Broad-Spectrum Antifungal Activities and Mechanism of Drimane Sesquiterpenoids
Bibliographic record
Abstract
ABSTRACT Eight drimane sesquiterpenoids including (-)-drimenol and (+)-albicanol were synthesized from (+)-sclareolide and evaluated for their antifungal activities. Three compounds, (-)-drimenol, (+)-albicanol, and (1 R ,2 R ,4a S ,8a S )-2-hydroxy-2,5,5,8a-tetramethyl-decahydronaphthalene-1-carbaldehyde ( 4 ) showed strong activity against C. albicans . (-)-Drimenol, the strongest inhibitor of the three, (at concentrations of 8 – 64 μg/ml, causing 100% death of fungi), acts not only against C. albicans as a fungicidal manner, but also inhibits other fungi such as Aspergillus , Cryptococcus , Pneumocystis , Blastomyces , Fusarium , Rhizopus , Saksenaea and FLU resistant strains of C. albicans , C. glabrata, C. krusei , C. parapsilosis and C. auris. These observations suggest drimenol is a broad-spectrum antifungal agent. At high concentration (100 μg/ml), drimenol caused a rupture of the fungal cell wall/membrane. In a nematode model of C. albicans infection, drimenol rescued the worms from C. albicans -mediated death, indicating drimenol is tolerable and bioactive in a metazoan. Genome-wide fitness profiling assays of both S. cerevisiae (nonessential homozygous and essential heterozygous) and C. albicans (Tn-insertion mutants) collections revealed putative genes and pathways affected by drimenol. Using a C. albicans mutants spot assay, the Crk1 kinase associated gene products, Ret2, Cdc37, and novel putative targets orf19.759, orf19.1672, and orf19.4382 were revealed to be the potential targets of drimenol. Further, computational modeling results suggest possible modification of the structure of drimenol including the A ring for improving antifungal activity.
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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 | 0.001 | 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".