The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
Tackling COVID-19 with neutralizing monoclonal antibodies
Open accessAbstract
Monoclonal antibodies (mAbs) have revolutionized the treatment of several human diseases, including cancer and autoimmunity and inflammatory conditions, and represent a new frontier for the treatment of infectious diseases. In the last 20 years, innovative methods have allowed the rapid isolation of mAbs from convalescent subjects, humanized mice, or libraries assembled in vitro and have proven that mAbs can be effective countermeasures against emerging pathogens. During the past year, an unprecedentedly large number of mAbs have been developed to fight coronavirus disease 2019 (COVID-19). Lessons learned from this pandemic will pave the way for the development of more mAb-based therapeutics for other infectious diseases. Here, we provide an overview of SARS-CoV-2-neutralizing mAbs, including their origin, specificity, structure, antiviral and immunological mechanisms of action, and resistance to circulating variants, as well as a snapshot of the clinical trials of approved or late-stage mAb therapeutics.
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Cites
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Cited by
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Self-citationSARS-CoV-2 immune evasion by the B.1.427/B.1.429 variant of concern
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Metadata
- Authors
- Corti, D., Purcell, L. A., Snell, G., & Veesler, D.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 184
- Issue
- 12
- Pages
- 3086-3108
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W3165032628
- Cited by
- 383
- References
- 67
- FWCI
- 24.65
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Corti, D., Purcell, L. A., Snell, G., & Veesler, D. (2021). Tackling COVID-19 with neutralizing monoclonal antibodies. Cell. https://doi.org/10.1016/j.cell.2021.05.005
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
