Key result
Multivariate models successfully quantified longitudinal associations between 6-minute walk distance and muscle fat fraction to guide biomarker use in Duchenne muscular dystrophy trials.
Why the study?
Industry receives minimal guidance on how to most beneficially use regulatory agency-encouraged imaging biomarkers in Duchenne muscular dystrophy clinical trials.
Multivariate modeling of muscle fat fraction and 6-minute walk distance can guide the efficient use of imaging biomarkers in Duchenne muscular dystrophy clinical trials.
Muscle fat fraction modeling may optimize DMD trial endpoints; leaves open prospective validation before clinical adoption.
Although regulatory agencies encourage inclusion of imaging biomarkers in clinical trials for Duchenne muscular dystrophy (DMD), industry receives minimal guidance on how to use these biomarkers most beneficially in trials. This study aims to identify the optimal use of muscle fat fraction biomarkers in DMD clinical trials through a quantitative disease-drug-trial modeling and simulation approach. We simultaneously developed two multivariate models quantifying the longitudinal associations between 6-minute walk distance (6MWD) and fat fraction measures from vastus lateralis and soleus muscles. We leveraged the longitudinal individual-level data collected for 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 fivefold cross-validation further showed the model's adequacy. The multivariate models will guide drug developers on using fat fraction assessment most efficiently using available data, including the widely used 6MWD. The models 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 inform better 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.
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Kim et al. (2023) studied Duchenne muscular dystrophy. Muscle fat fraction biomarkers was evaluated on Longitudinal associations between 6-minute walk distance (6MWD) and fat fraction measures. Multivariate models successfully quantified longitudinal associations between 6-minute walk distance and muscle fat fraction to guide biomarker use in Duchenne muscular dystrophy trials.
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