Recent Successes in Phenotypic Drug Discovery and the Future of ML/AI Methods

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In this blog post, we discuss recent examples of successfully approved treatments identified through phenotypic drug discovery methods. </p> </div> </div> </div> </div> </div> </div> </section> <section class="post tc" uk-scrollspy="cls:uk-animation-slide-bottom-medium"> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <p><strong>Table of Contents:</strong></p> <ol> <li><span style="color: #000000;"><a style="color: #000000;" href="#History of Phenotypic Drug Discovery">History of Phenotypic Drug Discovery</a></span></li> <li><span style="color: #000000;"><a style="color: #000000;" href="#Advantages of Phenotypic Drug Discovery">Advantages of Phenotypic Drug Discovery</a></span></li> <li><span style="color: #000000;"><a style="color: #000000;" href="#Recently Approved Treatments Identified through Phenotypic Drug Discovery">Recently Approved Treatments Identified through Phenotypic Drug Discovery</a></span></li> <li><span style="color: #000000;"><a style="color: #000000;" href="#AI Tools for a New Era of Phenotypic Drug Discovery">AI Tools for a New Era of Phenotypic Drug Discovery</a></span></li> </ol> <p><span style="font-weight: 400;">Phenotypic drug discovery has contributed to the development of dozens of novel treatments in recent years, many being first-in-class medicines. This drug discovery approach uses screening methods that do not require knowledge of the molecular target. Instead, it relies on identifying phenotypic changes associated with the effect of an active molecule on cells, tissues or whole organism systems relevant to human disease. </span></p> <p><span style="font-weight: 400;">Phenotypic drug discovery process involves using a diverse repertoire of methods, such as different types of cellular assays or </span><a href="https://ardigen.com/high-content-screening-redefining-what-is-possible-with-artificial-intelligence-and-machine-learning/"><span style="font-weight: 400;">high-content screening (HCS)</span></a><span style="font-weight: 400;">. The latter is arguably the most powerful and has been further enhanced by the recent advances in computational image analysis, including </span><a href="https://ardigen.com/multimodal-approach-improves-mode-of-action-and-bioactivity-prediction/"><span style="font-weight: 400;">machine learning (ML) and Artificial Intelligence (AI) approaches</span></a><span style="font-weight: 400;">. </span></p> <p><span style="font-weight: 400;">In contrast to target-based drug discovery, which relies on the meticulous investigation of a single target, phenotypic drug discovery empowers researchers to screen compound libraries against thousands of potential targets in a single experiment. This unbiased investigation method promotes the discovery of novel mechanisms, targets, pathways and lead molecules. Additionally, testing molecules directly in living systems that mimic disease states presents a significant advantage for generating insights that are more relevant to clinical outcomes [</span><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431967/"><span style="font-weight: 400;">1</span></a>]<span style="font-weight: 400;">.</span></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h2><a id="History of Phenotypic Drug Discovery" name="History of Phenotypic Drug Discovery"></a><b>History of Phenotypic Drug Discovery<br /> </b></h2> <p><span style="font-weight: 400;">Renewed interest in phenotypic drug discovery was an outcome of a systematic analysis of the list of new FDA-approved treatments between 1999 and 2008. A</span><a href="https://www.nature.com/articles/nrd3480"><span style="font-weight: 400;"> review</span></a><span style="font-weight: 400;"> published in </span><i><span style="font-weight: 400;">Nature Reviews Drug Discovery</span></i><span style="font-weight: 400;">, reports that phenotypic drug discovery methods were responsible for 28 first-in-class small molecule drugs discovered compared to 17 from the target-based methods [</span><a href="https://www.nature.com/articles/nrd3480"><span style="font-weight: 400;">2]</span></a><span style="font-weight: 400;">. Another report by Haasen et al [</span><a href="https://www.liebertpub.com/doi/abs/10.1089/adt.2017.796"><span style="font-weight: 400;">3]</span></a><span style="font-weight: 400;"> showed a dramatic increase in the percentage of phenotypic screens done by Novartis from 2011 to 2015. </span></p> <p><span style="font-weight: 400;">From 2012 to 2022, application of phenotypic drug discovery methods has grown from less than 10% to an estimated 25-40% of the project portfolio of large pharma companies such AstraZeneca and Novartis [</span><a href="https://www.nature.com/articles/nrd.2017.244"><span style="font-weight: 400;">4]</span></a><span style="font-weight: 400;">. The success of this pivot has been evident in the track record of recently approved treatments developed through phenotypic drug discovery efforts. </span></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h2><a id="Advantages of Phenotypic Drug Discovery" name="Advantages of Phenotypic Drug Discovery"></a>Advantages of Phenotypic Drug Discovery</h2> <p><span style="font-weight: 400;">An important benefit of phenotypic drug discovery is that its unbiased nature allows for the identification of therapeutic interventions for novel and diverse targets. In contrast to target-based drug discovery, which typically goes after enzymes and receptors, phenotypic approaches can reveal therapeutic intervention routes acting via membranes, ion channels, ribosomes, microtubules, or large complex molecular structures like the ATP synthase, as well as other unknown targets. </span></p> <p><span style="font-weight: 400;">With such a wide diversity of novel targets and mechanisms, phenotypic drug discovery projects have identified game-changing medicines, including first-in-class treatments f</span><span style="font-weight: 400;">or</span><span style="font-weight: 400;"> Duchenne muscular dystrophy, spinal muscular atrophy, cystic fibrosis, hepatitis C virus, and others. </span></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h2><a id="Recently Approved Treatments Identified through Phenotypic Drug Discovery" name="Recently Approved Treatments Identified through Phenotypic Drug Discovery"></a><strong>Recently Approved Treatments Identified through Phenotypic Drug Discovery</strong></h2> <p><b>Vamorolone</b><span style="font-weight: 400;">. One of the most recent examples of medicines that were identified through phenotypic drug discovery is a therapy for Duchenne muscular dystrophy, one of the most severe forms of hereditary neuromuscular diseases. A potent antagonist of the mineralocorticoid receptor (MR), Vamorolone (AGAMREE®), was developed by </span><a href="https://www.santhera.com/"><span style="font-weight: 400;">Santhera Pharmaceuticals</span></a><span style="font-weight: 400;"> and received approval in 2023. This first-in-class drug binds to the same receptors as corticosteroids but modifies the downstream activity of the receptors by ‘dissociating’ efficacy from typical steroid safety concerns, making it a valuable alternative to corticosteroids for children and adolescent. In the case of vamorolone, phenotypic profiling enabled the elucidation of the sub-activities of this drug [</span><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5354737/"><span style="font-weight: 400;">5]</span></a><span style="font-weight: 400;">. </span></p> <p><b>Risdiplam</b><span style="font-weight: 400;">. Another recently approved medicine that was developed using phenotypic drug discovery methods is Risdiplam, a Spinal Muscular Atrophy (SMA) treatment that can be administered at home. Risdiplam (sold under the brand name Evrysdi®) was developed by Genentech in collaboration with </span><a href="https://en.wikipedia.org/wiki/PTC_Therapeutics"><span style="font-weight: 400;">PTC Therapeutics</span></a><span style="font-weight: 400;"> and the SMA Foundation and approved for use in adults, children, and infants by the FDA in 2020. This systemically distributed small-molecule drug works through the modulation of SMN2 pre-mRNA splicing and increases levels of full-length SMN protein. Due to lack of known activity, SMN2 would have been an unlikely target in a traditional, target-based drug discovery campaign [</span><a href="https://pubs.acs.org/doi/full/10.1021/acs.jmedchem.8b00741"><span style="font-weight: 400;">6]</span></a><span style="font-weight: 400;">. </span></p> <p><em><strong>Table 1: Recently approved therapies identified using phenotypic drug discovery methods</strong></em></p> </div> </div> <div class="container"> <div class="tc-images-row"> <div class="tc-images-row-flex"> <div class="tc-images-row-flex-item"> <div class="tc-images-row-item"> <img src="https://ardigen.com/wp-content/uploads/2024/08/Untitled-presentation-2.svg" alt="Table 1: Recently approved therapies identified using phenotypic drug discovery methods"> </div> </div> </div> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <p><b>Daclatasvir</b><span style="font-weight: 400;"> (</span><span style="font-weight: 400;">Daklinza</span><span style="font-weight: 400;">) was developed by </span><a href="https://www.bms.com/"><span style="font-weight: 400;">Bristol-Myers Squibb</span></a><span style="font-weight: 400;"> and approved in the European Union in 2014 and the USA in 2015. This antiviral agent is used in combination with other medications to treat hepatitis C (HCV). The drug was identified through phenotypic screening and later revealed to</span> <span style="font-weight: 400;">target NS5A, a non-structural protein that plays a key role in the HCV replication process [</span><a href="https://pubs.acs.org/doi/full/10.1021/jm500335h"><span style="font-weight: 400;">7]</span></a><span style="font-weight: 400;">. As a protein with no enzymatic activity and its mechanism of action being not fully understood, it remained an elusive target for many years. Daclatasvir became first in the class of NS5A inhibitors to reach the market. A common thread between Daclatasvir and Risdiplam is that both drugs’ targets lack known activity and functional roles in disease and are therefore unlikely to be identified by traditional drug discovery methods. </span></p> <p><b>Lumacaftor</b><span style="font-weight: 400;">. Another molecule identified through phenotypic drug discovery is lumacaftor, which used in combination with</span> <span style="font-weight: 400;">ivacaftor as a therapy for cystic fibrosis and marketed under the name ORKAMBI®. Developed by </span><a href="https://www.vrtx.com/"><span style="font-weight: 400;">Vertex Pharmaceuticals</span></a><span style="font-weight: 400;">, it was approved for medical use in the United States in 2015. It targets the defective transmembrane conductance regulator (CFTR) and is effective at treating the disease in patients homozygous for the F508del mutation in </span><i><span style="font-weight: 400;">CFTR</span></i><span style="font-weight: 400;"> gene. The molecule was discovered using target-agnostic compound screens that used cell lines expressing wild-type or disease-associated CFTR variants [</span><a href="about:blank"><span style="font-weight: 400;">8]</span></a><span style="font-weight: 400;">. </span></p> <p><b>Perampanel</b><span style="font-weight: 400;">, sold under the brand name Fycompa, is an epilepsy treatment that was developed by </span><a href="https://www.eisai.com/index.html"><span style="font-weight: 400;">Eisai Co., Ltd</span></a><span style="font-weight: 400;">. This drug was first approved in 2012 to treat partial seizures and generalized tonic-clonic seizures for people older than twelve years. Whole-system, multi-parametric modeling was needed in the development of perampanel, which is not a common approach used in traditional target-based drug discovery [</span><a href="https://www.tandfonline.com/doi/full/10.1517/17460441.2014.891580"><span style="font-weight: 400;">9]</span></a><span style="font-weight: 400;">. </span></p> <p><span style="font-weight: 400;">If we take a look at the list of new treatments approved from 1999 to 2017</span><span style="font-weight: 400;">3</span><span style="font-weight: 400;">, phenotypic drug discovery has contributed to the development of 58 out of 171 total drugs. Traditional target-based drug discovery follows the lead, with 44 approvals, and monoclonal antibody (mAb)-based therapies responsible for 29 approvals. In addition, numerous candidates identified through phenotypic drug discovery programs are currently in clinical trials, suggesting the numbers will continue to increase. </span></p> <p><em><strong>Figure 1: New therapies from different discovery strategies (1999-2017). Data sources: Swinney and Anthony (2011); Haasen, et al. (2017). </strong></em></p> </div> </div> <div class="container"> <div class="tc-images-row"> <div class="tc-images-row-flex"> <div class="tc-images-row-flex-item"> <div class="tc-images-row-item"> <img src="https://ardigen.com/wp-content/uploads/2024/08/Untitled-presentation-3.svg" alt="Figure 1: New therapies from different discovery strategies (1999-2017). Data sources: Swinney and Anthony (2011); Haasen, et al. (2017). "> </div> </div> </div> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h2><a id="AI Tools for a New Era of Phenotypic Drug Discovery" name="AI Tools for a New Era of Phenotypic Drug Discovery"></a><strong>AI Tools for a New Era of Phenotypic Drug Discovery</strong></h2> <p><span style="font-weight: 400;">Over the last two decades, phenotypic drug discovery contributed to a large number of first-in-class medicines with novel molecular mechanisms. It has proven to be a powerful strategy for identifying new molecular entities that are not derivatives of existing or previously investigated compounds. This enabled the discovery of therapies that have provided valuable interventions for unmet medical needs, such as </span><span style="font-weight: 400;">Duchenne muscular dystrophy, spinal muscular atrophy,</span><span style="font-weight: 400;"> hepatitis C, and cystic fibrosis. </span></p> <p><span style="font-weight: 400;">To further advance phenotypic drug discovery, the biotech industry is dedicated to improving the methods for phenotypic profiling, developing better disease models and novel computational approaches. In particular, ML and AI tools provide significant advantages for phenotypic drug discovery by enabling automated analysis of cell image data, extraction of diverse morphological features and clustering of cellular phenotypes to help identify potential drug candidates, as well as for many other applications.</span></p> <p><span style="font-weight: 400;">In addition, advanced computational methods can take advantage of multimodal data. For example, using chemical structure features and together with extracted image features for </span><a href="https://ardigen.com/multimodal-approach-improves-mode-of-action-and-bioactivity-prediction/"><span style="font-weight: 400;">elucidating the Mode of Action (MoA) and bioactivity properties</span></a><span style="font-weight: 400;"> significantly improves the prediction power of the method.</span> <span style="font-weight: 400;">Diverse types of large public datasets have become a valuable data source for phenotypic drug discovery. The phenotypic drug discovery community is collaborating by sharing datasets and analysis methods through consortia such as </span><a href="https://jump-cellpainting.broadinstitute.org/"><span style="font-weight: 400;">JUMP-CP</span></a><span style="font-weight: 400;">. Ardigen has been a supporting partner of the JUMP-CP, contributing resources such as the </span><a href="https://phenaid.ardigen.com/jumpcpexplorer/"><span style="font-weight: 400;">JUMP-CP Data Explorer</span></a><span style="font-weight: 400;"> tool. </span></p> <p><span style="font-weight: 400;">Incorporating machine learning and AI solutions into phenotypic drug discovery workflows can provide additional dimensionality and powerful insights, improve the success rate and accelerate the speed of drug discovery, as well as provide access to the greatest diversity of target types and identify novel mechanisms.</span></p> <p><span style="font-weight: 400;">Explore Ardigen’s phenAID platform, dedicated to reducing the analysis time and enhancing the quality of predictions for HCS datasets. </span><a href="https://ardigen.com/biomedical-imaging/"><span style="font-weight: 400;">Visit our website</span></a><span style="font-weight: 400;"> to learn more. </span></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <div style="display: flex; justify-content: center;"> <div style="width: 50%;"> <h3 id="hs_cos_wrapper_module_17242350140723_title" class="hs_cos_wrapper form-title" data-hs-cos-general-type="widget_field" data-hs-cos-type="text"> Are you interested in phenotypic screening and would like more details? Get in touch!<br /> </h3> <p> <script charset="utf-8" type="text/javascript" src="//js-eu1.hsforms.net/forms/embed/v2.js"></script><br /> <script> hbspt.forms.create({ region: "eu1", portalId: "25781697", formId: "79e245be-378e-49b9-a84f-c03e5b9da7a6" }); </script> </div> </div> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h3><span style="font-weight: 400;">Works Cited: </span></h3> <ol> <li><span style="font-weight: 400;">Swinney, David C., and Jonathan A. Lee. “Recent advances in phenotypic drug discovery.” </span><i><span style="font-weight: 400;">F1000Research</span></i><span style="font-weight: 400;">9 (2020).</span> <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431967/"><span style="font-weight: 400;">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431967/</span></a></li> <li><span style="font-weight: 400;">Swinney, David C., and Jason Anthony. “How were new medicines discovered?.” </span><i><span style="font-weight: 400;">Nature reviews Drug discovery</span></i><span style="font-weight: 400;">10.7 (2011): 507-519. </span><a href="https://www.nature.com/articles/nrd3480"><span style="font-weight: 400;">https://www.nature.com/articles/nrd3480</span></a></li> <li><span style="font-weight: 400;">Haasen, Dorothea, et al. “How phenotypic screening influenced drug discovery: lessons from five years of practice.” </span><i><span style="font-weight: 400;">Assay and drug development technologies</span></i><span style="font-weight: 400;">15.6 (2017): 239-246. </span><a href="https://www.liebertpub.com/doi/abs/10.1089/adt.2017.796"><span style="font-weight: 400;">https://www.liebertpub.com/doi/abs/10.1089/adt.2017.796</span></a></li> <li><span style="font-weight: 400;">Vincent, Fabien, et al. “Phenotypic drug discovery: recent successes, lessons learned and new directions.” </span><i><span style="font-weight: 400;">Nature Reviews Drug Discovery</span></i><span style="font-weight: 400;">21.12 (2022): 899-914. </span><a href="https://www.nature.com/articles/s41573-022-00472-w"><span style="font-weight: 400;">https://www.nature.com/articles/s41573-022-00472-w</span></a></li> <li><span style="font-weight: 400;">Wells, Elizabeth, et al. “Vamorolone, a dissociative steroidal compound, reduces pro-inflammatory cytokine expression in glioma cells and increases activity and survival in a murine model of cortical tumor.” </span><i><span style="font-weight: 400;">Oncotarget</span></i><span style="font-weight: 400;">8.6 (2017): 9366. </span><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5354737/"><span style="font-weight: 400;">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5354737/</span></a></li> <li><span style="font-weight: 400;">Ratni, Hasane, et al. “Discovery of risdiplam, a selective survival of motor neuron-2 (SMN2) gene splicing modifier for the treatment of spinal muscular atrophy (SMA).” (2018): 6501-6517. </span><a href="https://pubs.acs.org/doi/full/10.1021/acs.jmedchem.8b00741"><span style="font-weight: 400;">https://pubs.acs.org/doi/full/10.1021/acs.jmedchem.8b00741</span></a></li> <li><span style="font-weight: 400;">Belema, Makonen, and Nicholas A. Meanwell. “Discovery of daclatasvir, a pan-genotypic hepatitis C virus NS5A replication complex inhibitor with potent clinical effect.” (2014): 5057-5071. </span><a href="https://pubs.acs.org/doi/full/10.1021/jm500335h"><span style="font-weight: 400;">https://pubs.acs.org/doi/full/10.1021/jm500335h</span></a></li> <li><span style="font-weight: 400;">Van Goor, Fredrick, et al. “Correction of the F508del-CFTR protein processing defect in vitro by the investigational drug VX-809.” </span><i><span style="font-weight: 400;">PNAS</span></i><span style="font-weight: 400;">108.46 (2011): 18843-18848. </span><a href="https://www.pnas.org/doi/abs/10.1073/pnas.1105787108"><span style="font-weight: 400;">https://www.pnas.org/doi/abs/10.1073/pnas.1105787108</span></a></li> <li><span style="font-weight: 400;"> Hanada, Takahisa. “The discovery and development of perampanel for the treatment of epilepsy.” Expert opinion on drug discovery 9.4 (2014): 449-458. </span><a href="https://www.tandfonline.com/doi/full/10.1517/17460441.2014.891580"><span style="font-weight: 400;">https://www.tandfonline.com/doi/full/10.1517/17460441.2014.891580</span></a></li> </ol> </div> </div> </section> <section class="latest" style="background-image: url('https://ardigen.com/wp-content/uploads/2018/04/img_bg_call_to.png')"> <div class="container"> <div class="mirror-flex mirror"> <div class="mirror-flex-item"> <div class="mirror-item" uk-scrollspy="cls:uk-animation-slide-left-medium"> <div class="mirror-image" style="background-image: url('https://ardigen.com/wp-content/uploads/2024/07/posty-na-linked-in-900-x-500-px.png')"> <div class="mirror-dim"></div> <div class="mirror-mask"></div> <div class="mirror-date">24 July 2024</div> </div> <div class="mirror-content"> <div class="mirror-title"> <h6>Ardigen phenAID’s Multimodal Approach Improves MoA and Bioactivity Prediction When Applied to a HCS Dataset from a Big Pharma Company</h6> </div> <div class="mirror-btn"> <a 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