Human and Machine Intelligence Together Drive Drug Repurposing in Rare Diseases - Challa, Anup P.; Zaleski, Nicole M.; Jerome, Rebecca N.; Lavieri, Robert R.; Shirey-Rice, Jana K.; Barnado, April; Lindsell, Christopher J.; Aronoff, David M.; Crofford, Leslie J.; Harris, Raymond C.; Alp Ikizler, T.; Mayer, Ingrid A.; et al. (Frontiers in Genetics, 2021)
Repurposing is an increasingly attractive method within the field of drug development for its efficiency at identifying new therapeutic opportunities among approved drugs at greatly reduced cost and time of more traditional methods. Repurposing has generated significant interest in the realm of rare disease treatment as an innovative strategy for finding ways to manage these complex conditions. The selection of which agents should be tested in which conditions is currently informed by both human and machine discovery, yet the appropriate balance between this approaches, including the role of artificial intelligence (AI), remains a significant topic of discussion in drug discovery for rare diseases and other conditions. Our drug repurposing team at Vanderbilt University Medical Center synergizes machine learning techniques like phenome-wide association study (PheWAS)—a powerful regression method for generating hypotheses about new indications for an approved drug—with the knowledge and creativity of scientific, legal, and clinical domain experts. While our computational approaches generate drug repurposing hits with a high probability of success in a clinical trial, human knowledge remains essential for the hypothesis creation, interpretation, “go-no go” decisions with which machines continue to struggle. Here, we reflect on our experience synergizing AI and human knowledge towards realizable patient outcomes, providing case studies from our portfolio that inform how we balance human knowledge and machine intelligence for drug repurposing in rare disease.