Nystatin nanosizing enhances in vitro and in vivo antifungal activity against Candida albicans
Bibliographic record
Abstract
OBJECTIVES: In this study, we developed a nanoparticulate nystatin formulation and performed a comparative evaluation against a commercial nystatin preparation of its in vitro and in vivo antifungal activities. METHODS: A nystatin nanosuspension was prepared from a commercially available suspension by wet-media milling. The nanosuspension was characterized for particle size by laser diffraction and assayed for content by HPLC. Its in vitro activity was evaluated against Candida albicans strains SC5314 and LAM-1 (12.5-5000 μg/mL) using an agar plate assay and its in vivo efficacy was evaluated using a murine model of oral candidiasis. Briefly, DBA/2 mice were immunosuppressed with cortisone acetate, orally infected with C. albicans strain LAM-1, and treated for 14 days with conventional nystatin suspension, nystatin nanosuspension or saline control. Efficacy endpoints were oral fungal burden, mouse survival and organ histopathology. A single-dose pharmacokinetic study was also performed. RESULTS: The median particle size of the nystatin suspension was reduced from 6577 to 137 nm. The HPLC assay demonstrated a nystatin content of 98.7% ± 0.8% of the label claim. In vitro activity was superior to that of the conventional nystatin suspension at 100-5000 μg/mL concentrations. Beginning on day 3 of treatment, lower oral burdens of C. albicans were found in the nanosuspension group compared with the suspension and control groups. Mouse survival was also superior in the nanosuspension group. No systemic absorption was observed. CONCLUSIONS: Taken together, these data reveal that nanonization of nystatin provides a novel approach to enhancing its efficacy in the treatment of oral candidiasis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".