TITLE: Statistical Designs and Considerations for Dose Optimization
SPEAKERS: Ying Yuan, MD Anderson and Philip He, Daiichi Sankyo
MODERATOR: Kalyan Ghosh
Abstract:
The U.S. FDA launched Project Optimus to reform dose-finding practices by shifting the focus from identifying the maximum tolerated dose (MTD) to determining the optimal biological dose (OBD) that offers the best risk-benefit profile. In this talk, I will provide an overview of strategies for dose optimization, including efficacy-integrated and two-stage approaches, illustrated with real-world examples. I will also discuss recent advances in the design of randomized dose optimization trials and highlight the role of backfilling as a flexible and efficient tool in this process. Additionally, I will address the complexities of dose optimization in the context of multiple indications, multiple agents, and combination therapies. Finally, I will introduce software tools that support the practical implementation of these methodologies.
Instructors’ Biography:
Ying Yuan is the Bettyann Asche Murray Distinguished Professor and Chair of the Department of Biostatistics at the University of Texas MD Anderson Cancer Center. Dr. Yuan is internationally renowned for his pioneering research in innovative Bayesian adaptive designs, including early-phase trials, seamless trials, biomarker-guided trials, and basket and platform trials. The designs and software developed by Dr. Yuan’s lab (www.trialdesign.org) have been widely adopted by medical research institutes and pharmaceutical companies. Among these, the BOIN design, developed by Dr. Yuan’s team, is a groundbreaking oncology dose-finding method recognized by the FDA as a fit-for-purpose drug development tool. The BOP2 design is also widely used for Phase II trials and toxicity monitoring. Dr. Yuan is a member of the FDA Advisory Committee for Genetic Metabolic Diseases, an elected Fellow of the American Statistical Association, and the lead author of two books: Bayesian Designs for Phase I-II Clinical Trials and Model-Assisted Bayesian Designs for Dose Finding and Optimization, both published by Chapman & Hall/CRC.
Philip He, PhD, is a statistician at Daiichi Sankyo, Inc., with nearly two decades of experience in oncology drug development, spanning early-phase trials through regulatory approvals. He has led statistical teams in the successful development of multiple cancer therapies, including chemotherapy, tyrosine kinase inhibitors, monoclonal antibodies, and immunotherapies. Passionate about advancing oncology clinical development, Dr. He actively contributes to the scientific community through volunteer service in professional organizations, editorial and peer-review work, and conference organization. He has published extensively on topics such as adaptive designs, estimands, dose optimization, and Bayesian statistics. Dr. He currently serves as Head of Early Phase Statistics at Daiichi Sankyo and co-chairs the DahShu Innovative Design and Scientific Working Group (IDSWG) Oncology Team (https://oncologytrialdesign.org/), where he collaborates with industry peers to advance innovative trial designs. In recent work, the IDSWG Oncology Team contributed a book chapter on dose optimization in oncology.