Multivariate modeling of magnetic resonance biomarkers and clinical outcome measures for Duchenne muscular dystrophy clinical trials

Abstract

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.

FIGURE 1

Magnetic resonance (MR) spectra and images from a single subject capturing 4 years of disease progression. In these transaxial images and spectra, red indicates muscle water, and green indicates fat. MR spectroscopy fat fraction is measured from a single voxel, shown as the white rectangles in top images, and quantifies the ratio of the peak areas of fat (green) to the sum of peak areas of fat (green) and water (red).

FIGURE 2

Longitudinal correlations between dependent variables and correlations among covariates of 118 individuals with Duchenne muscular dystrophy who were ambulatory at derived baseline visits. (a) Observed individual trajectories of 6MWD and FFVL versus age at assessments. (b) Observed individual trajectories of 6MWD and FFSOL versus age at assessments. (c) 6MWD versus FFVL (the Pearson correlation coefficient, r = −0.81). (d) 6MWD versus FFSOL (the Pearson correlation coefficient, r = −0.76). (e) Estimated correlations among continuous covariates. (f) Distribution of baseline measures per subgroup. Sample size: nonsteroid use and young = 10 subjects, nonsteroid use and old = 8 subjects, steroid users and young = 27 subjects, and steroid users and old = 73 subjects. 6MWD, 6‐minute walk distance (m); BS6MWD, baseline 6‐minute walk distance; BSFFSOL, baseline fat fraction measure from the soleus muscle; BSFFVL, baseline fat fraction measure from the vastus lateralis muscle; FFSOL, fat fraction measure from the soleus muscle; FFVL, fat fraction measure from the vastus lateralis muscle.

FIGURE 3

Data exclusion. DMD, Duchenne muscular dystrophy.

FIGURE 4

Representative evaluation plots of the final multivariate model quantifying the associations between 6‐minute walk distance (6MWD) and fat fraction measure from the vastus lateralis muscle (FFVL). In the goodness‐of‐fit plots for 6MWD (a) and FFVL (b), observed data versus individual predictions are plotted in the left panel, conditional weighted residuals (CWRES) versus population predictions are plotted in the middle panel, and individual weighted residuals (IWRES) versus age are plotted in the right panel. The visual predictive check plots for 6MWD (c) and FFVL (d) show the median (red dashed curves) and 10th and 90th percentiles (lower and upper blue dashed curves, respectively) of the predicted profiles. The shaded areas indicate the 90% confidence intervals of each of the percentile curves. For 6MWD, the fraction of the below limit of quantification (BLQ) is plotted in the solid curve and the predicted median is plotted in the dashed line surrounded by the 90% prediction interval shown in the shaded area. The aqua color represents the censored (BLQ) data (when the data were missing because of the inability to perform the functional test to measure 6MWD).

FIGURE 5

Representative simulation results. The shaded intervals represent 20% prediction intervals around the median of the 500 simulated profiles generated by 50 replicates of 10 individuals. The simulation outputs were stratified into two subgroups: steroid users versus nonusers. (a, b) The simulation was performed for all steroid users with higher baseline 6MWD measures compared with nonsteroid users. (c, d) The individuals' characteristics entered for simulation were mixed. These simulation results demonstrate that the developed models can predict the trajectories of each individual by accounting for the combined effects of the covariates as well as multiple sources of the variability. 6MWD, 6‐minute walk distance; FFSOL, fat fraction measure from the soleus muscle; FFVL, fat fraction measure from the vastus lateralis muscle.