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Viribus Unitis: Drug Combinations as a Treatment Against COVID-19

preprint
submitted on 17.04.2020 and posted on 20.04.2020 by Eugene Muratov, Alexey Zakharov

The opportunities that may be provided by synergistic antiviral action of drugs for battling SARS-CoV2 are currently underestimated. Modern AI technologies realized as text, data, and knowledge mining and analytics tools provide the researchers with unprecedented opportunities for “smart” design of drug combinations with synergistic antiviral activities. The goal of this study is to emphasize the combination therapy as a potential treatment against COVID-19 and to utilize the combination of modern machine learning and AI technologies with our expertise to select the most promising drug combinations with further experimental validation. To the best of our knowledge, we are the first who applied the combination of data, text, and knowledge mining and modeling towards identification of drug combinations against SARS-CoV2. As a result, we have identified 281 combinations of 38 drugs that may serve as potential treatment for COVID-19. Among them, we selected twenty binary combinations that were submitted to experimental testing and twenty treble drug combinations that will be submitted for experimental testing as soon as necessary infrastructure will be developed. We hope that this study will promote the combination therapy as an efficient treatment for COVID-19.

Funding

Biomedical Data Translator Initiative of National Center for Advancing Translational Sciences, National Institute of Health (grants OT3TR002020, OT2R002514) and National Institute of Health (grant 1U01CA207160).

History

Email Address of Submitting Author

murik@email.unc.edu

Institution

University of North Carolina, Chapel Hill

Country

United States

ORCID For Submitting Author

0000-0003-4616-7036

Declaration of Conflict of Interest

No conflict of interest

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