Evaluation of the Synergistic Activity of Antimicrobial Peptidomimetics or Colistin Sulphate with Conventional Antifungals Against Yeasts of Medical Importance
Bibliographic record
Abstract
With rising multidrug-resistant yeast pathogens, conventional antifungals are becoming less effective, urging the need for adjuvants that enhance their activity at lower doses. This study evaluated the synergistic activity of antimicrobial peptidomimetics (TM8 and RK758) or colistin sulphate in combination with conventional antifungals against Candida albicans, C. tropicalis, C. parapsilosis, Meyerozyma guilliermondii, Nakaseomyces glabratus, Pichia kudriavzevii and Kluyveromyces marxianus, and Candidozyma auris using the checkerboard microdilution test. RK758 was synergistic with fluconazole in 78% of isolates, with the remaining 22% of isolates still showing partial synergy; it showed synergy with amphotericin B in 56% of isolates, and with caspofungin, 78% of isolates exhibited either synergy or partial synergy. TM8 showed synergy with fluconazole in 44% (with partial synergy in another 44%) of isolates, with amphotericin B in 67% of isolates, and with caspofungin in 44% (with partial synergy in another 44%) of isolates. Colistin with fluconazole or caspofungin exhibited synergy or partial synergy in 56% of the isolates. No antagonism was observed in any of the combinations. Additionally, a time-kill assay further demonstrated synergistic activity between fluconazole and TM8 or RK758. The effects of these peptidomimetics on cell membrane integrity were demonstrated in an ergosterol binding assay, supported by SYTOX Green and cellular leakage assays, both indicating a lytic effect. These results suggest that peptidomimetics can synergise with conventional antifungals, offering a potential strategy for combination therapy against yeast infections. The membrane lytic activity of the peptidomimetics likely plays a role in their synergistic interaction with antifungals, thereby enhancing the antimicrobial activities of both compounds at sub-MIC levels.
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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 | 0.000 | 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) | 0.001 | 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".