Triazole antifungal drug interactions—practical considerations for excellent prescribing
Bibliographic record
Abstract
Systemic antifungal therapy is critical for reducing the mortality from many invasive and chronic fungal infections. Triazole antifungals are the most frequently prescribed antifungals but require attention to dosing and drug interactions. Nearly 600 severe drug-drug interactions and over 1100 moderate interactions requiring dose modifications are described or anticipated with systemic antifungal agents (see https://www.aspergillus.org.uk/antifungal-drug-interactions/). In this article, we address the common and less common, but serious, drug interactions observed in clinical practice with triazole antifungals, including a group of drugs that cannot be prescribed with all or most triazole antifungals (ivabradine, ranolazine, eplerenone, fentanyl, apomorphine, quetiapine, bedaquiline, rifampicin, rifabutin, sirolimus, phenytoin and carbamazepine). We highlight interactions with drugs used in children and new agents introduced for the treatment of haematological malignancies or graft versus host disease (midostaurin, ibrutinib, ruxolitinib and venetoclax). We also summarize the multiple interactions between oral and inhaled corticosteroids and triazole antifungals, and the strategies needed to optimize the therapeutic benefits of triazole antifungal therapy while minimizing potential harm to patients.
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 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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".