Clinical Trial Risk in Hepatitis C: Endpoint Selection and Drug Action
Bibliographic record
Abstract
Background and Aims. This study analyzed the risk of clinical trial failure of new drugs for hepatitis C between January 1998 and January 2015. Methods. Hepatitis C drug development trials that were in phases I-III of clinical trial testing were obtained from the publicly accessible clinical trial repository and other publicly available databases. Drug compounds were excluded from the study if they began their phase I testing before 1998, if they were not industry sponsored, or if they treated secondary complications of hepatitis C. Clinical trial success rates were analyzed in comparison to industry expectations. Further analysis was conducted on the molecule classifications, the mechanisms of action, and the trial endpoints. Results. One hundred and twenty-three unique drug compounds were found to fulfill the inclusion criteria, eight of which had FDA approval. The overall cumulative pass rate for hepatitis C drugs was 20%, which is double the industry expectation rate. Viral inhibitor small molecule drugs significantly reduced the risk of drug failure during clinical trials compared to other mechanisms of action. Conclusion. On average, one in every five drugs that began clinical testing will be approved for market. Viral inhibitor small molecule drugs are the most promising and hold the least risk.
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 | 0.207 | 0.307 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".