Identification of Antiviral Drug Candidates against SARS-CoV-2 from FDA-Approved Drugs

  1. Sangeun Jeon
  2. Meehyun Ko
  3. Jihye Lee
  4. Inhee Choi
  5. Soo Young Byun
  6. Soonju Park
  7. David Shum
  8. Seungtaek Kim

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Abstract

Drug repositioning is the only feasible option to immediately address the COVID-19 global challenge. We screened a panel of 48 FDA-approved drugs against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which were preselected by an assay of SARS-CoV. We identified 24 potential antiviral drug candidates against SARS-CoV-2 infection. Some drug candidates showed very low 50% inhibitory concentrations (IC 50 s), and in particular, two FDA-approved drugs—niclosamide and ciclesonide—were notable in some respects.

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  1. Version published to 10.1128/aac.00819-20
  2. ScreenIT

    SciScore for 10.1101/2020.03.20.999730: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Anti-SARS-CoV-2 N protein antibody was purchased from Sino Biological Inc. (Beijing, China)
    Anti-SARS-CoV-2 N protein antibody
    suggested: None
    Anti-SARS-CoV-2 N protein
    suggested: None
    Alexa Fluor 488 goat anti-rabbit IgG (H + L) secondary antibody and Hoechst 33342 were purchased from Molecular Probes.
    Alexa Fluor 488 goat anti-rabbit IgG
    suggested: None
    anti-rabbit IgG
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Vero cells were seeded at 1.2 × 104 cells per well in DMEM, supplemented with 2% FBS and 1X Antibiotic-Antimycotic solution (Gibco) in black, 384-well, μClear plates (Greiner Bio-One), 24 h prior to the experiment.
    Vero
    suggested: RRID:CVCL_ZW93)

    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:
    • No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
    • 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.

  3. Version published to 10.1101/2020.03.20.999730v3 on bioRxiv
  4. Version published to 10.1101/2020.03.20.999730v2 on bioRxiv
  5. Version published to 10.1101/2020.03.20.999730v1 on bioRxiv