The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Fc-engineered antibody therapeutics with improved anti-SARS-CoV-2 efficacy
Open accessAbstract
Monoclonal antibodies with neutralizing activity against SARS-CoV-2 have demonstrated clinical benefits in cases of mild-to-moderate SARS-CoV-2 infection, substantially reducing the risk for hospitalization and severe disease1–4. Treatment generally requires the administration of high doses of these monoclonal antibodies and has limited efficacy in preventing disease complications or mortality among hospitalized patients with COVID-195. Here we report the development and evaluation of anti-SARS-CoV-2 monoclonal antibodies with optimized Fc domains that show superior potency for prevention or treatment of COVID-19. Using several animal disease models of COVID-196,7, we demonstrate that selective engagement of activating Fcγ receptors results in improved efficacy in both preventing and treating disease-induced weight loss and mortality, significantly reducing the dose required to confer full protection against SARS-CoV-2 challenge and for treatment of pre-infected animals. Our results highlight the importance of Fcγ receptor pathways in driving antibody-mediated antiviral immunity and exclude the possibility of pathogenic or disease-enhancing effects of Fcγ receptor engagement of anti-SARS-CoV-2 antibodies upon infection. These findings have important implications for the development of Fc-engineered monoclonal antibodies with optimal Fc-effector function and improved clinical efficacy against COVID-19 disease. Optimization of monoclonal antibodies against SARS-CoV-2 for enhanced Fc-effector function increase their effectiveness for prevention and treatment of COVID-19 in multiple animal models of SARS-CoV-2 infection.
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Cites
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Self-citationStructural and functional analysis of a potent sarbecovirus neutralizing antibody
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SARS-CoV-2 Neutralizing Antibody LY-CoV555 in Outpatients with Covid-19
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Self-citationNeutralizing monoclonal antibodies for treatment of COVID-19
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Bamlanivimab plus Etesevimab in Mild or Moderate Covid-19
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REGN-COV2 antibodies prevent and treat SARS-CoV-2 infection in rhesus macaques and hamsters
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Cited by
The Dawn of a New Era: Targeting the “Undruggables” with Antibody-Based Therapeutics
Qian, L., Lin, X., Gao, X., Khan, R. U., Liao, J.-Y., Du, S., Ge, J., Zeng, S., & Yao, S. Q. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00915 · Cited by 155
Kent, S. J., Khoury, D. S., Reynaldi, A., Juno, J. A., Wheatley, A. K., Stadler, E., John Wherry, E., Triccas, J., Sasson, S. C., Cromer, D., et al. (2022) · Nature Reviews Immunology · doi:10.1038/s41577-022-00716-1 · Cited by 139
Protective activity of mRNA vaccines against ancestral and variant SARS-CoV-2 strains
Ying, B., Whitener, B., VanBlargan, L. A., Hassan, A. O., Shrihari, S., Liang, C.-Y., Karl, C. E., Mackin, S., Chen, R. E., Kafai, N. M., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abm3302 · Cited by 85
Fc glycoengineering of a PD-L1 antibody harnesses Fcγ receptors for increased antitumor efficacy
Cohen Saban, N., Yalin, A., Landsberger, T., Salomon, R., Alva, A., Feferman, T., Amit, I., & Dahan, R. (2023) · Science Immunology · doi:10.1126/sciimmunol.add8005 · Cited by 39
Metadata
- Authors
- Yamin, R., Jones, A. T., Hoffmann, H.-H., Schäfer, A., Kao, K. S., Francis, R. L., Sheahan, T. P., Baric, R. S., Rice, C. M., Ravetch, J. V., & Bournazos, S.
- Venue
- Nature
- Published
- 2021-09-21
- Volume
- 599
- Issue
- 7885
- Pages
- 465-470
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3199974613
- Cited by
- 185
- References
- 13
- FWCI
- 10.96
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Yamin, R., Jones, A. T., Hoffmann, H.-H., Schäfer, A., Kao, K. S., Francis, R. L., Sheahan, T. P., Baric, R. S., Rice, C. M., Ravetch, J. V., & Bournazos, S. (2021). Fc-engineered antibody therapeutics with improved anti-SARS-CoV-2 efficacy. Nature. https://doi.org/10.1038/s41586-021-04017-w
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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