Loading...
Network-based approach to prediction and population-based validation of in silico drug repurposing
Cheng, Feixiong Desai, Rishi J. Handy, Diane E. Wang, Ruisheng Schneeweiss, Sebastian Barabási, Albert László Loscalzo, Joseph
Cheng, Feixiong
Desai, Rishi J.
Handy, Diane E.
Wang, Ruisheng
Schneeweiss, Sebastian
Barabási, Albert László
Loscalzo, Joseph
Title / Series / Name
Nature Communications
Publication Volume
9
Publication Issue
1
Pages
Editors
Keywords
General Chemistry
General Biochemistry,Genetics and Molecular Biology
Multidisciplinary
General Physics and Astronomy
SDG 3 - Good Health and Well-being
General Biochemistry,Genetics and Molecular Biology
Multidisciplinary
General Physics and Astronomy
SDG 3 - Good Health and Well-being
Files
Loading...
Barabasi_Laszlo_2018.pdf
Adobe PDF, 1.01 MB
URI
https://hdl.handle.net/20.500.14018/28886
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.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2018-07-12
Language
ISBN
Identifiers
10.1038/s41467-018-05116-5