The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Antibodies to watch in 2022
Open accessAbstract
In this 13th annual installment of the annual 'Antibodies to Watch' article series, we discuss key events in commercial antibody therapeutics development that occurred in 2021 and forecast events that might occur in 2022. Regulatory review of antibody therapeutics that target the SARS-CoV-2 coronavirus proceeded at an unprecedented pace in 2021, resulting in both emergency use authorizations and full approvals for sotrovimab, regdanvimab, REGEN-COV2, as well as others, in numerous countries. As of November 1, a total of 11 antibody therapeutics had been granted first approvals in either the United States or European Union in 2021 (evinacumab, dostarlimab loncastuximab tesirine, amivantamab, aducanumab, tralokinumab, anifrolumab, bimekizumab, tisotumab vedotin, regdanvimab, REGEN-COV2). The first global approvals of seven products, however, were granted elsewhere, including Japan (pabinafusp alfa), China (disitamab vedotin, penpulimab, zimberelimab), Australia (sotrovimab, REGEN-COV2), or the Republic of Korea (regdanvimab). Globally, at least 27 novel antibody therapeutics are undergoing review by regulatory agencies. First actions by the Food and Drug Administration on the biologics license applications for faricimab, sutimlimab, tebentafusp, relatlimab, sintilimab, ublituximab and tezepelumab are expected in the first quarter of 2022. Finally, our data show that, with antibodies for COVID-19 excluded, the late-stage commercial clinical pipeline of antibody therapeutics grew by over 30% in the past year. Of those in late-stage development, marketing applications for at least 22 may occur by the end of 2022.
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Citations
Cites
Donanemab in Early Alzheimer’s Disease
Mintun, M. A., Lo, A. C., Duggan Evans, C., Wessels, A. M., Ardayfio, P. A., Andersen, S. W., Shcherbinin, S., Sparks, J., Sims, J. R., Brys, M., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2100708 · Cited by 1,728 · cited 2×
“04).(137) Donanemab is currently undergoing safety and efficacy evaluation in two placebo-controlled Phase 3 studies,”
BackgroundMethodEarly treatment for Covid-19 with SARS-CoV-2 neutralizing antibody sotrovimab
Gupta, A., Gonzalez-Rojas, Y., Juarez, E., Crespo Casal, M., Moya, J., Falci, D. R., Sarkis, E., Solis, J., Zheng, H., Scott, N., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2107934 · Cited by 1,086
Tezepelumab in Adults and Adolescents with Severe, Uncontrolled Asthma
Menzies-Gow, A., Corren, J., Bourdin, A., Chupp, G., Israel, E., Wechsler, M. E., Brightling, C. E., Griffiths, J. M., Hellqvist, Å., Bowen, K., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2034975 · Cited by 1,056
Swanson, C. J., Zhang, Y., Dhadda, S., Wang, J., Kaplow, J., Lai, R. Y. K., Lannfelt, L., Bradley, H., Rabe, M., Koyama, A., et al. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00813-8 · Cited by 847 · cited 1×
“with a consistent reduction of clinical decline across several of the clinical and biomarker endpoints.(117)”
BackgroundBamlanivimab plus Etesevimab in Mild or Moderate Covid-19
Dougan, M., Nirula, A., Azizad, M., Mocherla, B., Gottlieb, R. L., Chen, P., Hebert, C., Perry, R., Boscia, J., Heller, B., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2102685 · Cited by 663
TIGIT as an emerging immune checkpoint
Harjunpää, H., & Guillerey, C. (2020) · Clinical and Experimental Immunology · doi:10.1111/cei.13407 · Cited by 552
FDA approves 100th monoclonal antibody product
Mullard, A. (2021) · Nature Reviews Drug Discovery · doi:10.1038/d41573-021-00079-7 · Cited by 503
Caimi, P. F., Ai, W., Alderuccio, J. P., Ardeshna, K. M., Hamadani, M., Hess, B., Kahl, B. S., Radford, J., Solh, M., Stathis, A., et al. (2021) · The Lancet Oncology · doi:10.1016/s1470-2045(21)00139-x · Cited by 441
Hutchings, M., Morschhauser, F., Iacoboni, G., Carlo-Stella, C., Offner, F. C., Sureda, A., Salles, G., Martínez-Lopez, J., Crump, M., Thomas, D. N., et al. (2021) · Journal of Clinical Oncology · doi:10.1200/jco.20.03175 · Cited by 437
Bimekizumab versus Secukinumab in Plaque Psoriasis
Reich, K., Warren, R. B., Lebwohl, M., Gooderham, M., Strober, B., Langley, R. G., Paul, C., De Cuyper, D., Vanvoorden, V., Madden, C., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2102383 · Cited by 424
Cited by
ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins
Abanades, B., Wong, W. K., Boyles, F., Georges, G., Bujotzek, A., & Deane, C. M. (2023) · Communications Biology · doi:10.1038/s42003-023-04927-7 · Cited by 298
Monoclonal antibody therapies against SARS-CoV-2
Focosi, D., McConnell, S., Casadevall, A., Cappello, E., Valdiserra, G., & Tuccori, M. (2022) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(22)00311-5 · Cited by 269
Designing antibodies as therapeutics
Carter, P. J., & Rajpal, A. (2022) · Cell · doi:10.1016/j.cell.2022.05.029 · Cited by 267
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
Fragment-based computational design of antibodies targeting structured epitopes
Aguilar Rangel, M., Bedwell, A., Costanzi, E., Taylor, R. J., Russo, R., Bernardes, G. J. L., Ricagno, S., Frydman, J., Vendruscolo, M., & Sormanni, P. (2022) · Science Advances · doi:10.1126/sciadv.abp9540 · Cited by 55
Metadata
- Authors
- Kaplon, H., Chenoweth, A., Crescioli, S., & Reichert, J. M.
- Venue
- mAbs
- Publisher
- Informa UK Limited
- Published
- 2022-01-14
- Volume
- 14
- Issue
- 1
- Pages
- 2014296-2014296
- ISSN
- 1942-0862 (ISSN-L)
- 1942-0870
- OpenAlex
- W4205628911
- Cited by
- 331
- References
- 55
- Type
- journal article
- Language
- en
- Open access
- gold
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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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