Characterization of anti-SARS-CoV-2 monoclonal antibodies focusing on antigen binding, neutralization, and FcγR activation via formation of immune complex

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV−2) causes coronavirus disease 2019 (COVID−19). Antibodies induced by SARS-CoV−2 infection or vaccination play pivotal roles in the body’s defense against the virus; many monoclonal antibodies (mAbs) against SARS-CoV−2 have been cloned, and some neutralizing mAbs have been used as therapeutic drugs. In this study, we prepared an antibody panel consisting of 31 clones of anti-SARS-CoV−2 mAbs and analyzed and compared their biological activities. The mAbs used in this study were classified into different binding classes based on their binding epitopes and showed binding to the SARS-CoV−2 spike protein in different binding kinetics. A multiplex assay using the spike proteins of Alpha, Beta, Gamma, Delta, and Omicron variants clearly showed the different effects of variant mutations on the binding and neutralization activities of different binding classes of mAbs. In addition, we evaluated Fcγ receptor (FcγR) activation by immune complexes consisting of anti-SARS-CoV−2 mAb and SARS-CoV−2 pseudo-typed virus, and revealed differences in the FcγR activation properties among the binding classes of anti-SARS-CoV−2 mAbs. It has been reported that FcγR-mediated immune-cell activation by immune complexes is involved in the promotion of immunopathology of COVID−19; therefore, differences in the FcγR-activation properties of anti-SARS-CoV−2 mAbs are among the most important characteristics when considering the clinical impacts of anti-SARS-CoV−2 mAbs.

KEYWORDS:

• biological activities
• COVID-19
• Fcγ receptor
• immune complexes
• mabs
• SARS-CoV-2

Acknowledgments

This research was supported by a grant for Research on Regulatory Science of Pharmaceuticals and Medical Devices from the Japan Agency for Medical Research and development (AMED) under grant numbers JP21mk010194. UCSF ChimeraX was developed by the Resources for Biocomputing, Visualization, and Informatics at the University of California, with support from NIH R01-GM129325 and the Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19420862.2023.2222874.

Additional information

Funding

The work was supported by the Japan Agency for Medical Research and Development [JP21mk010194].