Antifungal Drugs: The Current Armamentarium and Development of New Agents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 it