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

By Feixiong Cheng, R. Desai, Diane E. Handy, Ruisheng Wang, Sebastian Schneeweiss, Albert-László Barabási, Joseph Loscalzo. Nature Communications, 2018

487 citations recorded by the source corpus.

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. Repurposing approved drugs could accelerate treatment options for various diseases. Here, the authors use network proximity of disease gene products and drug targets in the human protein interactome to identify drug-disease associations for cardiovascular disease, and validate these using longitudinal healthcare data.

Subfields: Biological Psychiatry, Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/38b2eee3c5c6c39e621afe1e49636c1c9af3e0dc

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In Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

Network medicine framework for identifying drug-repurposing opportunities for COVID-19 (2020, methodology): “(1) First, it uses the current aggregated ranking R to update the importance weights K r for each ranked list.”