Most teams in pharma say they want breakthrough impact, yet R&D portfolios keep clustering around the familiar. That tension is where our conversation with Stuart Berkowitz gets practical fast: if a disease area is already “98% served” by existing interventions, why sink a decade into a new molecule that barely moves the needle?
We dig into how biology is changing shape as a field, from wet lab routines to computational biology, bioinformatics, and system-level thinking. Stuart shares stories from genomics funding and even engineered microbes, then uses those examples to explain a bigger constraint: once problems become complex enough, intuition stops scaling. That is where Bayesian causal analysis comes in, not as a buzzword, but as a way to turn published medical research into causal models, convert hazard ratios into joint probabilities, and search enormous combinations of interventions to find Pareto optimal treatment plans.
We also explore what that means for real-world decision-making: benchmarking doctor protocols against alternative plans using large datasets, avoiding “Me Too drugs”, and how the FDA’s openness to Bayesian approaches could change the cost and timeline of clinical development. We close with the human side: compliance, habit formation, and longevity levers that actually matter for healthspan, including VO2 max, sauna, and strength training, while supplements often deliver far less than people hope.
Find more information about the guests project at https://longevityresearch.ca/
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