PEGylated Bile Acids for Use in Drug Delivery Systems: Enhanced Solubility and Bioavailability of Itraconazole
Bibliographic record
Abstract
Itraconazole is a drug of choice for the treatment of severe fungal infections and parasitic diseases, but its use is limited by its low water solubility and varying bioavailability. New self-emulsifying drug delivery systems (SEDDS) based on PEGylated bile acids (BA-PEGs) were designed and prepared, where the number and length of PEG arms were varied to optimize the loading of itraconazole in the final drug formulation. The use of both BA-PEGs and oleic acid improved the solubilization and absorption of the drug, which was in a glassy state in the SEDDS prepared with the melting method. High loading efficiencies of itraconazole (up to 20%) and stable liquid formulations were obtained at neutral pH, and full dispersion of itraconazole was reached in 2 h in simulated intestinal fluid (pH 6.8). Aqueous emulsions consisting of spherical micelles with mean hydrodynamic diameters (Dh) of ca. 75-220 nm, as verified by transmission electron microscopy and dynamic light scattering, are expected to improve the intestinal absorption of the drug. The new SEDDS showed good cytocompatibility by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays of BA-PEGs with Caco-2 and RAW 264.2 cells, and a low degree of hemolysis of human erythrocytes. The SEDDS based on PEGylated bile acids provide a controlled release system with significant improvement of the bioavailability of itraconazole in rats, as demonstrated by the pharmacokinetic studies.
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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".