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Title: Computationally restoring the potency of a clinical antibody against Omicron

Journal Article · · Nature (London)
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  1. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
  2. Vanderbilt Univ., Nashville, TN (United States). Medical Center
  3. Washington Univ., St. Louis, MO (United States). School of Medicine
  4. Fred Hutchinson Cancer Center, Seattle, WA (United States)
  5. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Google, Alphabet Inc., Mountain View, CA (United States)
  6. Washington Univ., St. Louis, MO (United States). School of Medicine; Vir Biotechnology, San Francisco, CA (United States)
  7. US Dept. of Defense, Frederick, MD (United States)
  8. Joint Rsearch and Development Inc., Stafford, VA (United States)
  9. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
  10. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States). Center for Bioengineering
  11. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  12. Fred Hutchinson Cancer Center, Seattle, WA (United States); Howard Hughes Medical Institute, Seattle, WA (United States)

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs and revealed how quickly viral escape can curtail effective options. When the SARS-CoV-2 Omicron variant emerged in 2021, many antibody drug products lost potency, including Evusheld and its constituent, cilgavimab. Cilgavimab, like its progenitor COV2-2130, is a class 3 antibody that is compatible with other antibodies in combination4 and is challenging to replace with existing approaches. Rapidly modifying such high-value antibodies to restore efficacy against emerging variants is a compelling mitigation strategy. We sought to redesign and renew the efficacy of COV2-2130 against Omicron BA.1 and BA.1.1 strains while maintaining efficacy against the dominant Delta variant. Here we show that our computationally redesigned antibody, 2130-1-0114-112, achieves this objective, simultaneously increases neutralization potency against Delta and subsequent variants of concern, and provides protection in vivo against the strains tested: WA1/2020, BA.1.1 and BA.5. Deep mutational scanning of tens of thousands of pseudovirus variants reveals that 2130-1-0114-112 improves broad potency without increasing escape liabilities. Our results suggest that computational approaches can optimize an antibody to target multiple escape variants, while simultaneously enriching potency. Our computational approach does not require experimental iterations or pre-existing binding data, thus enabling rapid response strategies to address escape variants or lessen escape vulnerabilities.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
Contributing Organization:
Tri-lab COVID-19 Consortium
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
2406520
Report Number(s):
LLNL--JRNL-839587; 1060519
Journal Information:
Nature (London), Journal Name: Nature (London) Journal Issue: 8013 Vol. 629; ISSN 0028-0836
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English

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