Development of an inactivated vaccine candidate for SARS-CoV-2

  1. Qiang Gao
  2. Linlin Bao
  3. Haiyan Mao
  4. Lin Wang
  5. Kangwei Xu
  6. Minnan Yang
  7. Yajing Li
  8. Ling Zhu
  9. Nan Wang
  10. Zhe Lv
  11. Hong Gao
  12. Xiaoqin Ge
  13. Biao Kan
  14. Yaling Hu
  15. Jiangning Liu
  16. Fang Cai
  17. Deyu Jiang
  18. Yanhui Yin
  19. Chengfeng Qin
  20. Jing Li
  21. Xuejie Gong
  22. Xiuyu Lou
  23. Wen Shi
  24. Dongdong Wu
  25. Hengming Zhang
  26. Lang Zhu
  27. Wei Deng
  28. Yurong Li
  29. Jinxing Lu
  30. Changgui Li
  31. Xiangxi Wang
  32. Weidong Yin
  33. Yanjun Zhang
  34. Chuan Qin

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Abstract

Global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an urgent race to develop a vaccine. Gao et al. report preclinical results of an early vaccine candidate called PiCoVacc, which protected rhesus macaque monkeys against SARS-CoV-2 infection when analyzed in short-term studies. The researchers obtained multiple SARS-CoV-2 strains from 11 hospitalized patients across the world and then chemically inactivated the harmful properties of the virus. Animals were immunized with one of two vaccine doses and then inoculated with SARS-CoV-2. Those that received the lowest dose showed signs of controlling the infection, and those receiving the highest dose appeared more protected and did not have detectable viral loads in the pharynx or lungs at 7 days after infection. The next steps will be testing for safety and efficacy in humans.

Science , this issue p. 77

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  1. Version published to 10.1126/science.abc1932
  2. ScreenIT

    SciScore for 10.1101/2020.04.17.046375: (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

    Software and Algorithms
    SentencesResources
    Materials and Methods:
    Methods
    suggested: None

    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.

  3. Version published to 10.1101/2020.04.17.046375v1 on bioRxiv