Recent advances in the development of NS5B polymerase inhibitors for the treatment of hepatitis C virus infection
Bibliographic record
Abstract
BACKGROUND: 170 to 200 million people worldwide are believed to suffer from chronic hepatitis C virus (HCV) infection, a blood-born disease that targets the liver and progresses to organ cirrhosis and primary cancer in a significant proportion of patients. The currently available treatment has limited efficacy and suffers from restricting side effects. HCV infection is the principal cause of liver transplant in industrialized nations and between 8000 and 10,000 deaths result annually from the disease in the United States alone. Virus-specific, more efficacious, and better-tolerated anti-HCV therapies are thus required to address the unmet medical need. OBJECTIVE: To review progress achieved since 2005 in the development of HCV NS5B polymerase inhibitors as potential therapy for the treatment of HCV infection with a primary focus on available patent and medical literature. RESULTS/CONCLUSION: Several classes of small-molecule inhibitors of HCV NS5B have progressed into clinical development and demonstrated efficacy in reducing viral load in infected patients. The results so far provide an encouraging foundation for the development of novel, more tolerable therapies and addressing emergence of resistance through combination of antiviral agents with complementary mechanisms of action.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.005 | 0.002 |
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".