RT Journal Article
SR Electronic
T1 A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study
JF Life Science Alliance
JO Life Sci. Alliance
FD Life Science Alliance LLC
SP e202000904
DO 10.26508/lsa.202000904
VO 4
IS 5
A1 Wang, Mengran
A1 Withers, Johanna B
A1 Ricchiuto, Piero
A1 Voitalov, Ivan
A1 McAnally, Michael
A1 Sanchez, Helia N
A1 Saleh, Alif
A1 Akmaev, Viatcheslav R
A1 Ghiassian, Susan Dina
YR 2021
UL http://www.life-science-alliance.org/content/4/5/e202000904.abstract
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.