Design and Development of NS5B Polymerase Non‐nucleoside Inhibitors for the Treatment of Hepatitis C Virus Infection
Bibliographic record
Abstract
The hepatitis C virus (HCV) infects an estimated 130–170 million people worldwide and is associated with life‐threatening liver diseases. The recent introduction of the first two HCV direct‐acting antivirals (DAAs) as a complement to the interferon/ribavirin standard of care has provided patients with improved outcomes. Still, 25–30% of subjects infected with genotype 1 HCV do not respond adequately to treatment owing to the emergence of resistant virus and many suffer from severe side effects. A paradigm shift towards the development of interferon‐free combinations of DAAs with complementary modes of action is currently taking place. Virally encoded proteins and enzymes have become the target of HCV drug discovery efforts and several promising new agents are currently being evaluated in the clinic for treatment of chronic HCV infection. The NS5B RNA‐dependent RNA polymerase is responsible for replication of viral RNA and plays a pivotal role in the virus life cycle. NS5B is undoubtedly the most druggable HCV target and is susceptible to several classes of allosteric inhibitors that bind to four distinct sites on the enzyme. This chapter describes successful strategies that have led to the discovery of HCV NS5B antivirals. It is divided according to allosteric sites and describes how each of the known families of inhibitors was discovered, characterized and optimized to provide clinical candidates. When available, the strategies adopted by medicinal chemists to optimize initial leads and address challenges and liabilities encountered on the path to candidate selection are described, along with reported clinical outcomes.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".