2022 RDCA-DAP Workshop: Case Study 3, Duchenne Regulatory Science Consortia (D-RSC)

This video presents a case study from the Duchenne Regulatory Science Consortia (D-RSC) by the Critical Path Institute, focusing on efforts to accelerate therapeutic development for Duchenne Muscular Dystrophy (DMD). Speaker Terina Martinez highlights the significant challenges in DMD drug development, including high clinical trial failure rates, disease complexity, and difficulties in patient selection and endpoint measurement. To address these issues, D-RSC has developed a Clinical Trial Simulation (CTS) tool. This tool leverages a rich, integrated database of patient-level data from numerous clinical trials and natural history studies to train and validate quantitative disease progression models. The goal is to enable in silico evaluation and refinement of clinical trial designs, optimizing parameters like sample size and duration, and simulating drug effects. The tool is undergoing regulatory qualification with both the EMA and FDA, emphasizing its rigor and potential to improve trial efficiency, reduce participant burden, and stimulate further data sharing within the rare disease community.

Key Takeaways:

• Data-Driven Clinical Trial Optimization: The D-RSC's Clinical Trial Simulation (CTS) tool exemplifies how aggregating and analyzing extensive patient-level data from clinical trials and natural history studies can be leveraged to create quantitative disease progression models. This enables in silico evaluation and optimization of trial designs for rare diseases like DMDai.
• Regulatory Qualification of Novel Methodologies: The active pursuit of regulatory qualification for the CTS tool through both EMA's Qualification of Novel Methodologies and FDA's Fit for Purpose Initiative highlights the growing importance of formally validating advanced computational tools for use in drug development and decision-making. This directly
• Impact on Trial Efficiency and Data Sharing: Such simulation tools are crucial for improving clinical trial efficiency by optimizing sample size, trial duration, and patient selection, thereby reducing the burden on rare disease patient populations. Regulatory approval also acts as a strong catalyst for increased data sharing among stakeholders, underscoring the value of robust data integration and management.
• Addressing Rare Disease Challenges with Technology: The initiative directly tackles the unique complexities of rare disease drug development, including high failure rates, heterogeneity in disease progression, and the need for better clinical endpoints, by providing a data-driven approach to de-risk and accelerate therapeutic advancement.