AI in drug development is moving beyond “failure prevention” into something much bigger: redesigning how we discover, develop, and deliver medicines. In this episode, Dr Andree Bates speaks with Vitalay Fomin of Numenos about biomarker discovery, patient stratification, and why the next breakthroughs come from breaking down data silos across diseases, modalities, and even species.

Vitalay shares his background across biotech and pharma, including work in biomarker discovery, translational medicine, and data science, and how frustration with existing approaches led her to build a new architecture for clinical genomic insights. A core theme is that traditional methods often oversimplify biology by forcing outcomes into binary labels and treating each disease area as an isolated box, even when the available data is too limited to answer meaningful questions well.

The conversation explores how foundation model approaches can unify clinical, genomic, transcriptomic, proteomic and imaging signals to create a fuller “biological fingerprint” of each patient. Vitalay explains how this can enable earlier insight from single-arm trials by effectively benchmarking against standard-of-care cohorts, helping teams enrich later-stage trials with the right subpopulations sooner, and reducing time and cost.

They also discuss the real blockers to adoption: not only scientific conservatism, but commercial uncertainty around how Big Pharma structures deals with tech-bio companies that bring platforms rather than single assets. Vitalay argues that explainability is non-negotiable in this space, because clinicians, scientists, patients, and regulators will not trust black-box predictions.

Topics Covered

Why AI is shifting from failure prevention to pipeline redesign
Biomarker discovery beyond binary responder vs non-responder labels
Breaking disease silos to learn across indications
Multimodal integration: DNA, RNA, protein, imaging, and clinical data
Using foundation models to bridge trial data and real-world data
Patient stratification and trial enrichment from early studies
Reverse translation and identifying unmet need before target hunting
Explainability, trust, and regulatory readiness
Adoption barriers: culture, champions, and deal structures for tech-bio
Misconceptions about AI in drug development and why “press a button” is a myth

About the Podcast

AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.

This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.

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