Antifungal Drugs: The Current Armamentarium and Development of New Agents
Bibliographic record
Abstract
Invasive fungal infections are becoming an increasingly important cause of human mortality and morbidity, particularly for immunocompromised populations. The fungal pathogens Candida albicans, Cryptococcus neoformans, and Aspergillus fumigatus collectively contribute to over 1 million human deaths annually. Hence, the importance of safe and effective antifungal therapeutics for the practice of modern medicine has never been greater. Given that fungi are eukaryotes like their human host, the number of unique molecular targets that can be exploited for drug development remains limited. Only three classes of molecules are currently approved for the treatment of invasive mycoses. The efficacy of these agents is compromised by host toxicity, fungistatic activity, or the emergence of drug resistance in pathogen populations. Here we describe our current arsenal of antifungals and highlight current strategies that are being employed to improve the therapeutic safety and efficacy of these drugs. We discuss state-of-the-art approaches to discover novel chemical matter with antifungal activity and highlight some of the most promising new targets for antifungal drug development. We feature the benefits of combination therapy as a strategy to expand our current repertoire of antifungals and discuss the antifungal combinations that have shown the greatest potential for clinical development. Despite the paucity of new classes of antifungals that have come to market in recent years, it is clear that by leveraging innovative approaches to drug discovery and cultivating collaborations between academia and industry, there is great potential to bolster the antifungal armamentarium.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".