Back to: FDA-CRCG Workshop on Modeling and AI in Generic Drug Development and Product Lifecycle Management
About
Quantitative Medicine has long guided drug development by transforming biology into models, predictions, and decisions. Whether through pharmacometrics, systems pharmacology, or translational modeling, it has helped reduce uncertainty and increase precision across the drug lifecycle. Today, a new force is accelerating this transformation: Artificial Intelligence/Machine Learning. More than a buzzword, AI/ML approaches are becoming an indispensable extension of Quantitative Medicine—augmenting our ability to analyze massive, complex datasets, generate real-time insights, and simulate decisions at scale. In this session, we explore how AI is unlocking new possibilities for the entire lifecycle of drug development.
Presentations
Speaker Introductions
Rajanikanth Madabushi, PhD
Associate Director, Guidance & Scientific Policy at IO, OCP, OTS, FDA
Digital Twins: What are They? How Can They Facilitate Drug Development?
Adarsh Subbaswamy, PhD
Assistant Professor, Center for Translational Medicine, UMB SOP
AI-Driven Knowledge Management in PBPK Modeling: Challenges and Opportunities
Vladmir Chupakhin, PhD
Principal Scientist, Simulation Plus Inc.
Role of AI/ML Approaches in New Drug Development and Evaluation
Qi Liu, PhD, MStat, FCP
Associate Director for Innovation & Partnership, OCP, OTS, FDA
AI for Augmenting and Accelerating Computational Fluid Dynamics Predictions of Regional Lung Deposition
Ross Walenga, PhD
Senior Chemical Engineer, DQMM, ORS, OGD, FDA
Using GenAI to Support Regulatory Applications and Product Lifecycle Management: Lessons Learned and Solutions
Liang Zhao, PhD, MAS, MBA
Professor & VC, Dept Bioengineering & Therapeutic Sci, SOP & SOM UCSF