TITLE: Master Protocol and Its Application
INSTRUCTORS: Jingjing Ye, BeiGene and Nicole Li, Merck & Co. Inc
MODERATOR: Xiaoming Li
As the paradigm of drug development shifts to personalized medicine and targeted therapies, pool of eligible clinical trial patients becomes increasingly smaller and there is a need for rapid learning and confirmation of clinically meaningful treatment effect. Master protocol, including umbrella, basket, and platform trials, promotes innovation in clinical trials and aims at improving efficiency, avoiding duplication and competition, and accelerating the drug development process. Though master protocols used to be primarily sponsored by nonprofit organizations, academic institutes and government agencies mostly in oncology area, there is a growing trend of conducting clinical trials using master protocol in recent years from the pharmaceutical industry in oncology and other therapeutic areas. Regulatory agencies across the globe have issued guidance on master protocols. In 2018, the ASA Biopharmaceutical Section Oncology Scientific Working Group (SWG) was chartered to explore innovative statistics in oncology drug development and a sub-team on master protocol in oncology was formed. In this short course, instructors from the Oncology SWG master protocol sub-team will provide an overview on master protocol, its regulatory landscape, statistical methodologies, special statistical considerations, challenges and opportunities both statistically and operationally. The last part of this short course will include a case study of a Pediatric platform trial: NCI-COG Pediatric MATCH, with detailed illustrations on how to implement the considerations discussed in the earlier part of the short course. See below the structure of the short course:
Part 1: Overview of master protocol
- Overview of master protocol: Definitions, Examples
- Review of Regulatory Landscape: US and Rest-of-World regulatory guidance
Part 2: Novel statistical methodologies used in master protocol trials, statistical and operational consideration
Part 3: Case Study of Pediatric Platform Trial: NCI-COG Pediatric MATCH
Dr. Jingjing Ye currently is head of system and standard within Global Statistics and Data Sciences (GSDS) in BeiGene. She leads a team to promote statistical innovations in clinical trials, develop visualization tools to support pre-clinical and clinical development and establish standard and process within BeiGene. She has over 15 years experience in pharmaceutical industry and US FDA. Before BeiGene, she was most recently a statistics team leader in the Office of Biostatistics in CDER. At CDER, she supervised a team of statistical analysts and reviewers for designing, reviewing and analyzing clinical trials to support drug approvals throughout preIND, IND, NDA/BLA and post-approval studies in oncology and hematology. She was statistical representative within the Oncology Center of Excellence (OCE) Pediatric Review sub-committee, responsible for overseeing all pediatric review operations within the OCE. She was recognized as subject matter expert (SME) promoting innovative designs and analyses in pediatric oncology trials within CDER and OCE.
Before promoting to team leader, she was primary statistical reviewer focusing in hematology in CDER and diagnostic devices in CDRH. With ~ 10 years FDA experience expanding two centers, she was active in scientific community being FDA representative between industry leaders, academia institutions and the FDA on various scientific working groups, including pediatric, external controls in oncology, simulation in adaptive designs, and quantitative imaging biomarkers. Prior to FDA, she was manager in statistics in Pfizer, focusing on drug discovery and translational research mainly in oncology. She received her PhD degree in statistics from University of California, Davis and B.S. in Applied Mathematics from Peking University in China.
Nicole (Xiaoyun) Li received her BS in Probability and Statistics from Peking University in 2006 and her PhD degree in Statistics from Florida State University in 2010, after which she joined Merck and have been working as a statistician on oncology clinical trials. She was the lead statistician for Keytruda’s first regulatory submission and approval for advanced melanoma in the United States, Europe, and the rest of the world, and a key contributor to Keytruda’s Prix Galien USA 2015 Award for Best Biotechnology Product.
In the past 10 years, she has served as the lead statistician for multiple key clinical trials of pembrolizumab success including KEYNOTE-001 (base of first Keytruda’s approval in US), KEYNOTE-002 (full approval for first Keytruda indication in US) and KEYNOTE-024. She is also the lead unblinded DMC statistician for multiple phase III Keytruda studies and the statistical lead for multiple early oncology programs and Merck’s umbrella studies. She has published more than 10 peer-reviewed articles in statistical methodologies. Her statistical publications and interests include adaptive designs particularly for biomarker enrichement, complex clinical trial designs including basket and umbrella trial designs, and utilization of real-world evidence into clinical drug development. She also enjoys volunteering in the local statistics community. She is currently the president of the American Statistical Association (ASA) San Diego Chapter, to promote innovative statistical methodologies, statistical knowledge sharing, and strengthening of the local statistical community.
Chengxing (Cindy) Lu (PhD in Biostatistics from Emory University) is a director of Biostatistics in Biogen Inc, Cambridge, MA. She has been leading statistical aspects of multiple compounds in various disease areas, from early to late phases of clinical development to post-marking activities, resulting several successful regulatory approvals. Dr. Lu is currently the co-lead of master protocol design sub-team of ASA Biopharmaceutical Section Oncology Scientific Working Group. Her research interest is study designs in clinical trials, real-world evidence and designs, and oncology/rare disease drug development strategy.