The influence of co-solvents on the stability and bioavailability of rapamycin formulated in self-microemulsifying drug delivery systems
Bibliographic record
Abstract
OBJECTIVE: This work aims to investigate the influence of various types and different contents of co-solvent on the stability and bioavailability of rapamycin formulated in self-microemulsifying drug delivery systems (SMEDDS). METHODS: A series of SMEDDS of rapamycin were prepared with different co-solvents [including PEG 400/ethanol (F1), glycerol/ethanol (F2), propylene glycol (F3), glycerol formal (F4), transcutol P (F5)]. Drug stability in aqueous media at different pH values and in vitro dispersion of SMEDDS were investigated prior to bioavailability assessment. The storage stability of rapamycin in formulations was also evaluated. RESULTS AND DISCUSSION: The AUC values of rapamycin following oral administration of F1, F3-F5 to rats were significantly higher than those of Rapamune and F0 (SMEDDS without co-solvent). Interestingly, a tendency toward increased bioavailability was seen in F1-F5, which presented the better drug stability in pH 1.2 aqueous media. However, a further increase of the content of co-solvent did not effectively improve the oral bioavailability of rapamycin. Compared with F0, F1-F5 presented significant improvement of drug storage stability. More specifically, the more--OH per unit mass co-solvent had, the better stability rapamycin presented in formulation. CONCLUSIONS: The data obtained in present study highlight the importance of co-solvents on the stability and bioavailability of rapamycin formulated in SMEDDS. Besides solubilizing drug and increasing the dispersion rate, co-solvent could markedly affect the stability of rapamycin whether in different aqueous media or during storage and contribute to the improved oral bioavailability; it can also appropriately decrease the content of surfactant without compromising the absorption of drug.
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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".