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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".