Drug interactions between antiretrovirals and hormonal contraceptives
Bibliographic record
Abstract
INTRODUCTION: Significant advances in antiretroviral therapy have transformed HIV into a chronic manageable disease, and millions of women living with HIV now have the opportunity to reconsider their reproductive choices, be it contraception or pregnancy planning. Hormonal contraceptives are metabolized by cytochrome P450 isoenzymes and sulfate and glucuronide conjugation in the liver. Many antiretrovirals have inducing or inhibiting effects on the cytochrome P450 system. As such, the pharmacokinetics of hormonal contraceptives can be affected by antiretroviral therapy with potential for significant clinical impact. AREAS COVERED: This article presents the pharmacology and metabolism of selected antiretrovirals and hormonal contraceptives, and highlights the potential interactions between these two classes of drugs. Furthermore, the authors present the pharmacokinetic evidence of interactions from available clinical trials, product monographs, and international conference abstracts. EXPERT OPINION: Drugs most likely to interact with combined oral contraceptives, transdermal and implant contraceptives include protease inhibitors, the NNRTIs efavirenz and nevirapine, and cobicistat-boosted elvitegravir. There do not appear to be significant pharmacokinetic interactions with depo-medroxyprogesterone or intrauterine systems and antiretrovirals, although further study is needed. Clinicians working with HIV-positive women need to know the significance of these interactions in order to properly counsel patients and prevent unplanned pregnancies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".