Webinar
Presented on: March 19, 2025
What does it take to deploy digital twins and synthetic data in clinical evidence generation — and what do regulators expect when you do?
In this third webinar in the MRCT Center’s Digital Twins and Synthetic Data series, a multidisciplinary panel examines the real-world application of these technologies across the clinical trial lifecycle. The discussion covers evidence quality and validation, regulatory benchmarks, model transparency, and the evolving landscape of FDA and EMA expectations. Panelists draw on experience spanning machine learning, FDA policy development, and drug development leadership to offer practical, grounded perspectives on what adoption looks like today — and where the field is headed.
Topics include:
- Defining digital twins and synthetic data: key distinctions and appropriate uses
- Reducing control arms and enhancing statistical power in randomized and single-arm trials
- Applications across rare disease, oncology, and common conditions
- Machine learning vs. traditional statistical approaches: complementary, not competing
- Regulatory acceptance: FDA draft guidance, EMA qualification of PROCOVA, and engagement strategies
- Model evaluation benchmarks and performance validation across development phases
- Cultural and organizational barriers to adoption — and how to address them
Panelists: Daniele Bertolini, Principal Machine Learning Scientist, Unlearn.AI | Tala Fakhouri, VP Consulting AI & Digital Policy and Real World Evidence, Parexel | Karen Smith, Board Director, Context Therapeutics, Skye Bioscience, and Sangamo Therapeutics
Moderator: Barbara Bierer, Faculty Director, MRCT Center
