Pretreatment Resistance to Hepatitis C Virus Protease Inhibitors Boceprevir/Telaprevir in Hepatitis C Subgenotype 1A-Infected Patients from Manitoba
Bibliographic record
Abstract
BACKGROUND: Traditional therapy with pegylated interferon and ribavirin combined with the new protease inhibitors boceprevir or telaprevir has demonstrated improved outcomes in hepatitis C virus (HCV)-infected patients. Prevalence data regarding pre-existing drug-resistant variants to these two new virus inhibitors in the Canadian population are not available. OBJECTIVE: To detect pre-existing mutations conferring resistance to boceprevir and⁄or telaprevir in Canadian patients infected with HCV genotype 1a. METHODS: Resistance-associated mutations (RAMs) were evaluated in 85 patients infected with HCV genotype 1a who had not yet received antiviral therapy. The NS3 protease gene was sequenced and common RAMs were identified based on a recently published list. RESULTS: The overall prevalence of pre-existing RAMs to boceprevir and telaprevir was higher compared with other similar studies. All of the observed RAMs were associated with a low level of resistance. A surprisingly high proportion of patients had the V55A RAM (10.6%). None of the mutations associated with a high level of resistance were observed. The simultaneous presence of two low-level resistance mutations (V36L and V55A) was observed in only one patient. Three other patients had both T54S RAM and V55I mutations, which may require a higher concentration of the protease drugs. The prevalence of various mutations in Aboriginal Canadian patients was higher (37.5%) compared with Caucasians (16.39%) (P=0.038). CONCLUSIONS: The present study was the first to investigate pre-existing drug resistance to boceprevir⁄telaprevir in Canadian HCV-infected patients. A relatively high proportion of untreated HCV genotype 1a patients in Manitoba harbour low-level RAMs, especially patients of Aboriginal descent, which may contribute to an increased risk of treatment failure.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".