Rapid isolation of potent SARS-CoV-2 neutralizing antibodies and protection in a small animal model

  1. Thomas F. Rogers
  2. Fangzhu Zhao
  3. Deli Huang
  4. Nathan Beutler
  5. Alison Burns
  6. Wan-ting He
  7. Oliver Limbo
  8. Chloe Smith
  9. Ge Song
  10. Jordan Woehl
  11. Linlin Yang
  12. Robert K. Abbott
  13. Sean Callaghan
  14. Elijah Garcia
  15. Jonathan Hurtado
  16. Mara Parren
  17. Linghang Peng
  18. James Ricketts
  19. Michael J. Ricciardi
  20. Stephen A. Rawlings
  21. Davey M. Smith
  22. David Nemazee
  23. John R. Teijaro
  24. James E. Voss
  25. Raiees Andrabi
  26. Bryan Briney
  27. Elise Landais
  28. Devin Sok
  29. Joseph G. Jardine
  30. Dennis R. Burton

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Abstract

The development of countermeasures to prevent and treat COVID-19 is a global health priority. In under 7 weeks, we enrolled a cohort of SARS-CoV-2-recovered participants, developed neutralization assays to interrogate serum and monoclonal antibody responses, adapted our high throughput antibody isolation, production and characterization pipeline to rapidly screen over 1000 antigen-specific antibodies, and established an animal model to test protection. We report multiple highly potent neutralizing antibodies (nAbs) and show that passive transfer of a nAb provides protection against high-dose SARS-CoV-2 challenge in Syrian hamsters. The study suggests a role for nAbs in prophylaxis, and potentially therapy, of COVID-19. The nAbs define protective epitopes to guide vaccine design.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.11.088674: (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.

    Table 2: Resources

    No key resources detected.

    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 10.1101/2020.05.11.088674 on bioRxiv