Potentiation of Azole Antifungals by 2-Adamantanamine
Bibliographic record
Abstract
Azoles are among the most successful classes of antifungals. They act by inhibiting α-14 lanosterol demethylase in the ergosterol biosynthesis pathway. Oropharyngeal candidiasis (OPC) occurs in about 90% of HIV-infected individuals, and 4 to 5% are refractory to current therapies, including azoles, due to the formation of resistant biofilms produced in the course of OPC. We reasoned that compounds affecting a different target may potentiate azoles to produce increased killing and an antibiofilm therapeutic. 2-Adamantanamine (AC17) was identified in a screen for compounds potentiating the action of miconazole against biofilms of Candida albicans. AC17, a close structural analog to the antiviral amantadine, did not affect the viability of C. albicans but caused the normally fungistatic azoles to become fungicidal. Transcriptome analysis of cells treated with AC17 revealed that the ergosterol and filamentation pathways were affected. Indeed, cells exposed to AC17 had decreased ergosterol contents and were unable to invade agar. In vivo, the combination of AC17 and fluconazole produced a significant reduction in fungal tissue burden in a guinea pig model of cutaneous candidiasis, while each treatment alone did not have a significant effect. The combination of fluconazole and AC17 also showed improved efficacy (P value of 0.018) compared to fluconazole alone when fungal lesions were evaluated. AC17 is a promising lead in the search for more effective antifungal therapeutics.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".