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Session 5: Artificial Intelligence (AI) and Machine Learning (ML) in Drug Development
Session Chair(s)
Pabak Mukhopadhyay, PhD
Executive Director, Late Statistics, Head of Breast Cancer Strategy
AstraZeneca, United States
Paul Schuette, PhD, MA
Mathematical Statistician, Scientific Computing Coordinator
FDA, United States
Artificial intelligence and machine learning (AI/ML) have the potential to complement current processes and procedures as well as revolutionize drug discovery and patient selection. In this session, we will highlight key learnings, caveats, and issues as well as areas to consider through case study exploration. Examples shared will include early discovery and patient selection. The session will demonstrate the present utility and future potential of AI/ML for regulatory science and will discuss the challenges and considerations when using AI/ML methods with RWD in regulatory decision making.
Learning Objective : - Identify cases where AI/ML are used in drug development
- Discuss other areas of drug development where AI/ML can be utilized
- Recognize limitations with AI/ML methodology
Speaker(s)
Learnings from Developing an ML Prognostic Model of Early Risk of Mortality for Treatment of Patients with Immune Checkpoint Inhibitors (ICIs)
Jolyon Faria, PhD, MSc
AstraZeneca, United Kingdom
Data Science Director
Machine Learning Considerations In Causal Inference Using Real-World Data
Di Zhang, PhD
Teva, United States
Associate Director, RWE Statistics
Turbocharging Drug Discovery with Machine Learning - An Application
Gregory Steeno, PhD, MS
Pfizer, United States
Senior Director, Research Statistics