Deep Mutational Scanning to Predict Escape from Bebtelovimab in SARS-CoV-2 Omicron Subvariants

Area of Science:

• Virology
• Immunology
• Genetics

Background:

• Therapeutic monoclonal antibodies for COVID-19 face reduced efficacy against new SARS-CoV-2 variants.
• Emerging Omicron subvariants pose a significant challenge to existing antibody treatments.

Purpose of the Study:

• To predict the efficacy of therapeutic monoclonal antibodies against future SARS-CoV-2 Omicron subvariants.
• To evaluate the potential of deep mutational scanning (DMS) as a predictive tool for antibody escape.

Main Methods:

• Deep mutational scanning (DMS) was performed on the receptor-binding domain of the SARS-CoV-2 BA.2 strain.
• An inverted infection assay using an ACE2-harboring virus and spike-expressing cells was employed.
• The study analyzed single amino acid substitutions and their impact on antibody binding and neutralization.

Main Results:

• Specific mutations at K444, V445, G446, P499, and T500 in the BA.2 receptor-binding domain were identified as key for bebtelovimab escape.
• SARS-CoV-2 subvariants BA2.75 (G446S), XBB (V445P), and BQ.1 (K444T) showed partial to complete evasion of bebtelovimab neutralization.
• Observed antibody evasion in circulating subvariants correlated with DMS predictions based on the BA.2 strain.

Conclusions:

• Deep mutational scanning (DMS) is a valuable predictive tool for assessing therapeutic antibody efficacy against evolving SARS-CoV-2 variants.
• Understanding mutation-driven antibody escape is crucial for developing next-generation COVID-19 therapeutics.
• Continuous monitoring of viral evolution and antibody escape pathways is necessary to maintain effective COVID-19 treatments.