Evaluating Genetic Modifiers of Duchenne Muscular Dystrophy Disease Progression Using Modeling and MRI

Prognostic significance of ACTN3 genotype in Duchenne muscular dystrophy: Findings from an Argentine patient cohort

A wide phenotypic spectrum exists among DMD patients, with genetic modifiers seen as a putative cause of this variability. The main aim was to evaluate the effect of 4 genetic modifiers and the location of DMD variants on disease severity in a DMD Argentine cohort. A secondary objective was to provide a summary of the current state of knowledge and association of the tested loci with DMD's phenotype. Two groups of patients with extreme phenotypes (Severe/Mild) were defined based on the age at loss of ambulation. SNVs in SPP1, LTBP4, CD40, and ACTN3 were genotyped, and their distribution was compared between groups using Chi-square or Fisher exact tests. Concurrent effects with glucocorticoids treatment, DMD mutation location (proximal/distal) and the other loci were evaluated by multivariate logistic regression. Additionally, we performed a systematic literature review to summarize and interpret the impact of modifiers on various DMD traits. ACTN3-rs1815739 was the only modifier loci of DMD progression in our cohort. A concurrent damaging effect between DMD mutation and ACTN3 was detected, identifying a possible interaction between distal variants and ACTN3 TT-genotype that need to be validated in a larger cohort. The systematic review showed agreement in the results when significant differences were reported. The employment of extreme DMD phenotypic groups was an innovative approach for identifying risk loci for disease severity. The interaction between DMD mutation location and ACTN3, if confirmed, could help to avoid confounding elements in assembling study cohorts for clinical trials. Finally, this report's major highlight is being the first study conducted on an Argentine and Latin-American population.

Five multivariate Duchenne muscular dystrophy progression models bridging six-minute walk distance and MRI relaxometry of leg muscles

The study aimed to provide quantitative information on the utilization of MRI transverse relaxation time constant (MRI-T2) 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-T2. 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-T2 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-T2 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-T2. Sigmoid Imax and Emax models best captured the profiles of 6MWD and MRI-T2 over age. Steroid use, baseline 6MWD, and baseline MRI-T2 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-T2 is half of its maximum increase varied depending on the type of muscle. The models connecting 6MWD and MRI-T2 successfully quantified how individual characteristics alter disease trajectories. The models demonstrate a plausible correlation between 6MWD and MRI-T2, supporting the use of MRI-T2. The developed models will guide drug developers in using the MRI-T2 to most efficient use in DMD clinical trials.

A model-informed clinical trial simulation tool with a graphical user interface for Duchenne muscular dystrophy

Quantitative model-based clinical trial simulation tools play a critical role in informing study designs through simulation before actual execution. These tools help drug developers explore various trial scenarios in silico to select a clinical trial design to detect therapeutic effects more efficiently, therefore reducing time, expense, and participants' burden. To increase the usability of the tools, user-friendly and interactive platforms should be developed to navigate various simulation scenarios. However, developing such tools challenges researchers, requiring expertise in modeling and interface development. This tutorial aims to address this gap by guiding developers in creating tailored R Shiny apps, using an example of a model-based clinical trial simulation tool that we developed for Duchenne muscular dystrophy (DMD). In this tutorial, the structural framework, essential controllers, and visualization techniques for analysis are described, along with key code examples such as criteria selection and power calculation. A virtual population was created using a machine learning algorithm to enlarge the available sample size to simulate clinical trial scenarios in the presented tool. In addition, external validation of the simulated outputs was conducted using a placebo arm of a recently published DMD trial. This tutorial will be particularly useful for developing clinical trial simulation tools based on DMD progression models for other end points and biomarkers. The presented strategies can also be applied to other diseases.

Draft Guidance for Industry Duchenne Muscular Dystrophy, Becker Muscular Dystrophy, and Related Dystrophinopathies - Developing Potential Treatments for the Entire Spectrum of Disease

Background

Duchenne muscular dystrophy (DMD) and related dystrophinopathies are neuromuscular conditions with great unmet medical needs that require the development of effective medical treatments.

Objective

To aid sponsors in clinical development of drugs and therapeutic biological products for treating DMD across the disease spectrum by integrating advancements, patient registries, natural history studies, and more into a comprehensive guidance.

Methods

This guidance emerged from collaboration between the FDA, the Duchenne community, and industry stakeholders. It entailed a structured approach, involving multiple committees and boards. From its inception in 2014, the guidance underwent revisions incorporating insights from gene therapy studies, cardiac function research, and innovative clinical trial designs.

Results

The guidance provides a deeper understanding of DMD and its variants, focusing on patient engagement, diagnostic criteria, natural history, biomarkers, and clinical trials. It underscores patient-focused drug development, the significance of dystrophin as a biomarker, and the pivotal role of magnetic resonance imaging in assessing disease progression. Additionally, the guidance addresses cardiomyopathy's prominence in DMD and the burgeoning field of gene therapy.

Conclusions

The updated guidance offers a comprehensive understanding of DMD, emphasizing patient-centric approaches, innovative trial designs, and the importance of biomarkers. The focus on cardiomyopathy and gene therapy signifies the evolving realm of DMD research. It acts as a crucial roadmap for sponsors, potentially leading to improved treatments for DMD.

Multivariate modeling of magnetic resonance biomarkers and clinical outcome measures for Duchenne muscular dystrophy clinical trials

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.

Is it time for genetic modifiers to predict prognosis in Duchenne muscular dystrophy?

Patients with Duchenne muscular dystrophy (DMD) show clinically relevant phenotypic variability, despite sharing the same primary biochemical defect (dystrophin deficiency). Factors contributing to this clinical variability include allelic heterogeneity (specific DMD mutations), genetic modifiers (trans-acting genetic polymorphisms) and variations in clinical care. Recently, a series of genetic modifiers have been identified, mostly involving genes and/or proteins that regulate inflammation and fibrosis - processes increasingly recognized as being causally linked with physical disability. This article reviews genetic modifier studies in DMD to date and discusses the effect of genetic modifiers on predicting disease trajectories (prognosis), clinical trial design and interpretation (inclusion of genotype-stratified subgroup analyses) and therapeutic approaches. The genetic modifiers identified to date underscore the importance of progressive fibrosis, downstream of dystrophin deficiency, in driving the disease process. As such, genetic modifiers have shown the importance of therapies aimed at slowing this fibrotic process and might point to key drug targets.