Network-based approach to prediction and population-based validation of in silico drug repurposing

TLDR

The authors identified hundreds of new drug‑disease associations for over 900 FDA‑approved drugs by quantifying network proximity between disease genes and drug targets in the human protein–protein interactome. The study aims to test the causal relationships of four network‑predicted drug‑disease associations using large healthcare databases. The authors applied state‑of‑the‑art pharmacoepidemiologic analyses to data from more than 220 million patients. Propensity‑score matched analyses validated two of four predictions: carbamazepine increased coronary artery disease risk (HR 1.56, 95 % CI 1.12–2.18) while hydroxychloroquine decreased risk (HR 0.76, 95 % CI 0.59–.

Abstract

Abstract Here we identify hundreds of new drug-disease associations for over 900 FDA-approved drugs by quantifying the network proximity of disease genes and drug targets in the human (protein–protein) interactome. We select four network-predicted associations to test their causal relationship using large healthcare databases with over 220 million patients and state-of-the-art pharmacoepidemiologic analyses. Using propensity score matching, two of four network-based predictions are validated in patient-level data: carbamazepine is associated with an increased risk of coronary artery disease (CAD) [hazard ratio (HR) 1.56, 95% confidence interval (CI) 1.12–2.18], and hydroxychloroquine is associated with a decreased risk of CAD (HR 0.76, 95% CI 0.59–0.97). In vitro experiments show that hydroxychloroquine attenuates pro-inflammatory cytokine-mediated activation in human aortic endothelial cells, supporting mechanistically its potential beneficial effect in CAD. In summary, we demonstrate that a unique integration of protein-protein interaction network proximity and large-scale patient-level longitudinal data complemented by mechanistic in vitro studies can facilitate drug repurposing.