Hepatitis C therapy in non‐genotype 1 patients: the near future
Bibliographic record
Abstract
Worldwide, 50-70 million subjects are infected with an hepatitis C virus (HCV) genotype 2, 3, 4, 5 or 6. In these patients, the combination of PEG-INF-α and ribavirin remains the currently approved standard-of-care treatment. The identification of different potential therapeutic targets in the HCV life cycle has led to the development of both direct antiviral agents (DAAs) and reagents targeting host functions essential for viral replication. DAAs comprise so far first-generation, second-wave and second-generation NS3/4A protease inhibitors (PIs), nucleos(t)ide (NIs) and non-nucleoside inhibitors of the NS5B RNA polymerase and NS5A complex inhibitors. The main host-protein-directed antiviral agents are cyclophilin inhibitors and silibinin. Whereas the launch of first-generation PIs was a major landmark in the management of genotype 1 (GT-1)-infected patients, these drugs are inactive in most non-GT-1-infected patients. Several of these and other drugs have now reached phase II and even phase III clinical stage development. The purpose of this article is to provide an overview of the clinical results recently reported for the treatment for non-GT-1 HCV infection with a focus on the most promising new compounds and combinations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".