Antifungal Drug Resistance: Molecular Mechanisms in <i>Candida albicans</i> and Beyond
Bibliographic record
Abstract
Fungal infections are a major contributor to infectious disease-related deaths across the globe. Candida species are among the most common causes of invasive mycotic disease, with Candida albicans reigning as the leading cause of invasive candidiasis. Given that fungi are eukaryotes like their human host, the number of unique molecular targets that can be exploited for antifungal development remains limited. Currently, there are only three major classes of drugs approved for the treatment of invasive mycoses, and the efficacy of these agents is compromised by the development of drug resistance in pathogen populations. Notably, the emergence of additional drug-resistant species, such as Candida auris and Candida glabrata, further threatens the limited armamentarium of antifungals available to treat these serious infections. Here, we describe our current arsenal of antifungals and elaborate on the resistance mechanisms Candida species possess that render them recalcitrant to therapeutic intervention. Finally, we highlight some of the most promising therapeutic strategies that may help combat antifungal resistance, including combination therapy, targeting fungal-virulence traits, and modulating host immunity. Overall, a thorough understanding of the mechanistic principles governing antifungal drug resistance is fundamental for the development of novel therapeutics to combat current and emerging fungal threats.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".