CNSPulse

REGULATORY PLENARY: AI-BASED TOOLS IN CNS DRUG DEVELOPMENT: A REGULATORY FRAMEWORK FOR CLINICAL TRIAL APPLICATIONS

Tiffany Farchione — US Food and Drug Administration Valentina Mantua, Center for Drug Evaluation and Research, Food and Drug Administration Bernard Fischer, U.S. Food and Drug Administration Qi Liu, Center for Drug Evaluation and Research, Food and Drug Administration Roberta Rasetti, FDA/CDER/DP

2026 ASCP Annual Meeting

This session examines the evolving role of artificial intelligence (AI) and machine learning (ML) in FDA drug review processes, with particular focus on central nervous system (CNS) therapeutics. The session will explore how AI tools are currently integrated within FDA operations, their impact on clinical review practices, and emerging applications in psychiatric and neurological drug development. The first presentation will provide an overview of AI implementation across FDA’s Center for Drug Evaluation and Research (CDER) and Center for Devices and Radiological Health (CDRH), highlighting internal tools that enhance regulatory efficiency while maintaining scientific rigor. The second presentation will focus on AI applications in CNS drug development, reviewing FDA’s Risk-Based Evaluation Framework for AI/ML tools and examining real-world examples of how these technologies address traditional challenges in psychiatric research, including patient stratification, digital endpoints, and placebo response variability. Following the two presentations, we will have a panel discussion focused on how clinical reviewers integrate AI resources into their workflow, addressing concerns about maintaining independent clinical judgment and ensuring that AI augments rather than replaces critical thinking in regulatory decision-making. This session will provide attendees with practical insights into current FDA AI capabilities, regulatory expectations for AI-enabled submissions, and emerging opportunities for leveraging these technologies in CNS therapeutic development.

Learning Objective 1: Evaluate opportunities to incorporate AI/ML technologies into CNS trial design

Learning Objective 2: Understand FDA’s Risk-Based Evaluation Framework for AI/ML tools in CNS drug development and recognize key regulatory considerations when planning to incorporate these technologies into clinical trial design.

References

Liu, Q., Huang, R., Hsieh, J., Zhu, H., Tiwari, M., Liu, G., Jean, D., ElZarrad, M.K., Fakhouri, T., Berman, S., Dunn, B., Diamond, M.C. and Huang, S.-M. (2023), Landscape Analysis of the Application of Artificial Intelligence and Machine Learning in Regulatory Submissions for Drug Development From 2016 to 2021. Clin Pharmacol Ther, 113: 771-774. Podichetty JT, Bauer AM, Xu R, Henscheid N, Anderson W, Khan A, Ma SC, Huynh H, Romero K. How AI Transforms Regulatory Submission: Current Clinical Implementation and Future Prospects. Clin Transl Sci. 2025 Dec;18(12) 10:15 a.m. - 11:45 a.m. ASCP Awards Ceremony: ASCP Lifetime Awardee Talk and Nasrallah Award Winner