Use of whole genome sequencing to identify low‐frequency mutations in SARS‐CoV‐2 patients treated with remdesivir
Bibliographic record
Abstract
BACKGROUND: Remdesivir (RDV) has been shown to reduce hospitalization and mortality in COVID-19 patients. Resistance mutations caused by RDV are rare and have been predominantly reported in patients who are on prolonged therapy and immunocompromised. We investigate the effects of RDV treatment on intra-host SARS-CoV-2 diversity and low-frequency mutations in moderately ill hospitalized COVID-19 patients and compare them to patients without RDV treatment. METHODS: From March 2020 to April 2022, sequential collections of nasopharyngeal and mid-turbinate swabs were obtained from 14 patients with and 30 patients without RDV treatment. Demographic and clinical data on all patients were reviewed. A total of 109 samples were sequenced and mutation analyses were performed. RESULTS: Previously reported drug resistant mutations in nsp12 were not identified during short courses of RDV therapy. In genes encoding and surrounding the replication complex (nsp6-nsp14), low-frequency minority variants were detected in 7/14 (50%) and 18/30 (60%) patients with and without RDV treatment, respectively. We did not detect significant differences in within-host diversity and positive selection between the RDV-treated and untreated groups. CONCLUSIONS: Minimal intra-host variability and stochastic low-frequency variants detected in moderately ill patients suggests little selective pressure in patients receiving short courses of RDV. The barrier to RDV resistance is high in patients with moderate disease. Patients undergoing short regimens of RDV therapy should continue to be monitored.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".