When deep learning meets causal inference: a computational framework for drug repurposing from real-world data
This work addresses the challenge of translational issues in drug repurposing for medical applications by leveraging large-scale real-world data, though it is incremental as it builds on existing methods.
The authors tackled the problem of drug repurposing by developing a computational framework that combines causal inference and deep learning to analyze real-world data, achieving 6 significant drug candidates for coronary artery disease out of 55 tested.
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.