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Cited by 267

The DOI matches its Crossref record; 153 of the 153 references it cites were found in a registry or an index.

Designing antibodies as therapeutics

Open access
Carter, Paul J., Rajpal, Arvind·2022·Cell

Abstract

Antibody therapeutics are a large and rapidly expanding drug class providing major health benefits. We provide a snapshot of current antibody therapeutics including their formats, common targets, therapeutic areas, and routes of administration. Our focus is on selected emerging directions in antibody design where progress may provide a broad benefit. These topics include enhancing antibodies for cancer, antibody delivery to organs such as the brain, gastrointestinal tract, and lungs, plus antibody developability challenges including immunogenicity risk assessment and mitigation and subcutaneous delivery. Machine learning has the potential, albeit as yet largely unrealized, for a transformative future impact on antibody discovery and engineering.

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Crossref · 10.1016/j.cell.2022.05.029

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Metadata

Authors
Carter, P. J., & Rajpal, A.
Venue
Cell
Publisher
Elsevier BV
Published
2022-07
Volume
185
Issue
15
Pages
2789-2805
ISSN
0092-8674 (ISSN-L)
OpenAlex
W4286267340
Cited by
267
References
153
FWCI
26.26
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Designing antibodies as therapeutics. https://app.cochat.ai/papers/10.1016/j.cell.2022.05.029
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Carter, P. J., & Rajpal, A. (2022). Designing antibodies as therapeutics. Cell. https://doi.org/10.1016/j.cell.2022.05.029

Data sources

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.