Neutralizing monoclonal antibodies for treatment of COVID-19
Open accessAbstract
Several neutralizing monoclonal antibodies (mAbs) to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been developed and are now under evaluation in clinical trials. With the US Food and Drug Administration recently granting emergency use authorizations for neutralizing mAbs in non-hospitalized patients with mild-to-moderate COVID-19, there is an urgent need to discuss the broader potential of these novel therapies and to develop strategies to deploy them effectively in clinical practice, given limited initial availability. Here, we review the precedent for passive immunization and lessons learned from using antibody therapies for viral infections such as respiratory syncytial virus, Ebola virus and SARS-CoV infections. We then focus on the deployment of convalescent plasma and neutralizing mAbs for treatment of SARS-CoV-2. We review specific clinical questions, including the rationale for stratification of patients, potential biomarkers, known risk factors and temporal considerations for optimal clinical use. To answer these questions, there is a need to understand factors such as the kinetics of viral load and its correlation with clinical outcomes, endogenous antibody responses, pharmacokinetic properties of neutralizing mAbs and the potential benefit of combining antibodies to defend against emerging viral variants.
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Four clinically approved monoclonal antibodies target the RBD: bamlanivimab, etesevimab, casirivimab and imdevimab.
“We tested four clinically approved monoclonal antibodies that target the RBD 18,19 (bamlanivimab (LY-CoV555), etesevimab (LY-CoV016), casirivimab (REGN10933) and imdevimab (REGN10987))”
From Reduced sensitivity of SARS-CoV-2 variant Delta to antibody neutralization (2021) · cites this paper as [18]
Figure 3 in “Monoclonal antibodies for COVID-19” identifies all four antibodies as binding the RBD. The Introduction says their three treatment regimens had emergency use authorization, not full clinical approval; the claim overstates their regulatory status. (LLM, conf=0.86)
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Ouyang, J., Isnard, S., Lin, J. C.-Y., Fombuena, B., Peng, X., Routy, J., & Chen, Y. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.570063 · Cited by 19 · see citing works
Helmink, B. J., Ragsdale, C. E., Peterson, E. J., et al. (2016) · The Journal of Pediatric Pharmacology and Therapeutics · doi:10.5863/1551-6776-21.2.146 · Cited by 13 · see citing works
The basis of a more contagious 501Y.V1 variant of SARS-COV-2
Liu, H.-L., Zhang, Q.-Q., Wei, P., et al. (2021) · bioRxiv · doi:10.1101/2021.02.02.428884 · Cited by 2 · see citing works
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Research works printed but not linked: 1
- [29]The Antibody Society. Therapeutic monoclonal antibodies approved or in review in the EU or US. The Antibody Society https://www.antibodysociety.org/resources/approved-antibodies/ (2020).
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Reduced sensitivity of SARS-CoV-2 variant Delta to antibody neutralization
Planas, D., Veyer, D., Baidaliuk, A., Staropoli, I., Guivel-Benhassine, F., Rajah, M. M., Planchais, C., Porrot, F., Robillard, N., Puech, J., et al. (2021) · Nature · doi:10.1038/s41586-021-03777-9 · Cited by 1,992 · see citing works
Increased memory B cell potency and breadth after a SARS-CoV-2 mRNA boost
Muecksch, F., Wang, Z., Cho, A., Gaebler, C., Ben Tanfous, T., DaSilva, J., Bednarski, E., Ramos, V., Zong, S., Johnson, B., et al. (2022) · Nature · doi:10.1038/s41586-022-04778-y · Cited by 297 · see citing works
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 · see citing works
Siemieniuk, R. A., Bartoszko, J. J., Díaz Martinez, J. P., Kum, E., Qasim, A., Zeraatkar, D., Izcovich, A., Mangala, S., Ge, L., Han, M. A., et al. (2021) · BMJ · doi:10.1136/bmj.n2231 · Cited by 79 · see citing works
Peng, L., Renauer, P. A., Ökten, A., Fang, Z., Park, J. J., Zhou, X., Lin, Q., Dong, M. B., Filler, R., Xiong, Q., et al. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100634 · Cited by 21 · see citing works
- Authors
- Taylor, P. C., Adams, A. C., Hufford, M. M., de la Torre, I., Winthrop, K., & Gottlieb, R. L.
- Affiliations
- University of Oxford (GB)
- Eli Lilly (United States) (US)
- Oregon Health & Science University (US)
- Baylor University Medical Center (US)
- Corresponding author
- Taylor, Peter C.
- Venue
- Nature Reviews Immunology
- Published
- 2021-04-19
- Volume
- 21
- Issue
- 6
- Pages
- 382-393
- ISSN
- 1474-1733 (ISSN-L)
- 1474-1741
- PMC
- PMC8054133
- OpenAlex
- W3155123963
- Cited by
- 732
- References
- 86
- Citations by year
- 2026: 26 · 2025: 57 · 2024: 94 · 2023: 158 · 2022: 257 · 2021: 139
- FWCI
- 19.06
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature reviews. Immunology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Eli Lilly and Company
- Topics
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Keywords
- neutralizing monoclonal antibodies, COVID-19 pandemic, SARS-CoV-2, passive immunization, convalescent plasma, emergency use authorization, patient stratification, biomarkers, viral load dynamics, pharmacokinetics, SARS-CoV, Ebola virus, respiratory syncytial virus, viral variants, antibody combination therapy
- MeSH terms
- Drug Development, COVID-19, SARS-CoV-2, COVID-19 Serotherapy, Antibodies, Monoclonal, Antibodies, Viral, Humans, Immunization, Passive, Models, Immunological, Antibody-Dependent Enhancement, Drug Resistance, Viral, Antibodies, Neutralizing, Pandemics, Antibodies, Monoclonal, Humanized
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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