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

Mengran Wang, Johanna B Withers, Piero Ricchiuto, Ivan Voitalov, Michael McAnally, Helia N Sanchez, Alif Saleh, Viatcheslav R Akmaev, View ORCID ProfileSusan Dina Ghiassian  Correspondence email
Mengran Wang
Scipher Medicine Corporation, Waltham, MA, USA
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Johanna B Withers
Scipher Medicine Corporation, Waltham, MA, USA
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Piero Ricchiuto
Scipher Medicine Corporation, Waltham, MA, USA
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Ivan Voitalov
Scipher Medicine Corporation, Waltham, MA, USA
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Michael McAnally
Scipher Medicine Corporation, Waltham, MA, USA
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Helia N Sanchez
Scipher Medicine Corporation, Waltham, MA, USA
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Alif Saleh
Scipher Medicine Corporation, Waltham, MA, USA
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Viatcheslav R Akmaev
Scipher Medicine Corporation, Waltham, MA, USA
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Susan Dina Ghiassian
Scipher Medicine Corporation, Waltham, MA, USA
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  • ORCID record for Susan Dina Ghiassian
  • For correspondence: dina.ghiassian@scipher.com
Published 16 February 2021. DOI: 10.26508/lsa.202000904
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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.

  • Received September 8, 2020.
  • Revision received February 4, 2021.
  • Accepted February 5, 2021.
  • © 2021 Wang et al.
Creative Commons logoCreative Commons logohttps://creativecommons.org/licenses/by/4.0/

This article is available under a Creative Commons License (Attribution 4.0 International, as described at https://creativecommons.org/licenses/by/4.0/).

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Systems-based antiviral identification
Mengran Wang, Johanna B Withers, Piero Ricchiuto, Ivan Voitalov, Michael McAnally, Helia N Sanchez, Alif Saleh, Viatcheslav R Akmaev, Susan Dina Ghiassian
Life Science Alliance Feb 2021, 4 (5) e202000904; DOI: 10.26508/lsa.202000904

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Systems-based antiviral identification
Mengran Wang, Johanna B Withers, Piero Ricchiuto, Ivan Voitalov, Michael McAnally, Helia N Sanchez, Alif Saleh, Viatcheslav R Akmaev, Susan Dina Ghiassian
Life Science Alliance Feb 2021, 4 (5) e202000904; DOI: 10.26508/lsa.202000904
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Volume 4, No. 5
May 2021
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