Repurposing existing medications for coronavirus disease 2019: protocol for a rapid and living systematic review

  1. Benjamin P. Geisler
  2. Lara Zahabi
  3. Adam Edward Lang
  4. Naomi Eastwood
  5. Elaine Tennant
  6. Ljiljana Lukic
  7. Elad Sharon
  8. Hai-Hua Chuang
  9. Chang-Berm Kang
  10. Knakita Clayton-Johnson
  11. Ahmed Aljaberi
  12. Haining Yu
  13. Chinh Bui
  14. Tuan Le Mau
  15. Wen-Cheng Li
  16. Debbie Teodorescu
  17. Ludwig Christian Hinske
  18. Dennis L. Sun
  19. Farrin A. Manian
  20. Adam G. Dunn

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Abstract

Background

Coronavirus disease 2019 (COVID-19) has no confirmed specific treatments. However, there might be in vitro and early clinical data as well as evidence from severe acute respiratory syndrome and Middle Eastern respiratory syndrome that could inform clinicians and researchers. This systematic review aims to create priorities for future research of drugs repurposed for COVID-19.

Methods

This systematic review will include in vitro, animal, and clinical studies evaluating the efficacy of a list of 34 specific compounds and 4 groups of drugs identified in a previous scoping review. Studies will be identified both from traditional literature databases and pre-print servers. Outcomes assessed will include time to clinical improvement, time to viral clearance, mortality, length of hospital stay, and proportions transferred to the intensive care unit and intubated, respectively. We will use the GRADE methodology to assess the quality of the evidence.

Discussion

The challenge posed by COVID-19 requires not just a rapid review of drugs that can be repurposed but also a sustained effort to integrate new evidence into a living systematic review.

Trial registration

PROSPERO 2020 CRD42020175648

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  1. Version published to 10.1186/s13643-021-01693-7
  2. ScreenIT

    SciScore for 10.1101/2020.05.21.20109074: (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

    Software and Algorithms
    SentencesResources
    Data sources: Bibliographical databases for literature search include Medline (via the Entrez PubMed interface)
    Medline
    suggested: (MEDLINE, RRID:SCR_002185)
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    , Embase (via the Embase.com interface), ClinicalTrials.gov (including studies that have already posted results), and Google Scholar.
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    We also target the following preprint servers: MedRxiv, BioRxiv, chemRxiv, Preprints.org, and the Chinese-language server ChinaXiv.
    BioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    If more than one controlled study is available for a similar patient population, we will calculate a relative risk with a 95% confidence interval via meta-analysis in RevMan or STATA.
    RevMan
    suggested: (RevMan, RRID:SCR_003581)
    STATA
    suggested: (Stata, RRID:SCR_012763)

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2020.05.21.20109074v1 on medRxiv