# Optimizing Muscular Dystrophy Clinical Trial Designs using Modeling and Simulation

> **NIH NIH R21** · UNIVERSITY OF FLORIDA · 2022 · $223,562

## Abstract

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.

## Key facts

- **NIH application ID:** 10470246
- **Project number:** 5R21TR004006-02
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Sarah Kim
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $223,562
- **Award type:** 5
- **Project period:** 2021-09-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10470246

## Citation

> US National Institutes of Health, RePORTER application 10470246, Optimizing Muscular Dystrophy Clinical Trial Designs using Modeling and Simulation (5R21TR004006-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10470246. Licensed CC0.

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