Optimizing Muscular Dystrophy Clinical Trial Designs using Modeling and Simulation

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PROJECT SUMMARY Duchenne muscular dystrophy (DMD) is a phenotypically heterogeneous pediatric disease. Drug development for DMD has accelerated over the past decade but continues to face significant challenges in both endpoint and cohort selection. A recent FDA guidance for drug development in rare pediatric diseases emphasizes the value of model-informed drug discovery and development approaches to optimize drug development pipelines. Additionally, FDA explicitly encourages inclusion of imaging biomarkers in clinical trials for DMD. The overall objective of this project is to develop a quantitative model-based clinical trial simulation (CTS) tool to guide investigators on how to best incorporate quantitative magnetic resonance (qMR) imaging and spectroscopy biomarkers in clinical trials. The model-based CTS tool will help drug developers to optimize their clinical trial design to detect a therapeutic effect as efficiently as possible, reducing clinical trial time, expense, and participant burden. This project takes advantage of the rich ImagingDMD data set, and will be the first to link the longitudinal changes of qMR biomarkers and physical function measures using a non-linear mixed effects modeling approach, enabling assessment of inter-individual and intra-individual variabilities. In Aim 1, we will quantify how the variability of the longitudinal changes of four functional endpoints are explained by qMR biomarker values measured on eight leg muscles at screening visits. In Aim 2, we will identify subgroups of the population that differ in disease progression through a covariate analysis. In Aim 3, we will develop a DMD disease progression model-based CTS tool. The CTS tool will accelerate drug discovery and development by allowing users to simulate possible scenarios of a clinical trial prior to its actual execution. It will inform trial design by providing insights into key trial design aspects, including choice of muscles/biomarkers, inclusion/exclusion criteria, optimal number of participants, trial duration, and frequency of observations. Covariates identified in Aims 1 and 2, which are common screening criteria in clinical trials in DMD, will be incorporated in the CTS tool. The interdisciplinary and model-based approach proposed in this study will allow us to leverage existing clinical research data to markedly improve trial design in DMD. The CTS tool will be open and publicly available, and it will be disseminated as a web-based user-friendly graphical user interface in order to facilitate easy access, broad use, and high impact.