Progress and prospects for targeting Hsp90 to treat fungal infections
Bibliographic record
Abstract
Fungal pathogens pose a major threat to human health worldwide. They infect billions of people each year, leading to at least 1·5 million deaths. Treatment of fungal infections is difficult due to the limited number of clinically useful antifungal drugs, and the emergence of drug resistance. A promising new strategy to enhance the efficacy of antifungal drugs and block the evolution of drug resistance is to target the molecular chaperone Hsp90. Pharmacological inhibitors of Hsp90 function that are in development as anticancer agents have potential to be repurposed as agents for combination antifungal therapy for some applications, such as biofilm infections. For systemic infections, however, effective combination therapy regimens may require Hsp90 inhibitors that can selectively target Hsp90 in the pathogen, or alternate strategies to compromise function of the Hsp90 chaperone machine. Selectively impairing Hsp90 function in the pathogen could in principle be achieved by targeting Hsp90 co-chaperones or regulators of Hsp90 function that are more divergent between pathogen and host than Hsp90. Antifungal combination therapies could also exploit downstream effectors of Hsp90 that are critical for fungal drug resistance and virulence. Here, we discuss the progress and prospects for establishing Hsp90 as an important therapeutic target for life-threatening fungal infections.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".