A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study

Abstract

This study describes two complementary methods that use network-based and sequence similarity tools to identify drug repurposing opportunities predicted to modulate viral proteins. This approach could be rapidly adapted to new and emerging viruses. The first method built and studied a virus–host–physical interaction network; a three-layer multimodal network of drug target proteins, human protein–protein interactions, and viral–host protein–protein interactions. The second method evaluated sequence similarity between viral proteins and other proteins, visualized by constructing a virus–host–similarity interaction network. Methods were validated on the human immunodeficiency virus, hepatitis B, hepatitis C, and human papillomavirus, then deployed on SARS-CoV-2. Comparison of virus–host–physical interaction predictions to known antiviral drugs had AUCs of 0.69, 0.59, 0.78, and 0.67, respectively, reflecting that the scores are predictive of effective drugs. For SARS-CoV-2, 569 candidate drugs were predicted, of which 37 had been included in clinical trials for SARS-CoV-2 (AUC = 0.75, P-value 3.21 × 10−3). As further validation, top-ranked candidate antiviral drugs were analyzed for binding to protein targets in silico; binding scores generated by BindScope indicated a 70% success rate.

Network-based repurposing identifies anti-alarmins as drug candidates to control severe lung inflammation in COVID-19

“…Network-based drug repurposing relies on the hypothesis that the closer a target is to a group of disease related genes in the PPI network, the higher the chance of having a significant impact on the disease. Many approaches focus on the shortest path to determine proximity, with some variations in order to avoid hub protein bias [15,39]. The latter bias occurs from certain proteins that have an extremely high degree in the network and thereby cause a highly dense graph.…”