  Drug repurposing is an effective strategy to identify new uses for existing
drugs, providing the quickest possible transition from bench to bedside.
Existing methods for drug repurposing that mainly focus on pre-clinical
information may exist translational issues when applied to human beings. Real
world data (RWD), such as electronic health records and insurance claims,
provide information on large cohorts of users for many drugs. Here we present
an efficient and easily-customized framework for generating and testing
multiple candidates for drug repurposing using a retrospective analysis of
RWDs. Building upon well-established causal inference and deep learning
methods, our framework emulates randomized clinical trials for drugs present in
a large-scale medical claims database. We demonstrate our framework in a case
study of coronary artery disease (CAD) by evaluating the effect of 55
repurposing drug candidates on various disease outcomes. We achieve 6 drug
candidates that significantly improve the CAD outcomes but not have been
indicated for treating CAD, paving the way for drug repurposing.
