tetano
Editor, Senior Moderator
Sci Adv
. 2025 Mar 28;11(13):eadu0718.
doi: 10.1126/sciadv.adu0718. Epub 2025 Mar 28. Preemptive optimization of a clinical antibody for broad neutralization of SARS-CoV-2 variants and robustness against viral escape
Fangqiang Zhu[SUP] 1 [/SUP], Saravanan Rajan[SUP] 2 [/SUP], Conor F Hayes[SUP] 3 [/SUP], Ka Yin Kwong[SUP] 2 [/SUP], Andre R Goncalves[SUP] 3 [/SUP], Adam T Zemla[SUP] 4 [/SUP], Edmond Y Lau[SUP] 1 [/SUP], Yi Zhang[SUP] 5 [/SUP], Yingyun Cai[SUP] 5 [/SUP], John W Goforth[SUP] 4 [/SUP], Mikel Landajuela[SUP] 3 [/SUP], Pavlo Gilchuk[SUP] 2 [/SUP], Michael Kierny[SUP] 2 [/SUP], Andrew Dippel[SUP] 2 [/SUP], Bismark Amofah[SUP] 2 [/SUP], Gilad Kaplan[SUP] 2 [/SUP], Vanessa Cadevilla Peano[SUP] 5 [/SUP], Christopher Morehouse[SUP] 5 [/SUP], Ben Sparklin[SUP] 5 [/SUP], Vancheswaran Gopalakrishnan[SUP] 5 [/SUP], Kevin M Tuffy[SUP] 5 [/SUP], Amy Nguyen[SUP] 5 [/SUP], Jagadish Beloor[SUP] 5 [/SUP], Gustavo Kijak[SUP] 5 [/SUP], Chang Liu[SUP] 6 7 [/SUP], Aiste Dijokaite-Guraliuc[SUP] 7 [/SUP], Juthathip Mongkolsapaya[SUP] 6 7 8 [/SUP], Gavin R Screaton[SUP] 6 7 [/SUP], Brenden K Petersen[SUP] 3 [/SUP], Thomas A Desautels[SUP] 3 [/SUP], Drew Bennett[SUP] 1 [/SUP], Simone Conti[SUP] 1 [/SUP], Brent W Segelke[SUP] 1 [/SUP], Kathryn T Arrildt[SUP] 1 [/SUP], Samantha Kaul[SUP] 4 [/SUP], Emilia A Grzesiak[SUP] 4 [/SUP], Felipe Leno da Silva[SUP] 3 [/SUP], Thomas W Bates[SUP] 3 [/SUP], Christopher G Earnhart[SUP] 9 [/SUP], Svetlana Hopkins[SUP] 10 [/SUP], Shivshankar Sundaram[SUP] 11 [/SUP], Mark T Esser[SUP] 5 [/SUP], Joseph R Francica[SUP] 5 [/SUP], Daniel M Faissol[SUP] 3 [/SUP]; LLNL Generative Unconstrained Intelligent Drug Engineering (GUIDE) consortium
Affiliations
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.
. 2025 Mar 28;11(13):eadu0718.
doi: 10.1126/sciadv.adu0718. Epub 2025 Mar 28. Preemptive optimization of a clinical antibody for broad neutralization of SARS-CoV-2 variants and robustness against viral escape
Fangqiang Zhu[SUP] 1 [/SUP], Saravanan Rajan[SUP] 2 [/SUP], Conor F Hayes[SUP] 3 [/SUP], Ka Yin Kwong[SUP] 2 [/SUP], Andre R Goncalves[SUP] 3 [/SUP], Adam T Zemla[SUP] 4 [/SUP], Edmond Y Lau[SUP] 1 [/SUP], Yi Zhang[SUP] 5 [/SUP], Yingyun Cai[SUP] 5 [/SUP], John W Goforth[SUP] 4 [/SUP], Mikel Landajuela[SUP] 3 [/SUP], Pavlo Gilchuk[SUP] 2 [/SUP], Michael Kierny[SUP] 2 [/SUP], Andrew Dippel[SUP] 2 [/SUP], Bismark Amofah[SUP] 2 [/SUP], Gilad Kaplan[SUP] 2 [/SUP], Vanessa Cadevilla Peano[SUP] 5 [/SUP], Christopher Morehouse[SUP] 5 [/SUP], Ben Sparklin[SUP] 5 [/SUP], Vancheswaran Gopalakrishnan[SUP] 5 [/SUP], Kevin M Tuffy[SUP] 5 [/SUP], Amy Nguyen[SUP] 5 [/SUP], Jagadish Beloor[SUP] 5 [/SUP], Gustavo Kijak[SUP] 5 [/SUP], Chang Liu[SUP] 6 7 [/SUP], Aiste Dijokaite-Guraliuc[SUP] 7 [/SUP], Juthathip Mongkolsapaya[SUP] 6 7 8 [/SUP], Gavin R Screaton[SUP] 6 7 [/SUP], Brenden K Petersen[SUP] 3 [/SUP], Thomas A Desautels[SUP] 3 [/SUP], Drew Bennett[SUP] 1 [/SUP], Simone Conti[SUP] 1 [/SUP], Brent W Segelke[SUP] 1 [/SUP], Kathryn T Arrildt[SUP] 1 [/SUP], Samantha Kaul[SUP] 4 [/SUP], Emilia A Grzesiak[SUP] 4 [/SUP], Felipe Leno da Silva[SUP] 3 [/SUP], Thomas W Bates[SUP] 3 [/SUP], Christopher G Earnhart[SUP] 9 [/SUP], Svetlana Hopkins[SUP] 10 [/SUP], Shivshankar Sundaram[SUP] 11 [/SUP], Mark T Esser[SUP] 5 [/SUP], Joseph R Francica[SUP] 5 [/SUP], Daniel M Faissol[SUP] 3 [/SUP]; LLNL Generative Unconstrained Intelligent Drug Engineering (GUIDE) consortium
Affiliations
- PMID: 40153503
- PMCID: PMC11952088
- DOI: 10.1126/sciadv.adu0718
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.