Lead Investigator: Terina Martinez, Critical Path Institute
Title of Proposed Research: 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
Vivli Data Request: 5456
Funding Source: None
Potential Conflicts of Interest: 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
Summary of the Proposed Research:
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.
Statistical Analysis Plan:
We have developed mixed-effects models of each of the six endpoints independently and are currently working on development of joint models where possible. These models will form the backbone of the clinical trial simulation platform. We are looking for additional data to use for independent validation of the models. The models will be validated in each dataset requested, as per the included covariates. That is, not all studies include all endpoints, but models of each endpoint will be validated in each appropriate dataset.
The development process of the multivariate or univariate DMD progression model(s) is initiated through identification of mathematical functions that describe changes over years of age for each of the dependent measures, followed by the incorporation of two levels of random effects: between-individual variability (BIV) and residual variability (RV). If the models perform reasonably well in simulation-based diagnostics, the models will be attempted to be brought together into a multivariate model. Candidate covariates will then be incorporated into the multivariate or univariate model(s), which will be subjected to simulation-based diagnostics stratified by relevant covariates. Once reasonable model performance is achieved, incorporation of a third level of random effects (between-study variability, BSV) will be attempted. Simulation-based diagnostics will be repeated, followed by model validation to yield the final DMD progression model(s). As an alternative, the attempt to bring the models together may be performed after the inclusion of the covariates.
A Prospective Natural History Study of Progression of Physical Impairment, Activity Limitation and Quality of Life in Duchenne Muscular Dystrophy.
Study ID: NCT01753804
Sponsor ID: PRO-DMD-01
A Phase III, Randomized, Double Blind, Placebo-controlled Clinical Study to Assess the Efficacy and Safety of GSK2402968 in Subjects With Duchenne Muscular Dystrophy
Study ID: NCT01254019
Sponsor ID: 114044
A Phase II, Double Blind, Exploratory, Parallel-group, Placebocontrolled Clinical Study to Assess Two Dosing Regimens of GSK2402968 for Efficacy, Safety, Tolerability and Pharmacokinetics in Ambulant Subjects With Duchenne Muscular Dystrophy
Study ID: NCT01153932
Sponsor ID: 114117
An Exploratory Study to Assess Two Doses of GSK2402968 in the Treatment of Ambulant Boys With Duchenne Muscular Dystrophy
Study ID: NCT01462292
Sponsor ID: 114876
- Ramona Belfiore-Oshan, Varun Aggarwal, Rhoda Muse, Sarah Kim, Stephan Schmidt, Juan Francisco Morales, Karthik Linganeni, Sudhir Sivakumaran, Diane Corey, Klaus Romero, Terina Martinez. Acceleration of clinical trial design in Duchenne Muscular Dystrophy building a model-based Clinical Trial Simulation Tool. The Muscular Dystrophy Association (MDA) Clinical & Scientific conference, March 13-16, 2022 (Abstract). https://mdaconference.org/node/1556
- Modeling informative drop out in a disease progression model of Duchenne Muscular Dystrophy. Rhoda Muse, Jane Larkindale, Sarah Kim, Stephan Schmidt, Juan Francisco Morales, Sudhir Sivakumaran, Ramona Belfiore-Oshan, Varun Aggarwal, Klaus Romero, Jackson Burton and Diane Corey. Statistics and PMx e.g. MBMA, Bayesian application/method, trial design, optimal design, machine learning, data mining. ACoP12 (2021) STPM-198 www.go-acop.org/?abstract=198
- Belfiore-Oshan, V. Aggarwal, S. Sivakumaran, D. Corey, C. Ollivier, K. Romero, K. Vandenborne, S. Kim, J. Morales, K. Lingineni, T. Martinez. VP.85. C-Path’s Duchenne Regulatory Science Consortium: Accelerating drug development for Duchenne muscular dystrophy. Neuromuscular Disorders, Volume 32, Supplement 1. 2022. Page S128. doi: 10.1016/j.nmd.2022.07.359
- Varun Aggarwal, Rhoda Muse, Sarah Kim, Jackson Burton, Diane Corey, Lauren Quinlan, Klaus Romero, Ramona Belfiore-Oshan, Terina Martinez. Multivariate Joint Models to Predict Clinically Meaningful Decline in Duchenne Muscular Dystrophy as measured by NSAA Using Timed Function Test Trajectories. ACoP13 (2022) STPM-328.