Model-Based Meta-Analysis Leveraging Publicly Available Data to Support Drug Development in Weight-management

In the complex landscape of obesity drug development, Model-Based Meta-Analysis (MBMA) plays a critical role in integrating and interpreting diverse clinical data to inform decision-making. GLP-1 receptor agonists (GLP-1 RAs) and related multi-mechanism therapies have demonstrated significant weight loss effects, but variability in response remains a challenge. This analysis utilized an MBMA database comprising longitudinal body weight data and study characteristics from 120 clinical trials to characterize the dose-response relationship and identify covariates influencing pharmacodynamic (PD) performance. An Emax model was developed to describe the dose-response relationship for weight loss across mono- and dual-mechanism therapies, including GLP-1 RAs, GIPRAs, GCGRAs, and amylin analogs. The model effectively captured key sources of between-trial heterogeneity, including treatment-specific dose-response and onset dynamics (e.g., titration effects), population characteristics (i.e., diabetic vs. non-diabetic), background therapies (i.e., insulin use), study phase, and measurement methodology (i.e., in-trial vs. on-treatment population). Sensitivity analyses further explored the impact of study design factors on model performance. This MBMA provides a comprehensive synthesis of available clinical data, enabling a more precise understanding of drug effects and supporting optimized dose selection in obesity treatment. By systematically quantifying variability and predicting treatment outcomes, MBMA offers a data-driven approach to enhance trial design and regulatory decision-making. Beyond obesity, this study underscores MBMA’s broader applications in optimizing drug development strategies across therapeutic areas, ultimately improving efficiency and reducing late-stage attrition.