(M-109) Optimizing Duchenne Muscular Dystrophy Clinical Trial Design by Modeling and Simulation: Identifying Characteristics of Magnetic Resonance Imaging Fat Fraction and Timed Function Test Measures and Their Longitudinal Association

Mina Park – Pharmaceutics – Center for Pharmacometrics and Systems Pharmacology, University of Florida; Deok Yong Yoon – Pharmaceutics – University of Florida; Rebecca Willcocks – Physical Therapy – University of Florida; William Triplett – Physical Therapy – University of Florida; Michael Daniels – Statistics – University of Florida; Ramona Belfiore-Oshan – Critical Path Institute; Glenn Walter – Physiology and Aging – University of Florida; William Rooney – Advanced Imaging Research Center, Oregon Health & Science University; Krista Vandenborne – Physical Therapy – University of Florida; Sarah Kim – Pharmaceutics – Center for Pharmacometrics and Systems Pharmacology, University of Florida

Objectives: This study aims to support the clinical usage of magnetic resonance (MR) imaging biomarkers (i.e., fat fraction (FF)) for Duchenne muscular dystrophy (DMD) clinical trials [1][2] by quantitatively analyzing the longitudinal relationship between commonly used timed function tests (TFTs) (i.e., 10-meter walk run (TMW), supine to stand (STS), climb 4 stairs (CFS)) and FF measures from two leg muscles (i.e., soleus (SOL) and vastus lateralis (VL)). Additionally, the research aims to identify characteristics of imaging and functional measures, including the sensitive age range and relationships with clinically relevant covariates.

Methods: Six multivariate disease progression models linking the 3 TFT velocity endpoints and 2 FF measures were developed using natural history data of ImagingNMD study (NCT01484678). The models were validated with placebo data of 3 clinical trials. [3] Distributions of the simulated model parameters of the entire data population were compared across measures. To intuitively explore the combined effects of covariates, 16 virtual individual profiles were generated by pairing each profile with a counterpart differing in a single covariate. Each individual was simulated 500 times and the median and 40th–60th percentile bands of TFT velocity and FF disease progression trajectories were compared.

Results: Multiplication of Chapman-Richards growth and Imax function and sigmoid Emax function provided the best fit as a structural model for TFT velocity and FF measures, respectively. DPmax_FF,i (maximum change in FF) was positively correlated with γ_TFT,i (steepness of the velocity curve) and negatively with Gmax_TFT,i (maximum possible velocity) in TMW and CFS velocity models linked to FFSOL (0.36/0.35 and -0.31/-0.52), but not in STS velocity. DPT50_FF,i (age at which the change is half of its maximum) showed positive correlations with Gmax_TFT,i for all 3 models linked to FFVL (0.40, 0.47, 0.57) and negative with γ_TFT,i (-0.18 and -0.4) except for STS velocity. STS velocity models linked to both FFSOL and FFVL showed strong positive correlations between DPT50_TFT,i and DPT50_FF,i (0.79 and 0.82). The peaks of DPT50,i distribution from the population-based simulations were 12, 10 and 11.1 (years) for TMW, STS and CFS velocity, and 12.5, 10.6 (years) for FFSOL and FFVL, respectively. Baseline TFT velocity was the most influential covariate for TMW velocity progression, masking other covariate effects. Meanwhile, steroid use had the strongest effect for STS and CFS velocity progression, followed by baseline TFT velocity, and baseline age. In altering FF trajectories, baseline FF was the key covariate for both, followed by steroid use in SOL and baseline age in VL.

Conclusions: The models highlighted FF measures as reliable biomarkers, with significant correlations to the TFT velocity measures. Model simulation results demonstrated that the models capture the sensitive age range of each measure and identify key covariates that alter disease progression trajectories. Accordingly, the multivariate models have potential to serve as informative quantitative tools for guiding biomarker selection and the choice of inclusion/exclusion criteria, further optimizing DMD clinical trial design.

Citations: [1] Kim S, Willcocks RJ, Daniels MJ, et al. Multivariate modeling of magnetic resonance biomarkers and clinical outcome measures for Duchenne muscular dystrophy clinical trials. CPT Pharmacometrics Syst Pharmacol. 2023; 12(10): 1437-1449.
[2] Yoon DY, Daniels MJ, Willcocks RJ, et al. Five multivariate Duchenne muscular dystrophy progression models bridging six-minute walk distance and MRI relaxometry of leg muscles. J Pharmacokinet Pharmacodyn. 2024; 51(6): 671-683.
[3] Yoon DY, Daniels MJ, Willcocks RJ, et al. Use of quantitative magnetic resonance imaging biomarkers in clinical trials for Duchenne muscular dystrophy: multivariate disease progression models bridging timed motor function tests and fat fraction. Presented at: American Society for Clinical Pharmacology and Therapeutics (ASCPT) Annual Meeting; March 2024; Colorado Springs, CO.

Keywords: Duchenne Muscular Dystrophy, Multivariate Modeling, Model-informed Drug Development