mixing, which can be described as a combination of three physical processes ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":"): distribution, dispersion, and diffusion. Distribution is also called “macromixing,” diffusion is known as “micromixing,” and depending on the scale of fluid motion, dispersion can be classified as either macro- or micromixing."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Distribution "},{"type":"text","text":"is the process by which fluid is transported to all regions of a stirred vessel by bulk circulation currents. It is an important process in mixing and often the slowest step because fluid has a great distance to traverse in a long circulation path. Dispersion breaks up the bulk flow into smaller and smaller eddies, which facilitates rapid transfer of material through a vessel. The degree of homogeneity possible from dispersion is limited by the size of the smallest eddies formed in a particular fluid. To achieve mixing on a scale smaller than the smallest eddies, diffusion becomes an important process. Such micromixing occurs relatively rapidly and over shorter distances than are involved in distribution."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Mixing time ("},{"type":"text","marks":[{"type":"italic"}],"text":"t"},{"type":"text","marks":[{"type":"subscript"}],"text":"m"},{"type":"text","text":") is a useful parameter for assessing the overall speed of mixing in stirred vessels. It is the time required to achieve a given degree of homogeneity after starting with completely segregated materials. Mixing time can be measured by injecting tracer material — commonly acids, bases, or concentrated salt solutions — into a vessel and following its concentration at a fixed point in the tank."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"When a small pulse of tracer is added to fluid in a stirred tank that already contains the tracer material at a certain concentration ("},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","text":"), the concentration response curve in Figure 6 can be expected. Before mixing is complete, a relatively high concentration will be detected each time the bulk flow brings tracer to the measurement point. The resulting peaks in concentration will be separated by a period approximately equal to the average time required for fluid to traverse one bulk circulation loop, known as the circulation time ("},{"type":"text","marks":[{"type":"italic"}],"text":"t"},{"type":"text","marks":[{"type":"subscript"}],"text":"c"},{"type":"text","text":"). After several circulations, a desired degree of homogeneity will be achieved."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":null},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt73c4bee29610f80f/6671f11b7ab673c8f35496be/22-6-tech-Chaundhry-F6.jpg","alt":"22-6-tech-Chaundhry-F6.jpg","title":null,"style":{"max-width":"421px","width":"421px","height":"auto","text-align":"center"}}}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"Figure 6: Determination of mixing time ("},{"type":"text","marks":[{"type":"italic"}],"text":"t"},{"type":"text","marks":[{"type":"subscript"}],"text":"m"},{"type":"text","text":") by following the concentration ("},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","text":") response"}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"after a tracer dye is injected into a stirred tank (adapted from Reference 1); "},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","marks":[{"type":"subscript"}],"text":"f"},{"type":"text","text":" = final"}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"concentration, "},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","text":" = interim concentration, "},{"type":"text","marks":[{"type":"italic"}],"text":"t"},{"type":"text","marks":[{"type":"subscript"}],"text":"c"},{"type":"text","text":" = circulation time."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Mixing time generally is expressed as the time after which the tracer concentration differs from its final concentration ("},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","marks":[{"type":"subscript"}],"text":"f"},{"type":"text","text":") by \u003c5–10% of the total concentration difference ("},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","marks":[{"type":"subscript"}],"text":"f"},{"type":"text","text":" – "},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","text":"). At"},{"type":"text","marks":[{"type":"italic"}],"text":" t"},{"type":"text","marks":[{"type":"subscript"}],"text":"m"},{"type":"text","text":", the tracer concentration is relatively steady, and the fluid composition approaches uniformity. For industrial-scale bioreactors, that takes minutes."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Liquid-phase mixing is vitally important in large-scale bioreactors because feed components, bases, or antifoam detergents are added, in most cases, at the top surface of the fluid ("},{"type":"text","marks":[{"type":"bold"}],"text":"12, 13"},{"type":"text","text":"). Such added materials must be distributed quickly throughout an entire culture to reach homogeneity without causing localized concentration gradients. The homogenization process is dominated by prevailing flow patterns, and information about global mixing conditions in a bioreactor can be obtained from"},{"type":"text","marks":[{"type":"italic"}],"text":" t"},{"type":"text","marks":[{"type":"subscript"}],"text":"m"},{"type":"text","text":" measurements."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Sieblist et al. reported such a study of large-scale bioreactors, determining "},{"type":"text","marks":[{"type":"italic"}],"text":"t"},{"type":"text","marks":[{"type":"subscript"}],"text":"m "},{"type":"text","text":"using an iodine-decolorization technique ("},{"type":"text","marks":[{"type":"bold"}],"text":"14"},{"type":"text","text":"). The data in Figure 7, obtained using three Rushton turbines and three pitched-blade impellers, show that at the same power input the spatial decolorization patterns and speed of decolorization are very different for those agitator types. Those results provide experimental evidence that impellers inducing axial flow reduce "},{"type":"text","marks":[{"type":"italic"}],"text":"t"},{"type":"text","marks":[{"type":"subscript"}],"text":"m"},{"type":"text","text":" by about a factor of two. The reason for that is their notably improved top-to-bottom mixing due to a more widescale flow structure induced by the axially down-pumping pitched-blade impellers; Rushton turbines induce flow compartmentalization, leading to less efficient overall mixing and hence longer mixing times."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":null},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltcc05b5d9642d8e95/6671f17ccb4f8e6d9746ca58/22-6-tech-Chaundhry-F7.jpg","alt":"22-6-tech-Chaundhry-F7.jpg","title":null,"style":{"max-width":"464px","width":"464px","height":"auto","text-align":"center"}}}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"Figure 7: Determination of mixing time ("},{"type":"text","marks":[{"type":"italic"}],"text":"t"},{"type":"text","marks":[{"type":"subscript"}],"text":"m"},{"type":"text","text":") in a 400-L bioreactor fitted with either"}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"three Rushton turbines (circles) or three pitch-blade impellers (triangles) (adapted from"}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"Reference 14); data are fitted to the correlation reported by Ruszkowski ("},{"type":"text","marks":[{"type":"bold"}],"text":"15"},{"type":"text","text":"); "},{"type":"text","marks":[{"type":"italic"}],"text":"D"},{"type":"text","text":" ="}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"impeller diameter, "},{"type":"text","marks":[{"type":"italic"}],"text":"H"},{"type":"text","text":" = tank height,"},{"type":"text","marks":[{"type":"italic"}],"text":" T"},{"type":"text","text":" = tank diameter, "},{"type":"text","marks":[{"type":"italic"}],"text":"ε"},{"type":"text","text":" = the local rate of turbulent"}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"energy dissipation per unit mass of the fluid, "},{"type":"text","marks":[{"type":"italic"}],"text":"θ"},{"type":"text","marks":[{"type":"subscript"}],"text":"95% "},{"type":"text","text":"= time needed to reach 95%"}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"homogeneity in the bioreactor."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"A striking finding from that study is that mixing time depends only on bioreactor geometry rather than the agitation system. Different impeller configurations influence the constant factor (10.5 or 17.9) in the empirical correlation of Ruszkowski ("},{"type":"text","marks":[{"type":"bold"}],"text":"15"},{"type":"text","text":"). Another experiment reported by Sieblist et al. demonstrated that mixing times critically depend on experimental conditions, particularly the location in the bioreactor where the tracer is added ("},{"type":"text","marks":[{"type":"bold"}],"text":"14"},{"type":"text","text":"). Figure 8 shows that the effect of adding tracer close to the impeller is much larger than that at the reference point (the liquid surface), leading to shorter mixing times. That indicates the importance adding substrates and corrective agents (e.g., bases and antifoams) to cultures close to impellers, thus enabling rapid distribution to an entire culture. That implication has been understood for a long time; however, it is not general practice."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":null},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blta17cf642066f57fd/6671f1cbb3215a6f4dd09def/22-6-tech-Chaundhry-F8.jpg","alt":"22-6-tech-Chaundhry-F8.jpg","title":null,"style":{"max-width":"416px","width":"416px","height":"auto","text-align":"center"}}}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"Figure 8: Mixing times measured in a 400-L stirred bioreactor at 90 W/m"},{"type":"text","marks":[{"type":"superscript"}],"text":"3"},{"type":"text","text":" with tracer"}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"added to the system at different points (adapted from Reference 14); numbers on the"}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"bars show the difference in mixing time compared with a reference addition at the top"}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"of the liquid surface;"},{"type":"text","marks":[{"type":"italic"}],"text":" θ"},{"type":"text","marks":[{"type":"subscript"}],"text":"95%"},{"type":"text","text":" = time needed to reach 95% homogeneity in the bioreactor."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The product of mixing time and stirrer speed ("},{"type":"text","marks":[{"type":"italic"}],"text":"N"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","text":" · "},{"type":"text","marks":[{"type":"italic"}],"text":"t"},{"type":"text","marks":[{"type":"subscript"}],"text":"m"},{"type":"text","text":") is known commonly as "},{"type":"text","marks":[{"type":"italic"}],"text":"dimensionless mixing time"},{"type":"text","text":", which represents the number of stirrer rotations required to homogenize a fluid after addition of a tracer pulse. For turbulent flow conditions in a bioreactor, "},{"type":"text","marks":[{"type":"italic"}],"text":"N"},{"type":"text","marks":[{"type":"subscript"}],"text":"i "},{"type":"text","text":"·"},{"type":"text","marks":[{"type":"italic"}],"text":" t"},{"type":"text","marks":[{"type":"subscript"}],"text":"m "},{"type":"text","text":"approaches a constant value for most stirrer types, and mixing time reduces in direct proportion to increasing stirrer speed. Under turbulent flow conditions,"},{"type":"text","marks":[{"type":"italic"}],"text":" t"},{"type":"text","marks":[{"type":"subscript"}],"text":"m"},{"type":"text","text":" can be reduced by increasing "},{"type":"text","marks":[{"type":"italic"}],"text":"D"},{"type":"text","marks":[{"type":"subscript"}],"text":"i"},{"type":"text","text":". However, larger-diameter impellers also require more power consumption, so a tradeoff balance is needed when using that strategy for improving mixing and shortening mixing time."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Power requirements should be kept in mind for scaling up mixing systems. The power required to achieve a given mixing time for a larger bioreactor can be computed from Equation 7 ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":"):"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"subscript"}],"text":"2"},{"type":"text","text":" = "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"subscript"}],"text":"1"},{"type":"text","text":"("},{"type":"text","marks":[{"type":"italic"}],"text":"V"},{"type":"text","marks":[{"type":"subscript"}],"text":"L2"},{"type":"text","text":"/"},{"type":"text","marks":[{"type":"italic"}],"text":"V"},{"type":"text","marks":[{"type":"subscript"}],"text":"L1"},{"type":"text","text":")"},{"type":"text","marks":[{"type":"superscript"}],"text":"5/3"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"where "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"subscript"}],"text":"2"},{"type":"text","text":" is the power required for the larger bioreactor, "},{"type":"text","marks":[{"type":"italic"}],"text":"V"},{"type":"text","marks":[{"type":"subscript"}],"text":"L2"},{"type":"text","text":" is the liquid volume of the larger bioreactor, "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"subscript"}],"text":"1"},{"type":"text","text":" is the power required for the smaller bioreactor, and "},{"type":"text","marks":[{"type":"italic"}],"text":"V"},{"type":"text","marks":[{"type":"subscript"}],"text":"L1"},{"type":"text","text":" is the liquid volume of the smaller bioreactor. For example, consider scaling up from a 1-L to a 100-L bioreactor: "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"subscript"}],"text":"2 "},{"type":"text","text":"= "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"subscript"}],"text":"1"},{"type":"text","text":" × (100/1)"},{"type":"text","marks":[{"type":"superscript"}],"text":"5/3"},{"type":"text","text":" and "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"subscript"}],"text":"2"},{"type":"text","text":" = "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"subscript"}],"text":"1"},{"type":"text","text":" × 2155. Hence, the power required to achieve equal mixing times in the larger bioreactor would be ~2000× that of the smaller bioreactor. That is much greater than would be feasible economically or technically, illustrating why the criterion of constant mixing time seldom is applied in scaling up. Mixing time inevitably must increase with scale-up."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Installation of multiple impellers on the same stirrer shaft might be perceived as a simple solution to improve mixing in a bioreactor. However, the power required increases substantially when additional impellers are fitted onto the same shaft. Moreover, impeller design (axial or radial) and clearance between impellers significantly affects mixing performance and would need to be optimized to realize an improvement in overall efficiency."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"References"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Note: "},{"type":"text","text":"To refer back to part 1, see Chaudhry MA. Lessons in Bioreactor Scale-Up, Part 1: Exploring Introductory Principles"},{"type":"text","marks":[{"type":"italic"}],"text":". BioProcess Int. "},{"type":"text","text":"22(1–2) 2024: 22–27; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.bioprocessintl.com/bioreactors/lessons-in-bioreactor-scale-up-part-1-mdash-exploring-introductory-principles","target":"_blank","rel":null,"class":null}}],"text":"https://www.bioprocessintl.com/bioreactors/lessons-in-bioreactor-scale-up-part-1-mdash-exploring-introductory-principles"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"1 "},{"type":"text","text":"Doran PM. Fluid Flow."},{"type":"text","marks":[{"type":"italic"}],"text":" Bioprocess Engineering Principles, 2nd Edition."},{"type":"text","text":" Academic Press: Cambridge, MA, 2013: 201–254."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"2 "},{"type":"text","text":"Minow B, et al. Biological Performance of Two Different 1000 L Single-Use Bioreactors Applying a Simple Transfer Approach. "},{"type":"text","marks":[{"type":"italic"}],"text":"Eng. Life Sci."},{"type":"text","text":" 14(3) 2014: 283–291; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://doi.org/10.1002/elsc.201300147","target":"_blank","rel":null,"class":null}}],"text":"https://doi.org/10.1002/elsc.201300147"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"3 "},{"type":"text","text":"De Wilde D, et al. Bridging the Gap from Reusable to Single-Use Manufacturing with Stirred, Single-Use Bioreactors. "},{"type":"text","marks":[{"type":"italic"}],"text":"BioProcess Int."},{"type":"text","text":" 7(4) 2009: 36–41; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.bioprocessintl.com/single-use/bridging-the-gap-from-reusable-to-single-use- anufacturing-with-stirred-single-use-bioreactors","target":"_blank","rel":null,"class":null}}],"text":"https://www.bioprocessintl.com/single-use/bridging-the-gap-from-reusable-to-single-use- anufacturing-with-stirred-single-use-bioreactors"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"4 "},{"type":"text","text":"Nienow AW, Isailovic B, Barrett TA. Design and Performance of Single-Use Stirred-Tank Bioreactors. "},{"type":"text","marks":[{"type":"italic"}],"text":"BioProcess Int"},{"type":"text","text":". 14(11–12) 2016: 12–21; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.bioprocessintl.com/single-use/design-and-performance-of-single-use-stirred-tank-bioreactors","target":"_blank","rel":null,"class":null}}],"text":"https://www.bioprocessintl.com/single-use/design-and-performance-of-single-use-stirred-tank-bioreactors"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"5 "},{"type":"text","text":"Nienow AW. Reactor Engineering in Large-Scale Animal Cell Culture. "},{"type":"text","marks":[{"type":"italic"}],"text":"Cytotechnology "},{"type":"text","text":"50(1–3) 2006: 9–33; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://doi.org/10.1007/s10616-006-9005-8","target":"_blank","rel":null,"class":null}}],"text":"https://doi.org/10.1007/s10616-006-9005-8"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"6 "},{"type":"text","text":"Nienow AW. Impeller Selection for Animal Cell Culture. "},{"type":"text","marks":[{"type":"italic"}],"text":"Encyclopedia of Industrial Biotechnology: Bioprocess, Bioseparation, and Cell Technology"},{"type":"text","text":". Flickinger MC, Ed. Wiley: Hoboken, NJ, 2010; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://doi.org/10.1002/9780470054581","target":"_blank","rel":null,"class":null}}],"text":"https://doi.org/10.1002/9780470054581"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"7 "},{"type":"text","text":"Godoy-Silva R, et al. Physiological Responses of CHO Cells to Repetitive Hydrodynamic Stress. "},{"type":"text","marks":[{"type":"italic"}],"text":"Biotechnol. Bioeng."},{"type":"text","text":" 103(6) 2009: 1103–1117; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://doi.org/10.1002/bit.22339","target":"_blank","rel":null,"class":null}}],"text":"https://doi.org/10.1002/bit.22339"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"8 "},{"type":"text","text":"Chisti Y. Hydrodynamic Damage to Animal Cells."},{"type":"text","marks":[{"type":"italic"}],"text":" Crit. Rev. Biotechnol"},{"type":"text","text":". 21(2) 2001: 67–110; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://doi.org/10.1080/20013891081692","target":"_blank","rel":null,"class":null}}],"text":"https://doi.org/10.1080/20013891081692"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"9 "},{"type":"text","text":"Kunas KT, Papoutsakis ET. Damage Mechanisms of Suspended Animal Cells in Agitated Bioreactors with and without Bubble Entrainment. "},{"type":"text","marks":[{"type":"italic"}],"text":"Biotechnol. 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Life Sci"},{"type":"text","text":". 12(5) 2012: 518–528; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://doi.org/10.1002/elsc.201100163","target":"_blank","rel":null,"class":null}}],"text":"https://doi.org/10.1002/elsc.201100163"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Schirmer C, et al. Development, Engineering and Biological Characterization of Stirred Tank Bioreactors. "},{"type":"text","marks":[{"type":"italic"}],"text":"Biopharmaceuticals"},{"type":"text","text":". Yeh M-K, Ed. IntechOpen: London, UK, 2018; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://dx.doi.org/10.5772/intechopen.79444","target":"_blank","rel":null,"class":null}}],"text":"https://dx.doi.org/10.5772/intechopen.79444"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"A seasoned industry professional with a PhD in chemical and biological engineering from the University of British Columbia (Vancouver, Canada) and expertise in embryonic stem cell bioengineering, upstream biological process development, and fed-batch bioreactors, "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Muhammad Arshad Chaudhry"},{"type":"text","marks":[{"type":"italic"}],"text":" is associate director of process and formulation development at Mural Oncology, 852 Winter Street, Waltham, MA 02451; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"muhammad.chaudhry@muraloncology.com","target":"_blank","rel":null,"class":null}},{"type":"italic"}],"text":"muhammad.chaudhry@muraloncology.com"},{"type":"text","marks":[{"type":"italic"}],"text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"paragraph","attrs":{"textAlign":"left"}}],"mobileHeadline":"Lessons in Bioreactor Scale-Up, Part 2: A Refresher on Fluid Flow and Mixing","articleUrl":"/bioreactors/lessons-in-bioreactor-scale-up-part-2-a-refresher-on-fluid-flow-and-mixing","listPageUrl":"/bioreactors/lessons-in-bioreactor-scale-up-part-2-a-refresher-on-fluid-flow-and-mixing","linkAttrs":{},"date":"Jun 12, 2024","timeRead":0,"categoryName":"Bioreactors","categoryUrl":"/upstream-processing/bioreactors","contributors":[{"name":"Muhammad Arshad Chaudhry","avatar":"","link":"/author/muhammad-arshad-chaudhry"}],"paidGating":null}},{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltae3a42851646a07b/6605c9ed02442858f239693d/22-4-CR-Boehringer-Cover-Wide.png","alt":""},"articleName":"Empowering Biologic Breakthroughs: Transforming Tomorrow’s Healthcare Landscape Through Microbial-Based Development and Manufacturing","articleSummary":"","articleBody":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt76d0b0641f86bb6d/6605ca16743fc20d5c5dffd7/22-4-CR-Boehringer-Cover-Border.png","alt":"22-4-CR-Boehringer-Cover-Border.png","title":null,"style":{"max-width":"178px","width":"178","height":"auto"}}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Microbial expression systems are crucial in developing innovative medicines and expanding development pipelines due to the broad range of molecule formats that can be obtained using this technology. However, this approach can be challenging as it requires a variety of manufacturing processes to handle these diverse molecule formats. This is where collaboration with an experienced organization comes into play. Boehringer Ingelheim, known for its proficiency in this technical domain, has a proven track record with 19 commercial microbial products and successful audits and regulatory compliance. The company's commitment to quality and flexibility in implementing production processes make it a reliable partner. Its technology-platform neutrality also simplifies technology and process transfers."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Fill out the form below to read the full report."}]}],"mobileHeadline":"Empowering Biologic Breakthroughs: Transforming Tomorrow’s Healthcare Landscape Through Microbial-Based Development and Manufacturing","articleUrl":"/sponsored-content/empowering-biologic-breakthroughs-transforming-tomorrow-s-healthcare-landscape-through-microbial-based-development-and-manufacturing","listPageUrl":"/sponsored-content/empowering-biologic-breakthroughs-transforming-tomorrow-s-healthcare-landscape-through-microbial-based-development-and-manufacturing","linkAttrs":{},"date":"Mar 28, 2024","timeRead":0,"categoryName":"Sponsored Content","categoryUrl":"#","contributors":[{"name":"Rebekka Wuester","avatar":"","link":"/author/rebekka-wuester"}],"paidGating":null}}],"title":"Upstream Processing","moreContent":{"title":"View More","url":"/upstream-processing"}},{"contents":[{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltcce8ce015d020bd8/66abf9034f18a60cffd24002/22-7-8-SS-Cygnus-F2.jpg","alt":"quantitative venn diagram"},"articleName":"LC-MS Detection of Host Cell Proteins: Antibody Affinity Extraction Sample Preparation Outperforms Native Digestion","articleSummary":"","articleBody":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Host cell proteins (HCPs) can copurify with biological drug substance (DS) and pose potential risks for both patients and drug manufacturers by causing immunogenicity, diminishing DS efficacy, and/or compromising DS stability. Thus, the quantity and nature of residual HCPs in DS generally are considered to be critical quality attributes (CQAs). Enzyme-linked immunosorbent assays (ELISAs) are the gold-standard analytical method for measuring total HCP levels to support both in-process testing and product release. Liquid chromatography with mass spectrometry (LC-MS) has emerged as an orthogonal tool for better understanding of HCP properties and thus more effective removal."}]},{"type":"figure","attrs":{"figcaption":"Figure 1: Quantitative Venn diagram displays unique identifications of host cell proteins (HCPs) in drug substance 1."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltcffd8053d9d0971b/66abf7a6a5e3fb16a958e3f4/22-7-8-SS-Cygnus-F2.jpg","alt":"22-7-8-SS-Cygnus-F2.jpg","title":null,"style":{"height":"auto","text-align":"center"}}},{"type":"text","text":"Figure 1: Quantitative Venn diagram displays unique identifications of host cell proteins (HCPs) in drug substance 1."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"MS is a powerful complementary method to demonstrate ELISA suitability. When combined with antibody affinity extraction (AAE) technology using an HCP ELISA antibody, MS can be used to demonstrate that an HCP ELISA is suitable for monitoring purification process consistency and product lot release ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"A major challenge for LC-MS–based HCP identification methods is that there can be a \u003e5× difference in concentration between HCPs and DS (e.g., a monoclonal antibody, mAb) in solution. That precludes the effective identification of low-abundance HCPs. To overcome this challenge, efforts have been made to optimize sample preparation and improve the dynamic range (e.g., with on-line or off-line fractionation) for mAb removal by affinity depletion or molecular weight cut-off (MWCO) and HCP enrichment."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The native digestion (ND) protocol introduced by Huang et al. has become a popular method ("},{"type":"text","marks":[{"type":"bold"}],"text":"2"},{"type":"text","text":"). ND is simpler, faster, and more robust than a traditional “bottom-up” proteomics approach (denaturing digestion), with a limit of detection (LoD) \u003c10 ppm when combined with analytical flow-through LC. However, the ND technique carries an associated risk of recovering a small subset of low-level HCPs."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Developed by Cygnus Technologies in 2013, AAE sample preparation is highly effective for enriching HCPs and depleting DS. When comparing the relative abundance of DS and HCPs with extracted ion chromatograms (data not shown), most peptides in a pre-AAE sample belong to DS. Following AAE enrichment, the relative abundance of HCPs dramatically increases while DS heavy and light chains decrease."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Below we compare ND and AAE enrichment of HCPs to fully characterize HCP profiles for two separate DS samples. Results indicate that AAE-based sample preparation is superior to ND enrichment."}]},{"type":"figure","attrs":{"figcaption":"Figure 2: Quantitative Venn diagram displays unique identifications of host cell proteins (HCPs) in drug substance 2."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltae1546f6492950da/66abf7a6a7c76a41e44fe966/22-7-8-SS-Cygnus-F3.jpg","alt":"22-7-8-SS-Cygnus-F3.jpg","title":null,"style":{"height":"auto","text-align":"center"}}},{"type":"text","text":"Figure 2: Quantitative Venn diagram displays unique identifications of host cell proteins (HCPs) in drug substance 2."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Materials and Methods"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"AAE Method:"},{"type":"text","text":" A Chinese hamster ovary (CHO) 3G polyclonal antibody from an F550-1 ELISA kit (Cygnus Technologies) was immobilized covalently on a separate chromatography support. The column was conditioned to prevent significant antibody leaching and to minimize nonspecific binding. HCP-containing DS samples were passed over the column on an ÄKTA 25-L fast protein liquid chromatography (FPLC) system (Cytiva) for binding HCPs and collecting elution fractions. The latter were neutralized, pooled, buffer-exchanged, and concentrated."}]},{"type":"figure","attrs":{"figcaption":"Table 1: Total, shared, and unique host cell proteins (HCPs) identified using each sample-preparation strategy with drug substance #1 (Figure 1); AAE = antibody affinity extraction, ND = native digestion."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltb3a66eaf2d4358c0/66abf84c8d57c66c5b14b101/22-7-8-SS-Cygnus-T1.png","alt":"22-7-8-SS-Cygnus-T1.png","title":null,"style":{"height":"auto","text-align":"center"}}},{"type":"text","text":"Table 1: Total, shared, and unique host cell proteins (HCPs) identified using each sample-preparation strategy with drug substance #1 (Figure 1); AAE = antibody affinity extraction, ND = native digestion."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"ND Method: "},{"type":"text","text":"This procedure was adapted from Huang et al. ("},{"type":"text","marks":[{"type":"bold"}],"text":"2"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"LC-MS: "},{"type":"text","text":"Pre-AAE (pre-enrichment DS sample) and post-AAE proteins were dissolved, reduced, alkylated, tryptic-digested, desalted, and concentrated. For LC-MS analysis, we used a Vanquish Horizon ultrahigh-performance liquid chromatography (UHPLC) system coupled to an Orbitrap Eclipse Tribrid mass spectrometer (Thermo Fisher Scientific). A survey spectrum ("},{"type":"text","marks":[{"type":"italic"}],"text":"m/z"},{"type":"text","text":" range 350–1700) at 240K resolution preceded MS/MS (375–2000"},{"type":"text","marks":[{"type":"italic"}],"text":" m/z"},{"type":"text","text":") of the most intense, multiply charged ions using collision-induced dissociation. Pre-AAE and post-AAE samples were analyzed independently in technical triplicate (randomized sequence). To minimize sample carryover, blank washes ran between sample injections."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Database Search:"},{"type":"text","text":" Using Proteome Discoverer software, version 2.5 (Thermo Fisher Scientific), we searched raw spectra against a proprietary database of HCPs from CHO cells. HCPs were identified with a false-positive detection rate (FDR) 0.1% and two unique peptides."}]},{"type":"paragraph","attrs":{"textAlign":"center"}},{"type":"figure","attrs":{"figcaption":"Table 2: Total, shared, and unique host cell proteins (HCPs) identified using each sample-preparation strategy with drug substance #2 (Figure 2)."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt0679213f9e536528/66abf86c9d65e28ebf313379/22-7-8-SS-Cygnus-T2.png","alt":"22-7-8-SS-Cygnus-T2.png","title":null,"style":{"text-align":"center","height":"auto"}}},{"type":"text","text":"Table 2: Total, shared, and unique host cell proteins (HCPs) identified using each sample-preparation strategy with drug substance #2 (Figure 2)."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Results and Discussion"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"We used the results from AAE and ND enrichment of two different DS samples to compare the techniques’ strengths and weaknesses. Both methods led to identification of more HCPs than was possible with no treatment at all (standard, denaturing condition), so both effectively enriched for HCPs and depleted DS for deeper proteomic coverage (Tables 1 and 2, Figures 1 and 2). That was true for DS samples containing moderately different totals of HCPs: 16 for DS #1 and 27 for DS #2."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"In both cases, AAE outperformed ND in the number of proteins identified. AAE enrichment provided for 32 identified HCPs in DS #1, whereas ND enabled identification of 20 HCPs (Table 1); AAE enrichment provided for 44 identified HCPs in DS #2, whereas ND enabled identification of only 33 HCPs (Table 2). The AAE method led to identification of all HCPs found in unenriched samples, whereas ND captured only a subset (75% for DS #1, ~92.6% for DS #2). AAE sample preparation provided for identification of all HCPs that could be identified using ND, further supporting that although both enrich for similar populations of HCPs, the AAE method does so more effectively."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Discussion: "},{"type":"text","text":"Residual HCPs in biopharmaceuticals are undesired process-related impurities that need to be controlled. Understanding their physicochemical and biochemical properties is important for achieving maximal HCP removal. The key challenge of LC-MS–based methods for HCP characterization in DS is the limited dynamic range (3–4 orders of magnitude) of most high-resolution mass spectrometers compared with the broad dynamic ranges (\u003e5×) required to detect low-level HCPs (\u003c10 ppm). The ND method is a simple and efficient way to deplete therapeutic proteins, thus reducing the dynamic range requirement and improving the sensitivity of HCP detection. Despite its broad adaptation for HCP characterization, however, ND does not enrich low-abundance HCPs that can contribute to HCP totals in final DS as quantified by ELISA. Our results show that many HCPs are not detected when using ND but are detected with AAE sample preparation. The HCPs have been enriched beyond the LoD of LC-MS through their reactivity with the CHO HCP ELISA antibody. Note that those HCPs contribute to total HCP levels as quantified by the corresponding F550-1 CHO HCP ELISA kit (Cygnus Technologies)."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"AAE enrichment can miss low-level HCPs that do not react to the HCP ELISA antibody. Despite this limitation, AAE sample preparation adequately depleted the DS and enriched for many HCPs, giving it deeper proteomic coverage than ND provides. Our results confirm the superiority of AAE enrichment to detect and possibly quantify HCPs in DS samples."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"References"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"1 "},{"type":"text","text":"Zilberman A, et al. Host Cell Protein Analysis: Immunoassays and Orthogonal Characterization By Antibody Affinity Extraction and Mass Spectrometry Methods. "},{"type":"text","marks":[{"type":"italic"}],"text":"BioProcess Int. "},{"type":"text","text":"20(9)si 2022: "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.bioprocessintl.com/sponsored-content/host-cell-protein-analysis-immunoassays-and-orthogonal-characterization-by-antibody-affinity-extraction-and-mass-spectrometry-methods","target":"_blank","rel":null,"class":null}}],"text":"https://www.bioprocessintl.com/sponsored-content/host-cell-protein-analysis-immunoassays-and-orthogonal-characterization-by-antibody-affinity-extraction-and-mass-spectrometry-methods"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"2 "},{"type":"text","text":"Huang L, et al. A Novel Sample Preparation for Shotgun Proteomics Characterization of HCPs in Antibodies. "},{"type":"text","marks":[{"type":"italic"}],"text":"Anal. Chem"},{"type":"text","text":". 89(10) 2017: 5436–5444; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://doi.org/10.1021/acs.analchem.7b00304","target":"_blank","rel":null,"class":null}}],"text":"https://doi.org/10.1021/acs.analchem.7b00304"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Corresponding author "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Alla Zilberman "},{"type":"text","marks":[{"type":"italic"}],"text":"is vice president, technical marketing and business development at Cygnus Technologies, LLC, part of Maravai LifeSciences, 1523 Olde Waterford Way, Leland, NC 28451; 1-910-454-9442; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.cygnustechnologies.com/custom-development-services/home","target":"_blank","rel":null,"class":null}},{"type":"italic"}],"text":"https://www.cygnustechnologies.com/custom-development-services/home"},{"type":"text","marks":[{"type":"italic"}],"text":". "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Darren Bertin"},{"type":"text","marks":[{"type":"italic"}],"text":" is a research and development laboratory technician, "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Stephen Stahlschmidt"},{"type":"text","marks":[{"type":"italic"}],"text":" is a laboratory technician II, "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Johanna Barnhill "},{"type":"text","marks":[{"type":"italic"}],"text":"is a senior associate scientist, "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Timothy Licknack "},{"type":"text","marks":[{"type":"italic"}],"text":"is a scientist, and "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Jared Isaac "},{"type":"text","marks":[{"type":"italic"}],"text":"is associate director of chromatography and mass spectrometry, all at Cygnus Technologies. AAE is a trademark of Cygnus Technologies."}]}],"mobileHeadline":"LC-MS Detection of Host Cell Proteins: Antibody Affinity Extraction Sample Preparation Outperforms Native Digestion","articleUrl":"/chromatography/lc-ms-detection-of-host-cell-proteins-antibody-affinity-extraction-sample-preparation-outperforms-native-digestion","listPageUrl":"/chromatography/lc-ms-detection-of-host-cell-proteins-antibody-affinity-extraction-sample-preparation-outperforms-native-digestion","linkAttrs":{},"date":"Jul 29, 2024","timeRead":0,"categoryName":"Sponsored Content","categoryUrl":"#","contributors":[{"name":"Alla Zilberman","avatar":"","link":"/author/alla-zilberman"},{"name":"Darren Bertin","avatar":"","link":"/author/darren-bertin"},{"name":"Stephen Stahlschmidt","avatar":"","link":"/author/stephen-stahlschmidt"},{"name":"Johanna Barnhill","avatar":"","link":"/author/johanna-barnhill"},{"name":"Timothy Licknack","avatar":"","link":"/author/timothy-licknack"},{"name":"Jared Isaac","avatar":"","link":"/author/jared-isaac"}],"paidGating":null}},{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt59a945f05f9c6316/666a08d49ca93b26a7263891/22-6-CR-Repligen-Cover-Wide.png","alt":"Simplifying Downstream Processing: Innovative Solutions for Filtration and Chromatography Cover"},"articleName":"Simplifying Downstream Processing: Innovative Solutions for Filtration and Chromatography","articleSummary":"","articleBody":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Downstream processing comprises critical unit operations for harvesting, purifying, and recovering target biomolecules for use as biopharmaceuticals. Tangential-flow filtration (TFF) has been a cornerstone technology in biomanufacturing, enabling precise, efficient separation and concentration of protein products. Capture and polishing chromatography steps ensure the safety of such products by separating target molecules from impurities and contaminants. Though important, such operations are known to be time and resource-intensive. To minimize production costs and enhance process economics, biopharmaceutical manufacturers must investigate novel solutions. In this special report, Mark Perreault and Adam Nelson of Repligen explore two solutions designed to simplify downstream processes: TangenX SC TFF devices and OPUS prepacked chromatography columns. "}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"First, Perreault highlights how TangenX SC technology minimizes setup and decommission times, improving the productivity of ultrafiltration and diafiltration (UF/DF) operations. In a brief case study on monoclonal antibody (mAb) concentration, TangenX SC devices also are shown to be cross-scalable with TangenX SIUS single-use TFF devices. Nelson then presents a case study in which downstream teams from Sanofi applied OPUS prepacked columns to enable continuous downstream purification of a mAb solution at pilot scale. Together, the chapters of this special report show how such technologies optimize process performance, increase ease of use, and ensure reliable scalability throughout downstream manufacturing."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Fill out the form below to download the full report now."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt9f8e604329385625/666a08ab772178aedb55aaf9/22-6-CR-Repligen-Cover-Border.png","alt":"22-6-CR-Repligen-Cover-Border.png","title":null,"style":{"text-align":"left","float":"left","width":"170px"}}}]}],"mobileHeadline":"Simplifying Downstream Processing: Innovative Solutions for Filtration and Chromatography","articleUrl":"/sponsored-content/simplifying-downstream-processing-innovative-solutions-for-filtration-and-chromatography","listPageUrl":"/sponsored-content/simplifying-downstream-processing-innovative-solutions-for-filtration-and-chromatography","linkAttrs":{},"date":"Jun 12, 2024","timeRead":0,"categoryName":"Sponsored Content","categoryUrl":"#","contributors":[{"name":"Mark A. Perreault","avatar":"","link":"/author/mark-a-perreault"},{"name":"Adam Nelson","avatar":"","link":"/author/adam-nelson"}],"paidGating":null}},{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt3c172157b7d0fb39/6669da96b4d956069024cfc5/22-6-SS-Thermo-Fisher-Cover-Wide.png","alt":"Navigating Biotherapeutic Complexity: Tailored Chromatography Resins to Enable Purification Success"},"articleName":"Navigating Biotherapeutic Complexity: Tailored Chromatography Resins to Enable Purification Success","articleSummary":"","articleBody":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltc3d165de628f6a4f/6669daa5bc57e126df78c845/22-6-SS-Thermo-Fisher-Cover-Border.png","alt":"22-6-SS-Thermo-Fisher-Cover-Border.png","title":null,"style":{"max-width":"173px","width":"173px","height":"auto"}}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The power of affinity chromatography is well-known as the workhorse of standard monoclonal antibody purification. However, as biopharmaceutical formats rapidly expand, normal affinity chromatographic tools may be unsuitable. Therefore, the creation of novel affinity chromatography resins tailored to a specific therapeutic is an excellent strategy for capturing product and removing impurities, improving yield, purity, and quality in a single step. "}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"In this article, we share a process for the creation of tailored affinity chromatography resins based on powerful ligand development and resin bead technology. This process has a successful track-record of over 60 therapeutic targets across a variety of modalities, including enzymes, antibodies, viral vectors, nucleic acids, cell surface markers, and more. Dive into case studies, including resin development performance parameters for a carbohydrate impurity scavenging resin, amongst others."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Download this article to learn how this technique can streamline downstream processes, reduce manufacturing costs, and shorten time to market, by increasing yield and purity in a single chromatography step."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Fill out the form below to download the full article now."}]}],"mobileHeadline":"Navigating Biotherapeutic Complexity: Tailored Chromatography Resins to Enable Purification Success","articleUrl":"/sponsored-content/navigating-biotherapeutic-complexity-tailored-chromatography-resins-to-enable-purification-success","listPageUrl":"/sponsored-content/navigating-biotherapeutic-complexity-tailored-chromatography-resins-to-enable-purification-success","linkAttrs":{},"date":"Jun 12, 2024","timeRead":0,"categoryName":"Sponsored Content","categoryUrl":"#","contributors":[{"name":"Laurens Sierkstra","avatar":"","link":"/author/laurens-sierkstra"}],"paidGating":null}}],"title":"Downstream Processing","moreContent":{"title":"View More","url":"/downstream-processing"}},{"contents":[{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blte7ffffa13d5bd28c/667c7e1ac82c20129338891b/22-6-eBook-Drug-Delivery-Cover-Wide.png","alt":"eBook cover"},"articleName":"eBook: Drug Delivery — New Formats for Novel Biopharmaceuticals","articleSummary":"","articleBody":[{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":null},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blta1cf1939d28f8f2a/667c7fd111582062a3445242/22-6-eBook-Drug-Delivery-Cover-Border.png","alt":"22-6-eBook-Drug-Delivery-Cover-Border.png","title":null,"style":{"max-width":"205px","width":"205px","height":"auto","text-align":"center"}}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Advances related to drug delivery, formulation, and fill–finish have paralleled drug-modality research in recent years. Perhaps inevitably, the newest formulations and devices bring forth a number of technical and operating challenges in areas such as contamination control, standardization (especially for prefilled syringes), lyophilization, serialization, and management of outsourcing/partnering relationships. New product modalities such as oligonucleotides and advanced therapies are intensifying the traditionally sizable demands on formulation, fill and finish groups in the biopharmaceutical industry. The authors in this eBook address some such concerns. First, a CDMO executive highlights the latest options in container technology. Then, BPI’s managing editor discusses key aspects of LNP technology with a leading researcher."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Register or Login and hit Download to get the full eBook."}]}],"mobileHeadline":"eBook: Drug Delivery — New Formats for Novel Biopharmaceuticals","articleUrl":"/fill-finish/ebook-drug-delivery-new-formats-for-novel-biopharmaceuticals","listPageUrl":"/fill-finish/ebook-drug-delivery-new-formats-for-novel-biopharmaceuticals","linkAttrs":{},"date":"Jun 26, 2024","timeRead":0,"categoryName":"Sponsored Content","categoryUrl":"#","contributors":[{"name":"Cheryl Scott","avatar":"","link":"/author/cheryl-scott"},{"name":"Vincenza Pironti","avatar":"","link":"/author/vincenza-pironti"},{"name":"Brian Gazaille","avatar":"","link":"/author/brian-gazaille"},{"name":"Kurt Ristroph","avatar":"","link":"/author/kurt-ristroph"}],"paidGating":null}},{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt258b17e576f75c90/667c4e1a646a83fd7bb8abd8/22-6-FR-Ram-F1.png","alt":"buffer design workflow"},"articleName":"Buffer Design for Biopharmaceutical Processes: An Online Tool for Designing and Understanding Buffers","articleSummary":"","articleBody":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Buffers are used widely in molecular biology, biochemistry, and biopharmaceutical manufacturing. For the latter, buffers play a pivotal role in modulating the characteristics of biologics, influencing their interactions with other molecules and surfaces as well as the structure and stability of the biologics themselves"},{"type":"text","marks":[{"type":"bold"}],"text":" "},{"type":"text","text":"("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":"). Biopharmaceuticals invariably are formulated with buffers, which serve to stabilize them and affect their physiological transport and function. Buffers are used extensively in biopharmaceutical processes, particularly chromatography steps in downstream processing ("},{"type":"text","marks":[{"type":"bold"}],"text":"2"},{"type":"text","text":"). A buffer’s pH level can affect significantly the state of a chromatographic resin and protein of interest, determining the effectiveness of binding and elution of both products and impurities"},{"type":"text","marks":[{"type":"bold"}],"text":" "},{"type":"text","text":"("},{"type":"text","marks":[{"type":"bold"}],"text":"3"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"To design buffers effectively, we must understand basics of their chemistry and reactions, including how those relate to solution pH and ionic concentrations. Once buffers are designed, we can analyze them for compound choice and concentration, ion concentrations, and variability. Here we explore those aspects while highlighting the use of a free online tool that we recently launched: the “Expert Buffer Designer” at "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://bioprocess-tools.app","target":"_blank","rel":null,"class":null}}],"text":"https://bioprocess-tools.app"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Designing Buffers"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Phosphate, acetate, citrate, tris, and histidine are some of the most prevalent buffer families (species) used in biopharmaceutical processes. Here we use the Expert Buffer Designer to design and understand buffers step by step according to a typical workflow (Figure 1)."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 1: Buffer design workflow."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt4103cc58c11149e7/667c4b9ecf7a9885843f48ea/22-6-FR-Ram-F1.PNG","alt":"22-6-FR-Ram-F1.PNG","title":null,"style":{"text-align":"center"}}},{"type":"text","text":"Figure 1: Buffer design workflow."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Step 1: Selecting buffering species"},{"type":"text","text":" starts with determining the desired pH range for a given process step. For instance, during monoclonal antibody (mAb) purification, an acetate or citrate buffer might be optimal for antibody binding to cation-exchange (CEX) resin, whereas tris or phosphate might be optimal for affinity chromatography. Although pH is a crucial factor, we also must consider the charge of a buffering species and its effect on resin chemistry. The multivalency of phosphate and citrate, for example, can hinder anion-exchange (AEX) flowthrough performance ("},{"type":"text","marks":[{"type":"bold"}],"text":"4"},{"type":"text","text":")"},{"type":"text","marks":[{"type":"bold"}],"text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Understanding the dissociation reactions of buffering species provides insights into charge characteristics, effective pH ranges, and complexities in the interplay between pH and ion concentrations. Each buffer species undergoes ionization equilibrium reactions, sequentially dissociating hydrogen ions at characteristic dissociation constants (p"},{"type":"text","marks":[{"type":"italic"}],"text":"K"},{"type":"text","marks":[{"type":"subscript"}],"text":"a"},{"type":"text","text":") ("},{"type":"text","marks":[{"type":"bold"}],"text":"5, 6"},{"type":"text","text":"). Phosphate, for example, undergoes three dissociations, each with a corresponding p"},{"type":"text","marks":[{"type":"italic"}],"text":"K"},{"type":"text","marks":[{"type":"subscript"}],"text":"a"},{"type":"text","text":", so it is a fitting buffer for maintaining pH ~7.2 (Equation 1)."}]},{"type":"figure","attrs":{"figcaption":"Equation 1: Phosphate ionization reactions."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt74a2a793281d5cdf/667c4bc6cf7a98912f3f48ee/22-6-FR-Ram-E1.PNG","alt":"22-6-FR-Ram-E1.PNG","title":null,"style":{"text-align":"center"}}},{"type":"text","text":"Equation 1: Phosphate ionization reactions."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Choosing a buffer species is straightforward with the Expert Buffer Designer tool. Begin typing the first few letters into the autocomplete input box, and the tool will suggest relevant options (Figure 2). The tool contains an extensive library of buffer species used in bioprocessing, standard biochemical reactions, and Good’s buffers and a comprehensive collection of dual-species buffers. That makes the tool fast and convenient for many applications"},{"type":"text","marks":[{"type":"bold"}],"text":" "},{"type":"text","text":"("},{"type":"text","marks":[{"type":"bold"}],"text":"7"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 2: Buffer species selection (screenshot from the Expert Buffer Designer tool)."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltc44c13535e14c69d/6682d7a23e6ead6e5a9543d3/22-6-FR-Ram-F2.PNG","alt":"22-6-FR-Ram-F2.PNG","title":null,"style":{"max-width":"408px","width":"408px","height":"auto","text-align":"center"}}},{"type":"text","text":"Figure 2: Buffer species selection (screenshot from the Expert Buffer Designer tool)."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Step 2: Selecting buffer compounds "},{"type":"text","text":"has significant implications for both performance and operational efficiency. Typically, you can design a buffer by combining a species-containing compound with another (nonbuffering) compound that provides counterions to adjust the pH level. In designing a phosphate buffer, for example, one option is to use sodium phosphate monobasic (NaH"},{"type":"text","marks":[{"type":"subscript"}],"text":"2"},{"type":"text","text":"PO"},{"type":"text","marks":[{"type":"subscript"}],"text":"4"},{"type":"text","text":") and sodium hydroxide (NaOH), with sodium serving as the counterion to shift pH to a desired level."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Alternatively, you can choose compounds that both contain your buffering species (Figure 3a), such as when combining the monobasic NaH"},{"type":"text","marks":[{"type":"subscript"}],"text":"2"},{"type":"text","text":"PO"},{"type":"text","marks":[{"type":"subscript"}],"text":"4"},{"type":"text","text":" with sodium phosphate dibasic (Na"},{"type":"text","marks":[{"type":"subscript"}],"text":"2"},{"type":"text","text":"HPO"},{"type":"text","marks":[{"type":"subscript"}],"text":"4"},{"type":"text","text":"). This approach is rooted in classic biochemistry/molecular biology protocols, in which scientists typically combine stock solutions of those compounds proportionately to achieve a target pH."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 3a: Compound options."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt547efb1c0cc4fdab/667c4c04993f88b000074909/22-6-FR-Ram-F3.PNG","alt":"22-6-FR-Ram-F3.PNG","title":null,"style":{"text-align":"center"}}},{"type":"text","text":"Figure 3a: Compound options."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"When compounds dissolve in water, they dissociate into ions (Figure 3b). As such, you can use compounds such that ions in the solution remain essentially the same. Thus, we can design buffers with the same pH and composition based on either compound combination within a practical use range. However, consider operational aspects as well: e.g., the cost of raw materials, testing, and release processes; safe handling of chemicals; and factors such as solubility, hydration levels, and effects of storage, shipping, and handling."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 3b: Compound ionization."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blte5eb9eb347e6d9e5/667c4c1b87dfa4bbeb5cb578/22-6-FR-Ram-F3b.PNG","alt":"22-6-FR-Ram-F3b.PNG","title":null,"style":{"text-align":"center"}}},{"type":"text","text":"Figure 3b: Compound ionization."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Our Expert Buffer Designer tool simplifies compound selection. The tool intelligently presents only the compound choices that can be used to prepare a specified buffer to the target concentration. You can choose from a simple acid or base, a compound with the same buffer species, or a different buffer species that could act as a counterion (Figure 4)."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 4: Selecting compounds from options (screenshot)."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt775f47c0fa517074/6682d7f038e1137b88c57e31/22-6-FR-Ram-F4.PNG","alt":"22-6-FR-Ram-F4.PNG","title":null,"style":{"height":"auto","text-align":"center"}}},{"type":"text","text":"Figure 4: Selecting compounds from options (screenshot)."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Step 3: Determining the buffer concentration "},{"type":"text","text":"can be iterative. It requires understanding how pH and ion concentrations influence a solution’s buffering capacity, buffer-ion charges, and ionic interactions with proteins and resins."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"As Equation 1 shows, phosphate ions exhibit different degrees of dissociation in a solution based on the pH. You can manipulate that — and consequently, the degree of dissociation — by adjusting the counterion concentration while keeping the buffer species concentration constant. ("},{"type":"text","marks":[{"type":"italic"}],"text":"Buffer concentration"},{"type":"text","text":" describes the total concentration of all buffer ions in a solution.) From the dissociation reactions and their equilibria, we determine individual species concentrations as a function of the total ionic concentration and pH (Equations 2–7)."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":null},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt1fb9c5c0b97224d6/667c4c5387dfa4335f5cb57c/22-6-FR-Ram-E2-7.PNG","alt":"22-6-FR-Ram-E2-7.PNG","title":null,"style":{"text-align":"center"}}}]},{"type":"paragraph","attrs":{"textAlign":"center"},"content":[{"type":"text","text":"Equations 2–7: Ionic concentrations and p"},{"type":"text","marks":[{"type":"italic"}],"text":"K"},{"type":"text","marks":[{"type":"subscript"}],"text":"a"},{"type":"text","text":" adjustment."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Next, we incorporate a fundamental rule dictating the relative proportions of ions (including hydrogen ions) and the electroneutrality principle. Equation 8 represents that principle for the phosphate buffer system. Qualitatively, the relative level of sodium ions dictates the charge balance, which then influences the phosphate-ion dissociation, subsequently affecting the hydrogen-ion concentration and thus the solution pH."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Equation 8: Electroneutrality equation."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt2e933885053a322a/667c4cb184b2eb6f001c7255/22-6-FR-Ram-E8.PNG","alt":"22-6-FR-Ram-E8.PNG","title":null,"style":{"text-align":"center"}}},{"type":"text","text":"Equation 8: Electroneutrality equation."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"When deciding on buffer concentration and pH, you need to pinpoint those charged-ion concentrations and the pH level that will yield your desired ion distribution. Our tool streamlines those considerations: You can input different buffer concentrations and pH values, and it will display the ions’ relative proportions to help you determine the optimal buffer. If your requirements are more straightforward, you might choose a concentration based on best practices and a pH required for your specific process step. You also have the option to introduce salt into a buffer by selecting a suitable salt and its concentration. The online tool also corrects p"},{"type":"text","marks":[{"type":"italic"}],"text":"K"},{"type":"text","marks":[{"type":"subscript"}],"text":"a"},{"type":"text","text":" based on the solution’s ionic strength using a well-established approach and iterative calculation ("},{"type":"text","marks":[{"type":"bold"}],"text":"6"},{"type":"text","text":"). Once you have chosen all necessary inputs, click on the Design Buffer button to trigger the design calculations, the output of which will be returned to the user interface."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Buffer Design with Natural Language Input: "},{"type":"text","text":"Large language models (LLMs) make it possible to transform natural-language input from users into function inputs for application programming interfaces (APIs). We have leveraged the ChatGPT API (gpt-3.5-turbo-0125) to develop an algorithm that seamlessly enables the capability in our Expert Buffer Designer tool. A straightforward command such as “50-mM phosphate buffer, pH 7.4” returns a recipe equivalent to one derived from the multiple-input buffer designer interface featured in the subsequent tab (Figure 5). You can specify that you want to use sodium hydroxide as a counterion compound by requesting “50-mM Phosphate–NaOH buffer, pH 7.4” instead."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 5: Natural language input (screenshot)."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt11bc95c5bab1c191/6682d817eb11bbe2590f0fd3/22-6-FR-Ram-F5.PNG","alt":"22-6-FR-Ram-F5.PNG","title":null,"style":{"height":"auto","text-align":"center"}}},{"type":"text","text":"Figure 5: Natural language input (screenshot)."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Behind the scenes, the tool interfaces with the ChatGPT API in real-time. Using a wealth of training data and prompts, along with its own proficiency in buffers and compounds, the tool translates user queries into a structured format. Our API then parses the output and invokes the necessary functions to generate a buffer recipe."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"pH Calculation Sandbox: "},{"type":"text","text":"The Expert Buffer Designer toolbox includes a pH calculator, which gives you a testing ground for buffer development. It lets you freely select and combine chemicals, explore resulting pH levels, and understand the resulting compositions and their potential variations. The capability thus enables you to design unconventional buffers and understand extreme ranges of variability."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"This flexible feature is not just for complex buffer systems; it’s also adept at calculating pH for single-component solutions in concentrations at which the Debye–Hückel corrections apply. Figure 6 shows the interface where you can input variables and instantly receive both the pH and the associated recipe. That direct feedback loop makes the calculator an indispensable asset for buffer designers seeking to push the boundaries of buffer science."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 6: pH calculator input (screenshot)."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt54d637b738a4ffda/6682d85b628e4335b31eb5b1/22-6-FR-Ram-F6.PNG","alt":"22-6-FR-Ram-F6.PNG","title":null,"style":{"max-width":"466px","width":"466px","height":"auto","text-align":"center"}}},{"type":"text","text":"Figure 6: pH calculator input (screenshot)."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Methodology Behind pH Calculations:"},{"type":"text","text":" We’ve established a workflow for calculating pH (Figure 7). Our tool takes the buffer concentration, chosen compounds, and target pH as inputs, then calculates the compound concentrations that will provide the target buffer species concentration while achieving a target pH level. In the phosphate buffer example, the tool determines molar concentrations of sodium phosphate monobasic and dibasic required to achieve the desired concentration, considering stoichiometry and other factors. The ionic strength is calculated and used to adjust p"},{"type":"text","marks":[{"type":"italic"}],"text":"K"},{"type":"text","marks":[{"type":"subscript"}],"text":"a"},{"type":"text","text":" iteratively. When a target pH is achieved, the compound and ion concentrations are created and a heat map is provided to the user interface (Figure 8a)."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 7: Calculation methodology."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltaef1408333bcbfe3/667c4d96c82c20d2223888e8/22-6-FR-Ram-F7.PNG","alt":"22-6-FR-Ram-F7.PNG","title":null,"style":{"text-align":"center"}}},{"type":"text","text":"Figure 7: Calculation methodology."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"As evident herein, the calculations are intricate, involving the resolution of highly nonlinear equations spanning 14 orders of magnitude. Through years of dedicated development, we’ve honed our algorithm to deliver speed, accuracy, and robustness — all without relying on complex software packages. This advancement is pivotal in making the Expert Buffer Designer tool freely accessible online, enabling its democratization and global adoption."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 8a: Compound variability heatmap showing slight pH level changes."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt5a48491da13415b0/667c4dbbb8e3b81e5f790a82/22-6-FR-Ram-F8.PNG","alt":"22-6-FR-Ram-F8.PNG","title":null,"style":{"text-align":"center"}}},{"type":"text","text":"Figure 8a: Compound variability heatmap showing slight pH level changes."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Output: Insights and Optimization"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Regardless of the input method used — generative AI input, the Expert Buffer Designer interface, or the pH calculator — the online tool always provides an updated buffer list, a buffer-recipe summary, and a detailed view containing compound concentrations, ion concentrations, and a compositional variability analysis heat map. This consistency ensures that users have the necessary data to fine-tune their own buffer formulations. The tool also allows you to edit outputs seamlessly using the designer or calculator."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Figure 8b shows a summarized output of the pH calculation and buffer list. The tool provides a convenient way to build an optimized list of buffers you can print and analyze."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 8b: Buffer list, summary, and details (screenshot)."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt6066d51e8853a20a/6682d8aa628e4370bc1eb5b5/22-6-FR-Ram-F8b.PNG","alt":"22-6-FR-Ram-F8b.PNG","title":null,"style":{"height":"auto","text-align":"center"}}},{"type":"text","text":"Figure 8b: Buffer list, summary, and details (screenshot)."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Refining Compound Concentrations for Optimal Buffers:"},{"type":"text","text":" Compound choices can vary for specific buffer species. You can experiment with different combinations and assess their effects on compound concentrations by viewing compound molarities in the summary recipe and concentrations in the compound details (Figure 8b)."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Ion Concentrations: "},{"type":"text","text":"We’ve established how critical ion concentrations influence protein and resin interactions. Once compounds dissolve, ions shed their compound identity, merging into a unified pool. The Expert Buffer Designer tool provides an exhaustive view of all compounds and ions (Figure 8b)."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Understanding unique ion concentrations is critical to anticipating how buffers will interact with resins. If you observe, for instance, that the total concentration of negative ions (chloride and phosphate) is 0.2 M, and the total positive ion concentration (sodium) stands at 0.27 M, then you can infer the potential effects on binding interactions in CEX and AEX columns."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Additionally, the online tool provides a concentration of each ionic form. With that information, you can gauge the total charged-ion concentration in a given solution, which carries significant implications for ion-exchange chromatography and other applications in which charge interactions are critical."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Buffer Performance and Variability"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"One problem we face as buffer designers is predicting buffer robustness in practical scenarios. Since we cannot design buffers exclusively at their p"},{"type":"text","marks":[{"type":"italic"}],"text":"K"},{"type":"text","marks":[{"type":"subscript"}],"text":"a"},{"type":"text","text":", we must anticipate how pH might fluctuate due to inevitable variations during preparation and use. During use, those might include a resin’s adsorption or desorption of ions and the dynamic processes of protein binding and elution, which subsequently alters both pH and ion composition."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Preparation-related variability is critical to establishing an appropriate control strategy, taking into account variability in weighing and dispensing and volume additions due to the precision and accuracy limitations of measurement systems. Furthermore, when buffers are not made from preestablished recipes but rather prepared by titration, we encounter additional variabilities, such as the precision of acid/base addition, the pH sensor’s reliability, and the calibration of pH meters."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Traditionally, “buffering capacity”— the pH change resulting from adding a unit amount of acid or base — has been the standard metric for buffer characterization. However, that measure doesn’t help you understand variabilities. We advocate for a pragmatic solution that models variability as fluctuations in compound concentrations. Our Expert Buffer Designer tool, with its capacity for rapid and precise pH calculations, is engineered to vary compound concentrations over a range and display the resulting pH values on a heat map."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The heat map in Figure 8a for a phosphate buffer reveals the pH impact of a 30% variability in compound concentration. By setting visualization thresholds at 0.1, 0.2, and \u003e0.3 fractions of pH, we observe that the variability is not uniform across the board. That insight is important because it shows that pH might be more sensitive to fluctuations in one compound than it is in another. This counters the common but erroneous belief that achieving a target pH implies compositional accuracy — a fallacy that often is encountered with titration methods, in which the correct pH might be reached only at the cost of compositional integrity."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Leveraging such insights through in silico methods vastly enhances the consistency and performance of the resulting buffers. Our approach exhibits operational robustness and embeds an effective control strategy into the buffer-design process. We champion this approach as an efficient quality-by-design (QbD) practice ("},{"type":"text","marks":[{"type":"bold"}],"text":"8"},{"type":"text","text":"). In silico models enable such design for quality, removing the traditional reliance on characterization, verification, and validation."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Exploring Dual-Species Buffers — Insights from Tris–Acetate Systems: "},{"type":"text","text":"Dual-species buffers are well established in biochemistry and molecular biology. Their application in bioprocesses has been limited, however, because of complexity and limited understanding about their potential advantages. Studies demonstrate that dual-species systems such as the tris–acetate system have significant value in process simplification ("},{"type":"text","marks":[{"type":"bold"}],"text":"9"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Buffer systems typically are selected to provide effective buffering around their p"},{"type":"text","marks":[{"type":"italic"}],"text":"K"},{"type":"text","marks":[{"type":"subscript"}],"text":"a"},{"type":"text","text":" values. However, achieving robust pH control is paramount in processes that require precise pH adjustments (e.g., pH inactivation, neutralization, or sample preparation for CEX). An overshoot in pH, particularly at lower values (typically 3.5–3.7) necessary for pH inactivation, can damage proteins. In such cases, using an acetate buffer system with pH adjustments based on acetic acid, proves to be advantageous. Acetic acid becomes less ionized as a target pH approaches, leading to a gradual pH change overall. Conversely, tris exhibits similar behavior when pH is adjusted upwards and becomes less ionized near pH 8.0, ensuring robust control. That allows for rapid changes during transitions, enabling efficient attainment of target pH levels."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The Expert Buffer Designer tool makes designing such dual-species buffers straightforward. The designer interface presents a comprehensive list of potential pairings for specified buffer species, enabling creation and evaluation of several combinations under different process scenarios. That capability helps users determine the most effective system for their needs. We are excited to announce that we are working on modeling titrations in the next major version of this tool."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Filling an Industry Need"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"We’ve explored the intricacies of buffer design in the context of our Expert Buffer Designer online tool. Our design journey began with selecting buffer species and compounds, highlighting how those choices affect the overall effectiveness and operational feasibility of the resulting buffers. Herein we demonstrate the value and use of natural language inputs to simplify the input process and a pH calculator for broad exploration."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Through meticulous calculation of pH and compound concentrations, the tool can handle different input methods consistently, whether through AI-driven interfaces or traditional pH calculators. Buffer robustness is determined through simulations."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"This represents a unique effort to provide a comprehensive tool for buffer design, which we saw as an unmet need in the biopharmaceutical industry. Making such a tool freely accessible through the online interface is another step in our vision of democratizing technology to help accelerate the development of essential patient treatments."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"References"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":" Strickley RG, Lambert WJ. A Review of Formulations of Commercially Available Antibodies. "},{"type":"text","marks":[{"type":"italic"}],"text":"J. Pharmaceut. Sci."},{"type":"text","text":" 110(7) 2021: 2590–2608.e56; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://doi.org/10.1016/j.xphs.2021.03.017","target":"_self","rel":null,"class":null}}],"text":"https://doi.org/10.1016/j.xphs.2021.03.017."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"2"},{"type":"text","text":" Linderholm A, et al. Buffers in Biologics Manufacturing."},{"type":"text","marks":[{"type":"italic"}],"text":" BioProcess Int"},{"type":"text","text":". 15(2) 2017: 20–27; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.bioprocessintl.com/qa-qc/buffers-in-biologics-manufacturing","target":"_self","rel":null,"class":null}}],"text":"https://www.bioprocessintl.com/qa-qc/buffers-in-biologics-manufacturing"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"3"},{"type":"text","text":" Pabst TM, Carta G. pH Transitions in Cation Exchange Chromatographic Columns Containing Weak Acid Groups. "},{"type":"text","marks":[{"type":"italic"}],"text":"J. Chromatogr. 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Basic Principles of Electrolyte Chemistry for Microfluidic Electrokinetics, Part I: Acid-Base Equilibria and pH Buffers."},{"type":"text","marks":[{"type":"italic"}],"text":" Lab Chip"},{"type":"text","text":" 9(17) 2009: 2437–2453; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://doi.org/10.1039/b906465f","target":"_blank","rel":null,"class":null}}],"text":"https://doi.org/10.1039/b906465f"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"7 "},{"type":"text","text":"Goldberg RN, Kishore N, Lennen RM. Thermodynamic Quantities for the Ionization Reactions of Buffers. "},{"type":"text","marks":[{"type":"italic"}],"text":"J. Phys. Chem. Ref. 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Abbott Laboratories. 2012/0238730."},{"type":"text","marks":[{"type":"italic"}],"text":" Integrated Approach to Isolating and Purifying Antibodies."},{"type":"text","text":" US Patent and Trademark Office: 20 September 2012; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://ppubs.uspto.gov/dirsearch-public/print/downloadPdf/20120238730","target":"_blank","rel":null,"class":null}}],"text":"https://ppubs.uspto.gov/dirsearch-public/print/downloadPdf/20120238730"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Based in Worcester, MA, corresponding author "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Natraj Ram"},{"type":"text","marks":[{"type":"italic"}],"text":" ("},{"type":"text","marks":[{"type":"link","attrs":{"href":"natraj.ram@gmail.com","target":"_blank","rel":null,"class":null}},{"type":"italic"}],"text":"natraj.ram@gmail.com"},{"type":"text","marks":[{"type":"italic"}],"text":") is vice president of innovation at Thermo Fisher Scientific. He has worked in process development at Takeda, Alkermes, AbbVie, Pfizer, and Cambrex BioScience. He is also the project lead for BioPhorum’s buffer management team and a scientific advisor for BPI events. "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Adharsh Ravi"},{"type":"text","marks":[{"type":"italic"}],"text":" is a senior in the S program for computer and information systems at Framingham State University in Framingham, MA. The freely available Expert Buffer Designer tool at "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://buffer-designer.web.app","target":"_blank","rel":null,"class":null}},{"type":"italic"}],"text":"https://buffer-designer.web.app"},{"type":"text","marks":[{"type":"italic"}],"text":" is not affiliated with Thermo Fisher Scientific."}]}],"mobileHeadline":"Buffer Design for Biopharmaceutical Processes: An Online Tool for Designing and Understanding Buffers","articleUrl":"/information-technology/buffer-design-for-biopharmaceutical-processes-an-online-tool-for-designing-and-understanding-buffers","listPageUrl":"/information-technology/buffer-design-for-biopharmaceutical-processes-an-online-tool-for-designing-and-understanding-buffers","linkAttrs":{},"date":"Jun 19, 2024","timeRead":0,"categoryName":"Information Technology","categoryUrl":"/manufacturing/information-technology","contributors":[{"name":"Natraj Ram ","avatar":"","link":"/author/natraj-ram"},{"name":"Adharsh Ravi","avatar":"","link":"/author/adharsh-ravi"}],"paidGating":null}},{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt61b0de37edaf7f44/667479d8e805777d7a4bcfae/world.jpg","alt":"digital globe"},"articleName":"Keys to Successful Technology Transfer","articleSummary":"","articleBody":[{"type":"figure","attrs":{"figcaption":"STOCK.ADOBE.COM"},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blta566a635812dbad0/667477d9553c0b38aa8cd3dc/world.jpg","alt":"world.jpg","title":null,"style":{"height":"auto","text-align":"center"}}},{"type":"text","text":"STOCK.ADOBE.COM"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Technology transfer is a sophisticated and complex undertaking that requires careful planning to ensure successful transition and regulatory compliance. The biopharmaceutical industry is concerned with two main types of technology transfer: scale-up and transfer to a different site ("},{"type":"text","marks":[{"type":"bold"}],"text":"1, 2"},{"type":"text","text":"). "},{"type":"text","marks":[{"type":"italic"}],"text":"Scale-up technology transfer "},{"type":"text","text":"often occurs within an organization or facility and involves moving a technology or process from a laboratory or pilot-scale environment to a larger manufacturing scale. Such work can help to increase production capacity, improve efficiency, and optimize technology for commercialization."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Transfer to a different site"},{"type":"text","text":" involves relocating a technology or process from one location or organization to another. This type of transfer often is undertaken when a company wants to replicate or implement a technology at a new facility, either within the same organization or internationally. This article focuses primarily on the considerations that organizations should make when conducting technology transfer to another site."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Technology transfer begins when an organization evaluates a process to ensure its scalability, robustness, and efficiency. The organization also should consider the characteristics of the initial equipment and production environment; the quality assurance (QA) and control (QC) measures necessary to maintain product consistency and meet regulatory requirements; and the training and support needed for operating personnel. It is important to ensure that the supply-chain network is reliable and that scale up or transfer to a different site is financially viable."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Evaluating Site Suitability"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Companies should evaluate a number of key aspects before deeming a site suitable for technology transfer. They must consider site infrastructure, compliance with local laws and regulations, availability of skilled labor, market proximity, and logistics. An organization should conduct prequalification assessment of the new site and define qualification criteria according to specific needs the requirements of the technology being transferred. Qualification helps to ensure that a proposed site has the capabilities, infrastructure, and quality systems needed to implement a transferred technology effectively."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"A site evaluation should include assessment of manufacturing operations, infrastructure, quality systems, and overall regulatory compliance. Evaluators should note whether a nominated facility will require modifications to accommodate a transferred technology. Such modifications might include equipment installation, infrastructure upgrades, and operational changes to ensure compliance with safety and environmental standards."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Necessary Documentation:"},{"type":"text","text":" Next, organizations should establish a quality or technical agreement that defines roles, responsibilities, and expectations related to quality management, regulatory compliance, change control, risk management, and communication between the organization and the new site. Such information informs the development of a step-by-step technical-transfer plan that includes timelines, milestones, documentation requirements, and testing and validation protocols."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"It’s important to ensure that the personnel involved in the technology-transfer process are appropriately trained and that they can undertake skills development when necessary. Companies should conduct periodic audits and compliance checks of their site and keep comprehensive records of all qualification activities and ongoing monitoring, including reviews, feedback sessions, and key performance indicators."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Navigating the Regulatory Landscape"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"When organizations conduct a technology transfer to a different region or country, they must identify the regulatory authority responsible for overseeing their product’s manufacture and marketing in that location. Only then can they develop an understanding of local good manufacturing practice (GMP) guidelines. Such guidelines outline the requirements and standards for compliance as described in 21 CFR Parts 210/211 from the US Food and Drug Administration (FDA), or the EudraLex Volume 4 in the European Union (EU) ("},{"type":"text","marks":[{"type":"bold"}],"text":"3, 4"},{"type":"text","text":"). Assessing the manufacturing facility and processes at a new site ensures compliance with GMP requirements. Then, companies can prepare for an inspection and audit by establishing comprehensive documentation and record-keeping systems that show compliance."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"To implement a quality management system (QMS) that aligns with GMP principles, companies must establish standard operating procedures (SOPs) and a robust change control process. Sponsors that outsource should conduct regular audits and ensure the continuous improvement of manufacturing processes. Ongoing compliance with GMP standards must be maintained through periodic inspections, reporting, and regulatory submissions. Regulators follow agreed-upon submission timelines and check procedures for technology transfer. It is important to develop relevant procedures that include validation and risk analysis."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"The Need for Validation"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Validation "},{"type":"text","text":"demonstrates repeatability by showing that processes, methods, and equipment work as designed and consistently lead to expected results ("},{"type":"text","marks":[{"type":"bold"}],"text":"5"},{"type":"text","text":"). Almost every regulatory authority requires validation. Therefore, technology-transfer plans should contain a number of validation-related tasks and milestones."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Validation is required whenever a company makes a new product or performs a technology transfer. The process might include installation qualification/operational qualification (IQ/OQ), performance qualification (PQ), or process performance qualification (PPQ). Other elements such as cleaning validation and transport/shipping validation will apply in certain circumstances. The sequential activities validate manufacturing facilities, equipment, and with different requirements can differ depending on the industry, technology, and regions involved in a transfer ("},{"type":"text","marks":[{"type":"bold"}],"text":"6"},{"type":"text","text":"). It is important to identify regulatory approvals, certifications, and licenses that may be required for technology transfer."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Usually, validation is first required during the commercial phase, but there are two exceptions to that rule. The first is analytical-method validation, which regulators expect to occur in late clinical stages. Some regulators also enforce validation as part of quality-system implementation during clinical stages to assess intrabatch variability and show repeatability of results."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The FDA, however, expects sponsors to include process validation — whereby the agency considers an entire process, including early development work — as part of overall validation ("},{"type":"text","marks":[{"type":"bold"}],"text":"7"},{"type":"text","text":"). And the European Medicines Agency (EMA) refers to process validation as PPQ ("},{"type":"text","marks":[{"type":"bold"}],"text":"8"},{"type":"text","text":"). Although the agencies differ in terms of nomenclature and emphasis, they are aligned for most expectations and requirements. During late clinical stages, regulators enforce validation requirements for dosage forms. The EMA is explicit in its requirements for sterile dosage forms, as outlined in Annex 1 ("},{"type":"text","marks":[{"type":"bold"}],"text":"9"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Risk management is integral to the validation life cycle ("},{"type":"text","marks":[{"type":"bold"}],"text":"8, 10, 11"},{"type":"text","text":"). Although regulators generally do not expect companies to document early stages of commissioning in a corrective and preventative action (CAPA) or investigative system, such measures should be described as part of a validation master plan. On reaching the PQ or PPQ phase of development, companies should document unexpected results that deviate from the validation plan in an official investigation or CAPA system. Companies that take a holistic approach to their quality systems during early development can help to ensure compliance by determining the root causes of unexpected events and apply that knowledge in preventing recurrences."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"When phase 2 results are successful, companies should review their quality systems and ensure that their procedures address process validation in its entirety. Procedures can be modified over time, but it’s important to have a fundamental policy documented and connected with other quality systems. It is also important to understand best industry practices for both validation and technology transfer."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Assembling the Right Team"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Technology transfer unites specialists from diverse fields, including analysts who focus on method transfer, engineers who navigate the complexities of equipment considerations, and financial departments that seek to maximize profitability. But it is essential to go beyond individual perspectives and develop a comprehensive understanding of an entire business process. Companies should have dedicated technology-transfer teams that foster collaboration and communication among sites."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"An experienced project manager should oversee the technology-transfer process, identify optimum resources, coordinate team members, establish and manage timelines and milestones, collaborate with stakeholders, and ensure effective communication within the team. The team also should include technology and analytical experts; regulatory and compliance specialists; legal and intellectual property experts; operations, facility, and manufacturing specialists; QA/QC experts; training and knowledge-transfer specialists; and communication and stakeholder engagement professionals."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"A chemistry, manufacturing, and controls (CMC) team can support technology transfer. This is done by making required updates and modifications to a regulatory-submission dossier, working with the regulatory-affairs team, and providing comprehensive information about changes in manufacturing processes, equipment, analytical methods, specifications, and controls."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Dossier submissions to regulatory agencies are important during technology transfer, especially when a product, manufacturing process, or site location has changed. Regulators review submitted changes to assess their effects on the safety, efficacy, and quality of a product ("},{"type":"text","marks":[{"type":"bold"}],"text":"12"},{"type":"text","text":"). After a successful review, regulators grant approval or authorization for the proposed changes, enabling a company to implement the transferred technology at a new site and ensure the validity of the associated marketing authorization."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Posttransfer Monitoring"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Once a technology transfer is complete, a marketing-authorization holder can take steps to ensure its correct implementation and that products meet the required quality, safety, and efficacy standards ("},{"type":"text","marks":[{"type":"bold"}],"text":"13"},{"type":"text","text":"). The manufacturing process at the new site must align with the approved process described in a marketing authorization dossier. Making that happen might involve on-site inspections, audits, or quality assessments to ensure that the technology transfer has been implemented accurately."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Companies should conduct a batch-record review of their first commercial batches manufactured at new sites. Doing so verifies that all critical steps and parameters have been followed and that batch records are accurately documented. Similarly, it is best practice to collect representative samples of those batches and perform comprehensive analysis and testing to verify quality, consistency, and compliance with specifications."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Implementing a robust QA/QC system at a new site is critical to monitoring a manufacturing process. It’s also important to maintain documents of all postauthorization activities related to a site’s first commercial batches. Those include records of inspections, audits, testing results, stability data, and all corrective actions taken. Such postauthorization steps provide confidence in the quality, safety, and efficacy of a product after technology has been transferred to a new site."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"The Keys to Success"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Due diligence, careful planning, and robust oversight are key to successful technology transfer, whether scaling up within the same organization or transferring to a new site in another region. But it is just as critical to maintain open and effective communication throughout a transfer process, starting among the team members who will interact with internal and external stakeholders and the new manufacturing site to ensure that constructive feedback is implemented when required. The path to successful technology transfer is much smoother with quality procedures underpinning each step of the process."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"References"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"1 "},{"type":"text","marks":[{"type":"italic"}],"text":"Annex 7: WHO Guidelines on Transfer of Technology in Pharmaceutical Manufacturing."},{"type":"text","text":" World Health Organization: Geneva, Switzerland, 2011; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://extranet.who.int/prequal/sites/default/files/document_files/TRS_961_Annex7_2011.pdf","target":"_blank","rel":null,"class":null}}],"text":"https://extranet.who.int/prequal/sites/default/files/document_files/TRS_961_Annex7_2011.pdf"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"2 "},{"type":"text","marks":[{"type":"italic"}],"text":"TRS 1044 — Annex 4: WHO Guidelines on Technology Transfer in Pharmaceutical Manufacturing. "},{"type":"text","text":"World Health Organization: Geneva, Switzerland, 2022; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.who.int/publications/m/item/trs1044-annex4","target":"_blank","rel":null,"class":null}}],"text":"https://www.who.int/publications/m/item/trs1044-annex4"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"3 "},{"type":"text","marks":[{"type":"italic"}],"text":"21 CFR 210-211"},{"type":"text","text":". US Food and Drug Administration: Rockville, MD, 2023;"},{"type":"text","marks":[{"type":"link","attrs":{"href":" https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?CFRPart=211","target":"_blank","rel":null,"class":null}}],"text":" https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?CFRPart=211"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"4 "},{"type":"text","marks":[{"type":"italic"}],"text":"EudraLex — Volume 4 — Good Manufacturing Practice (GMP) Guidelines."},{"type":"text","text":" European Commission: Brussels, Belgium, 2024; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://health.ec.europa.eu/medicinal-products/eudralex/eudralex-volume-4_en","target":"_blank","rel":null,"class":null}}],"text":"https://health.ec.europa.eu/medicinal-products/eudralex/eudralex-volume-4_en"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"5 "},{"type":"text","marks":[{"type":"italic"}],"text":"Annex 3: Good Manufacturing Practices: Guidelines on Validation. "},{"type":"text","text":"World Health Organization: Geneva, Switzerland, 2019; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.who.int/docs/default-source/medicines/norms-and-standards/guidelines/production/trs1019-annex3-gmp-validation.pdf","target":"_blank","rel":null,"class":null}}],"text":"https://www.who.int/docs/default-source/medicines/norms-and-standards/guidelines/production/trs1019-annex3-gmp-validation.pdf"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"6 "},{"type":"text","marks":[{"type":"italic"}],"text":"Validation Master Plan Installation and Operational Qualification Non-Sterile Process Validation Cleaning Validation"},{"type":"text","text":". PIC/S, Geneva, Switzerland, 2007; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://picscheme.org/docview/3447","target":"_blank","rel":null,"class":null}}],"text":"https://picscheme.org/docview/3447"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"7 "},{"type":"text","marks":[{"type":"italic"}],"text":"Process Validation: General Principles and Practices."},{"type":"text","text":" US Food and Drug Administration: Rockville, MD, 2011; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.fda.gov/files/drugs/published/Process-Validation--General-Principles-and-Practices.pdf","target":"_blank","rel":null,"class":null}}],"text":"https://www.fda.gov/files/drugs/published/Process-Validation--General-Principles-and-Practices.pdf"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"8 "},{"type":"text","marks":[{"type":"italic"}],"text":"Guideline on Process Validation for Finished Products — Information and Data To Be Provided in Regulatory Submissions."},{"type":"text","text":" European Medicines Agency: Amsterdam, the Netherlands, 2016; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-process-validation-finished-products-information-and-data-be-provided-regulatory-submissions-revision-1_en.pdf","target":"_blank","rel":null,"class":null}}],"text":"https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-process-validation-finished-products-information-and-data-be-provided-regulatory-submissions-revision-1_en.pdf."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"9 "},{"type":"text","marks":[{"type":"italic"}],"text":"Annex 1: Manufacture of Sterile Medicinal Products. "},{"type":"text","text":"European Commission: Brussels, Belgium, 2022; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://health.ec.europa.eu/system/files/2022-08/20220825_gmpan1_en_0.pdf","target":"_blank","rel":null,"class":null}}],"text":"https://health.ec.europa.eu/system/files/2022-08/20220825_gmpan1_en_0.pdf"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"10"},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":" "},{"type":"text","marks":[{"type":"italic"}],"text":"ICH Q9(R1): Quality Risk Management."},{"type":"text","text":" International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use: Geneva, Switzerland, 2023; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://database.ich.org/sites/default/files/ICH_Q9%28R1%29_Guideline_Step4_2023_0126_0.pdf","target":"_blank","rel":null,"class":null}}],"text":"https://database.ich.org/sites/default/files/ICH_Q9%28R1%29_Guideline_Step4_2023_0126_0.pdf"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"11 "},{"type":"text","marks":[{"type":"italic"}],"text":"Annex 15: Qualification and Validation. "},{"type":"text","text":"European Commission: Brussels, Belgium, 2015; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://health.ec.europa.eu/system/files/2016-11/2015-10_annex15_0.pdf","target":"_blank","rel":null,"class":null}}],"text":"https://health.ec.europa.eu/system/files/2016-11/2015-10_annex15_0.pdf."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"12 "},{"type":"text","text":"EC No 1234/2008 Concerning the Examination of Variations to the Terms of Marketing Authorisations for Medicinal Products for Human Use and Veterinary Medicinal Products. "},{"type":"text","marks":[{"type":"italic"}],"text":"Off. J. Eur. Union:"},{"type":"text","text":" Mercier, Luxembourg, 2008; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2008:334:0007:0024:en:PDF","target":"_blank","rel":null,"class":null}}],"text":"https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2008:334:0007:0024:en:PDF"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"13 "},{"type":"text","marks":[{"type":"italic"}],"text":"ICH Guideline Q10 on Pharmaceutical Quality System"},{"type":"text","text":". European Medicines Agency: Amsterdam, the Netherlands , 2015; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.ema.europa.eu/en/documents/scientific-guideline/international-conference-harmonisation-technical-requirements-registration-pharmaceuticals-human-guideline-q10-pharmaceutical-quality-system-step-5_en.pdf","target":"_blank","rel":null,"class":null}}],"text":"https://www.ema.europa.eu/en/documents/scientific-guideline/international-conference-harmonisation-technical-requirements-registration-pharmaceuticals-human-guideline-q10-pharmaceutical-quality-system-step-5_en.pdf"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Corresponding author "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Olena Chervonenko"},{"type":"text","marks":[{"type":"italic"}],"text":" is associate director of quality management and compliance (QMC) at Cencora PharmaLex; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"olena.chervonenko@pharmalex.com","target":"_blank","rel":null,"class":null}},{"type":"italic"}],"text":"olena.chervonenko@pharmalex.com"},{"type":"text","marks":[{"type":"italic"}],"text":". Now at Moderna, "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Gerardo Gomez "},{"type":"text","marks":[{"type":"italic"}],"text":"is the former director of QMC, United States at Cencora PharmaLex."}]}],"mobileHeadline":"Keys to Successful Technology Transfer","articleUrl":"/information-technology/keys-to-successful-technology-transfer","listPageUrl":"/information-technology/keys-to-successful-technology-transfer","linkAttrs":{},"date":"Jun 12, 2024","timeRead":0,"categoryName":"Information Technology","categoryUrl":"/manufacturing/information-technology","contributors":[{"name":"Olena Chervonenko","avatar":"","link":"/author/olena-chervonenko"},{"name":"Gerardo Gomez","avatar":"","link":"/author/gerardo-gomez"}],"paidGating":null}}],"title":"Manufacturing","moreContent":{"title":"View More","url":"/manufacturing"}}]},"adConfig":{"nativeAdOption":false}},{"type":"module_three_column_highlight","uid":"blt8fd84db697ef1e1f","data":{"title":"Three Column Component Analytical Business Therapeutic Modalities","moduleLocation":"homepage","dateDisplay":"hide","data":[{"contents":[{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt8425a4a0fa8e1c3f/663e728753e02df99b9bf9b9/AdobeStock_91140751.jpg","alt":""},"articleName":"Analytical Considerations for Gene-Modified Hematopoietic Stem and Progenitor Cell Therapies: Part 1 — In-Process and Drug Product Control","articleSummary":"","articleBody":[{"type":"figure","attrs":{"figcaption":"HTTPS://STOCK.ADOBE.COM"},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt8425a4a0fa8e1c3f/663e728753e02df99b9bf9b9/AdobeStock_91140751.jpg","alt":"AdobeStock_91140751.jpg","title":null,"style":{"text-align":"center"}},"marks":[{"type":"link","attrs":{"href":"HTTPS://STOCK.ADOBE.COM","target":"_self","rel":null,"class":null}}]},{"type":"text","text":"HTTPS://STOCK.ADOBE.COM"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Genome-modified hematopoietic stem and progenitor cell (GM-HSPC) therapies represent a significant frontier in the realm of personalized medicine, holding the promise of targeted interventions for a spectrum of disorders far beyond hematological conditions. Development of these therapies is tied intricately to the rich history of and advancements in hematopoietic stem cell (HSC) grafts, which have served as the cornerstone for our understanding of hematopoiesis and bone-marrow transplantation ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The journey toward gene-edited stem cell therapies began with the foundational work of E. Donnall Thomas and colleagues during the 1950s and 1960s. Their studies enabled the refinement of protocols for bone-marrow transplantation and demonstrated regenerative potential of HSC grafts transplanted into recipients, thereby reconstituting their blood and immune systems ("},{"type":"text","marks":[{"type":"bold"}],"text":"2"},{"type":"text","text":"). The 1970s brought a shift toward allogeneic HSC transplants, necessitating further refinements in transplantation techniques and immunosuppression strategies ("},{"type":"text","marks":[{"type":"bold"}],"text":"3"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"During the 1980s, significant strides were made in improved conditioning regimens, optimizing the success of HSC grafts. Those regimens involved pretransplant chemotherapy or radiation to create a conducive environment for donor-cell engraftment, which helped improve patient outcomes ("},{"type":"text","marks":[{"type":"bold"}],"text":"4"},{"type":"text","text":"). The understanding about CD34 as a cell-surface antigen and marker for hematopoietic stem and progenitor states was a significant milestone. CD34 serves as a valuable tool for identifying and isolating crucial cell populations ("},{"type":"text","marks":[{"type":"bold"}],"text":"5, 6"},{"type":"text","text":"). Its discovery has played a pivotal role in advancing our understanding of hematopoiesis, improving characterization and manipulation of HSCs for therapeutic applications, including bone-marrow transplantation and gene therapy ("},{"type":"text","marks":[{"type":"bold"}],"text":"7"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Advancements in the 1990s introduced the use of peripheral blood stem cells (PBSCs) as an alternative source for HSC grafts ("},{"type":"text","marks":[{"type":"bold"}],"text":"8"},{"type":"text","text":"). Meanwhile, lower-intensity conditioning regimens using reduced doses of radiation and chemotherapy decreased transplant-related complications and expanded eligible patient populations ("},{"type":"text","marks":[{"type":"bold"}],"text":"9"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"We must acknowledge all those contributions both in shaping our understanding of transplantation biology and elucidating the potential for developing new GM-HSPCs. Lessons learned from decades of research, coupled with advancements in gene modification, have paved the way for commercialization of several such therapeutics (Table 1)."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Table 1: Gene-modified hematopoietic stem and progenitor cell (GM-HSPC) therapeutics commercially approved by the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA). *later withdrawn."},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltcc4c03c10b71b566/663e730eab06df4cc375b394/22-5-Morse-T1.PNG","alt":"22-5-Morse-T1.PNG","title":null,"style":{"float":"center"}}},{"type":"text","text":"Table 1: Gene-modified hematopoietic stem and progenitor cell (GM-HSPC) therapeutics commercially approved by the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA). *later withdrawn."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The first publications describing genetic modifications of mammalian cells appeared in the 1980s, showcasing advances using gammaretroviral vectors ("},{"type":"text","marks":[{"type":"bold"}],"text":"10, 11"},{"type":"text","text":"). Those were followed by a series of successful clinical trials that ultimately led to the first licensed GM-HSPC product in 2016: Strimvelis therapy for treatment of adenosine-deaminase–deficient severe combined immunodeficiency ("},{"type":"text","marks":[{"type":"bold"}],"text":"12, 13"},{"type":"text","text":"). Use of lentiviral vectors (LVVs) represented a significant advancement over that of gamma retroviruses, with the former being able to transduce nondividing cells and thus allow expansion of target cell populations. As demonstrated in studies such as Aiuti et al. in 2009 ("},{"type":"text","marks":[{"type":"bold"}],"text":"14"},{"type":"text","text":"), that breakthrough showcased the potential for improved clinical outcomes across a broader spectrum of conditions."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Notably, the approval of several lentivirus-based therapies — namely Zynteglo, Libmeldy, Skysona, and Lyfgenia treatments (Table 1) — emphasizes the clinical success and widespread acceptance of these advancements."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"More recently, the field has undergone a transformative shift with the discovery of new endonuclease technologies, particularly clustered regularly interspaced palindromic repeats (CRISPR) and CRISPR-associated protein 9 (Cas9) ("},{"type":"text","marks":[{"type":"bold"}],"text":"15"},{"type":"text","text":"). The CRISPR/Cas9 system facilitates precise and targeted genetic modifications, which has ushered in new frontiers in gene therapy. Casgevy (exagamglogene autotemcel) was the first CRISPR-based therapy to achieve commercial status, obtaining regulatory approvals in 2023 from the Medicines and Healthcare Products Regulatory Agency (MHRA) in the United Kingdom and the US Food and Drug Administration (FDA) in the United States, with conditional approval so far from the European Medicines Agency (EMA) ("},{"type":"text","marks":[{"type":"bold"}],"text":"16–18"},{"type":"text","text":"). Realizing the potential of hematopoietic gene therapies requires concerted efforts and careful considerations during their development, with emphasized and stringent quality control (QC) testing to ensure the safety, efficacy, and consistency of these innovative products before they can be administered to patients."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"In this two-part review, we focus on high-level analytical development considerations for ex vivo GM-HSPC products derived from primary donor cells. Part 1 addresses in-process and drug product control. Part 2, which will be published in BPI’s July-August featured report, examines concerns for HSPC source materials. Look to other recently published reviews for a broader discussion of chemistry, manufacturing, and controls (CMC) for GM-HSPCs ("},{"type":"text","marks":[{"type":"bold"}],"text":"19, 20"},{"type":"text","text":") and for development considerations with gene-edited pluripotent stem cells (PSCs) ("},{"type":"text","marks":[{"type":"bold"}],"text":"21"},{"type":"text","text":"). Herein we use the term "},{"type":"text","marks":[{"type":"italic"}],"text":"genome modified"},{"type":"text","text":" in a generic sense to include products that are manufactured by means of viral-vector transduction (typically by LVVs) or CRISPR/Cas9–based genome editing."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"In-Process and Drug-Product Analytical Controls"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"In addition to control of the cellular starting materials and genome modification reagents (see Part 2 in July—August), a comprehensive analytical strategy must be implemented to ensure the adequacy of in-process testing measures as well as release and characterization of final HSPC drug products. Some key considerations are addressed below."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Sampling Plan:"},{"type":"text","text":" A sampling plan must balance patient dosing needs with material availability in the context of limited cellular starting material and little to no opportunity for cellular expansion. The total recovery of CD34+ cells from mobilized donors can vary by tenfold or more and depends on factors such as donor age, disease status, and previous treatment history ("},{"type":"text","marks":[{"type":"bold"}],"text":"9"},{"type":"text","text":"). Excessive expansion of CD34+ cells in vitro has been associated with poor engraftment ("},{"type":"text","marks":[{"type":"bold"}],"text":"22–23"},{"type":"text","text":"). Manufacturing processes therefore tend to be designed for static culture, although some efforts are underway to develop approaches for expanding CD34+ cells in culture while maintaining their function ("},{"type":"text","marks":[{"type":"bold"}],"text":"24–25"},{"type":"text","text":"). Therapeutic doses of CD34+ cells typically are set at a minimum of ~2 × 10"},{"type":"text","marks":[{"type":"superscript"}],"text":"6"},{"type":"text","text":" cells/kg. Sponsors developing GM-HSPC products often need to set aside a “rescue” or backup dose of unmanipulated cells. Those would be used in case of manufacturing process or supply-chain failure before dosing, but after patient conditioning begins, or when modified cells fail to engraft in recipients ("},{"type":"text","marks":[{"type":"bold"}],"text":"26–27"},{"type":"text","text":"). A recent review by Li et al. provides more detailed discussion regarding how those limitations can affect available cell numbers ("},{"type":"text","marks":[{"type":"bold"}],"text":"19"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"In-Process Testing:"},{"type":"text","text":" In-process testing occurs at nearly every unit operation of GM-HSPC manufacturing processes (Figure 1). Minimally, cell count and viability measurements are assessed to monitor a process and establish its efficiency. Measurement of CD34+ cell predominance — as well as the presence of nontarget cell types — can help ensure appropriate enrichment of the target cells while monitoring depletion of unwanted cell types. Monitoring residual levels of genome-modification reagents also can demonstrate clearance after introduction of those reagents and during the genome-editing unit operation. Early in development, most such measurements will be used for information purposes only. As more experience is gained with a given process, it is expected that many (if not most) of those measurements will become control points. Note that assays used as control points should be qualified or validated."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 1: Standard manufacturing process for genome-modified hematopoietic stem and progenitor cell (GM-HSPC) therapies; sgRNA = single-guide ribonucleic acid."},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltae23010ae562c22e/663e72e34d256ea5faeb5be6/22-5-tech-Morse-F1.jpg","alt":"22-5-tech-Morse-F1.jpg","title":null,"style":{"float":"center"}}},{"type":"text","text":"Figure 1: Standard manufacturing process for genome-modified hematopoietic stem and progenitor cell (GM-HSPC) therapies; sgRNA = single-guide ribonucleic acid."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Release Testing:"},{"type":"text","text":" Table 2 provides a nonexhaustive list of typical attributes that might be included for release of a GM-HSPC product. Not every assay listed will be present on every GM-HSPC release specification. Cell count and viability assays form the cornerstone of nearly all analytical control strategies for CGTs because such assays are heavily used both for in-process monitoring and for defining drug product dosages. The assays are considered to be straightforward, although they do involve many sources of variation, and analysis of cryopreserved products can bring additional performance variability ("},{"type":"text","marks":[{"type":"bold"}],"text":"29–30"},{"type":"text","text":"). Flow-cytometry–based assays commonly are used to evaluate cellular purity and report the percentage of CD34+ cells present. To minimize risks of graft-versus-host disease (GvHD), the percentage of cells expressing the canonical T-cell marker CD3 also might be included within panels for allogeneic products ("},{"type":"text","marks":[{"type":"bold"}],"text":"31–32"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Table 2: Standard release panel for genetically modified hematopoietic stem and progenitor cell (GM-HSPC) therapeutics. Abbreviations: CFU = colony forming unit, ddPCR = droplet digital polymerase chain reaction (Thermo Fisher Scientific), ELISA = enzyme-linked immunosorbent assay, GVHD = graft-versus-host disease, LVV = lentiviral vector, MoA = mechanism of action, USP = United States Pharmacopeia, qPCR = quantitative polymerase chain reaction."},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltc81cabcf7361c377/663e733a46b7075ae274756b/22-5-Morse-T2.PNG","alt":"22-5-Morse-T2.PNG","title":null,"style":{"float":"center"}}},{"type":"text","text":"Table 2: Standard release panel for genetically modified hematopoietic stem and progenitor cell (GM-HSPC) therapeutics. Abbreviations: CFU = colony forming unit, ddPCR = droplet digital polymerase chain reaction (Thermo Fisher Scientific), ELISA = enzyme-linked immunosorbent assay, GVHD = graft-versus-host disease, LVV = lentiviral vector, MoA = mechanism of action, USP = United States Pharmacopeia, qPCR = quantitative polymerase chain reaction."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Potency testing "},{"type":"text","text":"is a critical aspect of ensuring the efficacy and biological activity of GM-HSPC products that represents a persistent challenge in their development. Salmikangas et al. noted that “major issues with potency tests were noted in almost 50% of all ATMP MAAs [advanced-therapy medicinal product market-authorisation applications] in the EU [European Union]” ("},{"type":"text","marks":[{"type":"bold"}],"text":"32"},{"type":"text","text":"). The authors cite a statement from Peter Marks’s (director of the FDA’s Center for Biologics Evaluation and Research (CBER)) that “issues related to potency” have been a key delaying factor for many product reviews ("},{"type":"text","marks":[{"type":"bold"}],"text":"33–35"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Potency evaluation is expected to reflect a product’s mechanism(s) of action (MoA) , which may be complex and multileveled; ideally, potency results should correlate with clinical responses ("},{"type":"text","marks":[{"type":"bold"}],"text":"36–37"},{"type":"text","text":"). However, current regulatory guidance provides a degree of flexibility based on a product’s stage of development. For example, a 2011 FDA guidance notes that, for phase 1 and early phase 2 studies, “limited quantitative information on relevant biological attributes may be sufficient”, whereas at later phases reviewers expect potency-testing strategies to “assure that product lots are well defined, biologically active, and consistently manufactured” ("},{"type":"text","marks":[{"type":"bold"}],"text":"37"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"A recent update to the potency guidance (currently in draft form) could expand further upon that regulatory flexibility by introducing the concept of a “potency assurance strategy,” whereby a sponsor may “reduce risks to potency by controlling aspects of the manufacturing process that may affect potency” ("},{"type":"text","marks":[{"type":"bold"}],"text":"37"},{"type":"text","text":"). This revised approach could provide sponsors with additional flexibility in how to establish a potency strategy, particularly early in development."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Despite significant regulatory flexibility with respect to a sponsor’s potency strategy, it is critical that GM-HSPC sponsors implement quantitative and fit-for-purpose potency assays and consider a matrixed testing approach to cover multiple aspects relating to MoAs ("},{"type":"text","marks":[{"type":"bold"}],"text":"36"},{"type":"text","text":"). As the draft updated potency guidance notes, release assays should be qualified before initiation of clinical studies to support licensure, and assays for release of licensed products must be validated ("},{"type":"text","marks":[{"type":"bold"}],"text":"37"},{"type":"text","text":"). Early product characterization plays a pivotal role in the identification of both potency-indicating product attributes and the cellular processes that require control for maintaining potency during release testing."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Assessment of the functional capabilities of gene-modified HSCs involves a complex interplay of several factors: cellular viability and ability to engraft, differentiate, and produce desired therapeutic effects ("},{"type":"text","marks":[{"type":"bold"}],"text":"33"},{"type":"text","text":"). Of significant importance is the development of a colony-forming unit (CFU) assay, which provides predictive value for successful hematopoietic engraftment by measuring the potential of HSCs to proliferate and differentiate ("},{"type":"text","marks":[{"type":"bold"}],"text":"38"},{"type":"text","text":"). For GM-HSPCs, assessments of potency also should consider the consequent effects of genomic modification. For products manufactured using viral-vector transduction, early stage potency assessment requires evaluation of both transduction efficiency (assessable by flow cytometry) and vector copy number, which typically is assessed through polymerase chain reaction (PCR)–based methods ("},{"type":"text","marks":[{"type":"bold"}],"text":"39, 40"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"For GM-HSPCs, an evaluation of on-target editing efficiency may be a suitable measure of potency early in development, and multiple approaches to doing so have been described ("},{"type":"text","marks":[{"type":"bold"}],"text":"41, 42"},{"type":"text","text":"). Regardless of the type of genetic modification used, the suite of potency assays for a commercial product should evaluate genomic modification functionality and the therapy’s intended MoA (33). Such inclusion can be extremely challenging for HSPCs because, depending on the gene, changes in expression and/or function might not be measurable until cells have differentiated. That can take two to three weeks, making it difficult to implement such testing in a QC-friendly way ("},{"type":"text","marks":[{"type":"bold"}],"text":"33"},{"type":"text","text":"). Thus, the multifaceted nature of potency testing reflects the complexity of GM-HSPC product characterization, requiring a comprehensive and tailored approach to ensuring safety and efficacy for therapeutic applications."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"A comprehensive genomic "},{"type":"text","marks":[{"type":"bold"}],"text":"safety assessment "},{"type":"text","text":"is critical to ensuring the safety of GM-HSPC products, with specifics depending on the genome-modification approach applied. For example, although CRISPR/Cas9 is high-precision in nature, unintended genetic events can occur when using the system: e.g., off-target insertions and deletions (INDELs), translocations, and generation of DNA structural variants ("},{"type":"text","marks":[{"type":"bold"}],"text":"43, 45"},{"type":"text","text":"). FDA guidance sets expectations for developers seeking to assess genomic safety ("},{"type":"text","marks":[{"type":"bold"}],"text":"44"},{"type":"text","text":"). As noted, developers should use “multiple methods (e.g., in silico, biochemical, cellular-based assays)” that include at least one genome-wide method to identify candidate off-target sites. Several in silico, biochemical, and cell-based methods have been established for off-target detection ("},{"type":"text","marks":[{"type":"bold"}],"text":"46–48"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Development strategies also should account for the possibility of sequence gaps or mismatches between single-guide RNA (sgRNA) and off-target sites as well as sequence diversity within a cell population (e.g., single-nucleotide polymorphisms, SNPs). Candidate off-target sites that are confirmed through testing of multiple drug-product batches should be assessed for risk, with high levels assigned to sites located in coding regions, associated with a known cellular function, and/or involved in disease (e.g., cancer). Sponsors should consider developing release assays for such high-risk off-target sites. Note that although the conceptual framework for evaluating the potential for translocations and structural variants is similar to that for INDELs, detection of such events often is not possible with methods typically used to identify off-target INDELs ("},{"type":"text","marks":[{"type":"bold"}],"text":"45, 49"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"For LVV-based products, the primary genomic safety risk is posed by uncontrolled integration, which can disrupt adjacent genes. To ensure safety in clinical applications, developers must aim for minimal integration copy numbers while maintaining an acceptable level of both transduction efficiency and drug-product potency ("},{"type":"text","marks":[{"type":"bold"}],"text":"50"},{"type":"text","text":"). High posttransduction copy numbers carry the risk of genotoxicity and could increase the likelihood of transgene integration near oncogenes ("},{"type":"text","marks":[{"type":"bold"}],"text":"51"},{"type":"text","text":"). Common techniques used for measuring viral copy numbers integrated into a host genome include quantitative PCR (qPCR) and digital PCR (dPCR). The field is progressing toward standardization of those assays, emphasizing the use of appropriate reference-standard materials to enable accurate comparisons ("},{"type":"text","marks":[{"type":"bold"}],"text":"50, 52"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Characterization Testing:"},{"type":"text","text":" In addition to release assays, developers also should develop a suite of assays to characterize their drug products fully. Such assays could be performed routinely on every manufactured batch or selectively on specific batches, depending on the level of risk identified with a given aspect measured. These assays could include flow-cytometry–based methods for evaluation of HSPC subpopulations and confirmation that a genome-modification process has not affected the distribution of subpopulations unintentionally ("},{"type":"text","marks":[{"type":"bold"}],"text":"39"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"GM-HSPC products can contain several residual materials, including nontarget cells (such as T cells, B cells, monocytes, and neutrophils from donor leukapheresis) or residual genome-modification reagents ("},{"type":"text","marks":[{"type":"bold"}],"text":"53"},{"type":"text","text":"). Cell-based residuals can be identified by flow cytometry. Enzyme-linked immunosorbent assays (ELISAs) and PCR-based methods typically are used to detect residual Cas9, sgRNA, or viral vectors."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The most important role for characterization testing might be to provide additional supportive evidence for potency and safety of a drug product. Assays that support drug-product potency are unsuitable for implementation in a QC environment — e.g., those that are only semiquantitative or highly variable in their performance — should be identified and performed for the purpose of characterization instead. Genomic-safety assays also might be performed to characterize drug products, such as when low-risk off-target editing sites are analyzed routinely to evaluate process consistency rather than for release of the drug product. Furthermore, vector–copy-number analyses could be conducted at a single-cell level to enhance understanding of viral integration dynamics with increased granularity ("},{"type":"text","marks":[{"type":"bold"}],"text":"54–55"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Finally, a thorough genomic safety evaluation of LVV-derived products should include analysis of their integration-site profiles within a host genome. Evaluating the specific genomic locations where LVVs integrate is imperative for determining the risks of genotoxicity and potential adverse effects, particularly when integration takes place near oncogenes ("},{"type":"text","marks":[{"type":"bold"}],"text":"56–58"},{"type":"text","text":"). Researchers use a number of molecular techniques such as PCR, next-generation sequencing (NGS), and transposase-based approaches to examine integration sites comprehensively ("},{"type":"text","marks":[{"type":"bold"}],"text":"59"},{"type":"text","text":"). The resulting information is pivotal to optimizing vector design, assessing the safety profiles of gene-therapy vectors, and guiding regulatory assessments for clinical applications ("},{"type":"text","marks":[{"type":"bold"}],"text":"60"},{"type":"text","text":"). The World Health Organization’s (WHO’s) recent establishment of a reference standard for integration-site analysis underscores the global importance of this pivotal aspect of gene therapy ("},{"type":"text","marks":[{"type":"bold"}],"text":"61"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Looking Ahead"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Whether manufactured through viral-vector–mediated gene delivery, nuclease-mediated editing, or both, genome modification of HSPCs represents a significant advancement in the potential to cure diseases that otherwise have suboptimal or no currently available treatment options. Building on a substantial therapeutic legacy of HSC transplants, these new therapies are complex to manufacture and require broad and deep analytical support to ensure adequate and consistent product quality ("},{"type":"text","marks":[{"type":"bold"}],"text":"13"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"GM-HSPCs face two key analytical challenges. First, the components used to manufacture them are often bespoke and require significant analytical oversight, as will be expanded upon "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.bioprocessintl.com/cell-therapies/analytical-considerations-for-gene-modified-hematopoietic-stem-and-progenitor-cell-therapies-part-2-starting-materials-and-drug-substances","target":"_self","rel":null,"class":null}}],"text":"in Part 2 of this review"},{"type":"text","text":". Second, the broad range of materials used to manufacture a GM-HSPC can include proteins, nucleic acids, viral vectors, and cellular materials. Each of those requires the development of a bespoke analytical approach, including specific analytical tools and methods. Challenges associated with appropriate characterization of GM-HSPC products will increase as the field matures, with the potential addition of in vivo targeting approaches and new gene-editing techniques — such as base and prime editing and gene writing ("},{"type":"text","marks":[{"type":"bold"}],"text":"62–64"},{"type":"text","text":") — and in vivo targeting approaches ("},{"type":"text","marks":[{"type":"bold"}],"text":"65"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Thus, GM-HSPC sponsors are advised to put significant thought into the development of appropriate analytical control strategies for each of their candidate therapies. 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Biotechnol"},{"type":"text","text":". 41(4) 2023: 500–512; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://doi.org/10.1038/s41587-022-01527-4","target":"_blank","rel":null,"class":null}}],"text":"https://doi.org/10.1038/s41587-022-01527-4"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"65 "},{"type":"text","text":"Psatha N, et al. In Vivo Hematopoietic Stem Cell Genome Editing: Perspectives and Limitations. "},{"type":"text","marks":[{"type":"italic"}],"text":"Genes "},{"type":"text","text":"13(12) 2022: 2222; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://doi.org/10.3390/genes13122222","target":"_blank","rel":null,"class":null}}],"text":"https://doi.org/10.3390/genes13122222"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Corresponding author "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Brent Morse"},{"type":"text","marks":[{"type":"italic"}],"text":" is a principal consultant, and "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Alicja Fiedorowicz "},{"type":"text","marks":[{"type":"italic"}],"text":"is an analytical consultant in cell and gene therapy at Dark Horse Consulting Group near Boston, MA; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"bmorse@darkhorseconsultinggroup.com","target":"_blank","rel":null,"class":null}},{"type":"italic"}],"text":"bmorse@darkhorseconsultinggroup.com"},{"type":"text","marks":[{"type":"italic"}],"text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"paragraph","attrs":{"textAlign":"left"}}],"mobileHeadline":"Analytical Considerations for Gene-Modified Hematopoietic Stem and Progenitor Cell Therapies: Part 1 — In-Process and Drug Product Control","articleUrl":"/cell-therapies/analytical-considerations-for-gene-modified-hematopoietic-stem-and-progenitor-cell-therapies-part-1-in-process-drug-substances-and-drug-products","listPageUrl":"/cell-therapies/analytical-considerations-for-gene-modified-hematopoietic-stem-and-progenitor-cell-therapies-part-1-in-process-drug-substances-and-drug-products","linkAttrs":{},"date":"May 10, 2024","timeRead":0,"categoryName":"Cell Therapies","categoryUrl":"/therapeutic-modalities/cell-therapies","contributors":[{"name":"Brent Morse","avatar":"","link":"/author/brent-morse"},{"name":"Alicja Fiedorowicz","avatar":"","link":"/author/alicja-fiedorowicz"}],"paidGating":null}},{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltbe14079eeec68109/663e4f93b6eaa380019208c2/22-5-Panjwani-P1.jpg","alt":""},"articleName":"Practical Applications of Process Capability Indices in the Biopharmaceutical Industry: A Simulation-Based Study","articleSummary":"","articleBody":[{"type":"figure","attrs":{"figcaption":"HTTPS://STOCK.ADOBE.COM"},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltbe14079eeec68109/663e4f93b6eaa380019208c2/22-5-Panjwani-P1.jpg","alt":"22-5-Panjwani-P1.jpg","title":null,"style":{"text-align":"center","height":"auto"}},"marks":[{"type":"link","attrs":{"href":"STOCK.ADOBE.COM","target":"_self","rel":null,"class":null}}]},{"type":"text","text":"HTTPS://STOCK.ADOBE.COM"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Process capability often serves as a metric for quantifying biomanufacturing-process performance. The term refers to the extent of variation in measurable property "},{"type":"text","marks":[{"type":"italic"}],"text":"X "},{"type":"text","text":"of a given biological product made by a given process relative to the width of that property’s specification limits. Thus, process capability provides insight into how consistently a manufacturing process generates products that meet specifications. In other words, the higher the process capability is, the lower the probability is for an out-of-specification (OoS) outcome."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Evaluating process capability is particularly important in biopharmaceutical manufacturing. Multiple analytical results (often with multiple replicates) are generated routinely for release of drug-product batches — hence the need to ensure consistent process performance and product safety and efficacy."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Definitions:"},{"type":"text","text":" Statisticians working in industry and academia have proposed several process-capability indices ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":"). Equations 1–3 list the most basic indices for a process with an upper specification limit (USL) only, a process with a lower specification limit (LSL) only, and a process with both upper and lower specification limits, respectively. The indices assume that a biomanufacturing process generates a biological product with property "},{"type":"text","marks":[{"type":"italic"}],"text":"X"},{"type":"text","text":", values for which follow normal distribution and have a mean of "},{"type":"text","marks":[{"type":"italic"}],"text":"μ "},{"type":"text","text":"and a variance of "},{"type":"text","marks":[{"type":"italic"}],"text":"σ"},{"type":"text","marks":[{"type":"superscript"}],"text":"2"},{"type":"text","text":". If a process is centered, meaning that it has a distribution mean at the center of the specification limit interval, then "},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","text":", "},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pl"},{"type":"text","text":", and "},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pu"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"values are equal."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Equations 1–7: Basic calculations for process-capability indices."},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt00b99bd75d64adcf/663e4fe2d435f1362513470d/22-5-Panjwani-E1-7.jpg","alt":"22-5-Panjwani-E1-7.jpg","title":null,"style":{"float":"center"}}},{"type":"text","text":"Equations 1–7: Basic calculations for process-capability indices."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"A process-capability index also can be understood as a quantification of the extent of observed process variation compared with the allowable amount of process variation within specification limits (Figure 1). Popular commercial software tools such as the JMP package sometimes present alternative process-capability indices: e.g., "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","text":", "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pu"},{"type":"text","text":", and "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pl"},{"type":"text","text":". The difference between the "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"and "},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk "},{"type":"text","text":"sets of indices lies primarily in their calculation of variability ("},{"type":"text","marks":[{"type":"italic"}],"text":"σ"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 1: Schematic for a typical biomanufacturing process with an upper (USL) and lower specification limit (LSL)."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltf0e6af244d717b21/663e4fdf06943011b6927870/22-5-Panjwani-F1.jpg","alt":"22-5-Panjwani-F1.jpg","title":null,"style":{"text-align":"center"}}},{"type":"text","text":"Figure 1: Schematic for a typical biomanufacturing process with an upper (USL) and lower specification limit (LSL)."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Because of the relatively high variability of biological-assay results, some drug attributes necessitate testing with multiple replicates for each batch. That practice creates subgroupings in the resulting data. In such cases, two kinds of variability can be calculated: within-subgroup variability (also called "},{"type":"text","marks":[{"type":"italic"}],"text":"short-term σ"},{"type":"text","text":") and overall variability ("},{"type":"text","marks":[{"type":"italic"}],"text":"long-term σ"},{"type":"text","text":"). "},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"calculation is based on within-subgroup variability, whereas "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","text":" is calculated using overall variability. In the absence of subgrouping, the "},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk "},{"type":"text","text":"and "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"sets of indices have no significant differences."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Calculation Methods for Nonnormal Distributions: "},{"type":"text","text":"Process-capability calculations are relatively straightforward for normally distributed data. However, if data do not follow normal distribution, then standard capability definitions might generate misleading results because those definitions assume normality. Therefore, several methods have been proposed in literature to address the issue of nonnormality ("},{"type":"text","marks":[{"type":"bold"}],"text":"2, 3"},{"type":"text","text":"). Each technique has merits and limitations."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Percentile/quantile-based methods ("},{"type":"text","marks":[{"type":"bold"}],"text":"4"},{"type":"text","text":") and z-score ("},{"type":"text","marks":[{"type":"italic"}],"text":"nonconformance"},{"type":"text","text":") methods ("},{"type":"text","marks":[{"type":"bold"}],"text":"5"},{"type":"text","text":") are used frequently for process-capability calculation for nonnormal data. As the name suggests, percentile-based methods leverage percentiles instead of "},{"type":"text","marks":[{"type":"italic"}],"text":"σ "},{"type":"text","text":"values (Equation 4). In a normal distribution, "},{"type":"text","marks":[{"type":"italic"}],"text":"x"},{"type":"text","marks":[{"type":"subscript"}],"text":"0.5"},{"type":"text","text":", "},{"type":"text","marks":[{"type":"italic"}],"text":"x"},{"type":"text","marks":[{"type":"subscript"}],"text":"0.99865"},{"type":"text","text":", and"},{"type":"text","marks":[{"type":"italic"}],"text":" x"},{"type":"text","marks":[{"type":"subscript"}],"text":"0.00135"},{"type":"text","text":" are equivalent to "},{"type":"text","marks":[{"type":"italic"}],"text":"μ"},{"type":"text","text":", ("},{"type":"text","marks":[{"type":"italic"}],"text":"μ "},{"type":"text","text":"+ 3"},{"type":"text","marks":[{"type":"italic"}],"text":"σ"},{"type":"text","text":"), and ("},{"type":"text","marks":[{"type":"italic"}],"text":"μ "},{"type":"text","text":"– 3"},{"type":"text","marks":[{"type":"italic"}],"text":"σ"},{"type":"text","text":"), respectively."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The percentile method is a general approach that can be used for any kind of distribution. However, its major drawback is its potentially misleading interpretation of process capability. A process-capability index provides a simple measure of the probability of OoS results. For normally distributed and centered process data with both USLs and LSLs, "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"= 1 is equivalent to an OoS risk of 0.27%. But the relationship between "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"and %OoS is invalid when a percentile-based method is used. Thus, the same "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"value corresponding to different fitted distributions will result in different %OoS values."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The z-score method provides the same %OoS result for a specific "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"value regardless of the type of fitted distribution. This method leverages the quantile function of the standard normal distribution to calculate equivalent limits that would yield the same probability on a standard normal scale."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Equations 5–7 show how to calculate an equivalent USL (USL"},{"type":"text","marks":[{"type":"subscript"}],"text":"eq"},{"type":"text","text":"), an equivalent LSL (LSL"},{"type":"text","marks":[{"type":"subscript"}],"text":"eq"},{"type":"text","text":"), and a process-capability index, respectively, for a given nonnormal probability distribution described by probability density function (PDF)"},{"type":"text","marks":[{"type":"italic"}],"text":" f"},{"type":"text","text":"("},{"type":"text","marks":[{"type":"italic"}],"text":"x"},{"type":"text","text":"). Therein, "},{"type":"text","marks":[{"type":"italic"}],"text":"F"},{"type":"text","text":"("},{"type":"text","marks":[{"type":"italic"}],"text":"x"},{"type":"text","text":") represents the cumulative distribution function (CDF) corresponding to f(x), and "},{"type":"text","marks":[{"type":"italic"}],"text":"Φ"},{"type":"text","marks":[{"type":"superscript"}],"text":"–1"},{"type":"text","text":" is the quantile function for a standard normal distribution."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Power-transformation methods such as the Box–Cox ("},{"type":"text","marks":[{"type":"bold"}],"text":"6"},{"type":"text","text":") and Johnson techniques ("},{"type":"text","marks":[{"type":"bold"}],"text":"7"},{"type":"text","text":") also can be used to transform nonnormal distributions to normal distributions, enabling application of standard formulas for process-capability calculation."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Focus of Current Work:"},{"type":"text","text":" Any of the above methods can be applied easily for process-capability calculation with the help of commercially available statistical-analysis software. However, selecting an appropriate method is generally not a straightforward process. Selection is especially challenging in cases with limited amounts of data because of the inherent difficulty in fitting the appropriate distribution. The biopharmaceutical industry often encounters such issues because of time-consuming experiments, high experimentation costs, and limitations on patient availability for clinical trials. Despite data-availability constraints, analysts must make critical decisions about manufacturing processes. Therefore, statistical methods must be selected to address such limitations. Herein, one of our goals is to compare the performance of process-capability calculation methods presented above for small data sets using simulations."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"In cases with subgrouped data, process-capability evaluation requires even more attention than usual. As mentioned above, two kinds of indices can be calculated for such data. Depending on the type of variability applied during calculation, the methods’ interpretations of process capability can differ. For instance, consider cases in which multiple replicates from the same batch are tested to provide an average result. Per regulatory guidance, both the individual and averaged results must be compared against the respective specification limits ("},{"type":"text","marks":[{"type":"bold"}],"text":"8"},{"type":"text","text":"). The overall OoS risk increases because values from multiple replicates are up for comparison."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Process-capability index calculation is not straightforward in such cases because more than one type of variability can be considered for calculation. If only assay variability is considered, then the resulting process-capability index will indicate the assay’s performance but not that of the manufacturing process. On the other hand, using only batch-to-batch (process) variability is insufficient too because increased %OoS risk for a given batch comes primarily from the differences in the replicates’ values. Total variability (the sum of assay and process variability) can provide an alternative for process-capability calculation. However, that method does not account for the number of replicates. Thus, that formula for process-capability calculation should not be used in cases with subgrouped data. Our second objective in this article is to propose a mathematical framework for determining %OoS risks when individual replicates are compared against specification limits."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Methods"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Simulation Approach for Comparison of z-Score and Percentile Methods: "},{"type":"text","text":"Below, we focus on the widely used z-score and percentile methods for process-capability evaluation in the case of nonnormal data distribution. We compare the methods’ performance under the constraint of small data samples. Because real-world process-capability data are generally unknown within the biopharmaceutical industry, we strategically generated synthetic data for our comparisons."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"For normally distributed data, both methods yield the same process-capability index. Therefore, only data with nonnormal distributions require comparison. Index calculation also requires specification limits. Considering those factors, we developed the following simulation-based procedure to compare z-score and percentile method performances for nonnormally distributed data:"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"1 — Select a nonnormal distribution with defined distribution parameters."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"2 — Calculate specification limits for the given coverage factor (c) or for the corresponding percentiles of the nonnormal probability distribution using a PDF. For example, a coverage factor of 2 is equivalent to a USL at the 97.5th percentile and an LSL at the 2.5th percentile."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"3 — Generate sample set of size "},{"type":"text","marks":[{"type":"italic"}],"text":"n "},{"type":"text","text":"using a nonnormal PDF with parameters defined in the first step."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"4 — Using the calculated specification limits, calculate process-capability indices using "},{"type":"text","marks":[{"type":"italic"}],"text":"z"},{"type":"text","text":"-score and percentile methods."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"5 — Calculate the difference ("},{"type":"text","marks":[{"type":"italic"}],"text":"∆"},{"type":"text","text":") between the two indices."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"6 — Repeat steps 4–6 "},{"type":"text","marks":[{"type":"italic"}],"text":"k"},{"type":"text","text":" times to generate the distribution of "},{"type":"text","marks":[{"type":"italic"}],"text":"∆"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"7 — Repeat all of the above steps for the same type of distribution but with different distribution parameters so that higher-order scaled moments (e.g., skewness and kurtosis) can be varied."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"In our study, we varied skewness but not kurtosis because both sets of values correlated strongly and had similar effects on the distribution of the difference ("},{"type":"text","marks":[{"type":"italic"}],"text":"∆"},{"type":"text","text":") (see Appendix)."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Process-Capability Evaluation for a Multiple-Replicate Testing Scenario: "},{"type":"text","text":"As mentioned before, analysts sometimes need to test multiple replicates for drug attributes at a given stage of a biopharmaceutical process — e.g., when using samples from drug-substance intermediates, drug substances, and final drug products. In cases with multiple replicates per batch, the %OoS risk for a batch equals the probability of having at least one replicate falling outside specification limits. As the number of replicates per batch increases, the %OoS risk increases because of assay variability. A batch’s %OoS risk also depends on manufacturing-process variability."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Variability Breakdown Analysis:"},{"type":"text","text":" Because %OoS risk depends on both sources of variability, the overall variability observed across multiple batch results for a specific assay must be broken down into process and assay variabilities. Variance component analysis (VCA) is the standard statistical approach for partitioning overall variability ("},{"type":"text","marks":[{"type":"bold"}],"text":"9"},{"type":"text","text":"). For batches tested over multiple experimental setups with multiple replicates per setup, VCA can break down the total variability into process- and assay-variability components."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"OoS Risk Evaluation:"},{"type":"text","text":" Figure 2 presents distributions of mean-attribute and individual-measurement values. Overall, process mean ("},{"type":"text","marks":[{"type":"italic"}],"text":"µ"},{"type":"text","text":") can be estimated as the average of individual-batch averages. Process ("},{"type":"text","marks":[{"type":"italic"}],"text":"σ"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"p"},{"type":"text","text":") and assay ("},{"type":"text","marks":[{"type":"italic"}],"text":"σ"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"a"},{"type":"text","text":") variability can be estimated using VCA."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":null},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltd2993ebf1810bbf1/663e4fe09dde293c33b4d92e/22-5-Panjwani-F2.jpg","alt":"22-5-Panjwani-F2.jpg","title":null,"style":{"text-align":"center"}}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Figure 2: (LEFT) Mean attribute distribution with an overall process mean of "},{"type":"text","marks":[{"type":"italic"}],"text":"μ"},{"type":"text","text":", process variability of "},{"type":"text","marks":[{"type":"italic"}],"text":"σ"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"p"},{"type":"text","text":", and a process mean "},{"type":"text","marks":[{"type":"italic"}],"text":"μ"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"i"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"for the randomly selected"},{"type":"text","marks":[{"type":"italic"}],"text":" i"},{"type":"text","text":"th batch; (RIGHT) distribution of a measured attribute’s individual values for the"},{"type":"text","marks":[{"type":"italic"}],"text":" i"},{"type":"text","text":"th batch, with an assay variability of "},{"type":"text","marks":[{"type":"italic"}],"text":"σ"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"a"},{"type":"text","text":" and an upper (USL) and lower specification limit (LSL)."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"For an attribute with single result "},{"type":"text","marks":[{"type":"italic"}],"text":"y"},{"type":"text","text":", the %OoS risk for the "},{"type":"text","marks":[{"type":"italic"}],"text":"i"},{"type":"text","text":"th batch is the conditional probability of"},{"type":"text","marks":[{"type":"italic"}],"text":" y"},{"type":"text","text":" being outside specifications given the batch mean ("},{"type":"text","marks":[{"type":"italic"}],"text":"µ"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"i"},{"type":"text","text":"). Graphically, that is the area of the distribution outside the USL and LSL (Figure 2, right). %OoS risk is calculated using Equation 8, in which "},{"type":"text","marks":[{"type":"italic"}],"text":"CDF "},{"type":"text","text":"denotes a cumulative distribution function."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Equations 8–11: Calculations of risk for a biomanufacturing process generating a product with an out-of-specification (OoS) attribute."},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt1d00bc4e50d33586/663e4fe3c59cacdba0367380/22-5-Panjwani-E8-11.jpg","alt":"22-5-Panjwani-E8-11.jpg","title":null,"style":{"float":"center"}}},{"type":"text","text":"Equations 8–11: Calculations of risk for a biomanufacturing process generating a product with an out-of-specification (OoS) attribute."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"However, %OoS risk needs to be adjusted to account for the multiple-replicates scenario. Assuming that each replicate for the "},{"type":"text","marks":[{"type":"italic"}],"text":"i"},{"type":"text","text":"th batch tested is an independent Bernoulli trial and that the probability of a within-specification result equals (1 – "},{"type":"text","marks":[{"type":"italic"}],"text":"OoS"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"i"},{"type":"text","text":"("},{"type":"text","marks":[{"type":"italic"}],"text":"µ"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"i"},{"type":"text","text":")), then the probability of at least one replicate falling outside of specification is given by Equation 9. Assuming that the measured attribute ("},{"type":"text","marks":[{"type":"italic"}],"text":"y"},{"type":"text","text":") follows normal distribution, that expression can be described further using a CDF for normal distribution (Equation 10)."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Estimation of overall average OoS risk requires summation over all possible realizations of the "},{"type":"text","marks":[{"type":"italic"}],"text":"i"},{"type":"text","text":"th batch, as shown in Equation 11. Therein, "},{"type":"text","marks":[{"type":"italic"}],"text":"Prob"},{"type":"text","text":"("},{"type":"text","marks":[{"type":"italic"}],"text":"m"},{"type":"text","text":") represents the probability density function of a normally distributed process. Note that %OoS risk must be evaluated numerically because Equation 11 is not amenable to an analytical solution."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Results and Discussion"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Here, we describe application of our simulation approach to compare the two process-capability calculation methods for small sets of nonnormally distributed data. Then, we evaluate %OoS risks for multiple-replicates testing scenarios using the mathematical framework discussed earlier."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Comparison of "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"z"},{"type":"text","marks":[{"type":"bold"}],"text":"-Score and Percentile Process-Capability Indices:"},{"type":"text","text":" Several types of nonnormal distributions are described in statistics literature. A general evaluation of all such types is impossible, so we selected log-normal and Weibull distributions as two representative types. We chose the former option for a couple of reasons. When an attribute is log-normally distributed, logarithmic transformation of that attribute results in normal distribution. Logarithmic transformation also is among the most frequently used nonlinear transformation methods in the biopharmaceutical industry ("},{"type":"text","marks":[{"type":"bold"}],"text":"10, 11"},{"type":"text","text":"). Regarding Weibull distribution, a three-parameter technique can be applied to represent exponential, Rayleigh, generalized-gamma, generalized–extreme-value, and other types of nonnormal distributions."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Table 1 summarizes the parameters used in our simulation-based comparison of z-score and percentile methods for process-capability index calculation. Distribution parameters were varied in such a way that skewness of the distribution lies in {0.3 to 7}. Using the two sets of specification limits and two types of distributions, we conducted a total of four simulation studies."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":null},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt0847702972f0c043/663e53f1bb52f6b1afcdf2df/22-5-Panjwani-T1.PNG","alt":"22-5-Panjwani-T1.PNG","title":null,"style":{"text-align":"center"}}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Table 1: Simulation parameters for comparing "},{"type":"text","marks":[{"type":"italic"}],"text":"z"},{"type":"text","text":"-score and percentile methods;"},{"type":"text","marks":[{"type":"italic"}],"text":" c"},{"type":"text","text":" = coverage factor, "},{"type":"text","marks":[{"type":"italic"}],"text":"k"},{"type":"text","text":" = shape, "},{"type":"text","marks":[{"type":"italic"}],"text":"n "},{"type":"text","text":"= number of samples, "},{"type":"text","marks":[{"type":"italic"}],"text":"μ"},{"type":"text","text":" = location, "},{"type":"text","marks":[{"type":"italic"}],"text":"σ "},{"type":"text","text":"= scale, "},{"type":"text","marks":[{"type":"italic"}],"text":"λ "},{"type":"text","text":"= scale, "},{"type":"text","marks":[{"type":"italic"}],"text":"θ"},{"type":"text","text":" = location"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Log-Normal Distribution: "},{"type":"text","text":"Figures 3 and 4 illustrate simulation results for log-normal distribution for specification limits based on coverage factors of 3 and 2, respectively. In both cases, the absolute median difference ("},{"type":"text","marks":[{"type":"italic"}],"text":"∆"},{"type":"text","text":") between the two capability-calculation methods increased with increases in skewness. Values for ∆ increased at a much faster rate under a coverage factor of 2."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":null},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt94b37fc7561350c7/663e4fe220086e664b3e2b32/22-5-Panjwani-F3.jpg","alt":"22-5-Panjwani-F3.jpg","title":null,"style":{"text-align":"center"}}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Figure 3: Boxplot illustrating the distribution of differences ("},{"type":"text","marks":[{"type":"italic"}],"text":"Δ"},{"type":"text","text":") between percentile- and "},{"type":"text","marks":[{"type":"italic"}],"text":"z"},{"type":"text","text":"-score–method estimation of processcapability indices ("},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","text":") at increasing values of skewness for simulated log-normally distributed data; limits for determining out-of-specification (OoS) results were based on three standard deviation limits."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":null},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt7554eac26adc9829/663e4fe1f758568178da0058/22-5-Panjwani-F4.jpg","alt":"22-5-Panjwani-F4.jpg","title":null,"style":{"text-align":"center"}}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Figure 4: Boxplot illustrating the distribution of differences ("},{"type":"text","marks":[{"type":"italic"}],"text":"Δ"},{"type":"text","text":") between percentile- and"},{"type":"text","marks":[{"type":"italic"}],"text":" z"},{"type":"text","text":"-score–method estimation of processcapability indices ("},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","text":") at increasing values of skewness for simulated log-normally distributed data; limits for determining out-of-specification (OoS) results were based on two standard deviation limits."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"For specification limits based on a coverage factor of 3, "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","text":" values obtained by the percentile and z-score methods ranged from 0.80 to 0.89 and from 0.89 to 0.92, respectively. Similarly, for specification limits based on a coverage factor of 2, "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"ranged from 0.23 to 0.48 and from 0.51 to 0.52, respectively."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Weibull Distribution: "},{"type":"text","text":"Figures 5 and 6 illustrate simulation results for Weibull distributions with specification limits based on coverage factors of 3 and 2, respectively. In both cases, once again, "},{"type":"text","marks":[{"type":"italic"}],"text":"∆"},{"type":"text","text":" values between the two process-capability calculation methods increased with increases in skewness."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":null},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltf398fef1d8ed13c2/663e4fe153e02d1e5f9bf997/22-5-Panjwani-F5.jpg","alt":"22-5-Panjwani-F5.jpg","title":null,"style":{"text-align":"center"}}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Figure 5: Boxplot illustrating the distribution of differences ("},{"type":"text","marks":[{"type":"italic"}],"text":"Δ"},{"type":"text","text":") between percentile- and "},{"type":"text","marks":[{"type":"italic"}],"text":"z"},{"type":"text","text":"-score–method estimation of processcapability indices ("},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","text":") at increasing values of skewness for simulated Weibull-distributed data; limits for determining out-of specification (OoS) results were based on three standard deviation limits."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":null},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt3b4f7cf70ff3138c/663e4fe49d48d9231ca0d03d/22-5-Panjwani-F6.jpg","alt":"22-5-Panjwani-F6.jpg","title":null,"style":{"text-align":"center"}}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Figure 6: Boxplot illustrating the distribution of differences ("},{"type":"text","marks":[{"type":"italic"}],"text":"Δ"},{"type":"text","text":") between percentile- and "},{"type":"text","marks":[{"type":"italic"}],"text":"z"},{"type":"text","text":"-score–method estimation of processcapability indices ("},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","text":") at increasing values of skewness for simulated Weibull-distributed data; limits for determining out-of specification (OoS) results were based on two standard deviation limits."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"For specification limits based on a coverage factor of 3, "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"values from the percentile and z-score methods ranged from 0.62 to 0.89 and from 0.88 to 0.94, respectively. Similarly, for specification limits based on a coverage factor of 2, values from the percentile and z-score methods ranged from 0.19 to 0.53 and 0.44 to 0.52, respectively."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Findings:"},{"type":"text","text":" We made the following observations based on those four case studies. The first point concerns the median estimate of the absolute difference ("},{"type":"text","marks":[{"type":"italic"}],"text":"Δ"},{"type":"text","text":") in "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"values, with "},{"type":"text","marks":[{"type":"italic"}],"text":"Δ"},{"type":"text","text":" values increasing alongside larger skewness values. "},{"type":"text","marks":[{"type":"italic"}],"text":"Skewness "},{"type":"text","text":"measures the asymmetry of the probability distribution of a random variable around its mean. Skewness has a zero value in cases of normal distribution and a nonzero value for nonnormal distributions. Therefore, we can conclude that the difference ("},{"type":"text","marks":[{"type":"italic"}],"text":"Δ"},{"type":"text","text":") in "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"values becomes larger as the extent of nonnormality increases."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Because of a small sample size, we observed a spread in the "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"values. Values obtained from the "},{"type":"text","marks":[{"type":"italic"}],"text":"z"},{"type":"text","text":"-score method were spread less widely relative to values from the percentile method, indicating that the "},{"type":"text","marks":[{"type":"italic"}],"text":"z"},{"type":"text","text":"-score method is more robust. The wider spread in percentile-method "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk "},{"type":"text","text":"values can be attributed to the percentile calculations ("},{"type":"text","marks":[{"type":"italic"}],"text":"x"},{"type":"text","marks":[{"type":"subscript"}],"text":"0.99865"},{"type":"text","text":" and "},{"type":"text","marks":[{"type":"italic"}],"text":"x"},{"type":"text","marks":[{"type":"subscript"}],"text":"0.00135"},{"type":"text","text":"), which are influenced significantly by the small sample size."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"We set specification limits such that %OoS could be controlled. Theoretically, %OoS would be 5% and 0.27% under coverage factors of 2 and 3, respectively. We observed a range of "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk "},{"type":"text","text":"values in simulations for a given coverage factor, but all "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"values corresponded to the same theoretical OoS risk. In general, a process-capability index ≥1.0 is desirable for a normally distributed process. That result corresponds to an OoS risk of 0.27% for two-sided limits. In cases of nonnormal (log-normal and Weibull) distributions with specification limits based on a coverage factor of 3, both methods yielded median "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"values \u003c1.0. The minimum "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"value for Weibull distribution based on the percentile method was 0.62. That value was achieved for a highly skewed distribution. The significant disparity between the expected (1.0) and estimated "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"values (0.62) reflects the limitations of the percentile method for highly skewed distributions."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The same conclusion can be drawn for specification limits based on a coverage factor of 2. The expected "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"value was 0.67, but the percentile-based minimum estimated "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"value for a Weibull distribution was only 0.19. On the other hand, "},{"type":"text","marks":[{"type":"italic"}],"text":"z"},{"type":"text","text":"-score–based "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"values were much closer to the expected values, ranging from 0.88 to 0.94 (expected "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"= 1.0) and from 0.44 to 0.52 (expected "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"= 0.67) for coverage factors of 3 and 2, respectively."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"In summary, "},{"type":"text","marks":[{"type":"italic"}],"text":"z"},{"type":"text","text":"-score–based "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk "},{"type":"text","text":"values for a nonnormal distribution carry the same meaning as "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"values for a normal distribution because the relationship between "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"and %OoS remains intact. The relatively small differences observed between the expected and calculated "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"values could be due to a small sample size."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Multiple-Replicates Testing Scenario: "},{"type":"text","text":"Based on the methodology that we set out earlier, we conducted simulations to illustrate the limitations of traditional process-capability calculations. As we mentioned, %OoS is a more appropriate metric for evaluating process capability than the traditional process-capability indices (e.g., "},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"and "},{"type":"text","marks":[{"type":"italic"}],"text":"P"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","text":") in cases when multiple replicates are tested. Table 2 lists the parameters that we used for simulation. We set specification limits such that the %OoS for a single-replicate testing scenario would be 5%."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":null},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blta0879923ad2d2899/663e54333b613d537e102a20/22-5-Panjwani-T2.PNG","alt":"22-5-Panjwani-T2.PNG","title":null,"style":{"text-align":"center"}}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Table 2: Simulation parameters for comparing"},{"type":"text","marks":[{"type":"italic"}],"text":" z"},{"type":"text","text":"-score and percentile methods; "},{"type":"text","marks":[{"type":"italic"}],"text":"c"},{"type":"text","text":" = coverage factor, "},{"type":"text","marks":[{"type":"italic"}],"text":"k"},{"type":"text","text":" = shape, "},{"type":"text","marks":[{"type":"italic"}],"text":"n "},{"type":"text","text":"= number of samples,"},{"type":"text","marks":[{"type":"italic"}],"text":" μ"},{"type":"text","text":" = location, "},{"type":"text","marks":[{"type":"italic"}],"text":"σ"},{"type":"text","text":" = scale, "},{"type":"text","marks":[{"type":"italic"}],"text":"λ"},{"type":"text","text":" = scale, "},{"type":"text","marks":[{"type":"italic"}],"text":"θ"},{"type":"text","text":" = location"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Figure 7 shows calculated overall %OoS values corresponding to several simulated scenarios. As expected, %OoS values for single-replicate testing scenarios were ~5% in all cases, whereas %OoS became larger with increases in assay variability for multiple replicates ("},{"type":"text","marks":[{"type":"italic"}],"text":"n"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"rep"},{"type":"text","marks":[{"type":"subscript"}],"text":" "},{"type":"text","text":"\u003e 1). The %OoS also increased with larger numbers of replicates."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"Figure 7: Overall risk of out-ofspecification results (%OoS) values for parameters listed in Table 2."},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt3bda1fd71da49d75/663e4fdffee7f3653453010d/22-5-Panjwani-F7.jpg","alt":"22-5-Panjwani-F7.jpg","title":null,"style":{"text-align":"center"}}},{"type":"text","text":"Figure 7: Overall risk of out-ofspecification results (%OoS) values for parameters listed in Table 2."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"In single-replicate testing, an OoS risk of 5% corresponds to a "},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk "},{"type":"text","text":"value of 0.667 for normally distributed data. Because the traditional method of process-capability calculation does not account for the number of replicates, it yields the same "},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"pk"},{"type":"text","marks":[{"type":"italic"}],"text":" "},{"type":"text","text":"(0.667) even for scenarios with multiple replicates ("},{"type":"text","marks":[{"type":"italic"}],"text":"n"},{"type":"text","marks":[{"type":"italic"},{"type":"subscript"}],"text":"rep"},{"type":"text","text":" \u003e 1). Thus, it overestimates process capability in cases with multiple replicates. Differences in estimates between the traditional and proposed methods become larger with increases in both the number of replicates and the proportion of overall variability due to assay performance."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Conclusion"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Our purpose in this article was to address some practical challenges related to process-capability calculations for biopharmaceutical-manufacturing processes. Specifically, we wanted to address the fact that measured attributes do not always follow normal distribution. Therefore, a reliable and interpretable method is needed for process-capability evaluation. The "},{"type":"text","marks":[{"type":"italic"}],"text":"z"},{"type":"text","text":"-score method not only generates robust estimates for process-capability indices for nonnormal distributions, but also preserves the relationship between process-capability indices and OoS rates. Outcomes from our simulations highlighted limitations of the percentile method for process-capability calculation for nonnormal distributions. Therefore, we recommend using the "},{"type":"text","marks":[{"type":"italic"}],"text":"z"},{"type":"text","text":"-score method for such cases."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"We also introduced a mathematical framework for process-capability evaluation for situations in which individual results for an attribute are subjected to specification limits in addition to average results. Although our proposed analytical solution applies to normal distributions, it can be extended to nonnormal distributions using numerical computations."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"table","content":[{"type":"tableRow","content":[{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":null},"content":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Appendix: Kurtosis, Skewness, and Distribution of Difference"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"For log-normal distributions, Johnson, Kotz, and Balakrishnan ("},{"type":"text","marks":[{"type":"bold"}],"text":"12"},{"type":"text","text":") define skewness ("},{"type":"text","marks":[{"type":"italic"}],"text":"s"},{"type":"text","text":") as"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt2a0f65c257167533/663e4fde5a8859439b80cc06/22-5-Panjwani-Appendix-Equations.jpg","alt":"22-5-Panjwani-Appendix-Equations.jpg","title":null,"style":"max-width: 242px;width: 242;height: auto;"}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Therein, "},{"type":"text","marks":[{"type":"italic"}],"text":"σ "},{"type":"text","text":"represents the distribution’s scale parameter. Thus, skewness of a log-normal distribution depends only on its scale, not on its location parameter ("},{"type":"text","marks":[{"type":"italic"}],"text":"μ"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"For Weibull distributions, "},{"type":"text","marks":[{"type":"italic"}],"text":"skewness "},{"type":"text","text":"("},{"type":"text","marks":[{"type":"italic"}],"text":"s"},{"type":"text","text":") is defined ("},{"type":"text","marks":[{"type":"bold"}],"text":"12"},{"type":"text","text":") as"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt97ce2b555511ad96/663e4fdc4d256e0520eb5bcb/22-5-Panjwani-Appendix-E2.jpg","alt":"22-5-Panjwani-Appendix-E2.jpg","title":null,"style":"max-width: 232px;width: 232;height: auto;"}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Therein, the distribution mean ("},{"type":"text","marks":[{"type":"italic"}],"text":"μ"},{"type":"text","text":") ="}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltdb4a3ada6982b14a/663e4fdd9dde295b6ab4d92a/22-5-Panjwani-Appendix-E3.jpg","alt":"22-5-Panjwani-Appendix-E3.jpg","title":null,"style":"max-width: 113px;width: 113;height: auto;"}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"variance ("},{"type":"text","marks":[{"type":"italic"}],"text":"σ"},{"type":"text","marks":[{"type":"superscript"}],"text":"2"},{"type":"text","text":") ="}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltde9c3c9658d0bd84/663e4fe0947307a4b44b7432/22-5-Panjwani-Appendix-E4.jpg","alt":"22-5-Panjwani-Appendix-E4.jpg","title":null,"style":"max-width: 203px;width: 203;height: auto;"}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"and "},{"type":"text","marks":[{"type":"italic"}],"text":"k"},{"type":"text","text":", "},{"type":"text","marks":[{"type":"italic"}],"text":"λ"},{"type":"text","text":", and "},{"type":"text","marks":[{"type":"italic"}],"text":"γ "},{"type":"text","text":"represent the shape, scale, and gamma function, respectively."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"By substituting the mean and variance into the formula for skewness, both the numerator and denominator can be defined as third-order functions of scale ("},{"type":"text","marks":[{"type":"italic"}],"text":"λ"},{"type":"text","text":"):"}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blte959d75120cbd36c/663e4fdee2ac79598805d6d9/22-5-Panjwani-Appendix-E5.jpg","alt":"22-5-Panjwani-Appendix-E5.jpg","title":null,"style":"max-width: 533px;width: 533;height: auto;"}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Therein,"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltb247b27eeb2f1211/663e4fde8df8e1612dc78638/22-5-Panjwani-Appendix-E6.jpg","alt":"22-5-Panjwani-Appendix-E6.jpg","title":null,"style":"max-width: 422px;width: 422;height: auto;"}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"and"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltbd3606e192d1df43/663e4fddb6eaa33c159208c6/22-5-Panjwani-Appendix-E7.jpg","alt":"22-5-Panjwani-Appendix-E7.jpg","title":null,"style":"max-width: 239px;width: 239;height: auto;"}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Thus, skewness of a Weibull distribution depends only on its shape ("},{"type":"text","marks":[{"type":"italic"}],"text":"k"},{"type":"text","text":") and is independent of scale ("},{"type":"text","marks":[{"type":"italic"}],"text":"λ"},{"type":"text","text":") and location ("},{"type":"text","marks":[{"type":"italic"}],"text":"θ"},{"type":"text","text":") parameters."}]}]}]}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"References"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"1 "},{"type":"text","text":"Montgomery DC."},{"type":"text","marks":[{"type":"italic"}],"text":" Introduction to Statistical Quality Control"},{"type":"text","text":" (Eighth Edition). 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"},{"type":"text","marks":[{"type":"italic"}],"text":"Analytical Data-Interpretation and Treatment."},{"type":"text","text":" US Pharmacopeial Convention: Rockville, MD, 2024."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"11 "},{"type":"text","text":"USP \u003c1032\u003e. "},{"type":"text","marks":[{"type":"italic"}],"text":"Design and Development of Biological Assays"},{"type":"text","text":". US Pharmacopeial Convention: Rockville, MD, 2024."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"12 "},{"type":"text","text":"Johnson NL, Kotz S, Balakrishnan N. "},{"type":"text","marks":[{"type":"italic"}],"text":"Continuous Univariate Distributions, "},{"type":"text","text":"Vol. 1 (Second Edition). Wiley-Interscience: Hoboken, NJ, 1994."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Corresponding author "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Shyam Panjwani"},{"type":"text","marks":[{"type":"italic"}],"text":" is principal data scientist, and "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Konstantinos Spetsieris"},{"type":"text","marks":[{"type":"italic"}],"text":" is head of data science and statistics at Bayer US, LLC, 800 Dwight Way, Berkeley, CA 94710; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"shyam.panjwani@bayer.com","target":"_blank","rel":null,"class":null}},{"type":"italic"}],"text":"shyam.panjwani@bayer.com"},{"type":"text","marks":[{"type":"italic"}],"text":". Now at Genentech, "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Darrick Shen"},{"type":"text","marks":[{"type":"italic"}],"text":" was a data scientist at Bayer US. Data supporting the findings of this study are available from the corresponding author upon reasonable request."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"paragraph","attrs":{"textAlign":"left"}}],"mobileHeadline":"Practical Applications of Process Capability Indices in the Biopharmaceutical Industry: A Simulation-Based Study","articleUrl":"/qa-qc/practical-applications-of-process-capability-indices-in-the-biopharmaceutical-industry-a-simulation-based-study","listPageUrl":"/qa-qc/practical-applications-of-process-capability-indices-in-the-biopharmaceutical-industry-a-simulation-based-study","linkAttrs":{},"date":"May 10, 2024","timeRead":0,"categoryName":"QA/QC","categoryUrl":"/analytical/qa-qc","contributors":[{"name":"Shyam Panjwani","avatar":"","link":"/author/shyam-panjwani"},{"name":"Darrick Shen","avatar":"","link":"/author/darrick-shen"},{"name":"Konstantinos Spetsieris","avatar":"","link":"/author/konstantinos-spetsieris"}],"paidGating":null}},{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt043bb25fa09f2a05/65ccffc81ad587040a462b9f/iStock-1282370470.jpg","alt":"Corrective and preventive actions"},"articleName":"Determining Sample Size for Demonstrating Zero Failures: Ensuring the Effectiveness of Corrective and Preventive Actions","articleSummary":"","articleBody":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Biopharmaceutical manufacturers often implement corrective and preventive actions (CAPAs) as a mitigation strategy to address deviations based on root causes identified in investigations of process failures. CAPA implementation frequently is followed by effectiveness checks. The effectiveness of a CAPA is demonstrated by ensuring that a specific parameter can meet acceptable requirements."},{"type":"hardBreak"},{"type":"hardBreak"},{"type":"text","text":"If a monitored parameter for ensuring CAPA effectiveness does not follow any specific data distribution, or if it reports results as pass or fail, then the statistical approach for selection of sample sizes to ensure reliability of the effectiveness check will not necessarily be straightforward. Therefore, herein we detail one such appropriate statistical approach to calculate sample size: “Reliability demonstration tests” (RDTs) can be used to verify whether a product or process has met a certain reliability requirement at a stated statistical confidence level when the underlying data distribution is unknown."},{"type":"hardBreak"},{"type":"hardBreak"},{"type":"text","text":"A number of different RDT approaches are available for process engineers to choose from, including cumulative binomial, nonparametric binomial, exponential chi-squared, and nonparametric Bayesian methods. Here we focus on two relatively simple options that are easy to adopt: nonparametric binomial and exponential chi-squared methods. The others have limitations such as a requirement for feeding prior information based on assumptions. Because there is no single “correct way” of calculating prior information, using different approaches can give different results. Finally, we recommend a risk-assessment methodology for selecting acceptable reliability and statistical confidence levels that may be used in the two RDT methods highlighted."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltffad298aafaf3d16/65e0b1f41d1f05040ac82151/22-1-2-FR-Wombaker-Equations_(1).jpg","alt":"22-1-2-FR-Wombaker-Equations_(1).jpg","title":null,"style":{"max-width":"383px","width":"383px","height":"auto"}}}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Equations 1–5"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Nonparametric Binomial Method"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"This method is also known as the classical reliability demonstration test. When "},{"type":"text","marks":[{"type":"italic"}],"text":"x"},{"type":"text","text":" is the number of observed failures out of i trials, and the probability of each trial p is an independent Bernoulli trial, then the probability of at least c failures can be calculated using a binomial distribution (Equation 1). For zero failures (assuming "},{"type":"text","marks":[{"type":"italic"}],"text":"c"},{"type":"text","text":" = 0), that formula reduces to Equation 2, which can be rewritten as Equation 3. Now the number of sample sizes can be determined by equating Equation 3 to a significance value ("},{"type":"text","marks":[{"type":"italic"}],"text":"α"},{"type":"text","text":") type 1 error — in other words, the probability of falsely rejecting a true null hypothesis (Equation 4). Taking logarithms on both sides in that equation and solving for sample size n, you obtain Equation 5, in which the term "},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","text":" is confidence. Thus, to have"},{"type":"text","marks":[{"type":"italic"}],"text":" C"},{"type":"text","text":"% confidence when the CAPA is R% reliable, a minimum of n samples needs to be tested to ensure zero failure as an acceptance criteria."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt5e86a537d419b347/65e0afda7ace69040a35c9f2/Table_1.jpg","alt":"Table_1.jpg","title":null,"style":{"float":"center"}}},{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt5f1c01a29802c0ef/65e0afda3ee698040a51752e/Table_2.jpg","alt":"Table_2.jpg","title":null,"style":{"float":"center"}}}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Exponential Chi-Squared Method"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"In the exponential chi-squared method, you also can calculate the sample size for zero failure as a function of reliability and confidence, as shown in Equation 6:"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"where χ2(1–"},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","text":",2) represents the inverse of the right-tailed probability of a chi-squared distribution at"},{"type":"hardBreak"},{"type":"hardBreak"},{"type":"text","text":"1 – "},{"type":"text","marks":[{"type":"italic"}],"text":"C"},{"type":"text","text":" probability and two degrees of freedom. In a Microsoft Excel spreadsheet, that value can be calculated using this function:"},{"type":"hardBreak"},{"type":"hardBreak"},{"type":"text","text":"=CHISQ.INV.RT(probability, degrees of freedom)."},{"type":"hardBreak"},{"type":"hardBreak"},{"type":"text","text":"Risk Assessment: The American Society for Testing and Materials (ASTM) outlines three criteria for use in creating a risk-assessment matrix for attributes/parameters to be monitored: the severity of the consequences if a problem or failure occurs "},{"type":"text","marks":[{"type":"italic"}],"text":"(S)"},{"type":"text","text":", the likelihood of occurrence "},{"type":"text","marks":[{"type":"italic"}],"text":"(O)"},{"type":"text","text":", and the likelihood of detection (of a problem or of a detection failure, "},{"type":"text","marks":[{"type":"italic"}],"text":"D"},{"type":"text","text":") ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":"). Each criterion can be scored on a scale from 1 to 5, as shown in Table 1 ("},{"type":"text","marks":[{"type":"bold"}],"text":"2"},{"type":"text","text":")."},{"type":"hardBreak"},{"type":"hardBreak"},{"type":"text","text":"The product of those three scores — ("},{"type":"text","marks":[{"type":"italic"}],"text":"S) × (O) × (D"},{"type":"text","text":") — gives a risk priority number (RPN). Based on the RPN, the risk associated with a given attribute or parameter is classified as high, medium, or low (Table 2). For each risk classification, Table 2 also lists recommendations for confidence ("},{"type":"text","marks":[{"type":"italic"}],"text":"C)"},{"type":"text","text":" and reliability "},{"type":"text","marks":[{"type":"italic"}],"text":"(R)"},{"type":"text","text":". The latter is based on rules of thumb for Cronbach’s alpha coefficient, which measures the reliability or internal consistency of a measurement method ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Confidence and Reliability"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"We recommended documenting the statistical methods and rationality for selecting sample sizes to ensure that a CAPA is effective. For determining sample sizes, both nonparametric binomial and exponential chi-squared methods are widely accepted for practice in industries regulated by the US Food and Drug Administration (FDA). As Table 2 shows, calculated sample sizes do not differ significantly between these two approaches. Thus, either method can be used."},{"type":"hardBreak"},{"type":"hardBreak"},{"type":"text","text":"Note that confidence and reliability values herein are for illustration purposes only. Based on the nature of a given process, batch run rate, and associated costs, we recommend that drug manufacturers use the most suitable confidence and reliability values for adherence to their own quality policies."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"References"},{"type":"hardBreak"},{"type":"hardBreak"},{"type":"text","text":"1 M"},{"type":"text","marks":[{"type":"italic"}],"text":"uralidharan N. Process Validation: Calculating the Necessary Number of Process Performance Qualification Runs. BioProcess Int. 21(5) 2023: 37–43;"},{"type":"text","text":" "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://bioprocessintl.com/analytical/upstream-validation/process-validation-calculating-the-necessary-number-of-process-performance-qualification-runs.","target":"_blank","rel":null,"class":null}}],"text":"https://bioprocessintl.com/analytical/upstream-validation/process-validation-calculating-the-necessary-number-of-process-performance-qualification-runs."},{"type":"hardBreak"},{"type":"hardBreak"},{"type":"text","text":"2"},{"type":"text","marks":[{"type":"italic"}],"text":" Salkind NJ, Ed. Encyclopedia of Measurement and Statistics. Sage Publications: London, UK, 2007;"},{"type":"text","text":" "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://doi.org/10.4135/9781412952644.","target":"_self","rel":null,"class":null}}],"text":"https://doi.org/10.4135/9781412952644."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"https://istockphoto.com"},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt94c9eb93a5f344bd/65c2262c494bdc040a975170/iStock-1282370470.jpg","alt":"iStock-1282370470.jpg","title":null,"style":{"width":"403px","height":"auto"}},"marks":[{"type":"link","attrs":{"href":"https://istockphoto.com","target":"_self","rel":null,"class":null}}]},{"type":"text","text":"https://istockphoto.com"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.istockphoto.com","target":"_self","rel":null,"class":null}}],"text":"https://www.istockphoto.com"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Further Reading"},{"type":"hardBreak"},{"type":"hardBreak"},{"type":"text","marks":[{"type":"italic"}],"text":"Gorski A. CHI-Square Probabilities Are Poisson Probabilities in Disguise. IEEE Transactions on"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Reliability. R-34(3) 1985:"},{"type":"text","text":" "},{"type":"text","marks":[{"type":"italic"}],"text":"209–211;"},{"type":"text","text":" "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://doi.org/10.1109/tr.1985.5222117.","target":"_self","rel":null,"class":null}}],"text":"https://doi.org/10.1109/tr.1985.5222117."},{"type":"hardBreak"},{"type":"hardBreak"},{"type":"text","marks":[{"type":"italic"}],"text":"Romeu JL. Determining the Experimental Sample Size. Quality, Reliability and Continuous Improvement Institute: Syracuse, NY;"},{"type":"text","text":" "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://web.cortland.edu/romeu/ExperSampSizeQR\u0026CII.pdf.","target":"_blank","rel":null,"class":null}}],"text":"https://web.cortland.edu/romeu/ExperSampSizeQR\u0026CII.pdf."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Corresponding author "},{"type":"text","marks":[{"type":"bold"}],"text":"Naveenganesh Muralidharan"},{"type":"text","marks":[{"type":"italic"}],"text":" is senior manager of manufacturing science and technology (MSAT), and Dan Larson is manager of investigations, both at AGC Biologics, 5550 Airport Boulevard, Boulder, CO 80301; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"mailto:nmural@agcbio.com","target":"_self","rel":null,"class":null}},{"type":"italic"}],"text":"nmural@agcbio.com"},{"type":"text","marks":[{"type":"italic"}],"text":"; https://www.agcbio.com."}]}],"mobileHeadline":"Determining Sample Size for Demonstrating Zero Failures: Ensuring the Effectiveness of Corrective and Preventive Actions","articleUrl":"/qa-qc/determining-sample-size-for-demonstrating-zero-failures-ensuring-the-effectiveness-of-corrective-and-preventive-actions","listPageUrl":"/qa-qc/determining-sample-size-for-demonstrating-zero-failures-ensuring-the-effectiveness-of-corrective-and-preventive-actions","linkAttrs":{},"date":"Feb 6, 2024","timeRead":0,"categoryName":"QA/QC","categoryUrl":"/analytical/qa-qc","contributors":[{"name":"Naveenganesh Muralidharan","avatar":"","link":"/author/naveenganesh-muralidharan"},{"name":"Dan Larson","avatar":"","link":"/author/dan-larson"}],"paidGating":null}}],"title":"Analytical","moreContent":{"title":"View More","url":"/analytical"}},{"contents":[{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt8b72061a8c045efe/66745a2d75b6dfc1eaa0937c/AdobeStock_446010531.jpg","alt":"scientist at computer"},"articleName":"US–India Collaborative Innovations for Rapid Drug Discovery","articleSummary":"","articleBody":[{"type":"figure","attrs":{"figcaption":"WWW.STOCK.ADOBE.COM"},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blte9df1381caf6f786/667459e7c709989c6650103f/AdobeStock_446010531.jpg","alt":"AdobeStock_446010531.jpg","title":null,"style":{"height":"auto","text-align":"center"}}},{"type":"text","text":"WWW.STOCK.ADOBE.COM"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Technology is a key driver in the deepening partnership between the United States and India. This collaboration has a longstanding history: The two countries have cooperated previously in critical areas such as disease control and surveillance, laboratory integration, and enhancement of public-health capacities ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":"). In recent years, that collaboration has intensified, especially in the field of biotechnology. Joint efforts now focus on using artificial intelligence (AI) for matching patients with treatments, applying nanotechnology for targeted drug delivery, and improving bioinformatic tools for comprehensive data analysis. Given the complexity of such initiatives, a collaborative approach will be crucial to accelerate outcomes in research, development, and drug discovery. The ongoing US–India connection could advance research significantly in those areas by fostering swift progress in biotechnology and healthcare, particularly in rapid drug discovery (RDD)."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The typical drug-discovery process is time-consuming and financially intensive, often spanning several years and incurring billions of dollars in costs. From 2009 to 2018, estimated median capitalized research and development (R\u0026D) costs per product stood at $1.1 billion in the United States, including expenses for unsuccessful drug trials ("},{"type":"text","marks":[{"type":"bold"}],"text":"2"},{"type":"text","text":"). Collaborative initiatives must be adopted to shorten timelines and lower costs in the creation of life-saving drugs. Incorporating advanced technologies can improve R\u0026D process efficiencies."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The global drug-discovery market is valued at $55.46 billion and projected to increase to about $133.11 billion by 2032 ("},{"type":"text","marks":[{"type":"bold"}],"text":"3"},{"type":"text","text":"). Today, US–India trades and investments help to facilitate global growth, with bilateral trade exceeding $191 billion in 2022, nearly doubling the value in 2014 ("},{"type":"text","marks":[{"type":"bold"}],"text":"3"},{"type":"text","text":"). Innovations resulting from the US–India partnership could shape the future of global health."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Forging Research Partnerships"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Both US- and India-based pharmaceutical companies must continue to make rapid advances in drug discovery. The two countries recognize the promise of cooperative research in science and innovation and, as a result, have formed a research partnership between the US National Science Foundation (US-NSF) and the Indian Department of Biotechnology (DBT) ("},{"type":"text","marks":[{"type":"bold"}],"text":"4"},{"type":"text","text":"). That partnership and subsequent implementation agreements establish groundwork for collaboration to expedite innovations in biotechnology and biomanufacturing ("},{"type":"text","marks":[{"type":"bold"}],"text":"5"},{"type":"text","text":"). The initiative aims to propel knowledge and investigations through partnered research, empowering biotechnology industries and fostering global bioeconomies."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Such partnerships could expand international collaboration in several areas, including AI, quantum technologies (QTs), and advanced wireless capabilities. One partnership, the US–India Quantum Coordination Mechanism, leverages participation from industry, academia, and government to facilitate R\u0026D."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Similarly, the US–India initiative on Critical and Emerging Technology (iCET) has called for proposals on transformative AI and QTs. The Indo–US Science and Technology Forum (IUSSTF) also is offering a grant of $2 million for this program. The forum will select and support commercially viable and socially relevant US–India initiatives in technology innovation and entrepreneurship. Joint projects originate from government, academic, and commercial facilities. Many such initiatives aid in the advancements of underlying technologies, with use cases in RDD and delivery."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"To support those goals, the Indian government has allocated $71.37 billion to fund a National Quantum Mission (NQM). It will contribute resources to scientific and industrial research for QTs ("},{"type":"text","marks":[{"type":"bold"}],"text":"6"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Joint Investments in Technologies:"},{"type":"text","text":" In the past five years, the US-NSF has invested nearly $150 million to over 200 projects in India ("},{"type":"text","marks":[{"type":"bold"}],"text":"7"},{"type":"text","text":"). In 2022 alone, the NSF launched 35 emerging-technology projects with India’s Department of Science and Technology (S\u0026T). Joint funding has strengthened the foundation of cooperation, collaboration, and investment in science and engineering by providing opportunities for discovery and innovation across life-science industries. The US-NSF also has agreed to provide funding for joint projects in applied research areas while building on existing arrangements. Many of the technology fields receiving US-NSF investment can address society’s most pressing challenges, spur economic growth, and advance technologies in critical research areas."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"India’s Pharmaceutical Sector:"},{"type":"text","text":" Within months of the COVID-19 pandemic outbreak, the world recognized India’s efforts to support the healthcare efforts of other nations. The Indian government quickly provided medical diagnostic and test kits, infrared thermometers, and personal protective equipment (PPE). As a result of those swift actions, India has been hailed as the “pharmacy of the world” ("},{"type":"text","marks":[{"type":"bold"}],"text":"8"},{"type":"text","text":"). To address increasing demands for smart technology and RDD, the country has invested in building improved infrastructure. The Indian pharmaceutical industry has grown steadily in domestic and foreign markets over the past two decades ("},{"type":"text","marks":[{"type":"bold"}],"text":"9"},{"type":"text","text":"). By 2030, it is expected to be valued at $130 billion. As the industry embraces digital tools such as AI, it can create faster go-to-market capabilities. The United States and India also are cooperating in AI innovations and addressing challenges in the field through platforms such as the US–India Artificial Intelligence Initiative (USIAI)."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"AI plays an important role in streamlining RDD and accelerating overall drug-development processes. Nanotechnology and bioinformatics are two fields that leverage AI in such processes. Nanotechnology plays a vital role in developing diagnostic tools for early disease detection, whereas bioinformatics applies computational tools and techniques to analyze and interpret biological data. Alongside AI, those applications offer toolkits for developing effective, targeted healthcare. US–India collaborative efforts in the realm of technology indicate a commitment to advancing these transformative fields."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"India houses more than 800 biotechnology companies, increasing employment opportunities. The value of the country’s bioeconomy stood at $137.2 billion in 2022 ("},{"type":"text","marks":[{"type":"bold"}],"text":"10"},{"type":"text","text":") and is estimated to reach $300 billion by 2030 ("},{"type":"text","marks":[{"type":"bold"}],"text":"11"},{"type":"text","text":"). The US pharmaceutical industry can access growing talent and rapid R\u0026D in India. Start-ups are expected to lead much of the predicted growth by developing innovative technology solutions in drug discovery and delivery."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Building an Innovative Ecosystem"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The US–India collaboration provides opportunities for cooperation in the development and production of emerging technologies. For example, companies are offered a “soft landing” when they establish research and biomanufacturing facilities in India. Innovation requires networks of experts and mentors alongside capacity-building initiatives that are specialized in local common access infrastructure. The resulting ecosystem provides resource hubs for the biotechnology sector, with support for industry players, investors, and other stakeholders to promote entrepreneurship and innovation."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"India intends to assist large biopharmaceutical companies by providing supportive environments to establish facilities. The ecosystem will include corporate–start-up partnerships that help to apply global best practices. Other sectors available for investors to choose from include medical devices, agriculture, and environmental sciences."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Scientists showcased India’s potential in those fields at the Global Bio-India 2023 conference, organized by India’s Department of Biotechnology and the Biotechnology Industry Research Assistance Council (BIRAC). Japanese drug maker Takeda signed a three-year agreement with BIRAC, committing to provide advisory and mentoring support to innovators and entrepreneurs ("},{"type":"text","marks":[{"type":"bold"}],"text":"12"},{"type":"text","text":"). BIRAC also signed agreements with German company Miltenyi Biotec to collaborate in R\u0026D for immunology, stem-cell research, and cell and gene therapy (CGT). The partnership aims to expand CGT products and services from discovery stages to commercialization. Both organizations will enhance research and clinical capabilities for CGTs and establish capacities for good manufacturing practice (GMP). The collaboration will create specialized training forums for Indian clinicians, establish centers of excellence for CGTs, and share advanced technology to accelerate the development of patient-focused therapies, particularly for cancer ("},{"type":"text","marks":[{"type":"bold"}],"text":"13"},{"type":"text","text":"). Such programs intend to assist young entrepreneurs from ideation to the market entry of healthcare solutions. Similarly, biotechnology-based incubation centers in India have formed the Global Bio-incubators Network within BIRAC to promote collaboration as well. That network focuses on drug-delivery advancements in the biotechnology sector."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Symbiotic Partnerships: "},{"type":"text","text":"India stands to benefit significantly from technological advancements in the United States, which in turn can access lucrative and more affordable market opportunities offered by India. Consider the global magnetic resonance imaging (MRI) equipment industry, which reached a value $6.13 billion in 2022 and is predicted to attain $10.53 billion by 2031 ("},{"type":"text","marks":[{"type":"bold"}],"text":"14"},{"type":"text","text":"). The market is dominated by large multinational brands with an 80% market share. Despite significant industry growth, however, 70% of the world’s population still lacks access to MRI machines. Factors driving their increased adoption include technological advancements and the rising prevalence of chronic diseases. However, the high cost of traditional MRI scanners remains a significant barrier."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Indian company Voxelgrid (funded and mentored by BIRAC) unveiled an advanced, affordable MRI scanner in 2023 that can increase healthcare accessibility not only in India, but also around the world ("},{"type":"text","marks":[{"type":"bold"}],"text":"15"},{"type":"text","text":"). The scanner is more affordable than its traditional counterparts, breaking down access barriers for millions of people previously unable to afford an MRI procedure. The machine is priced at about $400,000, a fraction of the $670,000–1,200,000 cost of conventional 1.5T whole-body scanners. That substantial cost difference could create a profound impact on healthcare accessibility across the world."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"To unlock the US–India partnership’s potential fully, clear and balanced frameworks for intellectual property (IP) protection must be implemented. Open-source knowledge alongside robust IP frameworks can ensure that both countries benefit by accelerating innovation. The US–India partnership in the biotechnology, healthcare, and biomanufacturing sectors is currently in its early stages. However, the potential for global rewards is immense. Solutions that can be developed from the resulting collaborations not only will pave the way for advancements in several fields, but also could help shape a future that transcends borders and brings global change. As these two nations continue to pool their expertise, resources, and shared visions for progress, they can leave a lasting positive imprint on the global biotechnology and healthcare landscapes."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The United States is a financial powerhouse for funding innovations and providing growth capital; India is home to a substantial market with a constantly rising innovation index ("},{"type":"text","marks":[{"type":"bold"}],"text":"16"},{"type":"text","text":"). Both countries must leverage each other’s strengths to solve pressing healthcare problems around the globe."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"References"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"1 "},{"type":"text","marks":[{"type":"italic"}],"text":"CDC and India."},{"type":"text","text":" Centers for Disease Control and Prevention: Atlanta, GA, 2021; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.cdc.gov/globalhealth/countries/india/anniversary-report/pdf/India-Anniversary-Report.pdf","target":"_blank","rel":null,"class":null}}],"text":"https://www.cdc.gov/globalhealth/countries/india/anniversary-report/pdf/India-Anniversary-Report.pdf"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"2 "},{"type":"text","text":"Wouters OJ, McKee M, Luyten J. Estimated Research and Development Investment Needed To Bring a New Medicine to Market, 2009–2018. "},{"type":"text","marks":[{"type":"italic"}],"text":"JAMA "},{"type":"text","text":"323(9) 2020: 844–853; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://doi.org/10.1001/jama.2020.1166","target":"_blank","rel":null,"class":null}}],"text":"https://doi.org/10.1001/jama.2020.1166"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"3 "},{"type":"text","marks":[{"type":"italic"}],"text":"Drug Discovery Market — Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2023–2032."},{"type":"text","text":" Precedence Research: Ottawa, Canada, 2023; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.precedenceresearch.com/drug-discovery-market","target":"_blank","rel":null,"class":null}}],"text":"https://www.precedenceresearch.com/drug-discovery-market"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"4 "},{"type":"text","marks":[{"type":"italic"}],"text":"Fact Sheet: United States and India Elevate Strategic Partnership with the initiative on Critical and Emerging Technology (iCET)."},{"type":"text","text":" The White House: Washington, DC, 31 January 2023; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.whitehouse.gov/briefing-room/statements-releases/2023/01/31/fact-sheet-united-states-and-india-elevate-strategic-partnership-with-the-initiative-on-critical-and-emerging-technology-icet","target":"_blank","rel":null,"class":null}}],"text":"https://www.whitehouse.gov/briefing-room/statements-releases/2023/01/31/fact-sheet-united-states-and-india-elevate-strategic-partnership-with-the-initiative-on-critical-and-emerging-technology-icet"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"5 "},{"type":"text","marks":[{"type":"italic"}],"text":"DBT Signs Implementation Arrangement with United States-National Science Foundation"},{"type":"text","text":". BioSpectrum, 24 August 2023; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.biospectrumindia.com/news/22/23493/dbt-signs-implementation-arrangement-with-united-states-national-science-foundation.html","target":"_blank","rel":null,"class":null}}],"text":"https://www.biospectrumindia.com/news/22/23493/dbt-signs-implementation-arrangement-with-united-states-national-science-foundation.html"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"6"},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":" "},{"type":"text","marks":[{"type":"italic"}],"text":"Industry Will Be Expected To Be a Major Resource Contributor in All the Future Start-Up Ventures and Other New Technology Initiatives, Says Union Minister Dr. Jitendra Singh"},{"type":"text","text":". Ministry of Science and Technology: New Delhi, India, 5 October 2023; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://pib.gov.in/PressReleasePage.aspx?PRID=1964650","target":"_blank","rel":null,"class":null}}],"text":"https://pib.gov.in/PressReleasePage.aspx?PRID=1964650"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"7 "},{"type":"text","marks":[{"type":"italic"}],"text":"NSF Director Panchanathan Heads the US Delegation at the G20 Chief Science Advisers’ Roundtable in India, Signs Bilateral Agreement"},{"type":"text","text":". US National Science Foundation: Alexandria, VA, 31 August 2023; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://new.nsf.gov/news/nsf-director-panchanathan-heads-us-delegation-g20","target":"_blank","rel":null,"class":null}}],"text":"https://new.nsf.gov/news/nsf-director-panchanathan-heads-us-delegation-g20"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"8 "},{"type":"text","text":"Avhad P. "},{"type":"text","marks":[{"type":"italic"}],"text":"The Indian Pharmaceutical Industry: The “Pharmacy of the World?” "},{"type":"text","text":"Deloitte: London, UK, 20 March 2020; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://blogs.deloitte.co.uk/health/2020/03/the-indian-pharmaceutical-industry-the-pharmacy-of-the-world.html","target":"_blank","rel":null,"class":null}}],"text":"https://blogs.deloitte.co.uk/health/2020/03/the-indian-pharmaceutical-industry-the-pharmacy-of-the-world.html"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"9 "},{"type":"text","marks":[{"type":"italic"}],"text":"How the Indian Pharmaceutical Industry May Transform Post Pandemic."},{"type":"text","text":" EY India: New Delhi, India, 25 February 2021; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.ey.com/en_in/health/how-the-indian-pharmaceutical-industry-may-transform-post-pandemic","target":"_blank","rel":null,"class":null}}],"text":"https://www.ey.com/en_in/health/how-the-indian-pharmaceutical-industry-may-transform-post-pandemic"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"10 "},{"type":"text","marks":[{"type":"italic"}],"text":"India Bioeconomy Report 2023."},{"type":"text","text":" BIRAC: New Delhi, India, December 2023; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://birac.nic.in/webcontent/India_BioEconomy_Report_2023.pdf","target":"_blank","rel":null,"class":null}}],"text":"https://birac.nic.in/webcontent/India_BioEconomy_Report_2023.pdf"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"11 "},{"type":"text","marks":[{"type":"italic"}],"text":"Global Biotechnology - Market Size, Industry Analysis, Trends and Forecasts (2024-2029)."},{"type":"text","text":" IBISWorld: New York City, NY, September 2023; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.ibisworld.com/global/market-research-reports/global-biotechnology-industry","target":"_blank","rel":null,"class":null}}],"text":"https://www.ibisworld.com/global/market-research-reports/global-biotechnology-industry"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"12 "},{"type":"text","marks":[{"type":"italic"}],"text":"Takeda and BIRAC Forge Alliance To Propel Healthcare Solutions Development."},{"type":"text","text":" The Hindu Business Line: Chennai, India, 5 December 2023; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.thehindubusinessline.com/companies/takeda-and-birac-forge-alliance-to-propel-healthcare-solutions-development/article67606817.ece","target":"_blank","rel":null,"class":null}}],"text":"https://www.thehindubusinessline.com/companies/takeda-and-birac-forge-alliance-to-propel-healthcare-solutions-development/article67606817.ece"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"13 "},{"type":"text","marks":[{"type":"italic"}],"text":"Global Bio-India 2023 Sets New Target of $200 B for Bioeconomy in 2025"},{"type":"text","text":". BioSpectrum, 4 December 2023; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.biospectrumindia.com/news/96/23898/global-bio-india-2023-sets-new-target-of-200-b-for-bioeconomy-in-2025.html","target":"_blank","rel":null,"class":null}}],"text":"https://www.biospectrumindia.com/news/96/23898/global-bio-india-2023-sets-new-target-of-200-b-for-bioeconomy-in-2025.html"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"14"},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":" "},{"type":"text","marks":[{"type":"italic"}],"text":"Global Magnetic Resonance Imaging Equipment Market Report and Forecast"},{"type":"text","text":" 2023–2031. Research and Markets: Dublin, Ireland, April 2023; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.researchandmarkets.com/reports/5786940/global-magnetic-resonance-imaging-equipment","target":"_blank","rel":null,"class":null}}],"text":"https://www.researchandmarkets.com/reports/5786940/global-magnetic-resonance-imaging-equipment"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"15 "},{"type":"text","marks":[{"type":"italic"}],"text":"Game-Changing Indian MRI Scanner Launched: Targets 6 Billion People Without Access to Cutting-Edge Imaging Technologies"},{"type":"text","text":". BioSpace: West Des Moines, IA, 7 August 2023; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.biospace.com/article/releases/game-changing-indian-mri-scanner-launched-targets-6-billion-people-without-access-to-cutting-edge-imaging-technologies","target":"_blank","rel":null,"class":null}}],"text":"https://www.biospace.com/article/releases/game-changing-indian-mri-scanner-launched-targets-6-billion-people-without-access-to-cutting-edge-imaging-technologies"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"16 "},{"type":"text","marks":[{"type":"italic"}],"text":"India Retains 40th Rank in the Global Innovation Index"},{"type":"text","text":" 2023. NITI Aayog: New Delhi, India, 28 September 2023; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://pib.gov.in/PressReleasePage.aspx?PRID=1961576","target":"_blank","rel":null,"class":null}}],"text":"https://pib.gov.in/PressReleasePage.aspx?PRID=1961576"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Jitendra Kumar"},{"type":"text","marks":[{"type":"italic"}],"text":", PhD, is managing director at the Biotechnology Industry Research Assistance Council (BIRAC), 5th Floor, NSIC Business Park, NSIC Bhawan, Okhla Industrial Estate, New Delhi, India, 110020. For inquiries, please contact "},{"type":"text","marks":[{"type":"link","attrs":{"href":"birac@thegutenberg.com","target":"_blank","rel":null,"class":null}},{"type":"italic"}],"text":"birac@thegutenberg.com"},{"type":"text","marks":[{"type":"italic"}],"text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"paragraph","attrs":{"textAlign":"left"}}],"mobileHeadline":"US–India Collaborative Innovations for Rapid Drug Discovery","articleUrl":"/bioregions/us-india-collaborative-innovations-for-rapid-drug-discovery","listPageUrl":"/bioregions/us-india-collaborative-innovations-for-rapid-drug-discovery","linkAttrs":{},"date":"Jun 12, 2024","timeRead":0,"categoryName":"Bioregions","categoryUrl":"/business/bioregions","contributors":[{"name":"Jitendra Kumar","avatar":"","link":"/author/jitendra-kumar"}],"paidGating":null}},{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltad2c6062d1f891c1/664e636bbe90a0c3283ebdd9/22-5-eBook-Insider-Cover-Wide.png","alt":"Outsourcing — India's Burgeoning Biologics CDMO Marketplace eBook Cover"},"articleName":"eBook: Outsourcing — India's Burgeoning Biologics CDMO Marketplace","articleSummary":"","articleBody":[{"type":"figure","attrs":{"figcaption":null},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt592b2f4bb87a0137/664e635994a678506d5fe546/22-5-eBook-Insider-Cover-Border.png","alt":"22-5-eBook-Insider-Cover-Border.png","title":null,"style":{"text-align":"center"}}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The biopharmaceutical outsourcing market space is recognized widely as attractive. And yet, despite significant investment in vaccine manufacturing infrastructure catalyzed by the COVID-19 pandemic, Indian contract development and manufacturing organizations have yet to make their mark. No commercial-scale biomanufacturing capacity to match that of other countries is yet in place in India. In this eBook, industry veteran Roger Lias details the reasons for that situation and the trends that are changing India’s biopharmaceutical outlook."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Fill out the form to read the full eBook now."}]}],"mobileHeadline":"eBook: Outsourcing — India's Burgeoning Biologics CDMO Marketplace","articleUrl":"/regulations/ebook-outsourcing-indias-burgeoning-biologics-cdmo-marketplace","listPageUrl":"/regulations/ebook-outsourcing-indias-burgeoning-biologics-cdmo-marketplace","linkAttrs":{},"date":"May 22, 2024","timeRead":0,"categoryName":"Sponsored Content","categoryUrl":"#","contributors":[{"name":"Roger Lias","avatar":"","link":"/author/roger-lias"}],"paidGating":null}},{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltce65c7936187f4c9/66438f2ec37bbd5e84a5aa28/choppy.jpg","alt":"business man in choppy waters"},"articleName":"Navigating Choppy Waters: Three Resilient Marketing Tactics for Biotechnology Companies amid Market Turmoil","articleSummary":"","articleBody":[{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":"STOCK.ADOBE.COM"},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltce65c7936187f4c9/66438f2ec37bbd5e84a5aa28/choppy.jpg","alt":"choppy.jpg","title":null,"style":{"height":"auto","text-align":"center"}},"marks":[{"type":"link","attrs":{"href":"STOCK.ADOBE.COM","target":"_self","rel":null,"class":null}}]},{"type":"text","text":"STOCK.ADOBE.COM"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The biotechnology market is enduring historically low access to venture capital (VC), private equity (PE), mergers and acquisitions (M\u0026A), and initial public offerings (IPOs). As a result, biotechnology companies must chase after the fleeting funding opportunities out there today ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":"). By prioritizing the right marketing strategies immediately, companies can maximize available VC, PE, and M\u0026A opportunities, enabling them to establish long-term success and focus on science."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"According to Santollani, the bulk of biotechnology costs go to research and development (R\u0026D), employee compensation, scientific advisory boards (SABs), contract research organizations (CROs), rent, and legal services ("},{"type":"text","marks":[{"type":"bold"}],"text":"2"},{"type":"text","text":"). But that assessment did not discuss marketing as a budgetary consideration, which is an important topic to many biotechnology companies. For organizations that don’t sell products, allocating a percentage of revenue to marketing does not apply. However, for those that do, I recommend that 8–10% of a company’s budget be dedicated to marketing, as consistent with Gartner’s 2023 report that lists budget constraints among today’s market challenges ("},{"type":"text","marks":[{"type":"bold"}],"text":"3"},{"type":"text","text":"). Companies can apply the resilient marketing tactics herein to get the most from their money and to leverage that marketing budget to the fullest."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Understand Your Buyer Persona"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"As an agency owner who speaks to many founder-led and emerging biotechnology companies, I encounter many strategic approaches to marketing and answer questions regarding the next steps to take. For example, Shrishti Singh, a postdoctoral researcher at George Mason University, started her own diagnostic biotechnology company. She consulted me for marketing-cost analysis and recommendations. Her previous efforts focused on product–market-fit analysis."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"To obtain funding, her university required her to interview more than 100 people for customer research. Through those conversations, Singh identified a minimal viable product (MVP) and an ideal customer persona (ICP) ("},{"type":"text","marks":[{"type":"bold"}],"text":"4"},{"type":"text","text":"). Although she believed her process to be backward when compared with how many companies are launched, I commended her approach."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Research conducted through simple audience interviews can enable companies to save time, money, and resources. Information gathered from such interviews tells you what your target audience likes based on objective, value-driven findings. That removes the compulsion to rely on a “gut feeling” by drawing on tangible data instead. This also can be done on a small scale: 10–15 interviews might provide enough research on your MVP to ensure that it is desired by prospective customers and available at a budget-friendly price. Singh asked open-ended questions to glean the most information possible and construct a sense of the ICP’s day-to-day work as it related to her MVP. Consider asking questions similar to the following examples:"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• How would you describe the day-to-day activities at your current job?"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• What problems or pain points do you experience in your role?"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• What imaging instruments do you commonly work with or manage, and do they have any drawbacks?"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• What knowledge is required to work with those instruments? How would you describe the ease of training to use them?"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• How receptive is your workplace to adopting new techniques, and what is the process of adoption?"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Work with Suppliers on Innovative Marketing Tactics"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"According to Gartner, labor costs account for about 24% of a biotechnology organization’s budget ("},{"type":"text","marks":[{"type":"bold"}],"text":"3"},{"type":"text","text":"). Although companies argue that top-tier scientific talent is their lifeblood and cannot be squeezed, vendor relationships with marketers often can be outsourced to help reduce costs."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Few biotechnology companies require in-house specialists for public relations, graphic design, marketing, or website development, but many benefit from campaigns that use them. Outsourced marketing partners can provide project-based or retainer work with expertise that would be prohibitive for an in-house team on a budget."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"For example, when my organization conducts research for a client, we seek content marketing opportunities that both are related to a client’s products and services and have a high search volume. Upon reverse engineering a major competitor’s best content using search-engine optimization (SEO), we discovered a resource for cell-culture dishes and sizes that drove an estimated 7,500 visits per month and provided a natural source of backlink acquisition. We used that to inspire our client’s content marketing strategy. Creating and developing such a resource constitutes an advanced SEO and content marketing tactic that promotes brand awareness, and drives traffic. In this case, it also targeted bottom-of-the-funnel searchers who wished to purchase cell-culture dishes."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Finding Biotechnology Marketing Specialists:"},{"type":"text","text":" Biotech-focused incubators and accelerators provide a great resource for finding marketing agencies or consultants. Such programs often provide access to a network of vetted vendors and service providers that are well versed in the marketing needs of early stage biotechnology companies. For example, I joined the Alchemist Accelerator mentorship program to provide marketing expertise to biotechnology company founders. Academic collaborations also can provide meaningful resources to help biotechnology companies find vendors. Universities often have connections to specialized suppliers and research partners, as well."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Companies with biotechnology experience usually have better understanding than general marketing agencies of buyer personas, business goals, and scientific emphasis. But such specialized experience is expensive, so it’s important to weigh your business requirements against your need for specialization."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Showcase Your Investment Deck"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"As you seek to secure funding and capture the attention of potential investors through strategic marketing, your toolkit should include an investment slide deck that provides an illustrative narrative to highlight the strength of your investment proposition. Use that deck to outline the reasons that your company provides an enticing investment opportunity. Include a comprehensive fact sheet that discusses the scope of investment potential. Inform potential investors of your unique capacity to address prevailing challenges and present a roadmap to monetization, including plans for partnerships, mergers, and commercialization. Finally, assess whether your investors seek expertise or extensive data."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"You can leverage such investor-centric focal points to extend outreach for your website. That site serves as a central hub for presenting your corporate narrative and investment pitch. Establish a dedicated investors’ page that demonstrates the viability of your biotechnology venture clearly and outlines its path to success. Finally, consider making your investment deck available for visitors to download and enable investors to initiate further engagement directly from that page."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Appeal to Investors with Web Presence:"},{"type":"text","text":" Your website is a pivotal marketing tool even beyond its dedicated investors’ page. The site can provide accurate information about your scientific endeavors that investors will rely on to make informed decisions. Pages highlighting your leadership team, scientific pursuits, clinical trials, pipeline progress, press releases, and contact details should be infused with content tailored to investors. Links to clinical trials, access to press releases, and in-depth pipeline information cater to investors who seek a deeper understanding of your company’s operations."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Consider integrating text with graphics or animations to simplify intricate scientific concepts and make them easily scannable and digestible. Approach graphics knowing that investors often need a basic understanding of your scientific capabilities, how they translate into practical solutions, and their potential market implications."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Marketing Enables You To Focus on Science"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"For many biotechnology companies, budgetary prioritizations for top talent, R\u0026D, legal services, rent, and SAB result in underresourced marketing budgets. However, public exposure to your scientific discoveries can fuel the relationships needed to drive funding or M\u0026A talks. The organization with the loudest microphone often gets the most opportunities."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"References"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"1 "},{"type":"text","text":"Boissevain B. "},{"type":"text","marks":[{"type":"italic"}],"text":"Operational and Finance Tips for Early-Stage Startups in a Tough Market."},{"type":"text","text":" TechCrunch: San Francisco, CA, 3 August 2023;"},{"type":"text","marks":[{"type":"link","attrs":{"href":" https://techcrunch.com/2023/08/03/operational-and-finance-tips-for-early-stage-startups-in-a-tough-market","target":"_blank","rel":null,"class":null}}],"text":" https://techcrunch.com/2023/08/03/operational-and-finance-tips-for-early-stage-startups-in-a-tough-market"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"2 "},{"type":"text","text":"Santollani L. "},{"type":"text","marks":[{"type":"italic"}],"text":"A Founder’s Guide to Data-Driven Budgeting in Biotech"},{"type":"text","text":". Pillar VC: Boston, MA, 2024; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.pillar.vc/playlist/article/a-founders-guide-to-data-driven-budgeting-in-biotech","target":"_blank","rel":null,"class":null}}],"text":"https://www.pillar.vc/playlist/article/a-founders-guide-to-data-driven-budgeting-in-biotech"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"3 "},{"type":"text","marks":[{"type":"italic"}],"text":"The CMO’s Guide to Crafting a Marketing Budget"},{"type":"text","text":". Gartner, Inc.: Stamford, CT, 2024; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.gartner.com/en/marketing/topics/marketing-budget","target":"_blank","rel":null,"class":null}}],"text":"https://www.gartner.com/en/marketing/topics/marketing-budget"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"4 "},{"type":"text","text":"Martinez J. "},{"type":"text","marks":[{"type":"italic"}],"text":"Don’t Wait To Identify Your Startup’s Ideal Customer Personas. "},{"type":"text","text":"TechCrunch: San Francisco, CA, 26 June 2023; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://techcrunch.com/2023/06/26/dont-wait-to-identify-your-startups-ideal-customer-personas","target":"_blank","rel":null,"class":null}}],"text":"https://techcrunch.com/2023/06/26/dont-wait-to-identify-your-startups-ideal-customer-personas"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Corresponding author "},{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Daniel Lofaso"},{"type":"text","marks":[{"type":"italic"}],"text":" is founder and chief executive officer of Digital Elevator, a cutting-edge marketing agency specializing in biotech; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"dan@thedigitalelevator.com","target":"_blank","rel":null,"class":null}},{"type":"italic"}],"text":"dan@thedigitalelevator.com"},{"type":"text","marks":[{"type":"italic"}],"text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"}}],"mobileHeadline":"Navigating Choppy Waters: Three Resilient Marketing Tactics for Biotechnology Companies amid Market Turmoil","articleUrl":"/economics/navigating-choppy-waters-three-resilient-marketing-tactics-for-biotechnology-companies-amid-market-turmoil","listPageUrl":"/economics/navigating-choppy-waters-three-resilient-marketing-tactics-for-biotechnology-companies-amid-market-turmoil","linkAttrs":{},"date":"May 10, 2024","timeRead":0,"categoryName":"Economics","categoryUrl":"/business/economics","contributors":[{"name":"Daniel Lofaso","avatar":"","link":"/author/daniel-lofaso"}],"paidGating":null}}],"title":"Business","moreContent":{"title":"View More","url":"/business"}},{"contents":[{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt7a903736b34bad2f/667464268423f84d67fc3dd4/handshake.jpg","alt":"handshake"},"articleName":"Elevating the CGT Manufacturing Game: How Outsourcing Enables Success","articleSummary":"","articleBody":[{"type":"figure","attrs":{"figcaption":"STOCK.ADOBE.COM"},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blta725a7dba40171f7/667463798012682fd4c8bb3c/handshake.jpg","alt":"handshake.jpg","title":null,"style":{"height":"auto","text-align":"center"}}},{"type":"text","text":"STOCK.ADOBE.COM"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The tides of change have reached the banks of cell and gene therapy (CGT) manufacturing, washing ashore new concepts and capabilities that are inspiring the industry to rethink traditional business practices. During the relatively short history of CGT manufacturing, drug-sponsor companies and contract development manufacturing organizations (CDMOs) have changed their business expectations, operating models, and partnership dialogues. CGT developers no longer ask themselves whether they want to partner with a CDMO, but instead must consider when and how they want to do it."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"During the first years of the CGT boom, companies often built their own manufacturing facilities to ensure capacity because the market lacked alternatives. But recently, some companies have sold their in-house manufacturing facilities and partnered with CDMOs to maintain financial and operational sustainability."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Our latest analysis suggests that the outsourced CGT-manufacturing market will reach a value of US$57.1 billion by 2027, benefiting from a compound annual growth rate (CAGR) of 29% since 2021. It is important for drug sponsors and CDMOs to monitor macroeconomic developments as they plan for the future. Such trends can affect financial metrics, investments, cash on hand, and business priorities. The US Food and Drug Administration (FDA) already has approved four CGTs in 2024, with more expected throughout the year, highlighting the need for CDMOs to upscale their capabilities to meet client demands."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"The Evolutionary Tale of CGT Outsourcing"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Historically, CDMOs have struggled to acquire and retain the talent needed to build and drive quality processes and technology optimization for CGTs. Inconsistent performance and limited experience from struggling CDMOs created in a weak value proposition and significant trust gaps that led some pioneering drug sponsors to fend for themselves and build their own manufacturing facilities."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Critical manufacturing decisions arise in a product life cycle at four typical points for drug developers. The first occurs at the investigational new drug (IND) application stage, when a company prepares for its first human trial. During that time, a company must establish a way to supply enough investigational product to support early phase trials. A second inflection point occurs when a company prepares for later trials because the size and scale of manufacturing become critical to a program’s success. Companies face further inflection points when they commercialize and scale up products for market, then again when that market expands internationally."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"During each of those stages, budget constraints, regulatory risks, talent availability, and project timelines are assessed in the context of building, buying, or leasing manufacturing capabilities. Many executives emphasize the magnitude of such concerns, which could decide the fate of their companies. In an economic environment that often forces sponsors to tighten their purses, choosing the right manufacturing partnership is critical."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Dawn of the Next-Generation CDMOs"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Manufacturing capabilities have evolved, leading to the emergence of CDMOs that can offer all-inclusive, cost-effective, low-risk alternatives to traditional models. Competitive CDMOs develop and deploy integrated manufacturing strategies. Such companies have high-tech research capabilities with modern digital infrastructures and data analytics. CDMOs also focus on advanced manufacturing technologies that traditionally have incurred high costs and required specific resources such as automation. The best companies offer tracking and collaboration technologies that improve visibility for drug sponsors and help to build trust by ensuring transparency."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Analytical Testing In House: "},{"type":"text","text":"Every therapy has its own set of quality-release attributes and regulatory requirements, and the struggle to produce CGTs reliably and consistently has given executives many sleepless nights. By providing in-house regulatory expertise and analytical characterization services, CDMOs can relieve a pain point that has jeopardized commercial launches for sponsor companies."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Contracting with Innovative, Cobeneficial Mechanisms:"},{"type":"text","text":" Today’s CDMOs offer creative risk-sharing partnerships such as capacity leasing that entice drug sponsors to invest in services rather than take on complex facility projects. Such contracts enable sponsors to reserve capacity while enabling CDMOs to divert resources that are not needed. That way, sponsors have assured access to capacity, and CDMOs can minimize risk through securing a reliable stream of revenue."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Partnering for Strategic Growth and Expansion: "},{"type":"text","text":"Most modern CDMOs have strategic alliances with academic or research institutions that help establish new facilities and forge creative partnerships. Many CDMOs also have established investment arms to fund start-up companies and accelerate product development."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Anchoring Themselves as Enablers:"},{"type":"text","text":" Next-generation CDMOs are critical members of the CGT ecosystem. They understand regulatory requirements and support activities such as health-authority inspections. They also provide rapid scale-up capabilities and innovative manufacturing technologies."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Few sponsors ask why they need to outsource now. The creative services offered by CDMOs have grown because of mutual trust and shared goals that have emerged among CGT pioneers and their CDMO partners. CDMOs seek to match strides with drug sponsors, enabling CGT pioneers to rethink their complex manufacturing challenges as collaborative projects. Investments have helped service providers reinvigorate their value propositions and chip away at the traditional internal-build approach used by CGT developers."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Leveraging Information Technology"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Artificial intelligence (AI) and associated technological innovations are frequent drivers of industry advancement. In the next three to five years, the rise of AI and machine learning (ML) could transform operations across manufacturing and supply chains within the CGT industry. According to the industry experts that we spoke with, AI is poised to improve manufacturing in a number of ways:"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• maximizing capacity and resource use in manufacturing by multifactorial analysis"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• optimizing and streamlining supply and demand, sales, and operations planning"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• improving the accuracy of forecasts to plan against"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• predicting batch deviations, logistics and delivery delays, and quality-release issues early enough for corrective actions"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• compiling electronic batch records and regulatory submission documents using intelligent document processing"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• optimizing manufacturing yields while accounting for factors that are outside of human control, such as donor/patient raw-material variability and cell-growth patterns in vitro"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"• aiding in process development, analytical characterization processes, and scale-up."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"To derive the most benefit from such technologies, CDMOs and drug developers must work together and break down the barriers that separate them. They should share costs and data and collaborate on thought leadership. Sharing in technology adoption can increase efficiencies and help to ensure that life-saving therapies will be available and affordable to patients."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Resiliency Through Current Macroeconomic Conditions"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"In the past decade, the experiences of CGT companies have shown that well-orchestrated manufacturing practices figure into commercial success. Several promising therapies have not realized their potential because of manufacturing issues and bottlenecks. Even approved therapies still face challenges in providing consistent and reliable outcomes because of the complexities involved in manufacturing."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Evolving economic outlooks and changing government policies force drug sponsors to reevaluate their pipeline expectations. However, the CGT space is expected to continue expanding at a faster rate than that of the broader life sciences space. But if the current macroeconomic outlook continues, funding will be challenging, especially for start-up companies."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Outsourcing should integrate strategic approaches for managing risk, costs, and investments that align with market growth expectations. Such partnerships are different from traditional, transactional relationships between drug sponsors and vendors. Creative agreements and unprecedented collaborative models are likely to emerge as a new foundation for CGT manufacturing as the industry evolves. Such models will ensure that emerging CDMO solutions align with the changing needs of drug sponsors and will position both partners to respond quickly to challenges."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Talent Shortage Continues"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Despite industry-wide downsizing in 2022 and 2023, demand remains high in CGT jobs because of rapid growth in highly specialized positions and manufacturing-operator functions. According to the most recent Alliance for Regenerative Medicine (ARM) workforce report on the CGT industry (2023), 50% of CGT respondents indicated that talent shortages have impacted their manufacturing and clinical project timelines ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":"). Digging deeper, we find that 47% of respondents report having seven or more positions open, with 66% responding that it takes roughly two to three months to fill open roles."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Although the talent gap has not notably impeded the growth of the sector, the current wave of regulatory approvals underscores the need to prevent potential bottlenecks in manufacturing and clinical pipelines. The industry’s rapid evolution makes traditional educational pathways inadequate to prepare graduates for emerging roles and technologies. Additionally, competition from other sectors, such as biotechnology and pharmaceuticals, intensifies the talent shortage."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"To address recruitment challenges, some CDMOs adopt innovative strategies to attract and retain talent. One approach involves partnering with academic institutions and professional organizations to develop specialized training programs tailored to industry needs. For example, Swiss American CDMO established an alliance with BioNTX to support talent development in the Dallas–Fort Worth area of Texas, and the Center for Breakthrough Medicines (now SK pharmteco) has a similar collaboration with the University of Pennsylvania."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Market trends indicate sustained growth in the CGT industry with increasing investment from both public and private sectors. We expect growing demand for CDMO services alongside regulatory frameworks that continue to evolve to accommodate advanced therapies. The emergence of new modalities, such as RNA-based therapeutics, expands that demand by presenting more opportunities for industry increases and potential for a widening workforce gap."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Companies can enhance their talent development significantly by partnering with local academic and research institutions and medical facilities. Such organizations should also leverage state funding opportunities and engaging with industry collaborators. Such collaborations are imperative for success in the burgeoning field of cell and gene therapies, benefiting patients by accelerating new treatments and paving the way for future generations to pursue careers in regenerative medicine."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Reference"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"1 "},{"type":"text","marks":[{"type":"italic"}],"text":"Workforce Report: Gap Analysis for the Cell and Gene Therapy Sector."},{"type":"text","text":" Alliance for Regenerative Medicine: Washington, DC, March 2023; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://alliancerm.org/sector-report/workforce","target":"_blank","rel":null,"class":null}}],"text":"https://alliancerm.org/sector-report/workforce"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Corresponding author "},{"type":"text","marks":[{"type":"bold"}],"text":"Omkar Kawalekar"},{"type":"text","text":" is senior manager ("},{"type":"text","marks":[{"type":"link","attrs":{"href":"okawalekar@deloitte.com","target":"_blank","rel":null,"class":null}}],"text":"okawalekar@deloitte.com"},{"type":"text","text":"), and "},{"type":"text","marks":[{"type":"bold"}],"text":"Amit Agarwal"},{"type":"text","text":" is managing director of life sciences ("},{"type":"text","marks":[{"type":"link","attrs":{"href":"amitagarwal2@deloitte.com","target":"_blank","rel":null,"class":null}}],"text":"amitagarwal2@deloitte.com"},{"type":"text","text":"), both at Deloitte Consulting LLP. "},{"type":"text","marks":[{"type":"bold"}],"text":"Audrey Greenberg "},{"type":"text","text":"is cofounder of the Center for Breakthrough Medicines and chief business officer of SK pharmteco; ("},{"type":"text","marks":[{"type":"link","attrs":{"href":"agreenberg@cfbm.com","target":"_blank","rel":null,"class":null}}],"text":"agreenberg@cfbm.com"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"}}],"mobileHeadline":"Elevating the CGT Manufacturing Game: How Outsourcing Enables Success","articleUrl":"/cell-therapies/elevating-the-cgt-manufacturing-game-how-outsourcing-enables-success","listPageUrl":"/cell-therapies/elevating-the-cgt-manufacturing-game-how-outsourcing-enables-success","linkAttrs":{},"date":"Jun 12, 2024","timeRead":0,"categoryName":"Cell Therapies","categoryUrl":"/therapeutic-modalities/cell-therapies","contributors":[{"name":"Omkar Kawalekar","avatar":"","link":"/author/omkar-kawalekar"},{"name":"Audrey Greenberg","avatar":"","link":"/author/audrey-greenberg"},{"name":"Amit Agarwal","avatar":"","link":"/author/amit-agarwal"}],"paidGating":null}},{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt08cd228026138db1/665a34de81a854114a20409d/22-5-eBook-Allogenic-Cell-Therapy-Cover-Wide.png","alt":"Allogeneic Cell Therapy eBook cover"},"articleName":"eBook: Allogeneic Cell Therapy — From Clinical Concept to Viable Product","articleSummary":"","articleBody":[{"type":"paragraph","attrs":{"textAlign":"left"}},{"type":"figure","attrs":{"figcaption":null},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt919ae8176c504b68/665a34eb27985225f73d1d0d/22-5-eBook-Allogenic-Cell-Therapy-Cover-Border.png","alt":"22-5-eBook-Allogenic-Cell-Therapy-Cover-Border.png","title":null,"style":{"text-align":"center"}}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Allogeneic cell therapy has become an entrenched concept in the biopharmaceutical industry. Despite clear successes for autologous therapies, especially those based on chimeric antigen receptor (CAR) T cells, cell-therapy companies have yet to devise scalable means for making drug products based on cellular material from and for individual patients. Thus, many cancer patients are left without access to potentially life-saving treatment. Products based on starting material from multiple healthy donors would seem to solve the scalability dilemma as well as concerns relating to process reproducibility and product quality. For decades, hematopoietic progenitor cell (HPC) transplantation represented the only viable approach, leaving allogeneic options for novel immune-cell therapies (often based on genetically engineered material) in the realm of clinical concept. However, cell-therapy developers are steadily gathering the process and product knowledge necessary to transition allogeneic candidates from early clinical studies to late-stage trials."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"This eBook highlights recent industry efforts to bring a breadth of allogeneic cell therapies over the clinical hurdle. First, Diane McCarthy of the United States Pharmacopeia (USP) shines a spotlight on chemistry, manufacturing, and controls (CMC) considerations for cell and gene therapies (CGTs). Reporting on a recent meeting organized by the USP, US Food and Drug Administration, and Standards Coordinating Body, McCarthy shows how regulatory bodies and industry leaders are building alignment on quality expectations and best practices for CGT manufacture. Next, BPI’s managing editor speaks with Mark W. Frohlich (CEO of Indapta Therapeutics) about possibilities afforded by therapies based on natural killer (NK) cells. Frohlich calls particular attention to a naturally occurring NK subpopulation that could provide for scalable manufacture of allogeneic NK products. Tim Moore (executive vice president and chief technical officer of Allogene Therapeutics) underscores the critical need for allogeneic CAR-T products and describes promising clinical results from trailblazing candidates. Finally, Lorraine Borland and Julia Hengst from eBook sponsor Sartorius describe their company’s collaboration with Qasim Rafiq and other scientists at University College London (UCL) to establish scalable CAR-T production processes in stirred-tank bioreactors. "}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Fill out the form to read the full eBook now."}]}],"mobileHeadline":"eBook: Allogeneic Cell Therapy — From Clinical Concept to Viable Product","articleUrl":"/cell-therapies/ebook-allogeneic-cell-therapy-from-clinical-concept-to-viable-product","listPageUrl":"/cell-therapies/ebook-allogeneic-cell-therapy-from-clinical-concept-to-viable-product","linkAttrs":{},"date":"May 31, 2024","timeRead":0,"categoryName":"Sponsored Content","categoryUrl":"#","contributors":[{"name":"Diane McCarthy","avatar":"","link":"/author/diane-mccarthy"},{"name":"Brian Gazaille","avatar":"","link":"/author/brian-gazaille"},{"name":"Mark W. Frohlich","avatar":"","link":"/author/mark-w-frohlich"},{"name":"Tim Moore","avatar":"","link":"/author/tim-moore"},{"name":"Lorraine Borland","avatar":"","link":"/author/lorraine-borland"},{"name":"Julia Hengst","avatar":"","link":"/author/julia-hengst"},{"name":"Qasim Rafiq","avatar":"","link":"/author/qasim-rafiq"}],"paidGating":null}},{"type":"contentPreview","data":{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt4d754c78e81154e6/661994b74939c90ddf57f5ef/22-4-Klichinsky-P1a.jpg","alt":"Macroscopic macrophage image"},"articleName":"A Milestone in Macrophage-Based Cancer Treatment: Leveraging Chimeric Antigen Receptors To Address Solid Tumors","articleSummary":"","articleBody":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"image","attrs":{"textAlign":"left","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt4d754c78e81154e6/661994b74939c90ddf57f5ef/22-4-Klichinsky-P1a.jpg","alt":"22-4-Klichinsky-P1a.jpg","title":null,"style":{"max-width":"262px","width":"262px","height":"auto"}}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The biopharmaceutical industry has reached an inflection point regarding chimeric antigen receptor (CAR) technology. CAR-bearing T cells have become an established treatment modality for hematological indications. Consider that before such products emerged in clinical and commercial contexts, children and young adults with relapsed/refractory B-cell acute lymphoblastic leukemia (R/R B-ALL) expected five-year survival rates of 21% and 10%, respectively. Today, 70–90% of B-ALL patients who are treated with CD19-directed CAR T cells can achieve complete remission ("},{"type":"text","marks":[{"type":"bold"}],"text":"1"},{"type":"text","text":"). Since the 2017 debut of Kymriah (tisagenlecleucel) from Novartis, five other such products have received US Food and Drug Administration (FDA) approval ("},{"type":"text","marks":[{"type":"bold"}],"text":"2"},{"type":"text","text":"). CAR-T has become a second-line treatment option in some cases ("},{"type":"text","marks":[{"type":"bold"}],"text":"3"},{"type":"text","text":"). Drug developers recognize, however, that the modality continues to have limited efficacy against solid tumors, which represent 90% of cancers in human adults and 30% of those in children ("},{"type":"text","marks":[{"type":"bold"}],"text":"4–6"},{"type":"text","text":"). Thus, CAR-based approaches still have much ground to traverse if the goal is to treat the largest possible patient populations."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Emboldened by currently available CAR-T products and related advances in immunological and biomanufacturing understanding, drug developers are beginning to push the CAR concept in new directions. Such work often involves research into increasingly sophisticated CAR-T designs, such as those based on “armored” T cells redirected for universal cytokine-mediated killing (TRUCKs). Because TRUCKs are designed to express transgenic cytokines, enzymes, and/or costimulatory ligands upon receptor engagement, such cells hold promise for enhancing antitumor activity ("},{"type":"text","marks":[{"type":"bold"}],"text":"7, 8"},{"type":"text","text":") — although that advantage still needs to be demonstrated in clinical trials. In other cases, developers are engineering new cell types for CAR expression in the hope of leveraging other immune-system functions to navigate evasive and immunosuppressive tumor microenvironments."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Although industry veterans might be more familiar with CAR-expressing natural killer (NK) cells, another promising permutation of the CAR concept involves genetically engineered macrophages ("},{"type":"text","marks":[{"type":"bold"}],"text":"9–11"},{"type":"text","text":"). As this emerging modality begins to make headway in early stage clinical trials, I had the chance to speak with Michael Klichinsky, a coinventor of CAR-macrophage (CAR-M) technology and a cofounder of Carisma Therapeutics (originally Carma Therapeutics), at which he serves as chief scientific officer. He holds a PharmD degree from the University of Sciences in Philadelphia (now part of Saint Joseph’s University) and a PhD in pharmacology from the University of Pennsylvania, where he developed CAR-macrophage technology under the mentorship of Saar Gill and Carl June. During our discussion, Klichinsky highlighted clinical advantages that CAR-expressing macrophages could hold over their T-cell counterparts. He also described how recent advances in viral vectors could make CAR-Ms and related therapies viable from a manufacturing perspective."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Surmounting CAR-T Limitations"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"What are CAR-bearing macrophages, and why did your company begin researching such cells as therapeutics?"},{"type":"text","text":" CAR-T has treated leukemia successfully, and the modality is beginning to show promise against some lymphomas and myelomas. But solid tumors remain an intractable problem regardless of indication, target antigen, CAR design, and treatment regimen. While working in the June and Gill Laboratories at the University of Pennsylvania, we hypothesized that using macrophages could overcome some of the limitations facing CAR-T approaches in solid-tumor contexts."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Macrophages are innate immune cells. If you biopsy a solid tumor and ask which immune-cell type is most abundant in your sample, the answer is macrophages in almost every case. Tumors devote considerable energy to recruiting macrophages, whereas they generally try to exclude T cells. The reason for that is simple: T cells can recognize new antigens and thus pose a threat to tumor growth. On the other hand, macrophages can function as immunosuppressive cells when appropriately polarized to a suppressive phenotype, promoting tumor growth and immune escape."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Carisma takes a “Trojan horse” approach to cell therapy, provisioning the cells that tumors like to recruit with CARs. When a CAR-M binds with a target antigen, the cell is designed to activate as it would during typical antibody-driven immune responses: It will phagocytose the bound cancer cell, induce inflammatory cytokine release, and then present tumor-derived antigens to T cells."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"CAR-T therapies have improved significantly over the past 20 years, especially because of improvements in CAR design. Why do even current generations of CAR-T therapies show limited effectiveness against solid tumors, and how could CAR-M treatments improve outcomes for patients?"},{"type":"text","text":" Drug developers have put considerable effort into increasing CAR-T potency and modulating CAR signaling to give T cells specific phenotypes — e.g., a phenotype that shows higher activation, increased durability, or decreased susceptibility to T-cell exhaustion. Such advances are irrelevant if administered CAR T cells fail to reach a tumor in a sufficient quantity. If they do not enter a tumor and bind their target antigens, then it won’t matter how souped up the CAR is."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Other obstacles come from the many forms of immunosuppression that solid tumors leverage. When properly activated and expanded, T cells perform some of the fastest divisions known in biology. But speedy division requires considerable nutrients, oxygen, and other resources — not to mention conditions such as appropriate blood flow. Tumors simply lack such resources. Cancer cells also produce a host of immunosuppressive factors — including interleukin 10 (IL-10), transforming growth factor β (TGF-β), and programmed–cell-death receptor ligand 1 (PD-L1) — to hasten T-cell exhaustion."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"A third, often overlooked factor is that CAR-T usually has succeeded in the context of CD19-targeted therapies. CD19 is a highly expressed antigen that presents on nearly all B cells and B-cell cancers. But target-antigen expression is extremely heterogeneous in solid tumors. Some tumor cells express the antigen in varying quantities; other such cells do not present the antigen at all. Solid tumors effectively modulate antigen expression levels to their advantage. Let’s imagine that 20% of a patient’s tumor cells express that target antigen. The best-case scenario, then, is that CAR-T therapy kills all of those cells. But within a few weeks, the remaining 80% of tumor cells — which are inherently resistant because they do not bear the target antigen — will continue growing. Then, the tumor will be almost completely resistant to that targeted therapy."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Macrophages raise several advantages by comparison. Such cells can access tumors easily because they express different receptors and engage in distinct trafficking pathways. Some macrophages are unaffected by the immunosuppression strategies that diminish CAR-T efficacy in tumor microenvironments. Macrophages are phenotypically plastic. For simplicity’s sake, let’s say that they can assume one of two polarization states: M1 or M2. M1-state macrophages can maintain inflammatory pressure on tumor cells even in an immunosuppressive environment. Thus, at Carisma Therapeutics, we lock our CAR-Ms into the M1 phenotype."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Developers of CAR-T products cannot get around the problem of antigen heterogeneity because a CAR can target only one antigen. However, for CAR-M therapies, the receptor simply functions as a foothold — something to get phagocytosis going and initiate the cycle. Then, because macrophages are professional antigen-presenting cells (APCs), they will present tumor-specific neoantigens for other immune cells to recognize and destroy. Consider a cancer patient whose tumors show KRAS protooncogene mutations and overexpress human epidermal growth factor receptor 2 (HER2). Anti-HER2 CAR-M binding will lead to HER2-dependent phagocytosis, but then that macrophage will present the KRAS mutation to T cells, which in turn can attack tumor cells that do and do not express the initial CAR target. That process, called "},{"type":"text","marks":[{"type":"italic"}],"text":"epitope spreading"},{"type":"text","text":", is critical to the success of any immunotherapy for solid-tumor indications. T and NK cells are not APCs, so they cannot process or present antigens directly. Only monocytes, macrophages, and dendritic cells have that function."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"How much attention have macrophage-based therapies received from researchers and drug developers? Is the macrophage field still quite nascent?"},{"type":"text","text":" Our company was the first to work with CAR-M therapies. Over the past couple of years, though, the field has started to grow exponentially. Judging from the number of articles about CAR-macrophages on "},{"type":"text","marks":[{"type":"italic"}],"text":"PubMed"},{"type":"text","text":", we might say that the field has reached the same point as CAR-T did maybe 10 years ago."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Many studies have focused on genetically unmodified macrophages. German hematologist Reinhard Andreesen performed initial experiments in the 1990s ("},{"type":"text","marks":[{"type":"bold"}],"text":"12"},{"type":"text","text":"). His hypothesis was that, because macrophages are phagocytic cells, they could (without modification) take up cocultured bacteria, limiting colony formation in the culture vessel. Subsequent studies showed that macrophages activated with cytokines could phagocytose cancer cells, such that those cells showed a diminished growth rate."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"With that early concept, developers took autologous monocyte-derived macrophages into the clinic studies, which demonstrated the safety of macrophage therapy ("},{"type":"text","marks":[{"type":"bold"}],"text":"13"},{"type":"text","text":"). In some cases, cells trafficked to tumors, achieving mild improvement in ascites for patients with peritoneal carcinomatosis (a complication of ovarian cancer in the peritoneal cavity), but did little to diminish tumor growth substantially. Trial outcomes were lackluster because macrophages need a way to recognize a tumor as a target. Moreover, simply readministering macrophages to a patient does not enable the cells to attack a tumor; they need to be polarized in the M1 direction or else the tumor will recruit the cells to become immunosuppressive."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Our CAR-M concept is meant to overcome both of those limitations. A CAR is essentially a sense-and-response system. The receptor binds to a target antigen (e.g., HER2) and emits a signal to activate a cascade that leads to further immune-cell activation and killing of cancer cells. We also polarize the cells to the M1 phenotype so that tumors cannot make them immunosuppressive."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"A natural question to ask is, why did it take so long to put CARs on macrophages? Such cells are difficult to modify genetically. The vectors that are used to produce CAR T and NK cells — lentiviruses and retroviruses — simply do not work on human macrophages. Being RNA viruses, lentivirus and retrovirus release RNA genomes into infected cells. That material needs to be reverse-transcribed into DNA and then get integrated into a host cell’s genome. Reverse transcription requires that cells have DNA nucleotides available. But because macrophages actively suppress their pools of free nucleotides, reverse transcription cannot take place. Thus, lentivirus and retrovirus can enter macrophages but cannot convert their RNA genomes into DNA genomes."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"As a graduate student, I researched gene delivery into macrophages. Our team found a robust way to accomplish that using an engineered adenoviral (Ad) vector called Ad5.F35 ("},{"type":"text","marks":[{"type":"bold"}],"text":"9"},{"type":"text","text":"). It is designed to express chimeric fiber proteins comprising the knob and shaft of Ad serotype 35 (Ad35) and the fiber tail of Ad serotype 5 ("},{"type":"text","marks":[{"type":"bold"}],"text":"14, 15"},{"type":"text","text":"). Ad5.F35 vectors can transduce human macrophages and monocytes with high efficiency. Our laboratory at Carisma routinely observes \u003e90% CAR expression and high macrophage viability after Ad5.F35 transduction, whereas researchers typically observe \u003c5% CAR expression when using lentiviruses and retroviruses. Identifying the Ad5.F35 vector was one of the key findings that enabled the development of CAR-M therapy."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Making Clinical Headway"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"How would you describe your company’s CT-0508 candidate? How does it leverage the immunological advantages of macrophages that you mentioned earlier? "},{"type":"text","text":"CT-0508 is the first CAR-macrophage treatment to enter human clinical trials. It is an autologous monocyte-derived macrophage engineered with Ad5.F35 to express an anti-HER2 CAR. The candidate is undergoing a phase 1 safety study as a monotherapy ("},{"type":"text","marks":[{"type":"bold"}],"text":"16"},{"type":"text","text":"). In a distinct substudy, clinicians also are evaluating the therapy in combination with pembrolizumab, a T-cell checkpoint inhibitor ("},{"type":"text","marks":[{"type":"bold"}],"text":"17"},{"type":"text","text":"). The trial is treating patients with HER2-overexpressing solid tumors. The format is a basket trial, so it is not limited to a particular disease. But among 14 patients for whom we have presented data publicly, about half of our subjects have breast cancer, and the other half have gastroesophageal cancers, as we would expect in the HER2 space."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"CT-0508 has a few key characteristics. It is M1 polarized. Early evaluations showed that macrophages from all patients took on that proinflammatory phenotype by the end of manufacturing. We also analyzed manufactured CAR-macrophages from each patient to assess key functionality requirements — e.g., the abilities to phagocytose HER2-positive tumor cells and produce inflammatory cytokines. Results from those experiments showed that manufactured CAR-macrophages were functional in all patient lots."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Feasibility and functionality were key parameters going into the clinical phase. Trial subjects have extremely advanced diseases. Patients in our study have undergone five to 12 prior lines of treatment. All have undergone multiple rounds of chemotherapy; others have also had radiation; and all of them have received HER2-targeted therapies such as monoclonal antibodies (mAbs) and antibody–drug conjugates (ADCs). The big question, then, was whether patients’ myeloid cells were too beaten up to be manufactured into effective CAR-Ms. But the autologous macrophages worked. Cells resulting from our process showed the right phenotype, were produced in sufficient quantities, and exhibited properties that made them indistinguishable from CAR-Ms made from healthy-donor material."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"In our current first-in-human (FiH) study, safety is the focus. So far, CT-0508’s safety profile has been quite good. When you think about CAR-T therapeutics, you worry about severe cytokine release syndrome (CRS) and neurotoxicity, both of which have earned black-box warnings for marketed products. To date, the CT-0508 candidate has not triggered severe CRS. Trial subjects sometimes have experienced mild (grades 1 and 2), self-limiting CRS, but no severe illness (grades 3 and 4) has occurred. We have observed no neurotoxicity. Overall, the candidate has been well tolerated. A related factor is that therapies based on CAR T cells, CAR NK cells, tumor-infiltrating lymphocytes (TILs), and T-cell receptors (TCRs) all require lymphodepletion. We do not believe that such procedures are necessary before CAR-M administration — a unique feature among gene-modified cell therapies that enhances our candidate’s safety profile."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"In terms of efficacy, we knew from preclinical studies that CT-0508 should be studied in a combination regimen. CAR-Ms have direct antitumor activity, but a significant part of the CAR-macrophage mechanism of action (MoA) derives from its propagation of epitope spreading, which further activates T cells. In late-stage solid tumors, T cells already are experiencing exhaustion. We believe that a combination regimen with a CAR-macrophage therapy and a checkpoint inhibitor could help to overcome that limitation. Although pembrolizumab and other anti-PD-1/PD-L1 drugs have been immensely successful, most patients do not respond to monotherapy. Our goal is to use CAR-macrophages to convert nonresponding cells into responders."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Thus far, the best overall response achieved in our monotherapy trial has been stable disease, which we observed in 28% of subjects. Using single-cell analysis, we evaluated the candidate’s MoA in patient biopsy material. As was the case during preclinical studies in animals, we found that CT-0508 activated the tumor microenvironment, recruited T cells, increased intratumoral inflammation, and induced antitumor responses from T cells. At the same time, we noticed a high level of T-cell exhaustion that increased during treatment. When you activate T cells, you also induce protective inhibitory pathways such as that for PD-1. Ultimately, the monotherapy trial has demonstrated the safety of CAR-M approaches and validated the MoA. Both the monotherapy and combination trials are ongoing."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"Manufacturing Matters"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"What is a typical workflow for CAR-macrophage production?"},{"type":"text","text":" First, patients receive a subcutaneous injection of filgrastim, a recombinant granulocyte colony-stimulating factor (G-CSF) that increases peripheral-blood monocyte counts. As with autologous CAR-T products, patients undergo apheresis to extract blood, which is shipped to a manufacturing site. (Novartis performs this step for our CT-0508 candidate at one of its New Jersey facilities). Then, we use a CliniMACS Prodigy instrument (Miltenyi Biotec) to isolate monocytes from the leukopak material, perform a purification step, and differentiate the purified cells into macrophages. Then, as we discussed, Ad5.F35 vectors are used to transduce the macrophages. That step has a dual effect, delivering gene sequences for CAR expression while polarizing the macrophages into an M1 phenotype. Ultimately, manufactured cells are washed, processed, and cryopreserved until thawing and intravenous administration."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The whole process takes about a week from blood draw to product cryopreservation. The vein-to-vein time is a bit longer than that because of quality control (QC) steps that are standard for autologous cell therapies — e.g., testing for sterility, mycoplasma, and endotoxins. Thus, vein-to-vein time is three to four weeks."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"How might vein-to-vein times be decreased?"},{"type":"text","text":" One option involves the monocyte-differentiation step. In November 2023, we announced investigational new drug (IND) clearance for our CT-0525 candidate, which leverages monocytes rather than monocyte-derived macrophages. CT-0525 is undergoing a phase 1 clinical study. We have found that monocytes present some advantages over macrophages. CAR-bearing monocytes still become CAR-macrophages, but rather than performing a differentiation step ex vivo, we can administer CAR-monocytes to patients and have the cells differentiate there."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Having manufactured cells differentiate in vivo shortens the production process from one week to about one day. That strategy also enables us to produce fivefold more cells than we could in a CAR-macrophage process. We believe that dosing matters to therapy efficacy. On average, we can produce about 2 billion CAR-macrophages per patient, but with CAR-monocytes, we could produce up to 10 billion cells, enabling patients to receive higher doses."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Monocytes also raise several pharmacologic benefits. Preclinical studies have shown that monocytes traffic into tumors more readily, persist long after administration, and exhibit greater potency than do macrophages. We have yet to share data about the candidate, but the CT-0525 candidate is essentially a next-generation approach to the CT-0508 CAR-M."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"The Future of Macrophage Therapy"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"You mentioned monocytes’ advantages for production capacity. Compared with other CAR modalities, how scalable might processes for CAR-bearing macrophages and monocytes be? "},{"type":"text","text":"The industry needs to address that question soon because the solid-tumor patient population is considerably larger than are those for leukemia, lymphoma, and myeloma. Consider that the number of breast cancer patients in the United States is higher than it is for all hematologic malignancies. That said, developers still are working on bringing CAR cell therapies to the solid-tumor space. To date, no CAR-based therapy has received regulatory approval for a solid-tumor indication."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Nevertheless, the field is growing, with exciting technologies emerging to facilitate scale-up. Several companies are developing innovative automation technologies for custom cell-therapy processes. From our point of view, the CT-0525 CAR-monocyte manufacturing process is about as streamlined as an autologous workflow can get because our systems are fully automated and closed."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"It is worth mentioning that Carisma entered into a collaboration with Moderna about two years ago ("},{"type":"text","marks":[{"type":"bold"}],"text":"18"},{"type":"text","text":"). The idea was to leverage Moderna’s lipid nanoparticle (LNP)–encapsulated mRNA technology to generate CAR-macrophages in vivo rather than manufacturing a cell therapy that undergoes manipulation outside a patient’s body. Moderna has developed an LNP that is particularly successful in transfecting macrophages in the body."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"From a scalability perspective, an off-the-shelf, LNP-encapsulated therapeutic would be ideal. It could be manufactured at large scales, and because it would work directly within a patient’s body, it could overcome many of the complexities associated with ex vivo cell therapy. At the November 2023 Society for Immunotherapy of Cancer (SITC) meeting, Carisma presented data showing that such a strategy works in animal models and a variety of tumor models ("},{"type":"text","marks":[{"type":"bold"}],"text":"19"},{"type":"text","text":"). We also announced the selection of our first lead candidate under the collaboration, and the program continues to move toward IND-enabling studies ("},{"type":"text","marks":[{"type":"bold"}],"text":"20"},{"type":"text","text":")."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"References"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"1 "},{"type":"text","text":"Xu X, et al. 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Leukocyte Biol. 64(4) 1998: 419–426; https://doi.org/10.1002/jlb.64.4.419."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"13 "},{"type":"text","text":"Moroni F, et al. Safety Profile of Autologous Macrophage Therapy for Liver Cirrhosis. Nat. Med. 25(10) 2019: 1560–1565; https://doi.org/10.1038/s41591-019-0599-8."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"14 "},{"type":"text","text":"Flickinger JC Jr., et al. Chimeric"}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Ad5.F35 Vector Evades Anti-Adenovirus Serotype 5 Neutralization Opposing GUCY2C-Targeted Antitumor Immunity. J. Immunother. Cancer 8(2) 2020: e001046; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://doi.org/10.1136/jitc-2020-001046","target":"_blank","rel":null,"class":null}}],"text":"https://doi.org/10.1136/jitc-2020-001046"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"15 "},{"type":"text","text":"Chen S, et al. Harnessing and Enhancing Macrophage Phagocytosis for Cancer Therapy."},{"type":"text","marks":[{"type":"italic"}],"text":" Front. Immunol"},{"type":"text","text":". 12, 2021; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://doi.org/10.3389/fimmu.2021.635173","target":"_blank","rel":null,"class":null}}],"text":"https://doi.org/10.3389/fimmu.2021.635173"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"16 "},{"type":"text","marks":[{"type":"italic"}],"text":"CT-0508 Study."},{"type":"text","text":" Carisma Therapeutics: Philadelphia, PA, 2024; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.her2macrophagetrial.com/about-the-study","target":"_blank","rel":null,"class":null}}],"text":"https://www.her2macrophagetrial.com/about-the-study"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"17 "},{"type":"text","marks":[{"type":"italic"}],"text":"CT-0508 in Combination with Pembrolizumab Substudy."},{"type":"text","text":" Carisma Therapeutics: Philadelphia, PA, 2024; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://www.her2macrophagetrial.com/pembrolizumab-combo-study","target":"_blank","rel":null,"class":null}}],"text":"https://www.her2macrophagetrial.com/pembrolizumab-combo-study"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"18 "},{"type":"text","text":"Moderna and Carisma Establish Collaboration To Develop In Vivo Engineered Chimeric Antigen Receptor Monocytes (CAR-M) for Oncology (press release). Moderna Therapeutics: Cambridge, MA, 10 January 2022; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://investors.modernatx.com/news/news-details/2022/Moderna-and-Carisma-Establish-Collaboration-to-Develop-in-vivo-Engineered-Chimeric-Antigen-Receptor-Monocytes-CAR-M-for-Oncology/default.aspx","target":"_blank","rel":null,"class":null}}],"text":"https://investors.modernatx.com/news/news-details/2022/Moderna-and-Carisma-Establish-Collaboration-to-Develop-in-vivo-Engineered-Chimeric-Antigen-Receptor-Monocytes-CAR-M-for-Oncology/default.aspx"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"19 "},{"type":"text","text":"Carisma To Present First Results From In Vivo CAR-M Collaboration with Moderna at SITC 2023 (press release). Carisma Therapeutics: Philadelphia, PA, 25 October 2023; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://ir.carismatx.com/news-releases/news-release-details/carisma-present-first-results-vivo-car-m-collaboration-moderna","target":"_blank","rel":null,"class":null}}],"text":"https://ir.carismatx.com/news-releases/news-release-details/carisma-present-first-results-vivo-car-m-collaboration-moderna"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"20 "},{"type":"text","text":"First In Vivo CAR-M Lead Candidate Nominated Under Carisma–Moderna Collaboration (press release). Carisma Therapeutics: Philadelphia, PA, 14 December 2023; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"https://ir.carismatx.com/news-releases/news-release-details/first-vivo-car-m-lead-candidate-nominated-under-carisma-moderna","target":"_blank","rel":null,"class":null}}],"text":"https://ir.carismatx.com/news-releases/news-release-details/first-vivo-car-m-lead-candidate-nominated-under-carisma-moderna"},{"type":"text","text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"},{"type":"italic"}],"text":"Brian Gazaille"},{"type":"text","marks":[{"type":"italic"}],"text":", PhD, is managing editor of BioProcess International, part of Informa Connect Life Sciences; "},{"type":"text","marks":[{"type":"link","attrs":{"href":"brian.gazaille@informa.com","target":"_blank","rel":null,"class":null}},{"type":"italic"}],"text":"brian.gazaille@informa.com"},{"type":"text","marks":[{"type":"italic"}],"text":"."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Please email "},{"type":"text","marks":[{"type":"link","attrs":{"href":"info@carismatx.com","target":"_blank","rel":null,"class":null}},{"type":"italic"}],"text":"info@carismatx.com"},{"type":"text","marks":[{"type":"italic"}],"text":" to speak with Carisma Therapeutics about CAR-macrophage and CAR-monocyte therapies."}]},{"type":"paragraph","attrs":{"textAlign":"left"}}],"mobileHeadline":"A Milestone in Macrophage-Based Cancer Treatment: Leveraging Chimeric Antigen Receptors To Address Solid Tumors","articleUrl":"/emerging-therapeutics-manufacturing/a-milestone-in-macrophage-based-cancer-treatment-leveraging-chimeric-antigen-receptors-to-address-solid-tumors","listPageUrl":"/emerging-therapeutics-manufacturing/a-milestone-in-macrophage-based-cancer-treatment-leveraging-chimeric-antigen-receptors-to-address-solid-tumors","linkAttrs":{},"date":"Apr 10, 2024","timeRead":0,"categoryName":"Emerging Therapeutics","categoryUrl":"/therapeutic-modalities/emerging-therapeutics-manufacturing","contributors":[{"name":"Brian Gazaille","avatar":"","link":"/author/brian-gazaille"},{"name":"Michael Klichinsky","avatar":"","link":"/author/michael-klichinsky"}],"paidGating":null}}],"title":"Therapeutic Modalities","moreContent":{"title":"View More","url":"/therapeutic-modalities"}}]},"adConfig":{"nativeAdOption":false}},{"type":"module_micro_list","uid":"blt6c1c1591c4535c2a","data":{"title":"Latest eBooks","ctaButton":{"buttonText":"See 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Central to this is a cell culture media and feed system that can provide the required nutrients for optimal cell growth, viability, and function, as well as facilitate a seamless transition from development to clinical and commercial manufacturing."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"However, choosing an appropriate system can be a complex and time-consuming process, often requiring the evaluation of multiple medium and feed options. Platform systems, consisting of a medium and complementary feed developed for a specific cell line and application, have emerged as a valuable option for developers to help simplify selection and increase scale-up efficiency."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"To meet the need for high-performance platform mAb manufacturing solutions, Thermo Fisher Scientific has developed the Gibco™ Efficient-Pro™ Medium and Feeds System. The Efficient-Pro System consists of a basal CHO cell medium with two single-part feeds formulated for use with CHO-K1 and CHO-S/DG44 cell lines, respectively."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"In this article, we demonstrate the ability of the Efficient-Pro system to maintain mAb titers and quality from development to pilot scales, highlighting its suitability for use as a scalable platform solution to support fed-batch mAb production. We also discuss the additional design features of the Efficient-Pro system, intended to help manufacturers maximize process efficiency and implement a reliable commercial workflow."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Register or Login and hit Download Now to get the full Special Report."}]}],"mobileHeadline":"Scaling Up mAb Production with a Platform Medium and Feed System","articleUrl":"/sponsored-content/scaling-up-mab-production-with-a-platform-medium-and-feed-system","listPageUrl":"/sponsored-content/scaling-up-mab-production-with-a-platform-medium-and-feed-system","linkAttrs":{},"date":"Aug 23, 2024","timeRead":0,"categoryName":"Sponsored Content","categoryUrl":"#","contributors":[{"name":"Christopher Brau","avatar":"","link":"/author/chris-brau"}],"paidGating":null},{"contentType":"Journal","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt3166d0f474b0eb80/66c8c0bdf4a8251bc469aa59/22-9-CR-Sartorius-Cover-Wide.png","alt":"Sartorius Special Report Cover"},"articleName":"From Pipettes to Infusion Pumps: Volume Accuracy in Regulated Industries and Its Critical Impact","articleSummary":"","articleBody":[{"type":"figure","attrs":{"figcaption":null},"content":[{"type":"image","attrs":{"textAlign":"center","src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltc09cdbc2f04027ba/66c8c0d464ee1280f096a94e/22-9-CR-Sartorius-Cover-Border.png","alt":"22-9-CR-Sartorius-Cover-Border.png","title":null,"style":{"text-align":"center"}}}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Precision in volume delivery is critically important for both the biopharmaceutical and medical device industries. This special report discusses how accurate volume measurement affects patient safety, product quality, and regulatory compliance. Sartorius emphasizes the roles that infusion pumps, laboratory pipettes, and quality control (QC) measures play in ensuring reliable and reproducible results."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Register or Login and hit Download Now to get the full Special Report."}]}],"mobileHeadline":"From Pipettes to Infusion Pumps: Volume Accuracy in Regulated Industries and Its Critical Impact","articleUrl":"/sponsored-content/from-pipettes-to-infusion-pumps-volume-accuracy-in-regulated-industries-and-its-critical-impact","listPageUrl":"/sponsored-content/from-pipettes-to-infusion-pumps-volume-accuracy-in-regulated-industries-and-its-critical-impact","linkAttrs":{},"date":"Aug 23, 2024","timeRead":0,"categoryName":"Sponsored Content","categoryUrl":"#","contributors":[{"name":"Lucas Foerster","avatar":"","link":"/author/lucas-foerster"}],"paidGating":null}],"columnTitle":"Special Reports","ctaButton":{"buttonTitle":"See all","buttonUrl":"/publications/special-reports/special-reports"}},{"contents":[{"contentType":"Webinar","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltcc8e38b7538dbf25/6740f228fbc6d82586601064/11-21-2024-Lonza-ATE-slide1.png","alt":"Lonza Ask the Expert webcast"},"articleName":"Navigating Biopharma Commercialization: The Interplay of Regulatory Affairs, Process Development, and Manufacturing to Ensure Drug Quality","articleSummary":"","articleBody":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"This webcast features: "},{"type":"text","text":"Yves Scheurer, "},{"type":"text","marks":[{"type":"italic"}],"text":"manager, regulatory affairs,"},{"type":"text","text":" Lonza \u0026 Robin Schumacher, "},{"type":"text","marks":[{"type":"italic"}],"text":"late-stage expert,"},{"type":"text","text":" Lonza."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"In this webinar, join Lonza to explore the essential alignment between regulatory affairs, process development, and manufacturing in biopharmaceutical commercialization. Their expert speakers will discuss how integrating these functions ensures drug quality, safety, efficacy, and enables navigation of the complex regulatory landscape."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"This webinar draws attention to the commercialization of microbial bioprocesses, though the playbook that is presented can also be applied to other technologies in Lonza’s portfolio."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"First, gain insight into regulatory guidelines, with a focus on chemistry, manufacturing, and controls (CMC) documentation. Then, learn how effective regulatory affairs support can expedite global compliance and accelerate the commercial filing process."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Finally, discover how regulatory guidelines shape manufacturing processes, with practical insights on applying quality risk management, defining critical quality attributes (CQAs), and building robust control strategies that ensure consistent product quality from development through commercialization."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Watch this webinar to understand how the synergy between regulatory and operational processes drives compliance, assures process control, and ultimately delivers high-quality products to market."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Register or Login and hit Watch on Demand to view the recorded webcast now."}]}],"mobileHeadline":"Navigating Biopharma Commercialization: The Interplay of Regulatory Affairs, Process Development, and Manufacturing to Ensure Drug Quality","articleUrl":"/sponsored-content/navigating-biopharma-commercialization-the-interplay-of-regulatory-affairs-process-development-and-manufacturing-to-ensure-drug-quality","listPageUrl":"/sponsored-content/navigating-biopharma-commercialization-the-interplay-of-regulatory-affairs-process-development-and-manufacturing-to-ensure-drug-quality","linkAttrs":{},"date":"Nov 21, 2024","timeRead":30,"categoryName":"Sponsored Content","categoryUrl":"#","contributors":[{"name":"BPI Contributor","avatar":"","link":"/author/bpi-contributor"}],"paidGating":{"paidContent":false,"paidProductTaxonomy":[]}},{"contentType":"Webinar","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt3ebae85a29c14385/673e45cd6fac62187a4d0ec3/11-19-2024-908-ATE-Site-Image.jpg","alt":"908 Devices Ask the Expert"},"articleName":"Linking mAb Product Quality Profile Changes to Your Bioprocess: Case Study of Dynamic Glucose Level Control in CHO Process Improves Product Quality","articleSummary":"","articleBody":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"This webcast features: "},{"type":"text","text":"Erin Redman, PhD,"},{"type":"text","marks":[{"type":"bold"}],"text":" "},{"type":"text","marks":[{"type":"italic"}],"text":"principal scientist, "},{"type":"text","text":"\u0026 Graziella Piras, PhD"},{"type":"text","marks":[{"type":"italic"}],"text":", senior director of strategic marketing, life science,"},{"type":"text","text":" 908 Devices."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Changes made to the bioprocess can be linked to product quality attributes, but only if there are frequent data points to show the development throughout the bioprocess. Access to good product quality data at frequent timepoints from a full bioprocess can be a tall order for the analytical department."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"In this webinar, Erin Redman, PhD, principal scientist at 908 Devices, guides us through an easy solution to acquire mAb quality attribute profiling (charge variant profile, sequence identity, and glycosylation profile). Presented here is a simple dilute-and-shoot analysis using ZipChip capillary electrophoresis separation coupled to a high-resolution mass spectrometer."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Then, Graziella Piras, PhD, senior director of strategic marketing, life science, describes a case study where a comparison of CHO fed-batch process runs with the usual daily bolus glucose feed strategy and a 2g/L dynamic glucose feed strategy, enabled by MAVEN monitoring and control, resulting in clear improvement in product quality profile."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Key takeaways:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Streamline mAb product quality assessment throughout a bioprocess run."}]}]},{"type":"listItem","content":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Charge variant profile, sequence identity and glycosylation profile, glycation."}]}]},{"type":"listItem","content":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Link process feeding strategy to product quality profile- Bioprocess monitoring enabling control."}]}]},{"type":"listItem","content":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Stabilize glucose level 2g/L by MAVEN control reducing glycation."}]}]},{"type":"listItem","content":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Enable off-line analytical and on-line process analytical technology (PAT) solutions."}]}]},{"type":"listItem","content":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Dilute-and-shoot ZipChip-MS CVA workflow enabling frequent measurements."}]}]}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"italic"}],"text":"Register or Login and hit Watch on Demand to view the recorded webcast now."}]}],"mobileHeadline":"Linking mAb Product Quality Profile Changes to Your Bioprocess: Case Study of Dynamic Glucose Level Control in CHO Process Improves Product Quality","articleUrl":"/sponsored-content/linking-mab-product-quality-profile-changes-to-your-bioprocess-case-study-of-dynamic-glucose-level-control-in-cho-process-improves-product-quality","listPageUrl":"/sponsored-content/linking-mab-product-quality-profile-changes-to-your-bioprocess-case-study-of-dynamic-glucose-level-control-in-cho-process-improves-product-quality","linkAttrs":{},"date":"Nov 19, 2024","timeRead":30,"categoryName":"Sponsored Content","categoryUrl":"#","contributors":[{"name":"BPI Contributor","avatar":"","link":"/author/bpi-contributor"}],"paidGating":{"paidContent":false,"paidProductTaxonomy":[]}},{"contentType":"Webinar","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/bltb698dd10ce0fff78/673783e6bdac76bd103e868b/slide1.jpg","alt":"Bio-Rad Ask the Expert webcast"},"articleName":"Evaluation of a New Multimodal Anion Exchange Resin for Emerging Biotherapeutic Process Challenges","articleSummary":"","articleBody":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","marks":[{"type":"bold"}],"text":"This webcast features: "},{"type":"text","text":"Michael Rauscher, MS, "},{"type":"text","marks":[{"type":"italic"}],"text":"associate principal scientist, process R\u0026D enabling technologies, "},{"type":"text","text":"Merck \u0026 Co., Inc."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Multimodal chromatography has a history of providing value for biopharmaceutical separations. Ligands that enable binding based on both charge and hydrophobic interactions provide more opportunity for effective separation of multiple product- and process-related impurities. However, more diverse protein therapeutics and evolving expression systems present separation challenges that may not be easily addressed by commercially available multimodal resins."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"As an alternative to current multimodal anion exchange (MMAEX) resins with quaternary ammonium groups, Bio-Rad’s new Nuvia wPrime 2A Resin offering provides MMAEX functionality with a weak anion exchanger."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"This webinar will discuss several molecules that were prioritized for high-throughput evaluation of Nuvia wPrime 2A Resin, including an IgG1 monoclonal antibody (mAb), IgG4 mAb, and a bispecific antibody (bsAb), with extra focus placed on proteins with low isoelectric points (pI) and known strong binding to alternative MMAEX resins."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"High-throughput screening in flowthrough mode was performed to assess product binding across a broad operating space. One resin was scaled up and further evaluated using miniature columns to assess impurity removal. Results indicate strong aggregate and host cell protein clearance in flowthrough mode across a range of conditions, including effective removal of some high-risk host cell proteins. Compared to MMAEX resins with Q functionality, low pI proteins bound less, allowing higher yields. Moving forward, Nuvia wPrime 2A Resin can be a promising polishing option for diverse protein therapeutics, with protein properties guiding experimental design."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Key Takeaways:"}]},{"type":"bulletList","content":[{"type":"listItem","content":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"The ability to modulate the resin's charge state provided a wide‑design space."}]}]},{"type":"listItem","content":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Screening results showed improved yields and reductions in impurities compared to traditional strong ion exchange and strong AEX‑HIC resins."}]}]}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"hardBreak"}]}],"mobileHeadline":"Evaluation of a New Multimodal Anion Exchange Resin for Emerging Biotherapeutic Process Challenges","articleUrl":"/sponsored-content/evaluation-of-a-new-multimodal-anion-exchange-resin-for-emerging-biotherapeutic-process-challenges","listPageUrl":"/sponsored-content/evaluation-of-a-new-multimodal-anion-exchange-resin-for-emerging-biotherapeutic-process-challenges","linkAttrs":{},"date":"Nov 14, 2024","timeRead":15,"categoryName":"Sponsored Content","categoryUrl":"#","contributors":[{"name":"BPI Contributor","avatar":"","link":"/author/bpi-contributor"}],"paidGating":{"paidContent":false,"paidProductTaxonomy":[]}}],"columnTitle":"Ask the Expert Webcasts","ctaButton":{"buttonTitle":"See all","buttonUrl":"/webinars"}}]}},{"type":"module_three_column_highlight","uid":"bltfbc615562f05cc11","data":{"title":"Three Column Content White papers Videos Posters","moduleLocation":"homepage","dateDisplay":"hide","data":[{"contents":[{"type":"contentPreview","data":{"contentType":"Document","variant":"card-horizontal","layoutVariant":"horizontal","thumbnail":{"src":"https://eu-images.contentstack.com/v3/assets/blt0a48a1f3edca9eb0/blt7ce772ec8fdb49d4/6733d16b8d8bc4c2f68a4bbe/BioProcess_International_Image.jpg","alt":"Emerson white paper"},"articleName":"Sustainability in Life Sciences: Actionable Solutions for Improving Energy Efficiency","articleSummary":"","articleBody":[{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Adopting energy-saving strategies that support uninterrupted operations, mitigate production risks, and maintain competitiveness is vital for life sciences manufacturers. 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As the pharmaceutical industry evolves, high-concentration formulations are becoming crucial, particularly for chronic disease treatments that require frequent or long-term administration. These formulations enable smaller injection volumes, enhancing therapeutic efficacy and improving patient compliance by making self-administration easier at home."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"WuXiHigh is an advanced technology platform designed to address the challenges of high-concentration biologics, such as increased viscosity and protein aggregation, which can impact manufacturability and drug delivery. WuXi Biologics resolves these issues with innovative technologies like viscosity reduction techniques, synergistic excipient combinations, and high-throughput formulation screening. The platform supports concentrations up to 230 mg/mL and has contributed to over 100 projects, including monoclonal antibodies, bispecific antibodies, fusion proteins, and antibody-drug conjugates."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Beyond patient convenience, WuXiHigh offers significant cost advantages. By allowing smaller injection volumes, these high-concentration formulations reduce dosing frequency, potentially lowering the overall cost of goods sold (COGS) by optimizing manufacturing processes and reducing the need for healthcare professionals. This progress allows patients to self-administer treatments at home."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"WuXi Biologics also operates a Drug-Device Combination Center of Excellence (CoE), specializing in the development, testing, and manufacturing of combination products like prefilled syringes (PFS) and autoinjectors. 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Thousands of clones are typically screened with the goal of finding those that are stable, produce high yields of the bioproduct and exhibit desired CQAs. Screening and process optimization activities are typically carried out at the small scale in bioreactor cultures and are aimed at ensuring that results translate to larger bioreactor scales. Performance monitoring is primarily based on cell growth, cell viability, metabolite analysis and product titer, and is assessed over the entire culture process duration."}]},{"type":"paragraph","attrs":{"textAlign":"left"},"content":[{"type":"text","text":"Octet® BLI instruments utilize the biolayer interferometry (BLI) technology to monitor response changes upon the interaction of biological molecules. They can be easily implemented in cell line development labs to facilitate rapid determination of product yield amongst other attributes. 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It relies on the robust and easy-to-use Dip and Read™ format, which provides faster time to results relative to technologies like ELISA and SPR. It also operates in a fluidics-free format, thereby minimizing the complexity in analyte detection by fluidics-based technologies like SPR. It provides high-throughput analysis, with the option of analyzing to 96 samples simultaneously, thereby increasing analytical productivity. It has high tolerance to a diverse array of sample types, making it compatible with in-process testing during drug substance manufacturing. In addition, the samples on analyzed on an Octet system can be reused for other analysis, minimizing depletion of precious samples and maximizing process economy. 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