DOI: 10.25934/00005456
title: Validation of a Model-based Clinical Trial Simulation Tool to Optimize Clinical Trial Design of Studies to Investigate Efficacy of Potential Therapies for Duchenne Muscular Dystrophy

description: Recent advances in research for DMD have resulted in a robust pipeline of drugs in development to treat the disease, along with the first drug approvals. Unfortunately, only one recent drug trial has showed a statistically significant change in its primary outcome, causing discussion in the field as to whether drugs recently tested were effective or not. These controversies could be resolved through a comprehensive understanding of the longitudinal disease dynamics across appropriate measures, which could also be used to optimize future clinical trial design. Optimized trial protocols that convincingly determine if a therapy works or not are needed. However, developing robust clinical trials in DMD is challenging due to several factors:
•	DMD is a rare disease, so the population of individuals available to enter trials is small, confounded further by the number of concurrent trials being run.
•	DMD is a progressive disease, such that endpoints that are appropriate at some disease stages (e.g. measures of ambulation) may not be adequate or measurable at other disease stages or across the disease spectrum. No endpoint has been developed that covers the entire disease continuum. This further limits the population available for some trials, as well as making it difficult to ascertain whether a drug is effective at all stages of disease.
•	DMD affects children, who are still growing and developing. Thus, in early stages of disease, individuals may gain strength and function, while in the longer term the disease will result in loss of that function. Loss of function is also non-linear due to the fact that most functional tests are affected by the motivation of the subject on a given day. Furthermore, events such as leg fractures (which are common in DMD boys) cause a temporary loss of a function that may be regained when the injury heals.
•	DMD progression is variable, with different individuals progressing at different rates, although all individuals eventually lose function in a predictable order of events.

Thus, the need for clinical trial design optimization tools is clear and patent, especially since therapies are greatly needed for this devastating disease.
Both FDA and the European Medicines Authority (EMA) have stated on multiple occasions the need for an improved understanding of disease progression in rare diseases to run effective trials. A recent FDA guidance for drug development in rare pediatric diseases emphasizes the need for modeling and understanding of disease progression to accelerate drug development tools for such diseases (1). Model-informed drug discovery and development (MID3) can inform: (a) understanding of disease-related targets; (b) selection of dose, schedule and regimens; (c) stage-gate decisions (d) optimization of study design; (e) individual selection; and (f) bridging studies in special populations. With this in mind, the D-RSC team proposes to develop models that will help to optimize study design and individual selection and may also prove valuable to inform bridging studies between different populations of individuals.
The rare pediatric disease drug development guidance states that “For drug development programs in pediatric rare diseases, it may be necessary to develop, validate, and employ age-specific endpoints”. Here, the D-RSC team proposes to model how individuals progress through a series of disease-relevant disease stage-specific measures in a predictable manner, to optimize trial design and endpoint selection. Thus, this project aims to address modeling of disease progression through a series of stage-specific endpoints to inform clinical trial design for DMD, developing models that describe disease progression in detail, both to inform inclusion criteria and to help select from appropriate age/function specific endpoints for new trials.
The Duchenne Regulatory Science Consortium (D-RSC) is developing models based on extensive clinical data from natural history studies and past clinical trial control arms that will help clinical trialists select smaller groups of individuals expected to have a defined disease course, and to pass clinically relevant milestones in a predictable timeframe. This will allow sponsors to run more efficient clinical trials that give definitive (statistically significant) results in shorter periods of time if the proposed therapeutic is effective.  We have developed such a tool and are in the process of getting it endorsed by the regulators (Federal Drug Administration, FDA, and European Medicines Authority, EMA) so that it can be used to inform future trial development in a way that will be accepted. However, EMA has asked that we validate the underlying models in an independent dataset or datasets.  The requested studies would cover at least some of the data needed for such validation work.  If such quantitative drug development tools are endorsed by the regulators this would provide the field with a valuable resource to accelerate drug development for Duchenne muscular dystrophy.