Discovery of Synergistic and Antagonistic Drug Combinations against SARS-CoV-2 In Vitro

  1. Tesia Bobrowski
  2. Lu Chen
  3. Richard T. Eastman
  4. Zina Itkin
  5. Paul Shinn
  6. Catherine Chen
  7. Hui Guo
  8. Wei Zheng
  9. Sam Michael
  10. Anton Simeonov
  11. Matthew D. Hall
  12. Alexey V. Zakharov
  13. Eugene N. Muratov

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Abstract

COVID-19 is undoubtedly the most impactful viral disease of the current century, afflicting millions worldwide. As yet, there is not an approved vaccine, as well as limited options from existing drugs for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro . Overall, we identified 16 synergistic and 8 antagonistic combinations, 4 of which were both synergistic and antagonistic in a dose-dependent manner. Among the 16 synergistic cases, combinations of nitazoxanide with three other compounds (remdesivir, amodiaquine and umifenovir) were the most notable, all exhibiting significant synergy against SARS-CoV-2. The combination of nitazoxanide, an FDA-approved drug, and remdesivir, FDA emergency use authorization for the treatment of COVID-19, demonstrate a strong synergistic interaction. Notably, the combination of remdesivir and hydroxychloroquine demonstrated strong antagonism. Overall, our results emphasize the importance of both drug repurposing and preclinical testing of drug combinations for potential therapeutic use against SARS-CoV-2 infections.

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    SciScore for 10.1101/2020.06.29.178889: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Vero E6 in media and dispensed into assay plates at 25 μL/well for a final cell density of 4000 cells/well.
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    These shared MeSH terms, depicting proteins, chemicals, etc. further allow us to hypothesize on how these two compounds may be connected, namely via shared biochemical pathways.
    MeSH
    suggested: (MeSH, RRID:SCR_004750)
    Chemotext is a publicly-available web server that mines the published literature in PubMed in the form of Medical Subject Headings (MeSH) terms.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.06.29.178889 on bioRxiv