SESSION F

Recent draft ICH and FDA guidance suggests that mechanistic evidence increasingly will be prioritized in drug efficacy evaluations (e.g., the “model-informed drug development” framework of ICH M15 and the more recent “plausible mechanism framework” for rare genetic disorders). This likely will include an increasing role for response biomarkers in decision making. These developments are particularly relevant to biomarker research for “First in Class” settings, where historical meta-analysis methods are not directly applicable. In this tutorial, we provide an overview of statistical issues relevant in this setting, emphasizing opportunities for statistical influence even in the development of “mechanistic” evidence.

Instructors’ Biography:

Scott Chasalow joined Metrum Research Group in 2025 as a Principal Scientist in Statistical Bioinformatics, contributing more than three decades of experience in applied statistics, bioinformatics, and translational science. He holds an M.A. in Biochemistry and a Ph.D. in Biostatistics from the University of California, Berkeley.

Scott’s early work focused on statistical genetics and genomics in plant breeding, where he helped advance data-driven strategies for crop science. He later shifted his focus to human health, joining Bristol Myers Squibb, where he spent 21 years in statistical and bioinformatics leadership roles across clinical and non-clinical research. At BMS, he established and led the Translational Bioinformatics Methodology group, delivering expertise in experimental design, advanced statistical analysis, and development of novel approaches to biomarker discovery and evaluation.

At Metrum RG, Scott collaborates across disciplines to integrate statistical, bioinformatic, and pharmacometrics insights – accelerating the development of meaningful biomarkers and enabling more informed, data-driven decisions in drug development.

 Jim Rogers is the Vice President of Statistics in the Quantitative Sciences Business Unit at Metrum Research Group. After receiving his doctorate in statistics from The Ohio State University in 2001, Jim worked for two years on genomic and metabonomic analyses for biotechnology companies, followed by five years at Pfizer Global Research and Development where he worked initially as a nonclinical statistician and later as a clinical biostatistician. In 2008, Jim joined Metrum Research Group in order to work in closer collaboration with quantitative biologists and pharmacometricians. Over the course of his 15 years at Metrum RG, Jim has worked on decision-making informatics across a wide range of therapeutic areas and therapeutic modalities. Recurring areas of focus have included problems related to dose selection and dose optimization, as well as platform development based on disease progression models and clinical trial simulation. From a methodological perspective, Jim’s focus in recent years has centered on the role of causal inference concepts in evidence integration. Jim believes that scientists trained in statistics can revolutionize the discipline of pharmacometrics and that scientists trained in pharmacometrics can revolutionize the discipline of statistics.