Common drug-drug interactions in antifungal treatments for superficial fungal infections
Bibliographic record
Abstract
INTRODUCTION: Antifungal agents can be co-administered alongside several other medications for a variety of reasons such as the presence of comorbidities. Pharmacodynamic interactions such as synergistic and antagonistic interactions could be the result of co-administered medications. Pharmacokinetic interactions could also transpire through the inhibition of metabolizing enzymes and drug transport systems, altering the absorption, metabolism and excretion of co-administered medications. Both pharmacodynamic and pharmacokinetic interactions can result in hospitalization due to serious adverse effects associated with antifungal agents, lower therapeutic doses required to achieve desired antifungal activity, and prevent antifungal resistance. Areas covered: The objective of this review is to summarize pharmacodynamic and pharmacokinetic interactions associated with common antifungal agents used to treat superficial fungal infections. Pharmacodynamic and pharmacokinetic interactions that impact the therapeutic effects of antifungal agents and drugs that are influenced by the presence of antifungal agents was the context to which these antifungal agents were addressed. Expert opinion: The potential for drug-drug interactions is minimal for topical antifungals as opposed to oral antifungals as they have minimal exposure to other co-administered medications. Developing non-lipophilic antifungals that have unique metabolizing pathways and are topical applied are suggested properties that could help limit drug-drug interactions associated with future treatments.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".