Exploiting diverse chemical collections to uncover novel antifungals
Bibliographic record
Abstract
The rise in drug resistance amongst pathogenic fungi, paired with the limited arsenal of antifungals available is an imminent threat to our medical system. To address this, we screened two distinct compound libraries to identify novel strategies to expand the antifungal armamentarium. The first collection wasthe RIKEN Natural Product Depository (NPDepo), which was screened for antifungal activity against four major human fungal pathogens: Candida albicans, Candida glabrata, Candida auris, and Cryptococcus neoformans. Through a prioritization pipeline, one compound, NPD6433, emerged as having broad-spectrum antifungal activity and minimal mammalian cytotoxicity. Chemical-genetic and biochemical assays demonstrated that NPD6433 inhibits the essential fungal enzyme fatty acid synthase 1 (Fas1). Treatment with NPD6433 inhibited various virulence traits in C. neoformans and C. auris, and rescued mammalian cell growth in a co-culture model with C. auris. The second compound library screened was adiversity-oriented collectionfrom Boston University. This chemical screen was focused on identifying novel molecules that enhance the activity of the widely deployed antifungal, fluconazole, against C. auris. Through this endeavour, we discovered a potent compound that enhanced fluconazole efficacy against C. auris through increasing azole intracellular accumulation. This activity was dependent on expression of the multidrug transporter geneCDR1, suggesting that this compound targets efflux mechanisms. Furthermore, this molecule significantly reduced fungal burden alone and in combination with fluconazole in a murine model of C. auris disseminated infection. Overall, this work identifies novel compounds with bioactivity against fungal pathogens, revealing important biology, and paving the way for the critical development of therapeutic strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".