Antimicrobial Agents and ChemotherapyMicrobiologyPeer-reviewed journal article
Frequent emergence of resistance mutations following complex intra-host genomic dynamics in SARS-CoV-2 patients receiving Sotrovimab
R. P. Cabrera, F. Tejerina, A. M. Salinas, M. Ferris, C. Veintimilla, P. Catalan, G. R. Macias, R. Alonso, P. Munoz, D. Garcia de Viedma, L. Perez-Lago
Abstract
The emergence of the Omicron variant of SARS-CoV-2 represented a challenge to the treatment of COVID-19 with monoclonal antibodies. Only Sotrovimab maintained partial activity, allowing it to be used in high-risk patients infected with the Omicron variant. However, the reports of resistance mutations to Sotrovimab call for efforts to better understand the intra-patient emergence of this resistance. A retrospective genomic analysis was conducted on respiratory samples from immunocompromised patients infected with SARS-CoV-2 who received Sotrovimab at our hospital between December 2021 and August 2022. The study involved 95 sequential specimens from 22 patients (1-12 samples/patient; 3-107 days post-infusion (Ct [≤] 32)). Resistance mutations (in P337, E340, K356, and R346) were detected in 68% of cases; the shortest time to detection of a resistance mutation was 5 days after Sotrovimab infusion. The dynamics of resistance acquisition were highly complex, with up to 11 distinct amino acid changes in specimens from the same patient. In two patients, the mutation distribution was compartmentalized in respiratory samples from different sources. This is the first study to examine the acquisition of resistance to Sotrovimab in the BA.5 lineage, enabling us to determine the lack of genomic or clinical differences between Sotrovimab resistance in BA.5 relative to BA.1/2. Across all Omicron lineages, the acquisition of resistance delayed SARS-CoV-2 clearance (40.67 vs 19.5 days). Close, real-time genomic surveillance of patients receiving Sotrovimab should be mandatory to facilitate early therapeutic interventions.
- organism: virus
- assay: whole-genome sequencing
- research focus: variant calling
- approach: statistical model
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