Responsive Regulation of AI in Drug Development

Owned by Nicola Newton
Last updated: about 2 hours ago
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This Community Forum took place on 21 November 2024. Catch up by viewing the recording and the presentation slides, which are coming soon.

Community Forum: Responsive Regulation of AI in Drug Development

The use of artificial intelligence (AI), including machine learning (ML), technologies across all stages of the drug product life cycle may accelerate the delivery of safe and effective high-quality drugs. As this data-driven technology continues to rapidly evolve across the landscape of drug development, a responsive regulatory approach may be warranted to calibrate the requirements needed to meet safety and evidentiary standards. This responsive regulatory approach can be based on an assessment of model risk, which is estimated by examining AI models’ influence on regulatory decision-making and the potential consequences of wrong decisions if the model is inaccurate. This responsive regulatory approach is rooted in an in-depth understanding of the specific application context and calibrates regulatory requirements in accordance with model risk.

Principles of trustworthy and responsible AI serve as the foundation for responsive policy development and provide valuable considerations for both AI tool developers and regulators. It is important to consider ways to continue to engage with all interested parties to remain responsive to the changing technological landscape. Scientific discussion around continuing our responsive risk-based regulation, our collaborative efforts across the AI ecosystem (i.e. academia, industry, biotech), and advancing regulatory science in this area, without adding unnecessary burden to developers or regulators, is warranted.

PresenterBio

Lan Mu, University of Georgia

Dr Lan Mu is a geography professor and the director of the GIScience Certificate Program at the University of Georgia (UGA). She also holds a courtesy appointment with the College of Public Health. Leading GIS.HEAL (GIScience for Health and Environment Analytics Lab), Lan drives efforts to promote geographical thinking in health and environmental studies, effectively bridging STEM approaches with social science research. Her research encompassed GIScience, health and environment, geospatial analysis for physical, socio-economic and social media data, environmental planning, and cartography and geovisualisation.

Lan collaborates with experts across public health, epidemiology, medicine, agricultural economics, computer science and other disciplines to develop innovative geospatial analyses of complex health and environmental issues. Her work has been supported by NIH, NSF, USDA, UCGIS and UGA.

Jielu Zhang, University of Georgia

Jielu Zhang is a PhD candidate in the Geography Department and a master’s student in the Computer Science Department at the University of Georgia (UGA). Jielu’s research focuses on geographically explainable AI, geographical health and environment, spatial optimisation for health facilities, and remote sensing foundation models.

Marsha Samson, FDA

Dr Marsha Samson manages key initiatives in the FDA Center for Drug Evaluation and Research (CDER) related to the use of artificial intelligence (AI) in the development of drugs. She applies her broad research interests and experiences to a variety of AI-related topics, including considerations for human-led governance, accountability, and transparency; quality, reliability, and representativeness of data; and model development, performance, monitoring, and validation. Additionally, she routinely conducts rigorous reviews of the peer-reviewed literature to support CDER initiatives (e.g. advancing innovative approaches, diversity in clinical trials) and applies her scientific expertise to respond to urgent requests.

Previously, Dr Samson worked as a Career Epidemiology Field Officer (CEFO) at the CDC, where she served as the subject matter expert assigned to the District of Columbia’s Health Department. Dr Samson earned her MPH/MSHSA from Barry University and her PhD in epidemiology from the University of South Carolina. She completed her postdoctoral training at both Georgetown University in cancer biology and at CDC as an Epidemic Intelligence Service (EIS) officer in applied epidemiology. Dr Samson is a Lieutenant Commander in the U.S. Public Health Service and received her Regulatory Affairs Certification (RAC) for medical devices and pharmaceutical drugs to further support CDER and the Office of Medical Policy.

Jian Dai, Roche

Jian Dai is an expert data scientist at Roche and he leads the AI team in the Digital Strategy and Enablement (DSE) group. His current focus is on applying large language models (LLMs) to enhance and optimise the product development value chain. From 2018 to 2023, Jian played a crucial role in the Personalised Health Care department, making foundational contributions to Roche’s capabilities in medical imaging.

In 2013, he joined Genentech Research & Early Development as a data scientist, providing support for both clinical science and clinical operations. Before joining Genentech, Jian worked at Abbott Vascular and Affymax, where he supported label expansion and conducted post-market analysis. He holds a PhD in Theoretical Physics from Peking University.