Explore every episode of the podcast Data in Biotech
| Title | Pub. Date | Duration | |
|---|---|---|---|
| How Standardized Cell Labeling Could Fix Biotech's Data Problem | 30 Sep 2026 | 01:13:04 | |
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
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
Connect With the Show
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 | |||
| DrugBank CEO: Why Your AI Model Is Only Giving You Half the Answer | 17 Sep 2026 | 00:52:12 | |
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 | |||
| How to Identify the Blind Spots in Your Biotech's Genomic Data Before They Cost You a Drug Target | 02 Sep 2026 | 01:10:08 | |
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. | |||
| How to Turn Single-Cell Data Into a New Class of Cell-Depleting Therapies | 19 Aug 2026 | 00:54:12 | |
Why treating the cell, not the protein, could turn chronic disease treatment into something closer to a cure. You've built single-cell pipelines that spit out clusters, p-values and target lists, but nothing that survives contact with the clinic. What if the clustering method itself is quietly leading you astray? Adam Freund is Founder and CEO of Arda Therapeutics, a biotech using single-cell sequencing to find the pathogenic cells driving chronic disease. He spent seven years as a Principal Investigator at Calico Life Sciences, building a research lab on the biology of ageing and helping grow the company from 15 to more than 200 people, and holds a PhD in Molecular and Cell Biology from UC Berkeley. You'll get a working model for how Arda's discovery engine turns single-cell and spatial transcriptomic data into causal cell targets. Adam explains why a common statistical shortcut in single-cell analysis produces disease signals that don't hold up and how cell depletion could replace daily dosing with a handful of treatments that reset the immune system. Ross and Adam cover how Arda finds pathogenic cell populations across hundreds of donors, why chi-squared tests on cell clusters can substitute cell count for donor count without anyone noticing, and how B-cell depletion therapies proved that removing a cell can beat blocking its pathway. This one is for data science leaders and computational biologists building single-cell pipelines, not listeners after a general intro to drug discovery. Key Takeaways - Chi-squared tests on cell clusters draw their statistical power from the number of cells, not the number of donors, so a single oversampled patient can produce the same p-value as a hundred-donor study. - Rituximab clears 100% of B cells from circulation yet does nothing for lupus because the disease-driving cells live in tissue, not blood, a lesson now shaping where Arda tests its own molecules. - Neighborhood analysis scores each cell by the donor identity of its nearest neighbours rather than forcing cells into predefined clusters, producing a continuous disease-enrichment map with no cluster boundaries. - When depleted cells regrow, they often come back without the trait that made them harmful in the first place, which means a handful of doses can hold a chronic disease in remission for months. Chapter Markers 00:00 Why cell depletion beats pathway blocking 01:05 Welcome Adam Freund to the show 01:30 From Calico Life Sciences to founding Arda 03:29 Why blocking one pathway rarely works 05:32 B-cell depletion as the proof of concept 08:14 Building a modular library of depletion tools 10:46 Single-cell sequencing removes the need for a hypothesis 11:43 Why clustering is a dial, not ground truth 15:24 The chi-squared trap in single-cell analysis 20:40 Neighbourhood analysis and donor-weighted scoring 23:44 Moving from enrichment to causality 26:32 Inside Arda's lead fibrosis program 30:33 Why solid tissue testing beats blood samples 34:25 Simulating depletion in spatial transcriptomic data 38:49 The case for intermittent dosing over daily pills 43:58 The data infrastructure behind Arda's platform 48:46 Where spatial and protein data are heading Useful Links & Resources - Adam Freund on LinkedIn: https://www.linkedin.com/in/adam-freund-0657654 - CorrDyn: https://corrdyn.com Connect With the Show - Host Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ - Host Ross Katz on X: https://x.com/brosskatz - CorrDyn on LinkedIn: https://www.linkedin.com/company/corrdyn/ If your team runs single-cell pipelines, how do you currently decide on the number of clusters, and have you ever checked whether your significance scales with donor count rather than cell count? Tell us in the comments; we're building a running list of data QA checks for biotech data science teams. Visit corrdyn.com to learn how CorrDyn can help your organization extract value from data. | |||
| Why Biotech Talks About AI But Won't Pay for the Data It Needs | 05 Aug 2026 | 01:02:56 | |
Everyone in biotech agrees AI needs more data. Almost no one is willing to pay for it. If you're trying to build or buy a biotech AI model, you've hit the same wall: predictive performance depends on data your budget doesn't cover, and nobody in the field seems willing to close that gap. John Androsavich runs Ginkgo Datapoints, the bio AI data arm of Ginkgo Bioworks. He trained as an RNA scientist, spent years on the pharma side deciding which technologies were worth buying, and now sells the raw biological data everyone claims to want. Ross and John get into why biotech spends a fraction of what tech spends on data, how automation dropped ADME testing to $199 a compound, and what that unlocks for drug discovery pipelines and data science in biotech more broadly. You'll hear why single-cell foundation models don't scale the way the field expected, and how GPT-5 designed its own lab experiments inside an autonomous facility. This one's for data and analytics leaders in biotech who need a clearer read on where to spend on data generation, and where the field is still guessing. It's less useful if you're after a general AI overview with no biotech specifics. Key Takeaways - One Meta investment in a data-labelling vendor outweighs a full year of AI drug discovery venture funding combined, and dwarfs the entire single-cell data market. Biotech's data spend looks nothing like tech's. - Ginkgo's ADME-1 offering runs at roughly a tenth of standard pricing, which is changing when and how much companies test. Teams are now running full tier-one panels earlier instead of triaging molecules before they've generated the negative data models need. - A recent Microsoft Research paper found single-cell foundation model learning saturates at 200,000 to 2 million cells, out of a possible 20 million. Volume alone isn't the lever people assumed it was. - GPT-5 wrote its own experimental protocols for optimising cell-free protein expression, ran them through Ginkgo's autonomous Nebula lab, and hit the lowest price-per-titer ever recorded in the field. Chapter Markers 00:00 Introducing John Androsavich and Ginkgo Datapoints 01:12 Why Ginkgo launched a bio AI data business 05:03 Which companies benefit most from Datapoints 06:31 The paradox: everyone wants data, no one pays 09:00 How automation drives ADME-1's $199 price point 12:59 Testing the Jevons paradox in biotech data buying 16:05 Do we actually know biotech AI's scaling laws? 20:54 Why foundation model builders resist more data 24:59 What an empirical bake-off for bio AI could look like 29:32 The case against sitting on the sidelines 33:26 Inside the Virtual Cell Pharmacology Initiative 41:57 Where VCP fits among other virtual cell projects 44:50 The Antibody Developability Consortium with Apheris 53:57 Autonomous labs and GPT-5 designing its own experiments 59:38 Advice for mid-stage biotech data strategy 01:01:31 Final thoughts on where bio AI investment is heading Useful Links & Resources - Ginkgo Bioworks: [ginkgobioworks.com](https://www.ginkgobioworks.com) - Related episode: Apheris CEO Robin Rohm on federated co-folding (Data in Biotech) - Related episode: Eliza Appel on Lilly's TuneLab and federated learning (Data in Biotech) - CorrDyn: [corrdyn.com](https://www.corrdyn.com) Connect With the Show - Host LinkedIn (Ross Katz): [linkedin.com/in/b-ross-katz](https://www.linkedin.com/in/b-ross-katz/) - Host X: [x.com/brosskatz](https://x.com/brosskatz) - CorrDyn LinkedIn: [linkedin.com/company/corrdyn](https://www.linkedin.com/company/corrdyn/) Where does your organisation sit on the data investment paralysis John describes? Are you waiting for someone else to prove the scaling laws first, or are you buying the data now? Drop your take in the comments. Visit corrdyn.com to learn how CorrDyn can help your organisation extract value from data. #DataInBiotech #BiotechAI #DrugDiscovery #DataScience #GinkgoBioworks | |||
| Beyond Language: Why Drug Discovery Needs Physical AI, Not Just Large Language Models | 20 Jul 2026 | 00:58:07 | |
In this episode of Data in Biotech, host Ross Katz sits down with Woody Sherman, Founder and Chief Innovation Officer at PsiThera, for a conversation on why AI can transform drug discovery's paperwork and code while barely touching the hardest part of the problem: the molecules themselves. Woody's career runs through physical chemistry at MIT; over a decade at Schrödinger building tools the industry still relies on; founding Silicon Therapeutics (where his team took a small molecule STING agonist from concept to clinic in roughly three years); scaling that platform after Roivant's acquisition; and now leading PsiThera's effort to build oral small molecules for immunology targets that today are only reachable with injectable biologics. The conversation digs into why large language models excel at automation, coding, and regulatory writing but hit a wall when the task is predicting how a molecule behaves, what "physical AI" actually means as a category distinct from both LLMs and traditional physics-based simulation, and why representing molecules as quantum mechanical objects rather than text strings or 2D graphs changes what's predictable. Woody also walks through the STING program in detail, why the field's excitement over fast co-folding models like Boltz needs a strong dose of skepticism, and what it takes to build a database and team culture where chemists, biologists, and data scientists can actually understand each other. What you'll learn in this episode: >> Why the contradiction of "AI is transforming drug discovery" and "drugs still take a decade and billions of dollars" can both be true at once. >> How Silicon Therapeutics engineered a small molecule STING agonist to dimerize itself through a quantum mechanical interaction that had never been designed for before. >> What "physical AI" means as a new category built on embeddings from orbital-level, quantum mechanical representations of molecules, rather than language tokens or force-field simulations. >> Why molecular representation is the whole game: the limitations of SMILES strings and 2D graphs versus true 3D, quantum mechanical embeddings like PsiThera's Psiformer model >> Why a widely publicized claim of near-FEP-quality binding affinity at 1,000x the speed didn't hold up under scrutiny. >> How PsiThera captures not just simulation and wet lab data but human chemist judgment and reasoning as structured data, and why building a shared vocabulary across computational and experimental teams is as important as any model. Meet our guest: Woody Sherman, PhD, is Founder and Chief Innovation Officer at PsiThera, a biotechnology company designing oral small molecule drugs for immunology and inflammatory diseases, starting with the TNF superfamily. His career spans physical chemistry research at MIT, more than a decade at Schrödinger developing computational drug discovery tools, founding Silicon Therapeutics (acquired by Roivant), and leading the platform's evolution through PsiThera today. He has published more than 100 peer-reviewed papers spanning molecular dynamics, quantum mechanics, free energy simulations, and machine learning for drug design.
About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation.
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. https://www.linkedin.com/company/corrdyn/ | |||
| Synthesizable by Design: Rethinking AI's Role in Small Molecule Drug Discovery | 17 Jun 2026 | 00:59:29 | |
In this episode of Data in Biotech, host Ross Katz sits down with Paul Finn, Chief Scientific Officer at Oxford Drug Design, for a conversation on what it actually takes to find a drug molecule that works not just on paper but also in the lab, in the cell, and, ultimately, in the clinic. Paul brings four decades of experience across what became GSK, Pfizer, and a series of Oxford-area spinouts and has shepherded a compound all the way to a marketed drug. That perspective gives him a particular kind of skepticism toward AI results that look too good to be true because he's done the work of checking whether they are. The conversation moves through synthesizability as a first-class constraint, why chemistry has proven so much harder for AI than biology, how 3D molecular representation gets closer to the physics that actually matters, and what rigorous multi-parameter optimization looks like when you're trying to kill cancer cells and drug-resistant bacteria at the same time. What you'll learn in this episode: >> Why synthesizability is chronically underestimated and why changing a single atom in a structure can take a molecule from trivially easy to make to practically impossible >> How Oxford Drug Design constrains the generative search to reaction schemes and purchasable building blocks, and why that chemical space is still so vast that novelty is not meaningfully sacrificed >> Why most generative AI models learn from a 2D string representation of a molecule; two steps removed from the 3D physics that govern how a drug actually binds to its target >> How Bayesian optimization over reagent space, rather than molecular space, allows an active learning loop to focus on the structural patterns associated with activity >> Why benchmarking complex models against simple ones is the discipline that exposes false correlations and why Paul and his co-authors were able to recover the Halicin result using methods decades older than deep learning >> What a pharma company should actually ask an AI drug discovery vendor before buying what they're selling Meet our guest: Paul Finn is Chief Scientific Officer at Oxford Drug Design, a computational drug discovery company with roots in Oxford's chemistry department. His career spans over 40 years of computational drug discovery, from early structure-activity modeling in the 1980s through to modern generative AI methods, with deep experience at what became GSK and Pfizer before moving into the Oxford spinout ecosystem. At Oxford Drug Design, Paul leads internal programs in oncology and antibacterial resistance, combining novel computational methods with a rigorous, synthesizability-first approach to multi-parameter optimization.
About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation.
Connect with us:
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. https://www.linkedin.com/company/corrdyn/ | |||
| From Tissue to Mechanism to Decision: Building AI for Computational Oncology | 02 Jun 2026 | 00:46:54 | |
In this episode of Data in Biotech, host Ross Katz sits down with Arvind Rao, Professor of Computational Medicine and Bioinformatics at the University of Michigan, for a discussion on the gap between what biomedical AI can do and what it can reliably be trusted to do in clinical practice. Arvind's research sits at the intersection of computational oncology and AI governance and his lab works across H&E histopathology, multiplex immunofluorescence, spatial transcriptomics, and single-cell RNA sequencing, not just to build predictive models, but to understand the full lifecycle from data to model to inference, and to ask where that lifecycle can be trusted and where it can't. The conversation moves through two of his recent papers on SPIFEE, a graph-based framework that replaces scalar interaction scores in the tumor microenvironment with spatially resolved functional representations, and a multimodal framework that traces a path from stained tissue slides to nominated drug targets via morphological pattern discovery and spatial transcriptomic mapping. What you’ll learn in this episode: >> Why the field's central failure is not algorithmic but translational and the gap between a model that performs well on a benchmark and one that can be consistently trusted in a high-stakes clinical setting >> How SPIFEE replaces the conventional scalar edge representation of cell-cell interactions in the tumor microenvironment with spatially resolved functional edges >> How Arvind's multimodal framework moves from H&E pathology slides labeled with clinical outcomes, through morphological pattern discovery via multiple instance learning, to spatial transcriptomic mapping, to the nomination of molecular mechanisms and actionable drug targets >> Why Goodhart's Law applies directly to foundation model evaluation in biology >> What the AI literacy gap costs when it goes unaddressed in healthcare and pharma organizations Meet our guest: Arvind Rao is a Professor of Computational Medicine and Bioinformatics, with a joint appointment in Radiation Oncology, at the University of Michigan. His research focuses on establishing trust in biomedical AI predictions across the full data-to-decision pipeline, integrating H&E histopathology, spatial transcriptomics, multiplex immunofluorescence, and single-cell RNA sequencing to build models that are predictive, interpretable, and biologically credible. Alongside his research, Arvind develops AI literacy programs for healthcare and pharma professionals, helping clinical and procurement teams evaluate and govern AI systems with the rigor those decisions demand. About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation.
Connect with us:
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. https://www.linkedin.com/company/corrdyn/ | |||
| Cavities in the Data: Building FDA-Cleared AI for Dental Imaging with Overjet | 13 May 2026 | 00:58:42 | |
In this episode of Data in Biotech, host Ross Katz sits down with Sadegh Salehi, Director of Research and Principal Scientist at Overjet, to explore what rigorous model evaluation actually looks like when the stakes are clinical. Overjet builds FDA-cleared vision models that detect and quantify dental disease across billions of X-ray images from thousands of practices - a data problem with a staggering number of dimensions. Thirty-two teeth per adult patient, each with different morphology. Multiple image types capturing different anatomy. Fifteen to twenty sensor manufacturers producing perceptually distinct images, each with different contrast, resolution, and noise characteristics. And disease severity distributions ranging from barely visible early-stage decay to obvious pathology. Sadegh walks through what it takes to evaluate models responsibly across all of those dimensions and discusses why aggregate metrics like F1 score can mask catastrophic failures on specific subgroups, how models find and exploit shortcuts in training data, and why the same flawed sampling that creates gaps in your training set also creates them in your test set. He also traces Overjet's architectural evolution from over twenty narrow task-specific models to a single foundation model they call Unity, explains how treatment plan procedure codes provide a noisy but real production feedback signal, and describes how Overjet became one of the first companies to secure the FDA's Predetermined Change Control Plan (a framework that allows model updates without filing a new clearance each time.) What you’ll learn in this episode: >> Why aggregate evaluation metrics are insufficient for high-stakes medical AI >> How models exploit shortcuts in training data: if all images from a rare sensor in the training set happen to be healthy, the model doesn't learn to read that sensor, it learns that the sensor means healthy, bypassing the visual task entirely and producing systematic false negatives in production >> How Overjet evolved from over twenty narrow, sensor-specific and indication-specific models into a single foundation model called Unity, using noisy labels generated by the small models as the training signal for a much larger backbone, then building independent prediction heads for each clinical indication on top of it >> Why the decision to keep prediction heads architecturally independent from one another was driven as much by FDA regulatory strategy as by modeling considerations >> How Overjet uses dental treatment plan procedure codes as a production monitoring signal Meet our guest: Sadegh Salehi is Director of Research and Principal Scientist at Overjet, where he leads the team responsible for building, evaluating, and deploying FDA-cleared vision models for dental disease detection and quantification. About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation.
Connect with us:
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. https://www.linkedin.com/company/corrdyn/ | |||
| Data as a Moat: Why Biotech's Most Valuable Asset is Buried in a Hard Drive | 30 Apr 2026 | 00:42:52 | |
In this episode of Data in Biotech, host Ross Katz sits down with Jesse Johnson, founder of Merelogic, a software consulting firm specializing in data infrastructure for biotech organizations. Jesse brings a rare perspective to the conversation: having built data systems at Google where engineers control the data collection function end to end, before moving into biotech, where the biology does what it wants and bench scientists, not engineers, generate the data. The result is a grounded, pragmatic take on one of the most consequential and underappreciated questions in life sciences right now: as bio foundation models fundamentally change the value equation for experimental data, are biotech labs structured to capture that value? Jesse argues the answer is usually no and that the fix is less technical than most assume. It doesn't require a production-grade data pipeline or a cloud architecture. It requires lightweight, human-readable standard operating procedures, clear expectations between computational and wet lab teams, and a data strategy designed not just for the questions you're asking today, but for the ones you don't yet know you'll need to ask. What you’ll learn in this episode: >> Why the transition from tech to biotech requires a fundamental reset of assumptions about data infrastructure and why the biggest difference isn't technical, it's organizational. >> How bio foundation models have flipped the value equation for experimental data by reducing the cost of organizing it while dramatically increasing the potential return >> How the strategic value of proprietary data is evolving in the biotech ecosystem, from Tahoe Therapeutics building an acquirable single-cell dataset to Eli Lilly's Lowe lab using data as currency for partnerships >> Why electronic lab notebooks aren't going anywhere and how the real question facing biotech software teams isn't whether to use an ELN, but how to balance schema rigidity against the flexibility required for the long tail of one-off exploratory assays that no automation pipeline will ever fully capture Meet our guest: Jesse Johnson is the founder of Merelogic, a software consulting firm that works with biotech and biopharma organizations on data infrastructure and data operations strategy. Jesse writes regularly about data strategy for biotech on his Substack, covering topics from bio foundation model adoption to the evolving role of electronic lab notebooks in an AI-augmented research environment.
About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation.
Connect with us:
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Markus Gershater on Why Experimental Design in Biotech is Broken and How to Fix It | 15 Apr 2026 | 00:41:09 | |
This week, we're delighted to be joined by Markus Gershater, Chief Scientific Officer and CoFounder of Synthace - a digital experiment platform built for high-performance life science R&D teams to help them run more powerful experiments and accelerate scientific progress. Host Ross Katz speaks with Markus on what’s broken about:
--- If you’re a biotech company struggling to transform your business with data, CorrDyn can help. Whether you need to supplement existing technology teams with specialist expertise or launch a data program that lays the groundwork for future internal hires, you can partner with Corrdyn to unlock the potential of your business data - today. Visit connect.corrdyn.com/biotech to learn more. --- Data in Biotech is a fortnightly podcast exploring how companies leverage data to drive innovation in life sciences. | |||
| Data Science and Diagnostic Models - the What, Why and How with Michelle Wiest | 15 Apr 2026 | 00:44:05 | |
This week, we're delighted to be joined by Michelle Wiest, Director of IVD Biostatistics at Freenome - a high-growth biotech company that creates tools to help prevent, detect, and treat disease. Host Ross Katz speaks with Michelle on the use of biostatistics in the field of diagnostics, what biases can corrupt diagnostic tests and how to catch them early, the different types of data sets that are being used to develop diagnostic models and how to prepare to present data to regulatory bodies such as the FDA. --- If you’re a biotech company struggling to transform your business with data, CorrDyn can help. Whether you need to supplement existing technology teams with specialist expertise or launch a data program that lays the groundwork for future internal hires, you can partner with Corrdyn to unlock the potential of your business data - today. Visit connect.corrdyn.com/biotech to learn more. --- Data in Biotech is a fortnightly podcast exploring how companies leverage data to drive innovation in life sciences. | |||
| The Patient is Not a Document: Foundation Models for Biomedical AI with Standard BioModel | 15 Apr 2026 | 00:49:54 | |
In this episode of Data in Biotech, host Ross Katz sits down with Kevin Brown, co-founder of Standard BioModel, to explore one of the most ambitious projects in biomedical AI, building a multimodal foundation model that represents the full complexity of a patient across time. Drawing on a career spanning brain-computer interfaces, computer-aided diagnosis at Siemens Healthineers, and oncology data science at Bristol Myers Squibb, Kevin shares the scientific and philosophical journey that led him to a single conviction: a patient is not a document. Rather than reducing a patient to clinical notes, ICD-10 codes, or isolated test results, Standard BioModel's approach maps every available modality - CT imaging, digital pathology, genomics, EKGs, longitudinal EHR data - into a shared latent space, and models how that patient moves through time. The result is a framework designed not just for prediction, but for counterfactual reasoning, clinical trial matching, and personalized intervention, with open-source models already being validated across leading academic medical centers. What you’ll learn in this episode: >> Why reducing a patient to text - clinical notes, radiology reports, genomic assay summaries - and how mapping multimodal data into a shared latent embedding space preserves information that never makes it into the written record >> How Standard BioModel's temporal architecture models patients as trajectories through an abstract embedding space rather than static snapshots, enabling counterfactual reasoning about the likely impact of interventions on a patient's future health trajectory >> Why no single foundation model can own every clinical vertical and how building a highly generalizable base model that facilitates downstream fine-tuning is a more defensible and scalable strategy than building narrow, application-specific models >> How the model handles missing modalities in real-world clinical settings, and why the architecture is designed to function effectively even when not every data type is available for every patient >> Why Standard BioModel has chosen to open-source its models and why broad, institution-specific validation across diverse patient populations is not just a scientific priority, but a prerequisite for trustworthy clinical AI Meet our guest: Kevin Brown is the Founder and CEO of Standard Model Biomedicine, where he builds foundation models for biomedicine. He previously led AI work as Director of Artificial Intelligence at SimBioSys, and held data science and applied ML roles at Bristol Myers Squibb and Siemens Healthineers. With a neuroscience research background from New York University, Kevin’s work spans generative AI and machine learning for biomedical and medical imaging applications.
About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation.
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Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Physics, Free Energy, & Drug Discovery: Inside Schrödinger's Computational Platform | 01 Apr 2026 | 00:57:31 | |
In this episode of Data in Biotech, Ross Katz sits down with Robert Abel, Chief Scientific Officer of the Platform at Schrödinger, to explore how physics-based computational modeling is transforming drug discovery. Robert unpacks why machine learning alone isn't enough to navigate the vast complexity of chemical space - an estimated at 10⁶⁰ possible drug-like molecules - and how integrating atomistic simulations with ML creates a more accurate, reliable, and scalable approach to identifying viable drug candidates. From free energy perturbation calculations to generative AI, Robert offers a rare inside look at how Schrödinger's technology platform is accelerating the path from target identification to clinical candidate and where the field is headed next. What you’ll learn in this episode: >> Why chemical space (~10⁶⁰ molecules) makes purely data-driven ML approaches fundamentally insufficient for drug discovery, and how physics-based sampling solves the training data problem >> How free energy perturbation (FEP) calculations enable quantitative prediction of protein-ligand binding affinities at near-experimental accuracy (~1.2 kcal/mol RMSE) >> How Schrödinger's active learning framework combines physics-based simulations and ML to triage billions of candidate molecules before committing to wet lab synthesis >> Why Schrödinger operates across three business lines; software licensing, collaborative programs, and proprietary drug discovery and how each strengthens the underlying technology platform >> Where the next frontiers lie: routine anti-target selectivity profiling, retrosynthetic AI integration, and the expanding role of generative ML in de novo molecular design Meet our guest: Robert Abel is Chief Scientific Officer, Platform at Schrödinger, where he helps lead the scientific direction behind computational approaches that support modern drug discovery and molecular design. With a PhD in Chemical Physics from Columbia University and a deep background in computational chemistry, he has held multiple senior science leadership roles at Schrödinger, guiding teams that build and scale scientific methods into production-grade platforms used across research and industry.
About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation.
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Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| AI in biotech: separating hype from reality with Ben Locwin | 11 Mar 2026 | 00:30:29 | |
In this episode of Data in Biotech, host Ross Katz sits down with Ben Locwin, Vice President at Reliant Life Sciences, to explore the evolving landscape of artificial intelligence in biotechnology. Join us as we discuss why nearly every biotech claims to use AI but few actually do, examine successful applications like AlphaFold, and explore the challenges of implementing AI across drug development, manufacturing, and regulatory processes. Ben shares insights on maintaining healthy skepticism, understanding data provenance, and looking ahead to what this year may bring for AI in life sciences. What you’ll learn in this episode: >> The AI hype problem in biotech and why most companies claim to use AI but few actually do. >> AlphaFold as the gold standard and how DeepMind's protein structure prediction model represents the most successful application of AI in biotech >> Data quality over algorithmic sophistication and the critical importance of data provenance, examining primary sources, and understanding that data quality matters more than the complexity of the AI model >> The balance between optimism and evidence-based decision-making, distinguishing between sophisticated AI and advanced statistical modeling Meet our guest: Ben Locwin is a healthcare and life sciences executive and medical scientist known for helping bring pharmaceuticals, vaccines, and medical devices to market faster and with higher quality. A TEDx speaker and seasoned leader, he’s worked across major biotech hubs and has deep expertise in global regulatory pathways, having collaborated with the FDA, EMA, MHRA, PMDA, and more.
About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation.
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Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| 3D Printing Therapeutics at Scale with Aprecia Pharmaceuticals | 25 Feb 2026 | 00:49:01 | |
In this episode of Data in Biotech, Ross Katz sits down with Kyle Smith and Jacob Mayer from Aprecia Pharmaceuticals to explore how 3D printing is transforming pharmaceutical manufacturing. They dive into the unique binder jetting process, in-cavity printing, and how real-time data and automation are enabling agile, scalable, and precise drug production. Discover how Aprecia's approach is changing the game for clinical trials and personalized medicine. What you'll learn in this episode: >> How Aprecia developed the world’s first FDA-approved 3D printed drug >> Why binder jetting stands out among 3D printing methods in pharma >> How in-cavity 3D printing enables real-time tablet-level data collection >> The future of closed-loop control and digital twins in drug manufacturing >> Why 3D printing is key to agile, distributed, and personalized pharma production Meet our guests: Kyle Smith is President and COO of Aprecia Pharmaceuticals, leading strategic growth and innovation in GMP-regulated pharma manufacturing. With 12+ years at Aprecia, he brings deep expertise in engineering, operations, and technology transfer. Jacob Mayer is Director of Engineering Innovation at Aprecia Pharmaceuticals. With a decade of experience across automation, additive manufacturing, and life sciences, he leads the advancement of 3D printing technologies and integrated pharma systems. About the host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with our guests:
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Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Success-Driven Drug Discovery with OpenBench CEO James Yoder | 11 Feb 2026 | 00:53:37 | |
In this episode of Data in Biotech, host Ross Katz sits down with James Yoder, Founder and CEO of OpenBench, to unpack a radical new approach to early-stage drug discovery. James shares how OpenBench's "success-driven" model shifts risk away from biotech partners by only charging for validated hits. They dive deep into computational screening, molecular modeling, and the company's evolving tech stack that's making hit discovery smarter and more accessible. Discover how data, AI, and strategic collaboration are redefining biotech R&D. What you'll learn in this episode: >> Why OpenBench moved away from SaaS to a success-based service model >> How their computational platform predicts binding affinity and screens trillions of compounds >> The role of data flywheels and ML in improving drug discovery success rates >> Real-world case studies from biotech collaborations >> How OpenBench evaluates druggable targets in one week Meet our guest James Yoder is the Founder and CEO of OpenBench. With a background in statistics, data science, and applied machine learning, he leads OpenBench's mission to deliver validated drug discovery hits through computational innovation and a success-driven business model. About the host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with our guest: Connect with us:
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Brant Peterson on Valo Health’s patient-first approach to drug discovery | 29 Jan 2026 | 00:52:33 | |
Brant Peterson, Vice President & Fellow at Valo Health, joins Data in Biotech to explore how his team leverages real-world data, genetic insights, and machine learning to de-risk drug discovery. From building causal DAGs to identifying patient subtypes in neurodegenerative diseases like Parkinson’s, this episode dives deep into a patient-first, data-driven approach to biomedical innovation. What You'll Learn in This Episode: >> How Valo Health uses real-world evidence and EHR data to prioritize drug targets earlier in the development pipeline. >> Why integrating wet lab experiments and causal DAGs accelerates therapeutic validation. >> The importance of genetic pleiotropy and Mendelian randomization in refining disease hypotheses. >> How Valo Health identifies high-impact patient subgroups in neurodegenerative diseases like Parkinson’s and Alzheimer’s. >> Where machine learning models succeed and fall short, in uncovering mechanisms of disease from sparse longitudinal data. Meet Our Guest Brant Peterson is Vice President & Fellow in Data Science at Valo Health. He brings deep expertise in genetics, computational biology, and biomedical innovation. Formerly a Distinguished Data Scientist at Valo and Computational Biologist at Novartis, Brant focuses on leveraging patient-centric data to drive causal discovery in drug development. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Connect with Us:
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Inside Dash Bio’s productized CRO model with Ander Tallet | 14 Jan 2026 | 00:43:22 | |
Ander Tallet, co-founder and COO of Dash Bio and CEO of DigitalRadius, joins Ross Katz to discuss transforming the traditional CRO model through automation, transparency, and productization. Drawing on deep experience from Moderna, Science Exchange, and his leadership roles in digital transformation, Ander shares how Dash Bio is slashing turnaround times, improving data quality, and simplifying procurement for biotech companies. This episode unpacks the future of CRO services, strategic procurement, and the power of operational innovation in biotech. What you'll learn in this episode: >> Why traditional CRO models hinder speed and transparency in biotech >> How Dash Bio delivers 90% faster turnaround through automation >> What productizing CRO services really means for the customer experience >> How regulatory requirements shape innovation in clinical bioanalysis >> Why investor buy-in requires solving real, painful problems in biotech Meet our guest Ander Tallett is the co-founder and COO of Dash Bio and the CEO of DigitalRadius, where he leads digital transformation initiatives as one of the largest Smartsheet partners in the ecosystem. He previously served as Chief Strategy Officer at Science Exchange and co-founded Block Mill Capital, a B2B SaaS-focused investment fund shaped by his experience evaluating and implementing more than 100 SaaS platforms. About the host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with our guest: Connect with us:
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| From discovery to delivery: AI’s impact on nanomedicine | 31 Dec 2025 | 00:46:31 | |
In this episode of Data in Biotech, Ross Katz chats with Mitra Mosharraf, Chief Scientific Officer at HTD Biosystems, about how AI and machine learning are revolutionizing nanomedicine. They explore the use of AI in drug discovery, formulation, manufacturing, and clinical development, highlighting how data-driven strategies are improving safety, reducing costs, and enabling more personalized therapies in the biotech space. What you'll learn in this episode: >> How AI and ML reduce costs and increase success rates in nanomedicine development. >> Key challenges in nano drug delivery and how machine learning helps overcome them. >> How HTD Biosystems' iFormulate platform speeds up formulation with predictive modeling. >> How wearables and real-time data are reshaping clinical trial design. >> The future of personalized and automated drug delivery systems. Meet our guest Mitra Mosharraf is the Chief Scientific Officer at HTD Biosystems and co-founder of Engimata Inc. With 20+ years of experience, she leads innovation in biologics, nanomedicine, and lipid-based delivery systems. Mitra is a recognized thought leader in pharmaceutical sciences. About the host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Connect with Us:
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Jaidev Chakka on building the future of bioprinted medicine | 26 Dec 2025 | 00:45:24 | |
In this episode of Data in Biotech, Ross Katz chats with Jaidev Chakka, Principal Scientist at the University of Mississippi School of Pharmacy, about how 3D bioprinting and AI are reshaping pharmaceutical manufacturing. They explore the development of custom scaffolds for tissue engineering, the integration of gene delivery systems, and how data-driven approaches are enabling smarter, more scalable solutions in personalized medicine. What you'll learn in this episode: >> How 3D printing is enabling customized bone scaffolds and regenerative therapies >> The role of AI in optimizing pharmaceutical 3D printing parameters >> How organoids can act as micro-organs for testing and computation >> The promise and challenge of personalized, on-demand drug manufacturing >> Why collaboration between data scientists and biopharma researchers is critical Meet our guest LR Jaidev Chakka is a Principal Scientist at the University of Mississippi School of Pharmacy, pioneering 3D bioprinting, drug delivery, and organoid research to revolutionize patient care and pharma manufacturing. About the host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Connect with Us:
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| From screening to IND: How Veloxity accelerates critical drug decisions | 22 Dec 2025 | 00:25:34 | |
In this episode of Data in Biotech, Ross Katz sits down with Callie Celichowski and Isa Kupke from Veloxity Labs to discuss how their CRO leverages speed, precision, and innovation to support drug development. Learn how they use mass spectrometry, cloud-based infrastructure, and hands-on client partnerships to drive rapid, high-quality bioanalytical insights that support everything from preclinical studies to FDA submissions. What you'll learn in this episode: >> Why "speed with purpose" is essential for bioanalytical CROs supporting biotech and pharma clients >> The benefits and challenges of working with peptides and GLP-1 receptor agonists >> How the SCIEX 8600 enhances detection of low-concentration analytes Meet our guests Isa Kupke is Scientist II at Veloxity Labs, where she specializes in mass spectrometry and method development for preclinical and regulated bioanalytical programs. She also co-founded Blyde Botanics, bridging plant-based science and product development. Callie Celichowski is Senior Director of Business Development at Veloxity Labs, with over two decades in the pharmaceutical and CRO space. She's recognized for building strategic client partnerships and driving rapid, data-driven decision-making. About the host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest:
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Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Revolutionizing bioanalysis with high-resolution mass spec | 15 Dec 2025 | 00:32:31 | |
In this episode of Data in Biotech, host Ross Katz sits down with Eshani Galermo, Staff Scientist at SCIEX, to explore the next generation of mass spectrometry in pharma and biopharma. Eshani explains how innovations like the ZenoTOF 8600 are redefining sensitivity, selectivity, and workflow efficiency in bioanalytical chemistry. Discover how high-resolution accurate mass (HRAM) systems are unlocking new capabilities in drug discovery, clinical studies, and regulatory science. What you'll learn in this episode: >> Why traditional mass spectrometry falls short in modern bioanalysis >> How the ZenoTOF 8600 enhances sensitivity and reduces sample volume needs >> The role of high-resolution systems in detecting complex drug metabolites >> How automation tools are streamlining workflows for bioanalytical scientists >> The complementary role of AI and ML in mass spec data analysis Meet our guest Eshani Galermo is a Staff Scientist at SCIEX, where she leads global strategic marketing initiatives for pharma and biopharma quant applications. With deep expertise in bioanalytical chemistry and mass spectrometry, she has held multiple scientific roles across SCIEX, Emery Pharma, and Genentech. About the host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Connect with Us:
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Mastering solubility and stability in drug development with Serán BioScience | 03 Dec 2025 | 00:51:14 | |
In this episode of Data in Biotech, Ross Katz chats with Wesley Tatum, Principal Engineer at Serán BioScience, about the intricacies of formulating low-solubility drug products. They explore the science behind amorphous solid dispersions, how data informs formulation choices, and why balancing performance, manufacturability, and stability is critical in modern drug development. What you'll learn in this episode: >> How amorphous solid dispersions improve solubility and stability in drug products >> Why formulation decisions hinge on early data collection and modeling >> The role of data infrastructure in formulation R&D and knowledge transfer >> How Serán BioScience collaborates closely with clients to solve complex drug development challenges >> Where AI and automation are (and aren’t yet) transforming pharmaceutical formulation Meet our guest Wesley Tatum is a Materials Science PhD researcher working at the crossroads of materials innovation, data science, and machine learning. His work focuses on organic materials and polymer dispersions, and he’s especially passionate about how modern computational tools can transform the way we characterize and understand new materials. Wesley is well versed in PyTorch, Scikit-Learn, and a range of open-source scientific computing libraries, and he brings deep experience in chemical analysis, microscopy, and image analysis. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Connect with Us:
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Applying ML/AI to Drug Development with Anil Kane | 26 Nov 2025 | 00:35:48 | |
Dr. Anil Kane, Global Head of Technical & Scientific Affairs at Thermo Fisher Scientific, joins Ross Katz to discuss how AI, machine learning, and digital tools are reshaping drug development. From predictive modeling for formulation to digital manufacturing efficiencies, discover how data-driven approaches are reducing time, cost, and complexity in pharma innovation. What You'll Learn in This Episode: >> How predictive modeling eliminates trial-and-error in drug formulation >> The role of AI/ML in improving manufacturing efficiency and reducing downtime >> How Thermo Fisher’s ASAP program accelerates stability testing >> The future of digital transformation in pharma, including OpenAI partnerships >> Where human expertise fits in a digitally enhanced development pipeline Meet Our Guest Dr. Anil Kane is the Executive Director and Global Head of Technical & Scientific Affairs at Thermo Fisher Scientific, where he oversees a global team supporting drug development, scale-up, and technical strategy across sites in North America and Europe. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Connect with Us:
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Streamlining bioanalytical workflows with Watson LIMS and Thermo Fisher | 20 Nov 2025 | 00:49:51 | |
In this episode of Data in Biotech, host Ross Katz sits down with John Liberty, Senior Bioanalysis Technical Sales Consultant at Thermo Fisher Scientific. They dive deep into how Watson LIMS™ supports regulated bioanalysis workflows, the evolving role of lab automation, and how Connect Enterprise aims to unify biotech digital ecosystems. What you'll learn in this episode: >> Why Watson LIMS is purpose-built for bioanalysis and how it enhances compliance and data traceability. >> The role of lab automation in boosting productivity and reducing manual tasks. >> How Connect Enterprise integrates lab systems across vendors into a seamless workflow. >> Key considerations for implementing LIMS in startup versus established biotech environments. >> The ROI of digital lab solutions in supporting scalable, compliant biotech operations. Meet Our Guests John Liberty is a GMP-trained scientist with a strong focus on ELISA method development, validation, and transfer. He pairs his hands-on scientific background with experience in project management, CRO coordination, data analysis, and training, making him someone who really understands how work moves from the lab bench to real-world application. John has also spent time on the product and customer side, doing technical sales and demos for Watson LIMS™ software, giving him a rare blend of technical depth and communication skills. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Connect with Us:
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Scaling AI infra in biotech with Saturn Cloud and Nebius | 05 Nov 2025 | 00:52:17 | |
Discover how biotech and healthcare teams are fast-tracking research and development through AI and high-performance cloud infrastructure. Ross Katz sits down with Hugo Shi of Saturn Cloud and Ilya Burkov of Nebius to explore scalable, secure solutions for GPU-heavy AI workloads. From compliance to cost savings, this episode unpacks what it takes to innovate at scale in life sciences. What You'll Learn in This Episode: >> Why AI workloads in biotech demand specialized infrastructure >> How Saturn Cloud and Nebius simplify compliance, scale, and security for life sciences >> Real-world examples of gene editing, RNA sequencing, and medical imaging powered by cloud AI >> The trade-offs between hyperscalers and NeoClouds for GPU availability and cost >> Strategies for deploying, optimizing, and managing large-scale AI models Meet Our Guests Hugo Shi is the CTO and Founder of Saturn Cloud and a co-founder of Anaconda. He brings deep expertise in data science, AI infrastructure, and open-source development, helping teams scale complex workloads with minimal friction. Ilya Burkov is Global Head of Healthcare & Life Sciences Growth at Nebius. With a background in medicine and cloud technology, Ilya leads strategy and partnerships to empower biotech teams with secure, high-performance compute solutions. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest:
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Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| The Future of Co-Folding and Federated Learning with Apheris | 22 Oct 2025 | 00:49:08 | |
Robin Rohm, CEO and Co-Founder of Apheris, joins Ross Katz to explore how federated learning is unlocking secure, cross-company collaboration in pharma. Discover how Apheris is enabling biopharma leaders to train cutting-edge co-folding models without sharing sensitive data, why AlphaFold 3 wasn’t enough, and what OpenFold 3 means for the future of AI in drug discovery. What You'll Learn in This Episode >> Why federated learning is a game-changer for pharma data sharing and AI-driven research >> How OpenFold 3 builds on AlphaFold’s legacy to solve the protein-ligand interaction challenge >> The role of structural benchmarking in model development and validation >> How Apheris enables privacy-preserving collaboration between major pharma players >>The importance of high-quality, proprietary datasets in advancing co-folding model accuracy Meet Our Guest Robin Rohm is the CEO and Co-Founder of Apheris, a leader in federated data networks for life sciences. With a background in mathematics and computational genomics, Robin is advancing secure AI collaboration in pharma and biotech. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Connect with Us:
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Automating biopharma knowledge work with Convoke | 01 Oct 2025 | 00:56:24 | |
What if biotech teams could automate the most information-intensive parts of bringing a drug to market? In this episode of Data in Biotech, host Ross Katz speaks with Convoke co-founders Alex Telford and Maged Ahmed about building an AI-native knowledge acquisition and curation system for biopharma. Learn how they're transforming clinical research, regulatory writing, and competitive intelligence using LLMs, semantic search, and scalable data infrastructure. What You'll Learn in This Episode: >> How Convoke unifies public and private biotech data into a single workspace for smarter decision-making >> Why structured outputs and semantic layers are key to high-quality AI-driven insights >> Real-world use cases including clinical trial design, competitive landscape analysis, and regulatory documentation >> How feedback loops and model evaluations drive product reliability and user trust >> The future of AI in biotech: continuous decision-making and multimodal intelligence Meet Our Guests Alex Telford is Co-Founder and CEO of Convoke, an AI-native OS transforming drug development workflows. His background in life sciences consulting drives his mission to unify data and streamline outputs like clinical documents and regulatory submissions. Maged Ahmed is Co-Founder of Convoke and a former AI lead at Applied Intuition. He brings deep data infrastructure expertise to biotech, aiming to reduce friction in regulated environments by automating knowledge generation. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest:
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Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| How Eli Lilly’s new platform TuneLab codevelops AI models with biotech | 17 Sep 2025 | 00:40:44 | |
Dr. Aliza Apple, VP of Catalyze360 AI/ML at Eli Lilly, joins Ross Katz to unveil TuneLab—a first-of-its-kind federated learning platform accelerating preclinical drug discovery. Learn how Lilly’s billion-dollar models are empowering early-stage biotechs, the science behind federated learning, and what this means for the future of pharmaceutical R&D. What You'll Learn in This Episode: >> How Eli Lilly's TuneLab democratizes access to proprietary AI models trained on decades of pharmaceutical data >> What federated learning means for IP protection and collaborative data sharing in biotech >> Which two key use cases are driving TuneLab’s impact today: small molecule ADME and antibody developability >> How TuneLab’s early adopters like Firefly Bio and Superluminal are shaping the platform’s evolution >> Why generalizability—not just accuracy—is the true power of collaborative AI in drug development Meet Our Guest Dr. Aliza Apple is VP of Catalyze360 AI/ML at Eli Lilly and a biotech trailblazer with deep roots in AI-driven drug discovery. She has led initiatives from founding startups to heading Lilly Gateway Labs and now pioneers innovation at the intersection of data and biotech. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Connect with Us:
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Inside Merck's Computational Biology Approach with Jesper Ryge | 03 Sep 2025 | 00:46:33 | |
In this episode of Data in Biotech, host Ross Katz talks with Jesper Ryge, Director of Computational Biology at Merck Germany. Jesper shares his journey from neuroscience labs to leading computational teams, offering deep insights into disease modeling, target discovery, and multi-omics integration. Discover how AI and spatial transcriptomics are shaping the future of pharma R&D. What You'll Learn in This Episode >> How single-cell and spatial transcriptomics enhance disease mechanism discovery >> Why data integration and knowledge graphs are critical for target validation >> How computational biology teams interface with wet lab research >> What makes Merck Germany's data strategy unique in biotech >> How generative AI is changing how pharma interprets complex datasets Meet Our Guest Jesper Ryge is Director of Computational Biology at Merck Germany. A biophysicist by training, he brings deep expertise in neuroscience, single-cell analysis, and bioinformatics to pharmaceutical R&D. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Connect with Us:
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Where Biotech Innovation Really Happens: 50th Episode Special with Wolfgang Halter, Jacob Oppenheim, and Dave Johnson | 20 Aug 2025 | 00:31:54 | |
To mark our 50th episode, host Ross Katz brings back three visionary leaders—Dave Johnson (Dash Bio), Wolfgang Halter (Merck Life Science), and Jacob Oppenheim (RAVen)—together for a reflection on the evolution of biotech. They unpack the realities behind AI hype, the future of data-driven innovation, and what’s really changing in drug development. What You'll Learn in This Episode: >> Where real innovation is emerging across startups, big tech, and academia >> The biggest misconceptions about data in biotech—and why they persist >> What it takes to build trust in AI-powered biotech tools >> Why progress in biotech depends as much on execution as it does on breakthroughs >> How industry veterans see the future of automation, regulation, and global competition Meet Our Guests Dave Johnson is CEO and Co-Founder of Dash Bio and former Chief Data & AI Officer at Moderna. He’s pioneering automation in clinical bioanalysis to accelerate drug development. Wolfgang Halter leads Data Science at Merck Life Science, developing tools like BayBE to optimize R&D through smarter data modeling and open-source innovation. Jacob Oppenheim is a Venture Partner at RAVen and co-founder of Fresnel. With a PhD in Biological Physics, he champions the transition to digital-native biopharma. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest:
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Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| How BioRaptor is Modernizing Bioprocessing Workflows | 31 Jul 2025 | 00:49:40 | |
In this episode of Data in Biotech, Yaron David, CTO and Co-Founder of BioRaptor, joins host Ross Katz to explore how biotech companies can gain a holistic view of bioprocessing through real-time data integration, visualization, and modeling. Learn how BioRaptor’s full-stack platform helps scientists replace clunky spreadsheets with actionable insights—accelerating experiments and optimizing yields. What You'll Learn in This Episode: >> Why Excel is holding back innovation in bioprocessing labs >> How BioRaptor enables holistic bioprocess understanding across experiments >> The role of virtual sensors in enhancing real-time data visibility >> How BioRaptor drives ROI by onboarding customers in weeks, not months >> The difference between operational and optimization AI in biotech Meet Our Guest Yaron David is CTO and Co-Founder of BioRaptor, an AI-powered analytics platform transforming bioprocess data into scientific insights. With an MD and PhD in neuroscience, he blends medical, software, and startup experience to revolutionize data infrastructure for biotech. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest:
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Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Modernizing Clinical Data Analysis with Jesse Paquette | 11 Jul 2025 | 00:45:17 | |
In this episode of Data in Biotech, Jesse Paquette, co-founder and Chief Science Officer of Tag.bio, joins Ross Katz to explore how data mesh architecture and FAIR data principles are transforming clinical and research workflows in life sciences. From harmonizing legacy systems to enabling AI-readiness, Jesse shares how Tag.bio empowers domain experts to make data-driven decisions faster and more effectively. What You'll Learn in This Episode: >> How Tag.bio leverages data mesh and FAIR principles to democratize biomedical data access. >> The challenges biotech firms face with legacy infrastructure and clinical trial data. >> The importance of harmonized, version-controlled data products in AI-driven research. >> Why smaller pharma and life sciences firms benefit most from Tag.bio’s agile platform. Meet Our Guest Jesse Paquette is the co-founder and Chief Science Officer of Tag.bio, where he leads scientific strategy for AI-powered analytics. With over two decades in life sciences, he specializes in building tools that help researchers interpret complex biomedical data independently. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest:
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Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Building Scalable Data Platforms for Spatial Biology with Kenny Workman of LatchBio | 25 Jun 2025 | 00:55:17 | |
In this episode of Data in Biotech, Ross Katz sits down with Kenny Workman, co-founder and CTO of LatchBio, to unpack how biotech’s data infrastructure must evolve to meet the demands of next-gen assays. From scalable workflows to high-performance visualization tools, Kenny breaks down the shift from traditional biotech to a future defined by data-driven research and agile cloud platforms. What You'll Learn in This Episode:
Meet Our Guest Kenny Workman is co-founder and CTO of LatchBio, a company transforming biological data infrastructure. A UC Berkeley alum, Kenny brings deep expertise in bioengineering, machine learning, and cloud systems. He was named to Forbes' 2023 "30 Under 30" list for his contributions to computational biology. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest:
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Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Solving Chronic Disease with Life Insurance and Data Featuring Jeremy Shane | 11 Jun 2025 | 01:00:25 | |
Entrepreneur Jeremy Shane joins host Ross Katz to discuss Life for Health, a revolutionary framework aiming to tackle chronic disease through the integration of life insurance, longitudinal data, and preventative care. From drug discovery feedback loops to outcomes-based pricing, Shane outlines a bold new path for biotech and healthcare convergence. What You'll Learn in This Episode:
Meet Our Guest Jeremy Shane, Venture Partner at NextGen Venture Partners, is a healthcare innovator focused on chronic disease. With leadership experience at HealthCentral, WebMD, and 2U, he now pioneers Life for Health, blending life insurance with preventative care to realign incentives and extend health span. Life for Health, the book, will be released in July 2025. To learn more about Life For Health or get involved, go to www.lifeforhealth.com About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest:
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Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| How Etiome Is Redefining Preemptive Medicine with Scott Lipnick | 29 May 2025 | 00:58:54 | |
In this episode of Data in Biotech, host Ross Katz sits down with Scott Lipnick, Co-Founder and President of Etiome, to explore how preemptive medicine is changing the way biotech approaches therapeutic modeling and intervention. Discover how Etiome’s recently launched, AI-driven platform is built to detect disease earlier, identify precise biomarkers, and create stage-specific treatments—all before symptoms arise. It’s a visionary approach to healthcare focused on preserving health, not just managing illness. What You'll Learn in This Episode:
Meet Our Guest Scott Lipnick is the Co-founder and President of Etiome. At the forefront of preemptive medicine, Scott’s work focuses on predicting disease progression and delivering personalized, early-stage interventions using cutting-edge AI and molecular tools. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Connect with Us:
Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. | |||
| Transforming Houseplants with Synthetic Biology with Patrick Torbey | 14 May 2025 | 00:47:17 | |
In this episode of Data in Biotech, host Ross Katz speaks with Patrick Torbey, CEO and Co-Founder of Neoplants, about using genetic engineering and microbiome innovation to tackle indoor air pollution. Patrick explains how Neoplants is turning everyday houseplants into powerful air purifiers using synthetic biology, offering insights into VOC degradation, enzyme pathways, and data-driven R&D. What You'll Learn in This Episode:
Meet Our Guest: Patrick Torbey is the CEO and Co-Founder of Neoplants, a Paris-based biotech startup engineering plants to purify indoor air. With a PhD in genetic editing and deep expertise in synthetic biology, Patrick leads Neoplants in building functional, aesthetically unique, and sustainable plant systems for the future. About The Host: Ross Katz is the Principal Data Scientist at CorrDyn. He brings decades of experience across biotech, energy, and non-profit sectors, with a focus on building smarter data systems, machine learning pipelines, and actionable insights for complex industries. Connect with Our Guest:
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| How Phage Therapy is Scaling to Meet Antibiotic Resistance with Jessica Sacher | 30 Apr 2025 | 00:59:02 | |
Phage therapy is stepping into the spotlight as antibiotic resistance rises - and Jessica Sacher is helping lead the charge. In this episode, Ross Katz speaks with Jessica, Co-Founder of Phage Directory and Staff Scientist at Stanford, about sourcing phages, operationalizing therapy, and predicting efficacy through data. This conversation explores how personalized phage therapy works, its scalability, and the data challenges shaping its future. What You'll Learn in This Episode:
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| Targeting Transcription Factors with AI, featuring Will Fondrie from Talus Bio | 16 Apr 2025 | 00:46:54 | |
How do you drug the undruggable? In this episode of Data in Biotech, Ross Katz sits down with Will Fondrie, Head of Data Science and Engineering at Talus Bio, to explore how machine learning, mass spectrometry, and innovative computational models are transforming drug discovery. Learn how Talus Bio is targeting transcription factors—once considered out of reach—with scalable, high-impact data science. What You'll Learn in This Episode
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Connect with Will Fondrie on LinkedIn: https://www.linkedin.com/in/wfondrie/ Meet Our Guest Will Fondrie is the Head of Data Science and Engineering at Talus Bio, a biotech company pioneering the development of small molecule drugs targeting transcription factors. With a PhD in molecular medicine and a background in proteomics, Will brings deep expertise in computational biology, machine learning, and scalable data systems. About the Host Ross Katz is the Principal and Data Science Lead at CorrDyn. He hosts Data in Biotech to spotlight innovative thinkers and data-driven leaders pushing the boundaries of biotechnology. Enjoying the show? If you liked this episode, consider sharing it with a colleague and exploring more conversations at Data in Biotech. Your support helps us keep delivering expert insights on the future of biotech. Sponsored by CorrDyn This episode is brought to you by CorrDyn, a leader in data-driven solutions for biotech and healthcare. Learn more at CorrDyn. | |||
| Decoding the Dark Proteome: Biological Insights for Pharma with Dr. Jonathan Usuka from Sapient | 02 Apr 2025 | 00:46:45 | |
In this episode of the Data and Biotech Podcast, host Ross Katz sits down with Dr. Jonathan Usuka to discuss Usuka’s extensive background in bioinformatics and genomics, leading to his current role as CEO of Sapient. The conversation dives into the importance of deep molecular characterization of diseases, the role of proteomics and metabolomics in drug discovery, and the gaps between real-world data and clinical trial data. As a leader with a small but impactful team, Jonathan discusses the complexities of the dark proteome and metabolites, emphasizing the opportunity for deeper biological insights in pharmaceutical research using repeated, deep profiling of the same cohorts of patients. Jonathan also explores how advancements in computational approaches allow Sapient to build a robust data foundation for insight generation in biopharma. What You'll Learn in This Episode:
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| How AI Can Increase Clinical Trial Efficiency with Patrick Leung from Faro Health | 19 Mar 2025 | 00:39:02 | |
How can AI improve clinical trials and accelerate drug development? In this episode of Data in Biotech, Ross Katz sits down with Patrick Leung, CTO of Faro Health, to explore how AI-driven tools are reshaping clinical trial design. Patrick shares insights into document generation, patient burden analysis, and AI governance in biotech. Learn how Faro Health is developing clinical protocols and leveraging AI to optimize trial success while ensuring regulatory compliance. Whether you're in biotech or healthcare, this conversation offers valuable takeaways on the future of AI in increasing clinical trial efficiency. What You'll Learn in This Episode:
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Connect with Patrick Leung on LinkedIn: https://www.linkedin.com/in/puiwah/ Meet Our Guest: Patrick Leung is the Chief Technology Officer at Faro Health, where he leads AI-driven innovations in clinical trial design. With a background in data science and software engineering from companies like Google and Two Sigma, Patrick brings a fresh perspective to life sciences, focusing on optimizing clinical trials through AI and structured data models. About the Host: Ross Katz is the Principal and Data Science Lead at CorrDyn, specializing in applying data science to biotech and healthcare. As the host of Data in Biotech, Ross explores the latest trends and innovations shaping the industry. Enjoying the Show? Visit Faro Health to learn more about AI-driven clinical trial optimization. Don’t forget to rate and review Data in Biotech on Apple Podcasts! Sponsored by CorrDyn This episode is brought to you by CorrDyn, a leader in data-driven solutions for biotech and healthcare. Learn more at CorrDyn. | |||
| Organoids and Active Learning for Chronic Disease with Naren Tallapragada | 05 Mar 2025 | 00:50:25 | |
In this episode of Data In Biotech, Ross Katz interviews Naren Tallapragada, CEO and Co-founder of Tessel Bio, about his background in electrical engineering and physics and how personal circumstances led to him pivoting his focus to enter biotech and start Tessel Bio. Naren breaks down Tessel Bio’s unique approach to drug discovery, which involves "reverse engineering" chronic diseases. Instead of starting with a potential drug and testing its effects, they begin with a clear picture of the disease itself—specifically, how it appears and behaves in the body. A key part of their method is using human organoids—tiny, lab-grown versions of human tissues—to closely replicate real diseases and see how different treatments interact with them. This “small data” approach is made substantially more efficient with the addition of active learning. Join us for a fascinating conversation about Tessel Bio’s approach to finding cures for chronic diseases that impact hundreds of thousands of people every day. What You'll Learn in This Episode:
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| How Alex Junge from amass is Speeding Up Scientific Breakthroughs with AI | 19 Feb 2025 | 00:52:25 | |
In this episode of the Data and Biotech Podcast, host Ross Katz sits down with Alexander Junge, Co-Founder and CTO of amass, to break down how AI is reshaping scientific research in life sciences. Alex walks us through how his platform is creating professional tools for researchers, biotech companies, and venture firms to navigate the (rapidly!) growing breadth and depth of scientific knowledge by leveraging artificial intelligence - and what this will look like in the future. Alex also shares insights into his company’s work with Nordic Bio Ventures and how amass delivers reliability and builds trust in its platform. What You'll Learn in This Episode:
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| Advancing Therapeutic Design in Gene and Cell Therapy with Dipen Sangurdekar | 10 Feb 2025 | 00:40:34 | |
In this episode of Data in Biotech, Ross sits down with Dipen Sangurdekar, VP of Data Sciences at KSQ Therapeutics, to discuss the role of data-driven approaches in therapeutic design and development. The conversation explores the intersection of computational biology, machine learning, and bioinformatics in advancing personalized medicine and improving patient outcomes. Dipen shares his journey in the industry, emphasizing the importance of integrating data science with biological research and the challenges associated with working in the rapidly evolving field of cell therapies. From hypothesis-driven research to leveraging multimodal data for actionable insights, this episode explores the nuances of using statistical methods and AI to enhance drug development. Key Takeaways:
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| Democratizing Therapeutic Discovery for Neglected Diseases using AI with Timothy Jenkins of DTU Bioengineering | 22 Jan 2025 | 00:50:39 | |
This week on Data in Biotech, we welcome Timothy Jenkins, the Head of Data Science and Associate Professor at DTU Bioengineering, a leading scientific community dedicated to advancing areas of biotechnology, food technology, and health through innovative teaching and research. Timothy starts the conversation by walking us through his background and early career beginnings, from the first time he expressed interest in zoology and venomous snakes to now leading a research group focused on AI-guided drug discovery for snake antivenom. He and our host, Ross Katz, dive into one of DTU’s most exciting recent publications about "de novo" designed proteins to neutralize lethal snake venom toxins. Inspired by Nobel Prize winner David Baker’s groundbreaking paper and in collaboration with the Baker Lab on computational design methodology, this project holds great promise in therapeutic discovery and drug development. Tim explains how computational protein design and protein structure prediction are revolutionizing his field, highlighting compelling examples and milestones from his research on antivenom. He also provides an overview of the process used to discover new antivenoms, including the sourcing of biological data, model training, and integration of experimental feedback. Finally, we get Tim’s perspective on the future of AI-powered therapeutic discovery, and his take on the integration of quantum computing into protein design. Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences. Useful Links Nature - De novo designed proteins neutralize lethal snake venom toxins UW Institute for Protein Design | |||
| Revolutionizing Drug Discovery with AI: Fred Manby of Iambic Therapeutics | 08 Jan 2025 | 00:38:19 | |
In this episode, Ross Katz sits down with Fred Manby, Co-Founder and Chief Technology Officer of Iambic Therapeutics, to explore how cutting-edge AI technologies are reshaping the landscape of drug discovery. From building advanced machine learning platforms to designing user-friendly interfaces for scientists, Fred shares insights into the company’s approach to tackling some of the biggest challenges in biotech. Fred dives into the unique capabilities of Iambic’s Enchant multi-modal transformer model, its differentiation from other biological foundation models, and the importance of aligning model architecture with data creation and acquisition in modern drug discovery. We also discuss Iambic’s data-driven approach to developing oncology drugs, the exciting possibilities of incorporating new modalities like imaging, and the recent breakthroughs in protein-ligand structure prediction with NeuralPLexer3. Highlights:
Interface Design for Scientists: Iambic’s approach to integration between AI platforms and experimental workflows. Connect with Our Guest: Connect with Fred Manby on LinkedIn | |||
| Unlocking the Power of AI in Microscopy with Ilya Goldberg and Reese Findley | 11 Dec 2024 | 00:47:32 | |
In this episode of Data in Biotech, Ross Katz explores the transformative role of AI in microscopic imaging and life sciences with Ilya Goldberg, Chief Science Officer, and Reese Findley, an AI Data Scientist at ViQi. They discuss the cutting-edge applications of AI in automating high-content screening (HCS), enabling more efficient drug discovery, and unraveling complex biological processes. From streamlining time-course analysis to detecting off-target effects in drug compounds, ViQi’s tools are revolutionizing how scientists approach image-based data. Key Highlights:
Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences. Learn more about who was featured on the podcast:
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| Reflections & Predictions: One Year of Data in Biotech with Ross Katz | 27 Nov 2024 | 00:39:42 | |
In this episode of Data in Biotech, Ross Katz reflects on what he’s learned from one year of hosting the podcast. Diving deep into the intersection of data science and biotechnology, this episode covers topics like:
As we look to 2025, Ross shares his vision of the emerging democratization of the biotech data ecosystem by making domain knowledge, datasets and tools more accessible. He discusses the possibility of a future where decentralized collaboration, akin to open-source software projects, can tackle specific diseases through computational pipelines and cloud labs, enabling experiments without the need for costly infrastructure. Or where emerging trends like foundation models and ensemble modeling in drug discovery, cell and gene therapy, and the role of new data from advanced imaging and assay technologies can be unlocked to create novel insights. Finally, he invites regular listeners to contribute ideas, guest suggestions and resources as we build community and embrace more curiosity and openness. Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences. | |||
| From Moderna to Dash Bio - Revolutionizing Drug Development with Dave Johnson | 30 Oct 2024 | 00:32:54 | |
In this episode of Data in Biotech, Ross Katz sits down with Dave Johnson, CEO and co-founder of Dash Bio, a next-gen drug development services company with a mission to revolutionize clinical bioanalysis and streamline drug development. Dave begins the episode by taking us back to the early research days in Moderna, where he helped lay the groundwork for mRNA technology, which later enabled the development of a vaccine for COVID-19 at unprecedented speed. As he explains, this automated work and pre-built systems ultimately played a central role in responding to urgent health challenges. He also shares his firsthand experience of working in a rapidly scaling pharma company, discussing the potential challenges that arose along the way and the lessons he learned to overcome them. Dave then proceeds to highlight the most significant insufficiencies in drug development—particularly the lack of industrialization and standardization. He explains how Dash Bio aims to address these issues, focusing on clinical bioanalysis now and expanding to broader standardization later. The goal is ultimately to develop a more efficient, high-quality end-to-end system and improve the overall efficacy of the drug development process. Finally, Dave and Ross discuss the misconceptions surrounding lab automation and emphasize the need for a shift of perspective within the drug development space. They also touch upon Dave’s vision for the future of Dash Bio, plus his advice for aspiring biotech data leaders eager to contribute to industry transformation. Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences. Chapter Markers: [1:38] Introduction to Dave Johnson and his career journey from Moderna to founding a next-gen drug development company [2:57] Establishing mRNA technology groundwork in Moderna [4:36] The challenges of scaling up COVID-19 vaccine development [7:55] How rapid company growth impacts the organizational structure and engaging models [11:03] The role of AI, automation, and machine learning in drug development [12:45] Addressing the most significant insufficiencies in drug development and potential solutions [16:31] The need for standardization and automation in drug development [18:04] Current focus of Dash Bio on clinical bioanalysis [19:37] The misconceptions surrounding lab automation and the need for a shift of perspective within the drug development space [22:33] Dave’s vision for the future of Dash Bio and streamlining drug development [25:16] The current state of lab automation [27:41] The role of experimentation in Dash Bio's approach [29:47] Advice for aspiring data scientists and leaders in the biotech sector Useful Links | |||