Connect and Cure: Integrating Real-World Evidence in Drug Development
Overview
Abstract
Similar to how information technology maximizes system performance by increasing signal throughput and reducing noise, pharmaceutical development strives to optimize therapeutic benefit while minimizing toxicity. In this talk, Dr. Guanming Chen draws parallels between the Internet of Things (IoT) and modern biomedical innovation, where both depend on interconnected data streams, feedback control, and optimization under uncertainty. Artificial intelligence (AI) now plays a central role in this transformation, enabling deeper understanding of disease mechanisms, smarter trial design, and adaptive monitoring of patient responses. Meanwhile, real-world evidence (RWE) expands the bandwidth of medical knowledge, providing continuous feedback from clinical practice to inform regulatory and therapeutic decisions. Together, AI and RWE are reshaping drug development into a dynamic, data-driven system that learns from every patient and every data point.
Speaker Biography
Dr. Guanming Chen is a data science researcher in the School of Pharmacy at the University of California, San Francisco. He integrates real-world evidence (RWE), artificial intelligence (AI), and regulatory science to advance data-driven drug development. Before joining UCSF, Dr. Chen served as a reviewer in the U.S. Food and Drug Administration’s Division of Cancer Pharmacology, where he evaluated NDA/BLA/IND submissions aiming for drug approval. His research combines experience in clinical pharmacology and health outcomes to uncover factors influencing treatment response, safety, and health disparities. Dr. Chen’s recent projects apply machine learning and large language models to extract and analyze complex clinical data, supporting regulatory and translational decision-making. His long-term goal is to bridge AI innovation with regulatory rigor to improve patient outcomes and therapeutic safety.