Preparation of nobiletin in self-microemulsifying systems and its intestinal permeability in rats
Bibliographic record
Abstract
PURPOSE: The objective of this study was to prepare nobiletin self-microemulsifying drug delivery systems (SMEDDS) and investigate its intestinal transport behavior using the single-pass intestinal perfusion (SPIP) method in rat. METHODS: The characterizations of nobiletin SMEDDS were investigated. SPIP was performed in each isolated region of the small intestine (i.e. duodenum, jejunum, ileum and colon) over three concentrations of nobiletin (15, 30 and 60 microg/mL) at a flow rate of 0.2 ml/min. The concentrations of the samples were determined by HPLC and the effective permeability coefficients (Peff) in rats were calculated. Considering the high correlation of rat Peff values with those of human, the human intestinal permeability was predicted using the Lawrence compartment absorption and transit model. The intestinal permeability of nobiletin in SMEDDS, sub-microemulsions and micelles was compared. RESULTS: The particle size and zeta potential of nobiletin SMEDDS were (28.6+/-0.3) nm and (-22.6+/-3.5) mV, respectively. The Peff in jejunum at 15 microg/mL was significantly higher than that at 60 microg/mL (p>0.01). The Peff in colon was higher at the same concentration comparing to the other intestinal segments. Moreover, there was no statistical difference in Peff at each same concentration in jejunum, duodenum and ileum. The estimated human absorption of nobiletin for the SMEDDS dilutions was higher than that for sub-microemulsions (p>0.01) and similar to that of the micelles (p>0.05). CONCLUSIONS: Bases on the above results, the SMEDDS could enhance the intestinal permeability of the nobiletin, and may be presented as potential candidates for improving the oral absorption of the noblietin.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".