Innovation to Impact: Drug Development, AI, and Regulatory Strategy
Innovation to Impact is a podcast on decision-grade drug development in regulated environments. We examine how high-stakes go/no-go calls are made inside pharma and biotech, and what evidence is required for new tools to change those decisions without creating hidden risk. Each episode focuses on predictivity, translational risk, decision rights, and accountability (what breaks, who owns it, and what triggers a stop). This is not a podcast about technology trends. It is about disciplined innovation that can survive audit, scale, and real-world biology.
Episodes
More data sounds good until it starts making everyone anxious, confused, or falsely confident.
In this conversation, Brian Berridge, Nick Kelley, and Szczepan Baran explore how data shapes decisions across drug discovery, safety assessment, clinical translation, AI, digital health, and wearable monitoring. They discuss why legacy datasets are often not truly AI-ready, why context of use matters, and why the wrong data can create ris...
In this episode of Innovation to Impact: Ruminations & Ramblings, Szczepan Baran, Brian Berridge, and Nick Kelley tackle one of the biggest problems in modern drug development: we keep adding more technology, more data, and more complexity, yet clinical attrition remains painfully high. Across discussions on AI, NAMs, digital biomarkers, animal models, translational science, and organizational culture, they argue that innovatio...
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Drug development workflows only get heavier, and subtractive change requires predictivity and decision-grade evidence that can survive regulatory science scrutiny. In this episode we talk about how AI and translational science can earn the right to delete steps, not decorate them. The tension is uncomfortable: are you willing to remove something you have always done, or will you just...
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In drug development, go/no-go decisions concentrate translational risk and expose real consequences. When AI or digital biomarkers influence that call, decision-grade evidence in a regulated environment is not optional. This episode sits in the moment every executive recognizes: the slide is on the screen and someone asks, “Do we advance?” Here’s the tension. We lov...
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In drug development, ROI debates can drown out decision-grade evidence and the hard work of translational science. This episode asks a blunt question: when AI, digital biomarkers, or new assays change the work, who actually gets the return and who carries the downside? The tension is that finance wants clean numbers, while biology delivers messy truth. We challenge the habit of treat...
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Drug development punishes optimism, and predictivity is one of the few defenses we have against translational risk. In this episode we connect translational science and regulatory science to a simple operating idea: treat attrition as data, not embarrassment. The tension is real. Do you want a model that explains the past beautifully, or one that helps you be less wrong before the ne...
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In drug development, translational science can generate endless options, but decision-grade evidence starts with the patient decision. This episode looks at why AI and digital biomarkers often get adopted before we agree what “better” means for patients and for the teams who carry the risk. Here’s the uncomfortable question: are we improving outcomes, or just improv...
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