Current State-of-the-Art and Future Advancements of Model-Based Meta-Analysis

Model-based meta-analysis (MBMA) is an increasingly important component of model-informed drug development (MIDD), helping biopharmaceutical sponsors integrate evidence from multiple studies to make more informed decisions throughout the drug development lifecycle. This publication reviews the current state of MBMA, explores recent methodological advances, and highlights real-world applications demonstrating how MBMA supports dose optimization, clinical trial design, competitive landscape assessments, target product profile development, regulatory decision making, and market access strategy.

The publication also examines emerging innovations, including the integration of aggregate- and individual-level data, the use of MBMA alongside quantitative systems pharmacology (QSP), and the growing role of artificial intelligence and machine learning in accelerating evidence synthesis and predictive modeling. Together, these advances demonstrate how MBMA is strengthening model-informed drug development by improving the ability to predict outcomes, reduce uncertainty, and support faster, more confident drug development decisions.