In its 2025 draft guidance on AI-supported regulatory decision-making, followed by the joint FDA–EMA Guiding Principles of Good AI Practice in early 2026, regulators made their direction clear:
AI in drug development is no longer just about predictive performance. It is about scientific credibility.
The shift is real — away from black-box systems built for statistical accuracy, toward AI that is mechanistically grounded, interpretable, and one that regulators can actually trust.
What regulators are now emphasizing:
➤ Interpretability — explainable outputs, not just accurate ones
➤ Mechanistic Plausibility — biological grounding is expected
➤ Context–of–Use Validation — fit-for-purpose, not generic performance
➤ Translational Traceability — a clear scientific trail from model to decision
➤ Scientifically Defensible Inference — results that hold up under regulatory review
This directly impacts how AI is applied across PBPK modelling, translational PK/PD, MIDD workflows, toxicity prediction, dose optimisation, and clinical extrapolation.
The direction is toward mechanistically constrained AI — where machine learning works alongside PBPK, QSP, and mechanistic toxicology frameworks.
At InSilicoMinds, this is how we have always worked. AI and Computational Modelling & Simulation together — grounded in biology, built for regulatory acceptance. 🧬💻
Reach out today — we are here to help you navigate this. 🤝
📩 info@insilicominds.com | 🌐 insilicominds.com
Link: https://www.linkedin.com/posts/fda-ema-linked-ugcPost-7463525816363196416-y_Y1/?utm_source=share&utm_medium=member_desktop&rcm=ACoAABSeezgByE2EEvgLRb1yW0KYFVIdF_J-IF4
