A novel biparatopic antibody-ACE2 fusion that blocks SARS-CoV-2 infection: implications for therapy

  1. Xiaoniu Miao
  2. Yi Luo
  3. Xi Huang
  4. Suki M. Y. Lee
  5. Zhijun Yuan
  6. Yongzhou Tang
  7. Liandi Chen
  8. Chao Wang
  9. Wenchao Jiang
  10. Wei Gao
  11. Xuedong Song
  12. Yao Yan
  13. Tuling Pang
  14. Yuefeng Zou
  15. Weihui Fu
  16. Liping Wan
  17. Javier Gilbert-Jaramillo
  18. Michael Knight
  19. Tiong Kit Tan
  20. Pramila Rijal
  21. Alain Townsend
  22. Joanne Sun
  23. Xiaolin Liu
  24. William James
  25. Andy Tsun
  26. Yingda Xu

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Abstract

In the absence of a proven effective vaccine preventing infection by SARS-CoV-2, or a proven drug to treat COVID-19, the positive results of passive immune therapy using convalescent serum provides a strong lead. We have developed a new class of tetravalent, biparatopic therapy, 89C8-ACE2. It combines the specificity of a monoclonal antibody (89C8) that recognizes the relatively conserved N-terminal domain (NTD) of the viral S glycoprotein, and the ectodomain of ACE2, which binds to the receptor-binding domain (RBD) of S. This molecule shows exceptional performance in vitro, inhibiting the interaction of recombinant S1 to ACE2 and transduction of ACE2-overexpressing cells by S-pseudotyped lentivirus with IC50s substantially below 100 pM, and with potency approximately 100-fold greater than ACE2-Fc itself. Moreover, 89C8-ACE2 was able to neutralize authentic virus infection in a standard assay at low nanomolar concentrations, making this class of molecule a promising lead for therapeutic applications.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.14.147868: (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
    Recombinant antibody expression: The cDNAs encoding the variable regions of heavy (human IgG1 heavy chain) and light (kappa light chain constant regions) chains were cloned into expression plasmids (pCDNA3.1).
    human IgG1 heavy chain
    suggested: None
    Cell based blocking test: Gradient diluted antibodies or antibody-ACE2 fusion proteins were first pre-incubated with 0.1nM S1-mFc (SinoBiological) at 37 °C overnight, followed by incubation with CHO cells stably expressing ACE2 protein (CHO-ACE2) for 1h at RT.
    antibody-ACE2
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    The heavy- and light-chain encoding plasmids were transiently co-transfected into Expi293 cells (Life technologies) using PEI.
    Expi293
    suggested: RRID:CVCL_D615)
    Cell based blocking test: Gradient diluted antibodies or antibody-ACE2 fusion proteins were first pre-incubated with 0.1nM S1-mFc (SinoBiological) at 37 °C overnight, followed by incubation with CHO cells stably expressing ACE2 protein (CHO-ACE2) for 1h at RT.
    CHO
    suggested: CLS Cat# 603479/p746_CHO, RRID:CVCL_0213)
    HEK293T overexpressing hACE2 were seeded into the 96-well plates 16h before infection.
    HEK293T
    suggested: None
    Thereafter, 0.5 mL of a single cell suspension of Vero E6 cells in D1 at 5 E5/mL was added, and incubated for 2 h at 37 °C before being overlain with 0.5 mL of D1 supplemented with carboxymethyl cellulose (1.5 %).
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    The IC50 values were calculated with non-linear regression log (inhibitor) vs. response (four parameters) using GraphPad Prism 7 (GraphPad Software, Inc.)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    The PRNT90 and PRNT50 values were calculated using the “FindECanything” non-linear curve fitting procedure of GraphPad Prism 8.4.1, with F set to 10 and 50, respectively.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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 10.1101/2020.06.14.147868v1 on bioRxiv
  3. Version published to 10.1080/19420862.2020.1804241