The cyclophilin inhibitor Debio 025 combined with PEG IFNα2a significantly reduces viral load in treatment-naïve hepatitis C patients #
Bibliographic record
Abstract
UNLABELLED: The anti-hepatitis C virus (HCV) effect and safety of three different oral doses of the cyclophilin inhibitor Debio 025 in combination with pegylated interferon-alpha2a (PEG IFN-alpha2a) were investigated in a multicenter, randomized, double-blind, placebo-controlled escalating dose-ranging phase II study in treatment-naïve patients with chronic hepatitis C. Doses of 200, 600, and 1,000 mg/day Debio 025 in combination with PEG IFN-alpha2a 180 microg/week for 4 weeks were compared with monotherapy with either 1,000 mg/day Debio 025 or 180 microg/week PEG IFN-alpha2a. In patients with genotypes 1 and 4, the 600- and 1,000-mg combination treatments induced a continuous decay in viral load that reached -4.61 +/- 1.88 and -4.75 +/- 2.19 log(10) IU/mL at week 4, respectively. In patients with genotypes 2 and 3, HCV RNA levels at week 4 were reduced by -5.91 +/- 1.11 and -5.89 +/- 0.43 log(10) IU/mL, respectively, with the same treatment regimens. Adverse events were comparable between treatment groups apart from a higher incidence of neutropenia associated with PEG IFN-alpha2a and an increased incidence of isolated hyperbilirubinemia at the highest dose of Debio 025 (1,000 mg/day). CONCLUSION: These results confirm that Debio 025 has a potent activity and an additive effect on HCV RNA reduction in genotype 1 and 4 patients at 600 and 1,000 mg/day when combined with PEG IFN-alpha2a.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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 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".