Preemptive optimization of a clinical antibody for broad neutralization of SARS-CoV-2 variants and robustness against viral escape

Most previously authorized clinical antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have lost neutralizing activity to recent variants due to rapid viral evolution. To mitigate such escape, we preemptively enhance AZD3152, an antibody authorized for prophylaxis in immunocompromised individuals. Using deep mutational scanning (DMS) on the SARS-CoV-2 antigen, we identify AZD3152 vulnerabilities at antigen positions F456 and D420. Through two iterations of computational antibody design that integrates structure-based modeling, machine-learning, and experimental validation, we co-optimize AZD3152 against 24 contemporary and previous SARS-CoV-2 variants, as well as 20 potential future escape variants. Our top candidate, 3152-1142, restores full potency (100-fold improvement) against the more recently emerged XBB.1.5+F456L variant that escaped AZD3152, maintains potency against previous variants of concern, and shows no additional vulnerability as assessed by DMS. This preemptive mitigation demonstrates a generalizable approach for optimizing existing antibodies against potential future viral escape.

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Publication Details

Journal
Science Advances
Published
2025-03-28
DOI
https://doi.org/10.1126/sciadv.adu0718
Citations
5
Primary Topic
SARS-CoV-2 and COVID-19 Research
Type
article
Field-Weighted Citation Impact
6.98
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Preemptive optimization of a clinical antibody for broad neutralization of SARS-CoV-2 variants and robustness against viral escape

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Preemptive optimization of a clinical antibody for broad neutralization of SARS-CoV-2 variants and robustness against viral escape

André Gonçalves, Michael Kierny, Svetlana A. Hopkins, Christopher Morehouse, Joseph R. Francica, Thomas J. D. Bates, Christopher G. Earnhart, Conor F. Hayes, Edmond Y. Lau, Kevin M. Tuffy, Gilad Kaplan, Pavlo Gilchuk, Aiste Dijokaite-Guraliuc, Yíngyún Caì, Subuhi Kaul, Benjamin Sparklin, Drew Bennett, Felipe Leno da Silva, Brenden K. Petersen, Jagadish Beloor, Juthathip Mongkolsapaya, Brent W. Segelke, Andrew Dippel, Mikel Landajuela, Saravanan Rajan, Simone Conti, Gustavo H. Kijak, Kathryn T. Arrildt, Fangqiang Zhu, Mark T. Esser, Vancheswaran Gopalakrishnan, Adam Zemła, Emilia A. Grzesiak, Thomas Desautels, Daniel Faissol, Gavin Robert Screaton, Chang Liu, Yi Zhang, Shivshankar Sundaram, Ka Yin Kwong, Amy Nguyen, John W. Goforth, Bismark Amofah, Vanessa Cadevilla Peano
article en
5 citations

Abstract

Most previously authorized clinical antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have lost neutralizing activity to recent variants due to rapid viral evolution. To mitigate such escape, we preemptively enhance AZD3152, an antibody authorized for prophylaxis in immunocompromised individuals. Using deep mutational scanning (DMS) on the SARS-CoV-2 antigen, we identify AZD3152 vulnerabilities at antigen positions F456 and D420. Through two iterations of computational antibody design that integrates structure-based modeling, machine-learning, and experimental validation, we co-optimize AZD3152 against 24 contemporary and previous SARS-CoV-2 variants, as well as 20 potential future escape variants. Our top candidate, 3152-1142, restores full potency (100-fold improvement) against the more recently emerged XBB.1.5+F456L variant that escaped AZD3152, maintains potency against previous variants of concern, and shows no additional vulnerability as assessed by DMS. This preemptive mitigation demonstrates a generalizable approach for optimizing existing antibodies against potential future viral escape.

Science AdvancesVol. 11(13)
Lawrence Livermore National Laboratory (US), United States Department of Defense (US), Centre for Human Genetics (GB), Stafford College (GB), University of Oxford (GB), Mahidol Oxford Tropical Medicine Research Unit (TH), AstraZeneca (Japan) (JP), AstraZeneca (Poland) (PL), AstraZeneca (Finland) (FI), Lawrence Livermore National Security (US), Science Oxford (GB)
Good health and well-being
Openalex Percentile: Top 3%
SARS-CoV-2 and COVID-19 Research
6.98
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