Optimizing Muscular Dystrophy Clinical Trial Designs using Modeling and Simulation

项目总结 Duchenne肌营养不良症(DMD)是一种表型异质性的儿科疾病。药物开发 DMD在过去十年中加速发展，但在终端和 队列选择。FDA最近对罕见儿科疾病的药物开发指南强调了这一价值 建立模型知情的药物发现和开发方法，以优化药物开发管道。 此外，FDA明确鼓励将成像生物标记物纳入DMD的临床试验。整体而言 本项目的目标是开发一个基于定量模型的临床试验模拟(CTS)工具来指导 研究人员如何最好地将定量磁共振(QMR)成像和光谱学结合起来 临床试验中的生物标志物。基于模型的CTS工具将帮助药物开发商优化他们的临床试验 设计以尽可能有效地检测治疗效果，减少临床试验时间、费用和参与者 负担。 该项目利用了丰富的ImagingDMD数据集，并将第一个将纵向变化联系起来 使用非线性混合效应建模方法的QMR生物标记物和物理功能测量， 能够评估个体间和个体内的差异。在目标1中，我们将量化 四个功能终点纵向变化的变异性可以用QMR生物标志物的值来解释 在筛查时对八块腿部肌肉进行测量。在目标2中，我们将确定人口的亚群 通过协变量分析得出疾病进展的差异。在目标3中，我们将开发DMD疾病进展 基于模型的CTS工具。CTS工具将通过允许用户 在实际执行之前模拟临床试验的可能场景。它将通过提供以下内容来通知试验设计 对关键试验设计方面的见解，包括肌肉/生物标记物的选择、纳入/排除标准、最佳 参与者数量、试验持续时间和观察频率。目标1和目标2中确定的协变量，其中 是DMD临床试验中的常见筛查标准，将被纳入CTS工具。跨学科 这项研究中提出的基于模型的方法将使我们能够利用现有的临床研究数据 显著改善DMD的试验设计。CTS工具将是开放的和公开的，它将 作为基于网络的用户友好的图形用户界面进行传播，以便便于访问、广泛使用 而且影响很大。

PROJECT SUMMARY Duchenne muscular dystrophy (DMD) is a phenotypically heterogeneous pediatric disease. Drug development for DMD has accelerated over the past decade but continues to face significant challenges in both endpoint and cohort selection. A recent FDA guidance for drug development in rare pediatric diseases emphasizes the value of model-informed drug discovery and development approaches to optimize drug development pipelines. Additionally, FDA explicitly encourages inclusion of imaging biomarkers in clinical trials for DMD. The overall objective of this project is to develop a quantitative model-based clinical trial simulation (CTS) tool to guide investigators on how to best incorporate quantitative magnetic resonance (qMR) imaging and spectroscopy biomarkers in clinical trials. The model-based CTS tool will help drug developers to optimize their clinical trial design to detect a therapeutic effect as efficiently as possible, reducing clinical trial time, expense, and participant burden. This project takes advantage of the rich ImagingDMD data set, and will be the first to link the longitudinal changes of qMR biomarkers and physical function measures using a non-linear mixed effects modeling approach, enabling assessment of inter-individual and intra-individual variabilities. In Aim 1, we will quantify how the variability of the longitudinal changes of four functional endpoints are explained by qMR biomarker values measured on eight leg muscles at screening visits. In Aim 2, we will identify subgroups of the population that differ in disease progression through a covariate analysis. In Aim 3, we will develop a DMD disease progression model-based CTS tool. The CTS tool will accelerate drug discovery and development by allowing users to simulate possible scenarios of a clinical trial prior to its actual execution. It will inform trial design by providing insights into key trial design aspects, including choice of muscles/biomarkers, inclusion/exclusion criteria, optimal number of participants, trial duration, and frequency of observations. Covariates identified in Aims 1 and 2, which are common screening criteria in clinical trials in DMD, will be incorporated in the CTS tool. The interdisciplinary and model-based approach proposed in this study will allow us to leverage existing clinical research data to markedly improve trial design in DMD. The CTS tool will be open and publicly available, and it will be disseminated as a web-based user-friendly graphical user interface in order to facilitate easy access, broad use, and high impact.