Influence of Human Serum on Antifungal Pharmacodynamics with <i>Candida albicans</i>
Bibliographic record
Abstract
Antifungal susceptibilities (NCCLS, approved standard M27-A, 1997) were determined for the reference strain ATCC 90028 and 21 clinical isolates of Candida albicans with varying levels of fluconazole susceptibility using RPMI 1640 (RPMI) and 80% fresh human serum-20% RPMI (serum). Sixty-four percent (14 of 22) of the isolates tested demonstrated significant decreases (> or = 4-fold) in fluconazole MICs in the presence of serum, and the remaining eight isolates exhibited no change. Itraconazole and ketoconazole, two highly protein-bound antifungal agents, had MICs in serum that were increased or unchanged for 46% (10 of 22) and 41% (9 of 22) of the isolates, respectively. All 10 isolates tested against an investigational antifungal agent, LY303366, demonstrated significant increases in the MIC required in serum, while differences in amphotericin B MICs in the two media were not observed. Four of 10 isolates tested demonstrated fourfold higher flucytosine MICs in serum than in RPMI. Postantifungal effects (PAFEs) and 24-h kill curves were determined by standard methods for selected isolates. At the MIC, fluconazole, itraconazole, ketoconazole, flucytosine, and LY303366 kill curves and PAFEs in RPMI were similar to those in serum. Isolates of fluconazole-resistant C. albicans required lower MICs in serum than in RPMI, without relative increases in fungal killing or PAFEs. Isolates tested against amphotericin B demonstrated significantly reduced killing and shorter PAFEs in serum than in RPMI without observable changes in MIC. In conclusion, antifungal pharmacodynamics in RPMI did not consistently predict antifungal activity in serum for azoles and amphotericin B. Generally speaking, antifungal agents with high protein binding exhibited some form of reduced activity (MIC, killing, or PAFE) in the presence of serum compared to those with low protein binding.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".