PII-179 - A COMPUTATIONAL TOOL TO OPTIMIZE CLINICAL TRIAL DESIGN FOR DUCHENNE MUSCULAR DYSTROPHY: A PRACTICAL GUIDE AND CASE STUDIES.

J. Wilk¹, V. Aggarwal², M. Pauley², D. Corey², D. Conrado², K. Lingineni¹, J. Morales¹, D. Yoon¹, J. Burton², J. Larkindale², S. Ma², C. Hovinga², T. Martinez², K. Romero², R. Belfiore-Oshan², S. Kim³; ¹University of Florida, ²Critical Path Institute, ³Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida.

Background: Duchenne muscular dystrophy (DMD), a rare pediatric disease, presents numerous challenges when designing clinical trials, mainly due to scarcity of available trial participants. The DMD Clinical Trial Simulation (CTS) tool was created to inform the design of DMD clinical trials through the use of a non-linear mixed-effects modeling and simulation approach. The tool allows researchers to optimize the selection of the key design features for five functional measure assessments commonly used as endpoints in DMD clinical trials.

Methods: A full walkthrough of the DMD CTS tool was conducted, demonstrating the tool and its functionality while providing an easy-to-follow guide for users to reference. To replicate user experience with the tool for real-world applications, two case study scenarios were developed. Both used previous and ongoing clinical trial data to optimize parameter selection, with the goal of retaining adequate power (~0.80) while reducing patient population size and trial duration, respectively. Power curves were created for both scenarios.

Results: Case Study 1, a Phase 3 trial using the North Star Ambulatory Assessment (NSAA) as the primary functional measure, produced an initial power output of 0.92 with the given set of parameter selections with a population size of 150 patients. Population size was able to be reduced to 100 patients while still producing significant power (0.82), with the other parameters remaining unchanged. Case Study 2, a Phase 3 trial using Forced Vital Capacity (FVC) as the primary functional measure, produced a power of 0.99 during a 24-month trial period. Testing showed that the trial period could be reduced to 13 months and maintain significant power output (~0.81). Figure 1 visualizes the power curves from the two case studies.

Conclusion: This tutorial demonstrates realistic examples of how the model-based simulation tool can help optimize clinical trial design without the risk of decreasing statistical significance, i.e., power and significant level. The DMD CTS tool presents users with a unique and powerful approach to optimizing clinical trial design parameter selection, allowing researchers to mitigate the risk of designing trials that may be longer, larger, or more inclusive/exclusive than necessary.