Characterization of hepatitis C virus resistance to grazoprevir reveals complex patterns of mutations following on-treatment breakthrough that are not observed at relapse
Bibliographic record
Abstract
PURPOSE: A detailed analysis of hepatitis C virus (HCV) resistance-associated substitutions (RASs) is required to understand why people fail direct-acting antiviral therapies. This study was conducted to assess RASs in an analysis of 2 trials evaluating the second-generation NS3/4A protease inhibitor grazoprevir (GZR) in combination with peginterferon/ribavirin. PATIENTS AND METHODS: From a total of 113 participants with HCV genotype 1 infection, RASs were evaluated in 25 patients who relapsed and 6 patients with on-treatment virologic breakthrough using consensus Sanger and clonal sequence analysis of NS3/NS4a genes, with in vitro testing of replicon mutants. Next-generation sequencing (NGS) was used in a subset of participants to assess minority variants and the evolution of the whole viral genome. RESULTS: Baseline RASs did not predict treatment failure. Relapse was most commonly associated with RASs at D168, although additional RASs (Y56, R155 and A156) were also detected, particularly in participants with on-treatment breakthrough. Treatment-emergent RASs usually reverted to wild-type (WT), suggesting these mutations were associated with a negative fitness cost (confirmed using in vitro assays). NGS was the most sensitive assay for the detection of minor variants. Significant viral sequence divergence (up to 5.9% codons) was observed across whole genomes in association with the acquisition and reversion of RASs. CONCLUSION: Relapse with GZR and peginterferon/ribavirin is commonly associated with single RASs in NS3 that generally revert to WT, while breakthrough follows more complex patterns of viral resistance. NGS suggests that large diverse pools of viral quasispecies that emerge with RASs facilitate rapid viral evolution.
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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 | 0.001 | 0.002 |
| 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".