Drug development for rare diseases is challenged by small populations and limited data. This makes development of clinical trial protocols difficult and contributes to the uncertainty around whether or not a potential therapy is efficacious. The use of data standards to aggregate data from multiple sources, and the use of such integrated databases to develop statistical models can inform protocol development and reduce the risks in developing new therapies. Achieving regulatory endorsement of such models through defined pathways at the US Food and Drug Administration and European Medicines Authority allows such tools to be used by the drug development community for defined contexts of use without further need for discussion of the underlying model(s). The Duchenne Regulatory Science Consortium (D-RSC) has brought together multiple stakeholders to develop a clinical trial simulation tool for Duchenne muscular dystrophy using such an approach. Here we describe the work of D-RSC as an example of how such an approach may be effective at reducing uncertainty in drug development for rare diseases, and thus bringing effective therapies to patients faster.

中文翻译：

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寻求药物开发工具的监管认可，以促进模型信息化药物开发在杜氏肌营养不良症中的应用。

少数人群和有限的数据挑战了针对罕见疾病的药物开发。这使临床试验方案的开发变得困难，并且导致围绕潜在疗法是否有效的不确定性。使用数据标准来汇总来自多个来源的数据，以及使用此类集成数据库来开发统计模型，可以为方案开发提供信息并降低开发新疗法的风险。通过美国食品药品监督管理局和欧洲药品管理局规定的途径获得此类模型的监管认可，可以使药物开发界将此类工具用于确定的使用环境，而无需进一步讨论基础模型。杜兴管理科学协会（D-RSC）已将多个利益相关者召集在一起，以使用这种方法开发针对杜兴肌营养不良症的临床试验模拟工具。在这里，我们以D-RSC的工作为例，说明这种方法如何有效减少罕见病药物开发的不确定性，从而更快地为患者带来有效的治疗方法。