Systematically Prioritizing Targets in Genome-Based Drug Repurposing.

Drug repurposing is the use of currently-approved drugs to treat diseases separate and distinct from their originally-approved indications. With the rise of precision medicine and quantitative systems pharmacology, there is heightened emphasis on the application of genomic data to guide drug development. Herein, we propose an algorithmic approach to the selection of drug repurposing candidates using relevant drug profiles from DrugBank, a publicly available database of pharmacological agents, and genomic data from BioVU, a largescale DNA repository linked to de-identified longitudinal electronic health record information based at Vanderbilt University Medical Center. Specifically, we propose a method of repurposing candidate prioritization through the integration of structured data from DrugBank, such as marketing start date, number of targets with known mechanism of action, target names, and drug class, with quality control thresholds for the genomic data derived from the DNA samples housed within BioVU. Through the synergy of delineated "target-action pairs," along with target genetics, pharmacodynamics, and pharmacokinetics, we identify a new method of repurposability screening and candidate prioritization, which generates a select, manageable subset of approximately 250 drugs with unique mechanisms of action, target selectivity, and real-world repurposing potential from a total of nearly eleven thousand (11,000) agents.