Characteristics of hepatitis C virus resistance in an international cohort after a decade of direct-acting antivirals
Bibliographic record
Abstract
Background & Aims: Direct-acting antiviral (DAA) regimens provide a cure in >95% of patients with chronic HCV infection. However, in some patients in whom therapy fails, resistance-associated substitutions (RASs) can develop, limiting retreatment options and risking onward resistant virus transmission. In this study, we evaluated RAS prevalence and distribution, including novel NS5A RASs and clinical factors associated with RAS selection, among patients who experienced DAA treatment failure. Methods: SHARED is an international consortium of clinicians and scientists studying HCV drug resistance. HCV sequence linked metadata from 3,355 patients were collected from 22 countries. NS3, NS5A, and NS5B RASs in virologic failures, including novel NS5A substitutions, were examined. Associations of clinical and demographic characteristics with RAS selection were investigated. Results: The frequency of RASs increased from its natural prevalence following DAA exposure: 37% to 60% in NS3, 29% to 80% in NS5A, 15% to 22% in NS5B for sofosbuvir, and 24% to 37% in NS5B for dasabuvir. Among 730 virologic failures, most were treated with first-generation DAAs, 94% had drug resistance in ≥1 DAA class: 31% single-class resistance, 42% dual-class resistance (predominantly against protease and NS5A inhibitors), and 21% triple-class resistance. Distinct patterns containing ≥2 highly resistant RASs were common. New potential NS5A RASs and adaptive changes were identified in genotypes 1a, 3, and 4. Following DAA failure, RAS selection was more frequent in older people with cirrhosis and those infected with genotypes 1b and 4. Conclusions: Drug resistance in HCV is frequent after DAA treatment failure. Previously unrecognized substitutions continue to emerge and remain uncharacterized. Lay summary: Although direct-acting antiviral medications effectively cure hepatitis C in most patients, sometimes treatment selects for resistant viruses, causing antiviral drugs to be either ineffective or only partially effective. Multidrug resistance is common in patients for whom DAA treatment fails. Older patients and patients with advanced liver diseases are more likely to select drug-resistant viruses. Collective efforts from international communities and governments are needed to develop an optimal approach to managing drug resistance and preventing the transmission of resistant viruses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".