PT  - JOURNAL ARTICLE
AU  - Mengran Wang
AU  - Johanna B Withers
AU  - Piero Ricchiuto
AU  - Ivan Voitalov
AU  - Michael McAnally
AU  - Helia N Sanchez
AU  - Alif Saleh
AU  - Viatcheslav R Akmaev
AU  - Susan Dina Ghiassian
TI  - A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study
AID  - 10.26508/lsa.202000904
DP  - 2021 May 01
TA  - Life Science Alliance
PG  - e202000904
VI  - 4
IP  - 5
4099  - https://www.life-science-alliance.org/content/4/5/e202000904.short
4100  - https://www.life-science-alliance.org/content/4/5/e202000904.full
SO  - Life Sci. Alliance2021 May 01; 4
AB  - 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.