| Literature DB >> 31127458 |
Daniela J Conrado1, Jane Larkindale2, Alexander Berg2, Micki Hill3, Jackson Burton2, Keith R Abrams3, Richard T Abresch2, Abby Bronson4, Douglass Chapman5, Michael Crowther3, Tina Duong6, Heather Gordish-Dressman7, Lutz Harnisch5, Erik Henricson8, Sarah Kim9, Craig M McDonald8, Stephan Schmidt9, Camille Vong5, Xiaoxing Wang5, Brenda L Wong10, Florence Yong5, Klaus Romero2.
Abstract
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.Entities:
Keywords: Drug development tools; Duchenne muscular dystrophy consortium (D-RSC); Model-informed drug development; Rare diseases; Regulatory endorsement
Mesh:
Year: 2019 PMID: 31127458 DOI: 10.1007/s10928-019-09642-7
Source DB: PubMed Journal: J Pharmacokinet Pharmacodyn ISSN: 1567-567X Impact factor: 2.745