Large scale analysis of the SARS-CoV-2 main protease reveals marginal presence of nirmatrelvir-resistant SARS-CoV-2 Omicron mutants in Ontario, Canada, December 2021–September 2023
Bibliographic record
Abstract
Background: In response to the COVID-19 pandemic, a new oral antiviral called nirmatrelvirritonavir (Paxlovid TM ) was authorized for use in Canada in January 2022.In vitro studies have reported mutations in M pro protein that may be associated with the development of nirmatrelvir resistance.Objectives: To survey the prevalence, relevance and temporal patterns of M pro mutations among SARS-CoV-2 Omicron lineages in Ontario, Canada.Methods: A total of 93,082 M pro gene sequences from December 2021 to September 2023 were analyzed.Reported in vitro M pro mutations were screened against our database using in-house data science pipelines to determine the nirmatrelvir resistance.Negative binomial regression was conducted to analyze the temporal trends in M pro mutation counts over the study time period.Results: A declining trend was observed in non-synonymous mutations of M pro sequences, showing a 7.9% reduction (95% CI: 6.5%-9.4%;p<0.001) every 30 days.The P132H was the most prevalent mutation (higher than 95%) in all Omicron lineages.In vitro nirmatrelvir-resistant mutations were found in 3.12% (n=29/929) Omicron lineages with very low counts, ranging from one to 19.Only two mutations, A7T (n=19) and M82I (n=9), showed temporal presence among the BA.1.1 in 2022 and the BQ.1.2.3 in 2022, respectively. Conclusion:The observations suggest that, as of September 2023, no significant or widespread resistance to nirmatrelvir has developed among SARS-CoV-2 Omicron variants in Ontario.This study highlights the importance of creating automated monitoring systems to track the emergence of nirmatrelvir-resistant mutations within the SARS-CoV-2 virus, utilizing genomic data generated in real-time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".