Development of effective therapies for Duchenne Muscular Dystrophy (DMD) faces significant challenges, including the need for disease staging to optimize clinical trial design and de-risk drug development. Data sharing is pivotal for advancing a biological framework for staging DMD and developing regulatory-grade drug development tools (DDTs). To address these challenges, Critical Path’s (C-Path) Duchenne Regulatory Science Consortium (D-RSC) operates as a neutral pre-competitive partnership working with a diverse group of stakeholders, including industry, regulators, academia, and patient groups. D-RSC collaborates under the advisement of regulatory agencies worldwide. Current initiatives focus on developing a disease progression model for five individual endpoints, advancing biomarker research, and implementing staging to identify the biologically distinct phases of cardiac disease in DMD. These efforts are crucial for disease staging, optimizing patient stratification, and enhancing the predictive power of clinical trials. Progress includes the ongoing development of an integrated DMD patient-level database, which currently comprises 24 datasets and over 10,000 subjects, to support downstream DDT work. We secured EMA support and publicly launched a first of his kind DMD clinical trial simulation (CTS) platform, which is currently under review by the FDA. Collaborative data sharing initiatives have accelerated our comprehensive database that supports regulatory decision-making and therapeutic advancements. The power of data sharing from past studies and trials significantly advances regulatory acceptance of drug development tools, driving therapeutic advancement for DMD. By fostering a culture of collaboration and data sharing, we can overcome barriers to drug development in DMD and bring new, life-changing therapies to patients more quickly and efficiently. Continuation of data sharing by therapeutic development trials and observational studies will be crucial to continue moving these efforts forward. While individual data findings may be limited, together we can create a comprehensive view and a clear, strong package to support fit-for-purpose drug development tools.