Making Sense of 6MWT Variability: Developing a Disease Progression Model for Duchenne Muscular Dystrophy
Understanding disease progression is important for designing effective clinical trial for investigational drugs. Without an understanding of disease progression, it is very difficult to measure drug effects. Developing orphan drugs for rare diseases carries the additional limitation of recruitment challenges due to the limited number of patients.
Duchenne Muscular Dystrophy (DMD) is a rare, sex-linked genetic disease that causes progressive weakness and loss of muscle mass. Patients generally succumb to the disease in their early 20s.
While the standard of care (SOC) corticosteroids offer some benefit, there is an acute need for new drugs that can delay/prevent muscle function loss, improve quality of life, and reduce mortality. The 6-minute walk test (6MWT) is a clinical endpoint used to assess motor function in ambulatory DMD patients. It measures how far patients can walk on a flat, indoor course in 6 minutes.
The 6MWT is highly variable due to improvement during childhood development followed by disease-associated declines. Because of 6MWT variability, it is difficult to design a trial that can detect a motor function difference between the investigational drug and placebo or SOC.
Watch this webinar with Drs. Lora Hamuro and Joga Gobburu to learn how they developed a natural history progression model for DMD using the 6MWT. By watching this webinar, you will learn the following:
- How they leveraged literature data to develop and evaluate a population model to characterize the time course of 6MWT performance
- How they used this model to perform simulations that predicted the trends in improvement and decline in the 6MWT
- How this model can be used to evaluate patient prognostic factors that contribute to disease progression and design more effective trials for this rare disease
By having a disease progression model that describes the changes in the 6MWT trajectory, drug developers can use it to supplement placebo data and improve the likelihood of detecting potential drug efficacy.