Late Breaking: Multivariate modeling of magnetic resonance biomarkers and clinical outcome measures in clinical trials for Duchenne muscular dystrophy



Poster Number: Virtual


Sarah Kim, PhD, University of Florida, Rebecca Willcocks, PhD, University of Florida, Michael Daniels, ScD, University of Florida, Juan Francisco Morales, University of Florida, Deok Yong Yoon, University of Florida, William Triplett, BSc, University of Florida, Alison Barnard, PhD, University of Florida, Daniela Conrado, e-Quantify, Varun Aggarwal, PhD, Critical Path Institute, Ramona Belfiore-Oshan, PhD, Critical Path Institute, Terina Martinez, PhD, Critical Path Institute, Glenn Walter, PhD, University of Florida, William Rooney, PhD, Oregon Health & Science University, Krista Vandenborne, PT, PhD, University of Florida

While the FDA encourages inclusion of imaging biomarkers in clinical trials for Duchenne muscular dystrophy (DMD), industry has little guidance on how to use these biomarkers most beneficially in trials. This study aimed to optimize use of muscle fat fraction biomarkers in clinical trials for DMD through a quantitative disease-drug-trial modeling and simulation approach. We developed two multivariate models quantifying the longitudinal associations between six-minute walk distance (6MWD) and fat fraction measure from vastus lateralis and soleus muscles simultaneously. We leveraged the longitudinal patient-level data collected over 10 years through the ImagingDMD study. Age of the individuals at assessment was chosen as the time metric. After the longitudinal dynamic of each measure was modeled separately, the selected univariate models were combined using correlation parameters. Covariates, including baseline scores of the measures and steroid use, were assessed using the full model approach. The nonlinear mixed-effects modeling was performed in Monolix. The final models showed reasonable precision of the parameter estimates. Simulation-based diagnostics and 5-fold cross-validation further showed the model adequacy. The multivariate models will guide drug developers how to use fat fraction most efficiently using available data, including widely used 6MWD. The model will provide valuable information about how individual characteristics alter disease trajectories. We will extend the multivariate models to incorporate trial design parameters and hypothetical drug effects to better inform clinical trial designs through simulation, which will facilitate the design of clinical trials that are both more inclusive and more conclusive using fat fraction biomarkers.