Rare Disease Translational Research

National Center For Advancing Translational Sciences

Investigators

Abstract

RDTR has applied their advanced translational research workflow to provide support both for the broad landscape of rare diseases as well as specific conditions. For glioblastoma, RDTR successfully identified two promising drug candidates via a computational drug candidate prediction model. Work continues on this effort to include virtual screening of NCATS libraries, testing candidate drugs with glioblastoma organoids and continue to iterate on the predictive framework. Computational drug repurposing as well as computational biomarker discovery methods have been applied to Neuronal Ceroid Lipofuscinosis 3 (CLN3), Prader-Willi Syndrome, Niemann-Pick Disease -Type C1(NPC1), Sclerodema, and Pneumocystosis to date. In the broad landscape of rare diseases, rare disease clustering has resulted in basket trial design. Additionally, RDTR leverages NCATS data and tools, such as Tox21, Pharos, and Translator for novel target predictions. With an expertise in research data pertinent to rare diseases and using advanced technologies such as natural language processing and machine learning, RDTR has developed and shared rare disease knowledge graphs as well as the Rare Disease Alert System, designed to enhance the early identification and tracking of rare disease-related research and evidence. By systematically flagging non-interventional natural history studies, drug repurposing opportunities, and novel disease insights, RDAS supports research acceleration, clinical decision-making, and strategic funding in the rare disease ecosystem.