Assessment of Drug–Drug Interaction Risk Between Intravenous Fentanyl and the Glecaprevir/Pibrentasvir Combination Regimen in Hepatitis C Patients Using Physiologically Based Pharmacokinetic Modeling and Simulations
Bibliographic record
Abstract
INTRODUCTION: An unsafe injection practice is one of the major contributors to new hepatitis C virus (HCV) infections; thus, people who inject drugs are a key population to prioritize to achieve HCV elimination. The introduction of highly effective and well-tolerated pangenotypic direct-acting antivirals, including glecaprevir/pibrentasvir (GLE/PIB), has revolutionized the HCV treatment landscape. Glecaprevir is a weak cytochrome P450 3A4 (CYP3A4) inhibitor, so there is the potential for drug-drug interactions (DDIs) with some opioids metabolized by CYP3A4, such as fentanyl. This study estimated the impact of GLE/PIB on the pharmacokinetics of intravenous fentanyl by building a physiologically based pharmacokinetic (PBPK) model. METHODS: A PBPK model was developed for intravenous fentanyl by incorporating published information on fentanyl metabolism, distribution, and elimination in healthy individuals. Three clinical DDI studies were used to verify DDIs within the fentanyl PBPK model. This model was integrated with a previously developed GLE/PIB PBPK model. After model validation, DDI simulations were conducted by coadministering GLE 300 mg + PIB 120 mg with a single dose of intravenous fentanyl (0.5 µg/kg). RESULTS: The predicted maximum plasma concentration ratio between GLE/PIB + fentanyl and fentanyl alone was 1.00, and the predicted area under the curve ratio was 1.04, suggesting an increase of only 4% in fentanyl exposure. CONCLUSION: The administration of a therapeutic dose of GLE/PIB has very little effect on the pharmacokinetics of intravenous fentanyl. This negligible increase would not be expected to increase the risk of fentanyl overdose beyond the inherent risks related to the amount and purity of the fentanyl received during recreational use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".