Drug Interactions With Direct-Acting Antivirals for Hepatitis C
Bibliographic record
Abstract
OBJECTIVE: Review pharmacokinetics of new direct-acting antivirals (DAAs) for hepatitis C (HCV) infection and interactions with concomitant immunosuppressant and antiretroviral therapies (ART). DATA SOURCES: MEDLINE (1948-January 2015), EMBASE (1964-January 2015), International Pharmaceutical Abstracts (1970-January 2015), Google, and Google Scholar were searched combining the terms simeprevir, sofosbuvir, ledipasvir, daclatasvir, paritaprevir, ABT-450, ombitasvir, dasabuvir, pharmacokinetics, drug interaction, drug metabolism, HIV, antiretroviral, immunosuppressant, transplant. Articles, conference proceedings, abstracts, and product monographs were reviewed. STUDY SELECTION AND DATA EXTRACTION: Literature on pharmacokinetic or pharmacodynamic interactions with DAAs and immunosuppressants or ART was considered for inclusion. Pertinent information was extracted and summarized in the review. In the absence of data, pharmacokinetic and pharmacodynamic principles were used to predict the likelihood of interactions. DATA SYNTHESIS: DAA pharmacokinetics are reviewed and drug interaction data are presented with provision of management strategies. Fixed-dose combination paritaprevir/ritonavir/ombitasvir plus dasabuvir is most susceptible to drug interactions with immunosuppressants and ART mainly due to the influence of ritonavir on multiple enzymes. Simeprevir is also prone to drug interactions because of cytochrome P450(CYP) 3A4, CYP1A2, P-glycoprotein, and OATP1 involvement and is not recommended for use in combination with several HIV antiretrovirals (ARVs). Close therapeutic drug monitoring of calcineurin inhibitors is required with concomitant simeprevir. Few clinically significant interactions are expected with sofosbuvir or ledipasvir. Limited data suggest that daclatasvir may be coadministered with immunosuppressants but requires dose adjustments with certain ARVs. CONCLUSIONS: None of the DAAs are completely free of drug interactions. Awareness and management of drug interactions is critical to optimize outcomes and minimize adverse effects in these patient populations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".