Where can I listen to this podcast?

This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.

Innovation to Impact: Drug Development, AI, and Regulatory Strategy

Claim This Podcast

by Brian Berridge, Nick Kelly & Szczepan Baran

7 episodes

Updated Daily

Accepts GuestsHas Sponsors

Podcast Overview

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.

Language

🇺🇲

Publishing Since

2/15/2026

Visit Website View on Apple Podcasts RSS Feed

1 verified contact email on file for Innovation to Impact: Drug Development, AI, and Regulatory Strategy

Pitch yourself as a guest, propose sponsorships, or reach out directly to the host.

See contact details

Recent Episodes

June 6, 2026

The Danger of Data You Can’t Interpret

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 risk instead of reducing it.A practical, skeptical discussion for anyone working at the messy intersection of biomedical research, AI, translation, and decision-making.

May 20, 2026

De-Risking What Matters

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 innovation fails when tools become the strategy instead of serving clearly defined patient-centered decisions. The conversation explores why reverse translation matters, how AI should function as a decision-support system rather than a magic oracle, why “decision warranties” may become essential in AI-enabled science, and how the industry continues to confuse activity with progress. This is a candid, often uncomfortable discussion about predictivity, accountability, translational learning, and what it would actually take to build a drug development system optimized for patient outcomes instead of platform hype.

February 15, 2026

Innovation That Earns Deletion (Subtractive Trust)

<a href="https://www.youtube.com/watch?v=7TZMIz3pwNQ" title="Click here to watch a video of this episode.">Click here to watch a video of this episode.</a> 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 add another layer and call it progress? We introduce the idea of a Predictivity Ledger, a simple way to make miss rates visible and force clarity about what is being claimed, in what context, and with what failure modes. Deletion is not a vibe. It is a governance decision with receipts. Takeaway: pick one legacy step, define deletion criteria, and start logging misses like they cost time and money, because they do. <ul><li>(00:00) - Episode purpose, resolutions, and AI adoption risks </li> <li>(02:00) - Subtractive strategy and replacement opportunities in practice </li> <li>(04:00) - Evidence authority, familiarity, and higher validation bars </li> <li>(07:00) - Add-on endpoints and why adoption stalls </li> <li>(08:00) - Decision bias and the need to challenge interpretation </li> <li>(10:00) - Predictivity ledger components for accountable decisions </li> <li>(12:00) - AI capabilities, limits, and accountability requirements </li> <li>(14:00) - Administrative AI example and subtractive realization questions </li> <li>(18:00) - Process efficiency versus biology, fast-fail critique </li> <li>(25:00) - Breaking silos, expert finding, and organizational knowledge </li> <li>(29:00) - Historical knowledge, foundational literature, and AI leverage </li> <li>(33:00) - Closing remarks and next episode sign-off</li> </ul> If you liked this episode, steal the monthly cheat sheet at <a href="https://innovationimpact.beehiiv.com/">Innovation2Impact Newsletter </a>(we do the digging, you keep the credit).

7 total episodes available

Deep-dive analytics for Innovation to Impact: Drug Development, AI, and Regulatory Strategy

Frequently asked questions

Have a different question and can't find the answer you're looking for? Reach out to our support team by sending us an email and we'll get back to you as soon as we can.

What is 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.
How often does this podcast release new episodes?: This podcast updates daily.
Where can I listen to this podcast?: This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
Does this podcast accept guests?: Information about guest appearances is not available.

Legal Disclaimer

Pod Engine is not affiliated with, endorsed by, or officially connected with any of the podcasts displayed on this platform. We operate independently as a podcast discovery and analytics service.

All podcast artwork, thumbnails, and content displayed on this page are the property of their respective owners and are protected by applicable copyright laws. This includes, but is not limited to, podcast cover art, episode artwork, show descriptions, episode titles, transcripts, audio snippets, and any other content originating from the podcast creators or their licensors.

We display this content under fair use principles and/or implied license for the purpose of podcast discovery, information, and commentary. We make no claim of ownership over any podcast content, artwork, or related materials shown on this platform. All trademarks, service marks, and trade names are the property of their respective owners.

While we strive to ensure all content usage is properly authorized, if you are a rights holder and believe your content is being used inappropriately or without proper authorization, please contact us immediately at hey@podengine.ai for prompt review and appropriate action, which may include content removal or proper attribution.

By accessing and using this platform, you acknowledge and agree to respect all applicable copyright laws and intellectual property rights of content owners. Any unauthorized reproduction, distribution, or commercial use of the content displayed on this platform is strictly prohibited.