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

Feixiong Cheng, Rishi J. Desai, Diane E. Handy, Ruisheng Wang, Sebastian Schneeweiss, Albert-László Barabási and Joseph Loscalzo ()
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Feixiong Cheng: Northeastern University
Rishi J. Desai: Harvard Medical School
Diane E. Handy: Harvard Medical School
Ruisheng Wang: Harvard Medical School
Sebastian Schneeweiss: Harvard Medical School
Albert-László Barabási: Northeastern University
Joseph Loscalzo: Harvard Medical School

Nature Communications, 2018, vol. 9, issue 1, 1-12

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.

Date: 2018
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DOI: 10.1038/s41467-018-05116-5

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