Fitness Trade-Offs Associated with the Evolution of Resistance to Antifungal Drug Combinations
Bibliographic record
Abstract
The evolution of drug resistance threatens human health worldwide. An emerging strategy to mitigate drug resistance is combination therapy. The fate of multidrug-resistant pathogens depends on their fitness relative to susceptible counterparts, yet the fitness consequences of multidrug resistance remain enigmatic. Here, we dissect fitness consequences of the evolution of resistance to antifungal drug combinations in the leading human fungal pathogen, Candida albicans. We focus on the most widely deployed antifungals, the azoles, and inhibitors of the molecular chaperone Hsp90 and protein phosphatase calcineurin, which regulate cellular stress responses required for azole resistance. We find tradeoffs such that adaptation to drug combinations is associated with reduced fitness in distinct environments, including those relevant to the human host. We identify mutations associated with fitness tradeoffs in clinical isolates and that influence morphogenesis, a key virulence trait. Thus, we delineate evolutionary constraints that may minimize the evolution of resistance to antifungal combinations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".