Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, FL, USA.
Department of Statistics, University of Florida, Gainesville, FL, USA.
J Pharmacokinet Pharmacodyn. 2024 Dec;51(6):671-683. doi: 10.1007/s10928-024-09910-1. Epub 2024 Apr 12.
The study aimed to provide quantitative information on the utilization of MRI transverse relaxation time constant (MRI-T) of leg muscles in DMD clinical trials by developing multivariate disease progression models of Duchenne muscular dystrophy (DMD) using 6-min walk distance (6MWD) and MRI-T. Clinical data were collected from the prospective and longitudinal ImagingNMD study. Disease progression models were developed by a nonlinear mixed-effect modeling approach. Univariate models of 6MWD and MRI-T of five muscles were developed separately. Age at assessment was the time metric. Multivariate models were developed by estimating the correlation of 6MWD and MRI-T model variables. Full model estimation approach for covariate analysis and five-fold cross validation were conducted. Simulations were performed to compare the models and predict the covariate effects on the trajectories of 6MWD and MRI-T. Sigmoid I and E models best captured the profiles of 6MWD and MRI-T over age. Steroid use, baseline 6MWD, and baseline MRI-T were significant covariates. The median age at which 6MWD is half of its maximum decrease in the five models was similar, while the median age at which MRI-T is half of its maximum increase varied depending on the type of muscle. The models connecting 6MWD and MRI-T successfully quantified how individual characteristics alter disease trajectories. The models demonstrate a plausible correlation between 6MWD and MRI-T, supporting the use of MRI-T. The developed models will guide drug developers in using the MRI-T to most efficient use in DMD clinical trials.
本研究旨在通过使用 6 分钟步行距离(6MWD)和 MRI 弛豫时间常数(MRI-T)开发杜氏肌营养不良症(DMD)的多变量疾病进展模型,为 DMD 临床试验中 MRI 横向弛豫时间常数(MRI-T)的利用提供定量信息。临床数据来自前瞻性和纵向的 ImagingNMD 研究。使用非线性混合效应建模方法开发疾病进展模型。分别建立了 6MWD 和 5 块肌肉 MRI-T 的单变量模型。评估时的年龄是时间指标。通过估计 6MWD 和 MRI-T 模型变量的相关性来开发多变量模型。进行了全模型估计方法的协变量分析和五折交叉验证。进行了模拟以比较模型并预测协变量对 6MWD 和 MRI-T 轨迹的影响。Sigmoid I 和 E 模型最能捕捉 6MWD 和 MRI-T 随年龄的变化情况。类固醇的使用、基线 6MWD 和基线 MRI-T 是显著的协变量。在五个模型中,6MWD 下降到最大下降的一半的中位数年龄相似,而 MRI-T 增加到最大增加的一半的中位数年龄因肌肉类型而异。连接 6MWD 和 MRI-T 的模型成功地量化了个体特征如何改变疾病轨迹。这些模型表明 6MWD 和 MRI-T 之间存在合理的相关性,支持使用 MRI-T。所开发的模型将指导药物开发人员在 DMD 临床试验中最有效地使用 MRI-T。