Multivariate modeling of magnetic resonance biomarkers and clinical outcome measures for Duchenne muscular dystrophy clinical trials.
Multivariate modeling of magnetic resonance biomarkers and clinical outcome measures for Duchenne muscular dystrophy clinical trials.
复制标题
Duchenne型肌营养不良临床试验的磁共振生物标志物和临床结局测量的多变量建模。
DOI：
10.1002/psp4.13021
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发表时间：
2023-10
影响因子：
3.5
通讯作者：
Vandenborne, Krista
中科院分区：
文献类型：
--
作者：
Kim, Sarah;Willcocks, Rebecca J.;Daniels, Michael J.;Morales, Juan Francisco;Yoon, Deok Yong;Triplett, William T.;Barnard, Alison M.;Conrado, Daniela J.;Aggarwal, Varun;Belfiore-Oshan, Ramona;Martinez, Terina N.;Walter, Glenn A.;Rooney, William D.;Vandenborne, Krista
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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影响因子：
6
作者：
Barnard, Alison M.;Lott, Donovan J.;Walter, Glenn A.
通讯作者：
Walter, Glenn A.
影响因子：
9.9
作者：
Peverelli, Lorenzo;Testolin, Silvia;Moggio, Maurizio
通讯作者：
Moggio, Maurizio
影响因子：
3.7
作者：
Forbes, Sean C.;Willcocks, Rebecca J.;Vandenborne, Krista
通讯作者：
Vandenborne, Krista
影响因子：
9.9
作者：
Arpan, Ishu;Willcocks, Rebecca J.;Vandenborne, Krista
通讯作者：
Vandenborne, Krista
DOI：
10.1007/164_2019_256
发表时间：
2020-01-01
期刊：
PEDIATRIC PHARMACOTHERAPY, VOL. 0
影响因子：
--
作者：
Hoffman, Eric P.
通讯作者：
Hoffman, Eric P.