TITLE: Statistical Methods in the Clinical Development of Novel Cancer Immunotherapies
SPEAKERS: Bo Huang, Pfizer
MODERATOR: Naitee Ting
With decades of progress in medical research and advance in understanding the biology of cancer microenvironment, clinical development of novel cancer drugs including immunotherapies and targeted therapies is booming in recent years. In 2018 alone, the FDA approved 19 new cancer drugs and biologics. In 2013, Science names cancer immunotherapy as the scientific breakthrough of the year. Common immunotherapy approaches include cancer vaccine, effector cell therapy and T-cell stimulating antibody. Checkpoint inhibitors such as CTLA-4 and PD-1/PD-L1 antagonists, and CAR-T therapies have shown exciting results in many indications in solid tumors and hematology. However, the mechanisms of action of these novel drugs pose unique statistical challenges in the accurate evaluation of clinical safety and efficacy, such as late-onset toxicity, dose optimization, evaluation of combination agents, biomarkers, non-proportional hazards/delayed efficacy, and estimation of treatment benefit.
Traditional statistical methods may not be the most accurate or efficient. It is highly desirable to develop the most suitable methodologies and tools to efficiently develop cancer immunotherapies. In this presentation, I will go over the existing methods, main issues and challenges, and introduce and discuss novel analytical methods to meet the challenges in the clinical development of these drugs.
Dr Bo Huang graduated from Nankai University as the top student in his class with a bachelor’s degree in Mathematical Statistics. He joined Pfizer in 2008 after receiving his PhD in Statistics from the University of Wisconsin-Madison. He is currently Senior Director, Head of Immuno-Oncology Statistics at Pfizer. Dr. Huang has extensive experience in the pharmaceutical industry across all stages of global clinical development of medical products, with extensive global regulatory and submission related experience. He significantly contributed to all aspects related to major filings to Health Authorities and approval, including the development of strategy, planning, and execution leading up to the global submissions as well as rapid responses to global queries. He is the recipient of the Craig Saxton Clinical Excellence Award at Pfizer in 2019.
Dr. Huang is a recognized leader in developing and applying innovative quantitative methods, adaptive dose finding and confirmatory designs in drug development. He was one of the main drivers behind the increased use of innovative model-based and adaptive designs at Pfizer, and he designed the first protocol at Pfizer Oncology using a Bayesian model-based adaptive design in 2010. Dr. Huang is the author of more than 70 publications, with over 50 papers and book chapters, and over 20 abstracts, including as lead author or co-author of papers published in top journals such as NEJM, Journal of Clinical Oncology, The Lancet, JAMA Oncology, Biometrics, Statistics in Medicine etc. Both of his recent papers “Some statistical considerations in the clinical development of cancer immunotherapies” and “Comparison of the restricted mean survival time with the hazard ratio in superiority trials with a time-to-event endpoint” were ranked by Wiley among the top downloaded papers in Pharmaceutical Statistics between 2017 and 2018. The mean DOR method published in JAMA Oncology was included as a standard method in the Pfizer Oncology Statistics Rulebook, implemented to support regulatory submission and presentations in medical conferences. He also jointly holds a US patent in cancer treatment (US8435516B2). In addition, he received several poster and paper awards from the American Statistical Association and the International Biometric Society, and has been serving as Guest Editor, Associate Editor and reviewer for scientific journals, serving as chair or member on professional committees of statistical associations, and was an elected Board Director of the International Chinese Statistical Association (2016-2019).