Network Medicine

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


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.


More publications
Deisy Morselli Gysi, Ítalo do Valle, Marinka Zitnik, Asher Ameli, Xiao Gan, Onur Varol, Susan Dina Ghiassian, J. J. Patten, Robert A. Davey, Joseph Loscalzo, and Albert-László Barabási

PNAS May 11, 2021 118 (19) e2025581118

Italo F. do Valle, Harvey G. Roweth, Michael W. Malloy, Sofia Moco, Denis Barron, Elisabeth Battinelli, Joseph Loscalzo & Albert-László Barabási

Nature Food volume 2, pages143–155(2021)

Soodabeh Milanlouei, Giulia Menichetti, Yanping Li, Joseph Loscalzo, Walter C. Willett & Albert-László Barabási

Nature Communications volume 11, Article number: 6074 (2020)