Chair: Zhaoyang Teng, PhD (Servier)
Co-Chair: Wenqiong Xue, PhD (Boehringer Ingelheim Pharmaceuticals, Inc.)
Abstract: Artificial Intelligence (AI) and Machine Learning (ML) are set to revolutionize drug development, offering new ways to accelerate discovery, improve clinical trial efficiency, and deliver personalized treatments. However, the application of AI/ML in this highly regulated industry also presents significant challenges.
This session will bring together a distinguished group of industry leaders, FDA regulators, and academic experts to explore the transformative potential of AI and Machine Learning (ML) in drug development.
The session will feature two 15-minute presentations—one by an FDA representative and another by an industry expert—followed by a one-hour panel discussion. The conversation will focus on key opportunities, including AI-driven drug discovery and AI-powered clinical development, with improvements in patient selection and more efficient study designs. Additionally, AI/ML is showing promise in areas such as document automation, literature review, supply chain optimization, predictive modeling, and safety signal detection. The panel will also address critical challenges, including data quality and bias, regulatory hurdles, and the ethical considerations surrounding the use of AI/ML in healthcare.
Panelists will share their insights on emerging trends, the evolving regulatory landscape, and collaborative efforts needed to fully harness the power of AI/ML. The discussion will explore how industry, regulators, and academia can work together to ensure these technologies are responsibly and effectively integrated into the drug development process, ultimately benefiting patients through faster and more personalized therapeutic solutions.
Speaker & Panelist: Brian Jin, PhD (Boehringer Ingelheim Pharmaceuticals, Inc.)
Title: Applying AI/ML to Address Unmet Needs in Retinal Health
Abstract: The development of treatments for Retinal Health faces significant challenges, including the lack of reliable preclinical models, the slow progression of retinal diseases, and the absence of well-established biomarkers. This presentation will explore how artificial intelligence (AI) and machine learning (ML) can be leveraged to accelerate drug development and mitigate associated risks. The focus will be on the application of AI/ML to facilitate clinical trials in Retinal Health, providing meaningful examples of their use. By modeling disease progression and predicting outcomes based on patient-level data, AI/ML can enhance early signal detection, decrease variation, and reduce sample sizes, ultimately improving the efficiency and effectiveness of clinical trials. These advancements have the potential to lead to better treatment options and significantly improve the quality of life for patients suffering from retinal diseases.
Speaker & Panelist: Ruthanna Davi, PhD (Medidata Solutions)
Title: AI in Operational and Scientific Aspects of Medical Product Development
Abstract: This session will explore two applications of AI in medical product development. The first, on the operational side of clinical trials, is AI driven data cleaning and reconciliation. Based on the principle that most data collected during the conduct of a clinical trial is accurate we will illustrate how AI that first identifies overall patterns within this data and second highlights instances where specific patients variables or sites differ from these patterns, one can create an opportunity for much more efficient targeted data review without requiring rules or edit checks. Examples of the types of patterns and exceptions (i.e., potential data errors) that can be identified will be shared. In addition, we illustrate how historical clinical trials data can be used to form external controls to understand counterfactuals and allow more informed clinical decision making and/or more patient centric trials. These ideas will be illustrated through validation case studies comparing randomized and propensity score based external controls and application in a phase 2 and phase 3 setting.
Panelist: Sammi Tang, PhD (Astellas Pharma)
Panelist: Mark Chang, PhD (Boston University, AGInception)
Panelist: Weitong Zhang, PhD (University of North Carolina, Chapel Hill)
