Interactions of the hepatitis C virus protease inhibitor faldaprevir with cytochrome P450 enzymes: In vitro and in vivo correlation
Bibliographic record
Abstract
The potential inhibition of the major human cytochrome P450 (CYP) enzymes by faldaprevir was evaluated both in vitro and in clinical studies (healthy volunteers and hepatitis C virus [HCV] genotype 1-infected patients). In vitro studies indicated that faldaprevir inhibited CYP2B6, CYP2C9, and CYP3A, and was a weak-to-moderate inactivator of CYP3A4. Faldaprevir 240 mg twice daily in healthy volunteers demonstrated moderate inhibition of hepatic and intestinal CYP3A (oral midazolam: 2.96-fold increase in AUC(0-24 h)), weak inhibition of hepatic CYP3A (intravenous midazolam: 1.56-fold increase in AUC(0-24 h)), weak inhibition of CYP2C9 ([S]-warfarin: 1.29-fold increase in AUC(0-120 h)), and had no relevant effects on CYP1A2, CYP2B6, or CYP2D6. Faldaprevir 120 mg once daily in HCV-infected patients demonstrated weak inhibition of hepatic and intestinal CYP3A (oral midazolam: 1.52-fold increase in AUC(0-∞)), and had no relevant effects on CYP2C9 or CYP1A2. In vitro drug-drug interaction predictions based on inhibitor concentration ([I])/inhibition constant (Ki) ratios tended to overestimate clinical effects and a net-effect model provided a more accurate approach. These studies suggest that faldaprevir shows a dose-dependent inhibition of CYP3A and CYP2C9, and does not induce CYP isoforms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".