Model-Based Meta-Analysis: Optimizing Research, Development, and Utilization of Therapeutics Using the Totality of Evidence

Model-based meta-analysis (MBMA) is a valuable component of the quantitative pharmacology toolkit for model-informed drug discovery and development. It enables principled decision making with a totality of evidence mindset through integration of internal and external data across multiple dimensions (e.g., targets/mechanisms, molecules/drugs, doses/regimens, diseases/indications, populations, endpoints, and clinical trial designs). MBMA distinguishes itself from traditional meta-analysis by infusing pharmacologic plausibility into the statistical rigor that typifies meta-analytic data integration. This is possible through mechanism-informed formulation of pharmacologically inspired cause-effect and dose-response relationships, time course of treatment effects, and interrelationships between proximal and distal outcomes of modulation of disease biology and pathophysiology. In this review, we offer a question-based approach to enhance appreciation of the value of MBMA across the continuum from drug discovery and translational research through clinical development, comparative effectiveness research, and postapproval optimization of therapeutics using illustrative examples across therapeutic areas.