Data in Biotech

Four rival pharma companies build AI startups together. The science is rarely what decides which ones succeed. You've built a model that works on public data. Now pharma wants a security review, a test you can't design yourself, and proof your tool fits a program they already run. Yair Benita is CTO of AION Labs, a venture studio owned by Teva, Merck, Pfizer, AstraZeneca and AWS that builds AI drug discovery companies from scratch. His career runs from immuno-oncology at Merck in the early Keytruda days to immune checkpoint discovery at Compugen and patient data work at CytoReason. Ross Katz and Yair break down the barriers between good technology and a pharma team willing to use it. You'll hear how blind validation won over skeptical chemists, why a design tool sells where a prediction stalls, and how a year of compliance work became a one-click AWS deployment. Key Takeaways A startup's model ranked a pharma compound library it had never seen, against targets it didn't know. The chemists hoped for two binders in the top 100. The results kept them testing for ten targets.Telling a team its CRISPR guide is toxic after months and $1 million of work sends them back to the start. Yair's rule: if you can predict it, you can design it.Splitting antibody discovery and optimization into two companies made scientific sense. Commercially, pharma wanted the whole step.If pharma partners can't agree on a nonclinical test for success, don't start the company. That's why AION Labs never entered drug combination. Chapter Markers 00:00 Introduction: Rival pharma companies building AI companies together 01:30 From pharmacology to computational biology 03:13 Merck and Compugen: discovering novel immune checkpoints from genome structure 07:00 Go it alone or reveal early? Lessons on proving a computational discovery 11:14 Cytoreason: deconvolving cell composition to solve pharma problems 15:18 Start with low-hanging fruit: indication selection vs. target discovery 17:30 Business models: why tech-for-pharma is hard, and why that is changing 19:58 What AION Labs is and how four pharma companies and AWS built it 23:10 Limitations of the model: shared challenges and the data-for-training problem 27:13 Blind validation: the small molecule ranking story 32:30 Baby steps, blinded competitions and building pharma trust 35:39 How seed-stage startups innovate on limited data and funding 41:54 Lesson: predictions are useless without a fix (Cassidy Bio) 44:00 Lesson: pharma wants the full process, not a piece (antibody discovery and optimization) 46:29 Defining the problem and the test: why no drug combination company was started 51:00 Eon’s evolving model: access to pharma, co-development and funding partners 54:40 The compliance bottleneck: ISO 27001 and one-click compliant AWS 58:07 Vision: an end-to-end ecosystem of specialized AI companies 01:00:27 Who should reach out to AION Labs 01:01:24 Four barriers of data, product, proof and access Useful Links & Resources AION Labs: https://www.aionlabs.comCASP (Critical Assessment of Structure Prediction): https://predictioncenter.orgISO/IEC 27001 information security standard: https://www.iso.org/standard/27001AlphaFold Protein Structure Database: https://alphafold.ebi.ac.ukYair Benita on LinkedIn: https://www.linkedin.com/in/ybenita/ Connect With the Show Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/CorrDyn on LinkedIn: https://www.linkedin.com/company/corrdyn/CorrDyn website: https://www.corrdyn.com Which barrier has slowed your team most: data access, proof, product fit, or security review? Tell us in the comments. If you've run a blind validation with a pharma partner, we'd like to hear how you set it up. Visit corrdyn.com to see how CorrDyn can help your organization extract value from data. #DataInBiotech #DrugDiscovery #BiotechDataScience #MachineLearning #LifeSciences

AI can design a molecule in seconds, however someone still has to build it and test it before it means anything. You're pouring resources into AI-driven molecule design, but nobody on your team has answered the harder question: can you actually make what the model just designed? Patrick Finn is President and Chief Operating Officer of Twist Bioscience, a company writing DNA on silicon chips at a scale of roughly 32 million distinct oligonucleotides a day. Under his leadership, Twist has become the manufacturing layer behind antibody discovery, liquid biopsy, and molecular residual disease testing programs across biotech. You'll get a clear picture of what happens after an AI model produces a candidate molecule, and why the synthesis step decides whether that design ever becomes usable data. Paddy breaks down how pooled DNA synthesis and display selection turn hundreds of thousands of candidates into model feedback in weeks instead of months, and where the manufacturability gap bites hardest as AI pushes sequences into territory nature never explored. This episode covers high-throughput DNA synthesis, target enrichment for liquid biopsy, tumor-informed molecular residual disease testing, and the economics of scaling antibody discovery. It's built for data and R&D leaders investing in AI-driven drug discovery who haven't stress-tested the physical layer underneath their models, plus anyone in biotech data science curious how life sciences data pipelines actually generate their evidence. Key Takeaways Twist's silicon chip platform pushes roughly 32 million oligonucleotides through synthesis every day, and that scale is what turns a single AI-designed antibody into a dataset instead of a guess.Liquid biopsy tests need sequencing depth around 50,000x coverage to catch a signal in the noise, and target enrichment panels can cut total sequencing costs by roughly half.AI models are pushing sequence designs into territory that's genuinely hard to manufacture: homopolymers, long repeats, extreme GC content. Twist's acceptance rate on submitted sequences climbed from 96% to 99.5%, and that gap matters more than it sounds.Memorial Sloan Kettering sent Twist 100,000 antibody designs and had the entire set synthesized as a single pooled library, tested through phage or yeast display, instead of one sequence at a time. Chapter Markers 00:00 Introduction to Twist Bioscience and Paddy Finn 01:12 Overview of Twist's silicon DNA synthesis platform 04:13 How miniaturized chips drive 32 million oligos daily 05:35 Killer applications built on Twist's infrastructure 07:44 Liquid biopsy and the cost of sequencing depth 12:02 How target enrichment lowers diagnostic costs 13:15 Tumor-informed molecular residual disease testing 16:30 What "disease-free" really means in MRD testing 18:02 Producing and characterizing antibodies at scale 22:25 The assay menu behind antibody developability 24:04 Why AI-designed sequences are hard to manufacture 27:36 Predicting manufacturability before synthesis begins 31:16 Going from 96% to 99.5% sequence acceptance 33:16 Wet lab partnership with Amazon and Memorial Sloan Kettering 36:10 Why Twist stays an enabler, not a model builder 38:20 Is AI-driven biology durable, or a short cycle 41:53 What R&D leaders should ask before scaling AI design 43:00 Ross's takeaways: bottlenecks move, they don't disappear Useful Links & Resources Twist Bioscience: https://www.twistbioscience.comPatrick Finn on LinkedIn: https://www.linkedin.com/in/patrickjfinn/CorrDyn: https://www.corrdyn.comRoss Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ #DataInBiotech #BiotechDataScience #DrugDiscovery #LifeSciences #AIinBiotech

Flow cytometry can find a cell population in seconds, but naming it consistently across labs is a problem still unsolved. If you've ever tried to compare cytometry data across studies, CROs, or even two scientists in the same lab, you know the frustration: everyone gates their own way, and the same cell population ends up with three different definitions. That inconsistency is quietly limiting what AI can do with biotech data. Ryan Brinkman is VP Research Director of Flow Cytometry Bioinformatics at Dotmatics and Founding Director of SOULCAP, the Standard Ontology for Unambiguous Labeling in Cytometry and Phenotyping, built after years as an academic developing automated gating tools that kept running into the same naming problem. He's joined by Brian Wile, who is the General Manager of Flow Cytometry at KCAS Bio, a CRO that feels the cost of inconsistent labeling in client projects every day. You'll get a clear picture of how flow cytometry data moves from raw signal to labeled cell population, why that last step has resisted automation, and what a shared standard could unlock for machine learning models trained on this data. Ryan and Brian break down the gap between automated gating and consistent labeling, and why agreement, not just data volume, is what AI in biotech actually needs. This episode covers the mechanics of flow cytometry, an EVE Online citizen science project that trained a gating algorithm on hundreds of millions of human-labeled plots, and why cell population names like "Treg" or "natural killer cell" don't map to one agreed set of markers. Key Takeaways Gating automation solved half the problem: computers can now draw boundaries around cell populations, but nothing proves which name belongs on the result.A citizen science project turned hundreds of thousands of EVE Online players into an unlikely training set, collecting roughly half a billion labeled plots from people with zero biology background.The same cell type, like a regulatory T cell, gets defined by entirely different marker combinations depending on the lab or CRO, and those datasets can't simply be merged later.SOULCAP is running a Delphi-style consensus process among scientists worldwide to tie every cell population name to a specific, reproducible set of markers and experiments. Chapter Markers 00:00 Why naming cells is harder than naming genes 02:13 What flow cytometry measures, from cell to signal 06:21 The scale and complexity of high-dimensional cytometry data 09:20 How raw data becomes a gated cell population 12:02 Why gating has stayed manual for decades 16:19 Clusters of differentiation and the marker explosion 18:11 Training an algorithm with EVE Online players 25:31 Evaluating accuracy without a gold standard 31:34 Why solving gating doesn't solve labeling 36:32 What genomics got right that cytometry hasn't 38:09 Two definitions of a regulatory T cell, one label 44:28 The cost of inconsistent labeling for CROs and pharma 47:43 Introducing SoCAP and the Delphi consensus process 57:18 What automated labeling could make possible 65:38 How to get involved in SOULCAP Useful Links & Resources Ryan Brinkman on LinkedIn: https://www.linkedin.com/in/ryan-brinkman-9bb1103/Brian Wile on LinkedIn: https://www.linkedin.com/in/brianwile/KCAS Bio: https://www.kcasbio.comCorrDyn: https://www.corrdyn.com Connect With the Show Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/CorrDyn LinkedIn: https://www.linkedin.com/company/corrdyn/ Where do you land on the naming problem? Have you had to merge cytometry datasets that turned out to use different definitions for the same cell type? Tell us about it in the comments. Visit corrdyn.com to learn how CorrDyn can help your organization extract value from data. #DataInBiotech #FlowCytometry #BiotechDataScience #Bioinformatics #LifeSciences

Ask a general AI model how many approved drugs hit a target, and it might tell you three when the real answer is six, sounding just as confident either way. If your team is grounding drug discovery decisions in AI output with no way to trace where the answer came from, you're one regulator's question away from a very expensive problem. Lisa Downey is CEO of DrugBank, a structured biomedical intelligence platform cited in more than 60,000 papers and used by nine of the top 20 global pharma companies. She previously built Clarivate's genomic and rare disease data business from the ground up and held leadership roles at GlobalData, giving her almost 20 years across healthcare and life sciences data. Lisa breaks down why the bottleneck in AI-driven drug discovery has shifted from data scarcity to trustworthy grounding, and what that means for teams making target identification and go/no-go calls. You'll hear how DrugBank's knowledge graph separates causation from correlation, why reproducibility matters more than speed, and what questions to ask before building a reference data layer in-house. This episode covers deterministic versus probabilistic data, human-in-the-loop versus human-over-the-loop curation, and how biopharma teams connect grounding layers to their AI agents through MCP. It's built for data and analytics leaders, R&D teams, and anyone deciding whether to build or buy their biomedical data infrastructure. Key Takeaways - A general model asked how many approved drugs hit PD-L1 will answer with total confidence, and total inaccuracy, missing half the real number without any signal that it's wrong. - Anthropic's own benchmarks found frontier models pulling public genomic data got it right as little as 17% of the time, until a deterministic tool pushed accuracy past 99%. - DrugBank moved from human-in-the-loop curation to human-over-the-loop oversight once its data was connected enough that one expert validating one relationship could cascade trust across dozens of related facts. - Before building or buying a reference data layer, Lisa lays out four questions that separate real infrastructure from marketing, starting with whether every fact traces back to a source and a date. Chapter Markers 00:00 Why data scarcity isn't the real bottleneck anymore 01:22 What drew Lisa to DrugBank's mission 03:04 What DrugBank is and who relies on it 05:03 The grounding layer: completeness and reproducibility 07:28 Anthropic's benchmark on data infrastructure 09:21 The high-stakes decisions DrugBank data informs 12:23 Where lost cycle time actually comes from 14:13 DrugBank versus homegrown knowledge graphs 19:43 Human-in-the-loop versus human-over-the-loop curation 24:12 How DrugBank checks its own data quality 25:37 Deterministic versus probabilistic data explained 28:52 The J&J case: separating causation from correlation 33:06 Connecting DrugBank to your AI stack via MCP 37:28 Four questions to ask before you build or buy 42:15 Where DrugBank fits, and where it doesn't 44:44 AI as an amplifier of both good and bad decisions Useful Links & Resources - Connect with Lisa Downey on LinkedIn (https://www.linkedin.com/in/lisaldowney/) Connect With the Show - Ross Katz on LinkedIn (https://www.linkedin.com/in/b-ross-katz/) - CorrDyn on LinkedIn (https://www.linkedin.com/company/corrdyn/) Have you run into an AI model giving you a confident, wrong answer in your own R&D work? Tell us about it in the comments, we're always looking for real examples for future episodes. Visit corrdyn.com to learn how CorrDyn can help your organization extract value from data. #DataInBiotech #BiotechAI #DrugDiscovery #DataScience #LifeSciences

Most drug discovery genomic data comes from a thin slice of the world, and that bias follows every decision downstream. Your team can run a Mendelian randomization study on 35,000 patients and still walk away with a single signal that doesn't even apply to the population you care about. If your phenotype definitions are fuzzy, more data won't save you. Erika Kvikstad is a computational biologist who led precision medicine for cardiovascular disease at Bristol-Myers Squibb, working on therapies including Camzyos for hypertrophic cardiomyopathy. She now works independently on genomic data equity, focused on how reference populations shape everything from target discovery to clinical trial recruitment. You'll get a practical look at how to evaluate real-world data vendors, why heart failure is nearly impossible to define cleanly from billing codes, and where statistical power breaks down even with tens of thousands of patients. Erika also explains how her team used AI to reconstruct missing imaging data and validate cardiomyopathy diagnoses at scale. This episode covers GWAS studies, Mendelian randomization, UK Biobank, proteome-wide analysis, and the practical gap between biobank-scale data and disease-specific cohorts. It's built for data and analytics leaders working in life sciences who need to understand where genomic bias enters their pipeline, not just that it exists. Clarification Around 57:58–58:24, in discussing the proteome-wide Mendelian randomization study, Erika moved quickly between two related findings. BTN3A2 was identified as a candidate associated with ischemic stroke and potential immune-modulatory biology. Separately, single-cell expression data helped contextualize other candidate signals, including some with enriched expression in cardiomyocyte populations. Cardiomyocyte-enriched expression was not a specific finding for BTN3A2. Chapter Markers 00:00 Whose genome are we designing drugs for 01:34 Erika's path from academic genomics to BMS 03:48 Building the precision medicine strategy at BMS 06:37 Ross shares his own HCM diagnosis 07:09 Why heart failure resists clean definition 11:11 How medication use reclassifies patients 14:35 Imaging as a biomarker, and its data gaps 20:23 Data infrastructure gaps across regions 22:44 What to look for when evaluating a data vendor 27:35 Consortia and biobanked specimens for rare mutations 29:52 Cardiovascular data infrastructure versus oncology 32:29 Where statistical power breaks down 37:07 UK Biobank's strengths and its limits 40:01 Bridging broad biobanks with disease-specific cohorts 44:32 How reference population bias propagates downstream 48:53 Where genomic bias hits hardest in the pipeline 53:18 Inside a proteome-wide Mendelian randomization study 59:42 Choosing the right computational tool for the question 1:06:38 Building globally representative genomic infrastructure 1:08:04 Ross's takeaways on bias and statistical power Useful Links & Resources - Erika on LinkedIn: https://www.linkedin.com/in/erikakvikstad - UK Biobank: https://www.ukbiobank.ac.uk - Alliance for Genomic Discovery: https://alliancegenomicdiscovery.org - SHaRe Registry (DCM Foundation): https://dcmfoundation.org Connect With the Show - Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ - (Ross Katz on X: https://x.com/brosskatz - CorrDyn LinkedIn: https://www.linkedin.com/company/corrdyn/ Have you run into genomic reference bias in your own work? Tell us what it looked like and how your team caught it. Visit corrdyn.com to learn how CorrDyn can help your organization extract value from data. Subscribe to Data in Biotech so you don't miss the next conversation.