Drug-Drug Interactions With Antiviral Agents in People Who Inject Drugs Requiring Substitution Therapy
Bibliographic record
Abstract
OBJECTIVE: To describe potential drug-drug interactions in the area of HIV/hepatitis C virus (HCV) coinfection and injection drug use, including those between antiretrovirals (ARVs), direct-acting antivirals (DAAs), and opioid-agonist therapy, and to supply a practical approach to their management. DATA SOURCES: We searched PubMed for relevant articles published up until February 2015 as well as conference reports and drug-drug-interaction Web sites. DATA SELECTION AND DATA EXTRACTION: We used the following search terms: pharmacokinetic and pharmacodynamic drug-drug interaction, opioid substitution, HIV, hepatitis and the individual names of the relevant agents of the following drug classes and the drug classes itself: reverse transcriptase inhibitors, nonnucleoside reverse transcriptase inhibitors, protease inhibitors, direct-acting antivirals, opioide, benzodiazepines, anticonvulsants, antidepressants and antipsychotics. Additional references were identified from a review of literature citations and drug-drug interaction Web sites. In our evaluation, we included German- and English-language studies and reports addressing drug-drug interactions between opioid agonist therapy and ARVs or DAAs. DATA SYNTHESIS: Pharmacokinetic data were available for all ARVs and DAAs except rilpivirine, indinavir, saquinavir, maraviroc, dolutegravir, and MK-8742 with buprenorphine as well as maraviroc with methadone Drug-drug interactions of potential clinical relevance are most likely to occur between opioid-replacement therapy and ARVs, particularly the nonnucleoside reverse transcriptase inhibitors, efavirenz and nevirapine, and HIV protease inhibitors. CONCLUSION: Integrase inhibitors may be safely coadministered with opioid-replacement therapy. With respect to HCV DAAs, most currently approved and late-stage investigational agents do not have clinically significant interactions with opioid-replacement therapy. ARV and DAAs may interact with other drug classes commonly used in the opioid-dependent population, including benzodiazepines, antidepressants, anticonvulsants, and antipsychotics.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".