Model-Informed Approaches for Pediatric Type 2 Diabetes Evidence Generation: Insights from a Model-Based Longitudinal...

Evidence generation for antihyperglycemic therapies in pediatric type 2 diabetes mellitus (T2DM) remains challenging, with most randomized trials failing to demonstrate statistically significant reductions in glycated hemoglobin (HbA1c), partly due to substantial between-subject variability. To better understand the sources and implications of this variability, we conducted a model-based longitudinal meta-analysis of individual participant data from seven pediatric T2DM clinical trials submitted to the European Medicines Agency in support of marketing authorization applications. The analysis incorporated 3295 HbA1c observations from 809 participants and quantified the contributions of baseline disease severity, disease progression, placebo response, and treatment effects to longitudinal HbA1c dynamics. Insulin use and longer duration of diabetes were associated with higher baseline HbA1c and/or faster disease progression, while the placebo effect was substantial and highly variable across participants. Clinical trial simulations using the final model demonstrated that trials with fewer than 200 participants are unlikely to achieve 80% power to detect placebo-corrected treatment effects smaller than -0.70%-points over 26 weeks. Enrichment strategies targeting patients with lower variability, as well as model-based estimators-including Bayesian approaches leveraging historical information-substantially reduced the required sample size. These findings indicate that current pediatric T2DM trials are typically underpowered due to underestimation of HbA1c variability and highlight opportunities for more efficient trial designs. By quantifying disease- and treatment-related drivers of HbA1c trajectories and demonstrating the potential of model-informed strategies to improve power, this work provides a framework to enhance pediatric T2DM drug development and support regulatory decision-making.