Cheng, FeixiongDesai, Rishi J.Handy, Diane E.Wang, RuishengSchneeweiss, SebastianBarabási, Albert LászlóLoscalzo, Joseph2025-04-082025-04-082018-12-012041-172310.1038/s41467-018-05116-5https://hdl.handle.net/20.500.14018/27161Here 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.engcc-byhttps://creativecommons.org/licenses/by/4.0/General ChemistryGeneral Biochemistry,Genetics and Molecular BiologyGeneral Physics and AstronomySDG 3 - Good Health and Well-beinghttp://www.scopus.com/inward/record.url?scp=85050009917&partnerID=8YFLogxKCheng, F, Desai, R J, Handy, D E, Wang, R, Schneeweiss, S, Barabási, A L & Loscalzo, J 2018, 'Network-based approach to prediction and population-based validation of in silico drug repurposing', Nature Communications, vol. 9, no. 1, 2691. https://doi.org/10.1038/s41467-018-05116-5