Target Identification: The Cornerstone of the Drug Discovery Process

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By employing AI and ML methods in your target identification process, you can reduce the risk of drug failure at the subsequent stages, accelerate drug discovery and development, cut down the costs of R&D and improve your competitiveness on the market. </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="#Selecting the right therapeutic target"><span style="font-weight: 400;">Selecting the right therapeutic target</span></a></span></li> <li><span style="color: #000000;"><a style="color: #000000;" href="#AI and ML in target identification">AI and ML in target identification</a></span></li> <li><span style="color: #000000;"><a style="color: #000000;" href="#Large Language Models (LLMs) for target identification">Large Language Models (LLMs) for target identification</a></span></li> <li><span style="color: #000000;"><a style="color: #000000;" href="#Omics data analysis with AI and ML">Omics data analysis with AI and ML</a></span></li> <li><span style="color: #000000;"><a style="color: #000000;" href="#Target ranking using AI and ML tools">Target ranking using AI and ML tools</a></span></li> <li><span style="color: #000000;"><a style="color: #000000;" href="#Ardigen’s approach to target identification">Ardigen’s approach to target identification</a></span></li> <li><span style="color: #000000;"><a style="color: #000000;" href="#Target identification: the first step to successful drug development">Target identification: the first step to successful drug development</a></span></li> </ol> <p><span style="font-weight: 400;">Target identification is the cornerstone of developing successful therapies. As the first step of the drug discovery process, it sets the entire pipeline for success. If the drug target is not chosen appropriately, this could lead to significant losses in terms of both time and resources. The time it takes to develop new treatments exceeds 10 years and the average cost to bring a new therapy to market is in the $1-2 billion range [</span><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7054832/"><span style="font-weight: 400;">1</span></a><span style="font-weight: 400;">]. Therefore, you want to be sure that you have selected the right target to go after from the very beginning. </span></p> <p><span style="font-weight: 400;">The importance of the right target identification is evident in the following statistics: nine out of ten drug candidates fail at the clinical stages [</span><a href="https://www.sciencedirect.com/science/article/pii/S2211383522000521"><span style="font-weight: 400;">2</span></a><span style="font-weight: 400;">]. According to clinical trials data, the most common reason for failure is lack of clinical efficacy (with 40-50% failure rate), followed by toxicity ( 30%), poor drug-like properties (10-15%), and overestimated commercial potential (10%) [</span><a href="https://www.nature.com/articles/d41573-019-00074-z"><span style="font-weight: 400;">3</span></a><span style="font-weight: 400;">]. With the lack of clinical efficacy accounting for nearly half the failures, correct initial target selection is crucial for reducing the attrition rate.</span></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h2><a id="Selecting the right therapeutic target" name="Selecting the right therapeutic target"></a><b>Selecting the right therapeutic target</b></h2> <p><span style="font-weight: 400;">What does correct target identification mean? The most important consideration is that the target must be associated with the underlying disease. The second is that the target must be druggable</span><span style="font-weight: 400;">, which means it can be modulated by a drug, producing a desired therapeutic action</span><span style="font-weight: 400;">. This is where the nuances of target identification come into play. For example, in some cases, a target may be associated with the disease but not essential. In this case, a drug may act on a target but not produce the desired therapeutic effect. In other cases, there may be compensatory molecular mechanisms that yield the therapeutic intervention ineffective.</span></p> <p><span style="font-weight: 400;">Experimental approaches to target identification can sometimes generate targets that may turn out to be non-essential during subsequent validation stages. The complexity of biological systems makes it difficult to predict all the potential interactions that can yield a target ineffective. For this reason, artificial intelligence (AI) and machine learning (ML) methods have emerged as valuable complementary approaches, offering new and expanded capabilities for target identification.</span></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h2><a id="AI and ML in target identification" name="AI and ML in target identification"></a>AI and ML in target identification</h2> <p><span style="font-weight: 400;">Advanced computational approaches, including AI and ML, have supercharged the drug discovery and development process, including target identification. With the power to uncover patterns and insights lost in conventional data analysis, ML is an indispensable tool in the hands of bioinformaticians, biologists and clinicians who are dedicated to bringing drugs to market faster and with higher success rates.</span></p> <p><span style="font-weight: 400;">ML and AI methods can help optimize the signal-to-noise ratio and find actionable patterns in data. For example, one of the challenges in the traditional ‘one target, one drug’ model is the presence of redundant functions and compensatory signaling routes. AI enables complex network analysis and elucidation of compensatory functions that can facilitate the discovery of </span><b>synthetic lethality</b><span style="font-weight: 400;"> mechanisms [</span><a href="https://www.nature.com/articles/s41588-023-01557-x"><span style="font-weight: 400;">4</span></a><span style="font-weight: 400;">,</span><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9116379/"> <span style="font-weight: 400;">5</span></a><span style="font-weight: 400;">].</span></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <img src="https://ardigen.com/wp-content/uploads/2024/10/Transforming-our-future-with-daGama.svg" alt="" style="float:right" class="float-right"> <h2><a id="Large Language Models (LLMs) for target identification" name="Large Language Models (LLMs) for target identification"></a>Large Language Models (LLMs) for target identification</h2> <p><span style="font-weight: 400;">AI can be used to mine the literature for target selection. Domain-specific large language models (LLMs), like </span><a href="https://huggingface.co/microsoft/biogpt"><span style="font-weight: 400;">BioGPT</span></a><span style="font-weight: 400;"> from Microsoft [</span><a href="https://academic.oup.com/bib/article/23/6/bbac409/6713511"><span style="font-weight: 400;">6</span></a><span style="font-weight: 400;">], can support therapeutic target discovery by quickly mining biomedical texts. Trained on extensive data from millions of publications, these tools can link diseases, genes, and biological processes, enabling the rapid identification of disease mechanisms, potential drug targets, and biomarkers. However, this approach introduces an inherent bias towards mechanisms and targets that have already been previously identified.</span></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h2><a id="Omics data analysis with AI and ML" name="Omics data analysis with AI and ML"></a>Omics data analysis with AI and ML</h2> <p><span style="font-weight: 400;">Multiomic data offers a wealth of information for target identification. In the hands of domain experts such as omics researchers, AI and ML tools can provide powerful ways to uncover novel therapeutically relevant targets, as well as assess their druggability potential. AI and ML approaches can be used for analyzing data from high-throughput CRISPR screenings, as well as genomic, transcriptomic, proteomic and metabolomic studies. It has been demonstrated that AI and ML approaches can reduce the time to identify viable targets from 4-5 years with traditional methods down by 70% [</span><a href="https://www.cell.com/trends/pharmacological-sciences/fulltext/S0165-6147(23)00137-2"><span style="font-weight: 400;">7</span></a><span style="font-weight: 400;">].</span></p> <h2><a id="Target ranking using AI and ML tools" name="Target ranking using AI and ML tools"></a>Target ranking using AI and ML tools</h2> <p><span style="font-weight: 400;">AI and ML can also help rank targets according to their therapeutic relevance [</span><a href="https://ieeexplore.ieee.org/document/9121705"><span style="font-weight: 400;">8</span></a><span style="font-weight: 400;">]. The ranking includes different scoring metrics, such as how druggable or clinically relevant the target is, the drug specificity and safety, its novelty and economic potential, and others. The ranking process </span><span style="font-weight: 400;">helps </span><span style="font-weight: 400;">uncover indication-relevant biological patterns, evaluate the safety of your target (based on literature and clinical trial information), as well as predict drug responses </span><i><span style="font-weight: 400;">in vivo</span></i><span style="font-weight: 400;"> from preclinical data to improve translatability [</span><a href="https://www.sciencedirect.com/science/article/abs/pii/S0959440X23000209"><span style="font-weight: 400;">9</span></a><span style="font-weight: 400;">].</span></p> <p></p> <div style="border: 2px solid #000; padding: 10px; margin: 10px auto; font-size: 0.95em; width: 80%; text-align: center;"> <h4 style="margin: 5px 0;"> AI & ML Capabilities for Target Identification</h4> <ul style="list-style-type: disc; padding-left: 20px; text-align: left; margin: 5px 0;"> <li>Reveal insights from multi-omic data</li> <li>Identify therapeutically relevant targets</li> <li>Rank targets according to their relevance</li> <li>Uncover indication-relevant biological patterns</li> <li>Evaluate the safety of your target</li> <li>Predict drug responses in vivo from preclinical data</li> </ul> </div> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h2><a id="Ardigen’s approach to target identification" name="Ardigen’s approach to target identification"></a>Ardigen’s approach to target identification</h2> <p><span style="font-weight: 400;">As a leader in AI in drug discovery and development,</span><a href="https://ardigen.com/"> <span style="font-weight: 400;">Ardigen</span></a><span style="font-weight: 400;"> has spent the last nine years creating a framework that facilitates rapid and efficient target identification. Ardigen’s </span><a href="https://ardigen.com/ardigen-dagama/"><span style="font-weight: 400;">Target ID Services</span></a><span style="font-weight: 400;"> harnesses the power of its own data universe, combined with ML and AI techniques developed specifically to streamline target identification and reduce the risk of the drug discovery and development process.</span></p> <p><span style="font-weight: 400;">Our custom ML and AI-driven</span> <span style="font-weight: 400;">target identification approaches include curated literature searches, including annotated and unannotated databases, as well as analysis of proprietary biological datasets. ML and AI methods enable comprehensive analysis of multiomic data (including genomic, epigenomic, transcriptomic, proteomic and metabolomic profiling), functional genomics studies and phenotypic profiling image data analysis (such as </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</span></a><span style="font-weight: 400;">). </span></p> <p><span style="font-weight: 400;">In addition to target identification, our framework can also aid in the subsequent </span><b>target validation</b><span style="font-weight: 400;"> steps by providing initial </span><i><span style="font-weight: 400;">in silico</span></i><span style="font-weight: 400;"> validation to find associations with indications and phenotypes. We combine multiple modalities that enable not only a thorough target identification search but also a comprehensive assessment of the target’s viability, </span><b>developability</b><span style="font-weight: 400;"> and therapeutic potential.</span></p> <div style="border: 2px solid #000; padding: 10px; margin: 20px auto; font-size: 0.95em; width: 80%; text-align: center;"> <h4 style="margin: 5px 0;"> Capabilities of Ardigen’s Target ID Services</h4> <ul style="list-style-type: disc; padding-left: 20px; text-align: left; margin: 5px 0;"> <li>Target discovery and prioritization</li> <li>Target validation</li> <li>Target deconvolution</li> <li>Indication expansion</li> <li>Multi-omics AI for target mining</li> </ul> </div> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h2><a id="ATarget identification: the first step to successful drug development" name="Target identification: the first step to successful drug development"></a>Target identification: the first step to successful drug development</h2> <p><span style="font-weight: 400;">Thinking of target identification as the cornerstone of your research program will help you bring effective therapies to market faster, cut down the costs of R&D and reduce the attrition rate. You can elevate your target identification efforts by implementing AI and ML methods, which are essential to staying competitive in the market. </span></p> <p><span style="font-weight: 400;">With Ardigen’s </span><span style="font-weight: 400;">Target ID Services</span><span style="font-weight: 400;">, you can shorten your target identification timelines, ensure the target efficacy, druggability and therapeutic relevance, as well as tackle the initial steps of the target validation process. Discover the capabilities of Ardigen’s</span> <a href="https://app-eu1.hubspot.com/documents/25781697/view/732275603?accessId=babf28"><span style="font-weight: 400;">Target ID Services</span></a><span style="font-weight: 400;"> today—contact us 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 target discovery and would like more details? Get in touch!</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></p> </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;"> Wouters, Olivier J., et al. “Estimated research and development investment needed to bring a new medicine to market, 2009-2018.” </span><i><span style="font-weight: 400;">Jama</span></i><span style="font-weight: 400;"> 323.9 (2020): 844-853. </span><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7054832/"><span style="font-weight: 400;">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7054832/</span></a></li> <li><span style="font-weight: 400;"> Sun, D., et. al. “Why 90% of clinical drug development fails and how to improve it?” </span><i><span style="font-weight: 400;">Acta Pharmaceutica Sinica B </span></i><span style="font-weight: 400;">12(7) (2022): 3049-3062.</span><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9293739/"> <span style="font-weight: 400;">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9293739/</span></a></li> <li><span style="font-weight: 400;"> Dowden, H., and Munro, J. “Trends in clinical success rates and therapeutic focus.” </span><i><span style="font-weight: 400;">Nat Rev Drug Discov</span></i><span style="font-weight: 400;">, </span><i><span style="font-weight: 400;">18</span></i><span style="font-weight: 400;">(7) (2019): 495-496.</span><a href="https://www.nature.com/articles/d41573-019-00074-z"> <span style="font-weight: 400;">https://www.nature.com/articles/d41573-019-00074-z</span></a></li> <li><span style="font-weight: 400;"> Ryan, C. J., et al. “Complex synthetic lethality in cancer.” </span><i><span style="font-weight: 400;">Nature Genetics</span></i><span style="font-weight: 400;">, </span><i><span style="font-weight: 400;">55</span></i><span style="font-weight: 400;">(12) (2023): 2039-2048.</span><a href="https://www.nature.com/articles/s41588-023-01557-x"> <span style="font-weight: 400;">https://www.nature.com/articles/s41588-023-01557-x</span></a></li> <li><span style="font-weight: 400;"> Wang, J., et al. “Computational methods, databases and tools for synthetic lethality prediction.” </span><i><span style="font-weight: 400;">Briefings in Bioinformatics</span></i><span style="font-weight: 400;">, </span><i><span style="font-weight: 400;">23</span></i><span style="font-weight: 400;">(3) (2023): </span><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9116379/"><span style="font-weight: 400;">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9116379/</span></a></li> <li><span style="font-weight: 400;"> Luo, Renqian, et al. “BioGPT: generative pre-trained transformer for biomedical text generation and mining.” </span><i><span style="font-weight: 400;">Briefings in Bioinformatics</span></i><span style="font-weight: 400;"> 23.6 (2022): </span><a href="https://academic.oup.com/bib/article/23/6/bbac409/6713511"><span style="font-weight: 400;">https://academic.oup.com/bib/article/23/6/bbac409/6713511</span></a><b></b></li> <li><span style="font-weight: 400;"> Pun, F. W., et al. “AI-powered therapeutic target discovery.” </span><i><span style="font-weight: 400;">Trends in Pharmacological Sciences</span></i><a href="https://www.cell.com/trends/pharmacological-sciences/fulltext/S0165-6147(23)00137-2"> <span style="font-weight: 400;">(2023)</span><span style="font-weight: 400;">: </span><span style="font-weight: 400;">https://www.cell.com/trends/pharmacological-sciences/fulltext/S0165-6147(23)00137-2</span></a></li> <li><span style="font-weight: 400;">Muslu, Özlem, et al. “GuiltyTargets: prioritization of novel therapeutic targets with network representation learning.” </span><i><span style="font-weight: 400;">IEEE/ACM Transactions on Computational Biology and Bioinformatics</span></i><span style="font-weight: 400;"> 19.1 (2020): 491-500.</span> <a href="https://ieeexplore.ieee.org/document/9121705"><span style="font-weight: 400;">https://ieeexplore.ieee.org/document/9121705</span></a></li> <li><a href="https://endpts.com/the-endpoints-rd-15-how-many-blockbusters-does-124b-buy-the-biggest-spenders-in-drug-research-not-enough/"> <span style="font-weight: 400;">Obrezanova, Olga. “Artificial intelligence for compound pharmacokinetics prediction.” </span><i><span style="font-weight: 400;">Current Opinion in Structural Biology</span></i><span style="font-weight: 400;"> 79 (2023): 102546.</span></a> <a href="https://www.sciencedirect.com/science/article/abs/pii/S0959440X23000209"><span style="font-weight: 400;">https://www.sciencedirect.com/science/article/abs/pii/S0959440X23000209</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: 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