Interactions between antifungal and antiretroviral agents
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Since the advent of combination antiretroviral therapy, the incidence of opportunistic infections has declined and the life expectancy of HIV-infected people has significantly increased. However, opportunistic infections, including fungal diseases, remain a leading cause of hospitalizations and mortality in HIV-infected people. With the availability of several new antiretroviral and antifungal agents, drug-drug interactions emerge as a potential safety concern. AREAS COVERED IN THIS REVIEW: Relevant literature was identified using a Medline search of articles published up to March 2010 and a review of conference abstracts. Search terms included HIV, antifungal agents and drug interactions. Original papers and relevant citations were considered for this review. WHAT THE READER WILL GAIN: Readers will gain an understanding of the pharmacokinetic properties of antiretroviral and antifungal agents, and insight into significant drug-drug interactions which may require dosage adjustments or a change in therapy. TAKE HOME MESSAGE: Azole antifungal drugs, with the exception of fluconazole, pose the greatest risk of two-way interactions with antiretroviral drugs through CYP450 enzymes effects. Limited studies suggest the risk of interactions between antiretroviral drugs and echinocandins is much lower. The combination of tenofovir and amphotericin B should be used with caution and close monitoring of renal function is required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".