Nano-sized Droplets of Self-Emulsifying System for Enhancing Oral Bioavailability of Chemotherapeutic Agent VP-16 in Rats: A Nano Lipid Carrier for BCS Class IV Drugs
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to investigate the ability of a self-nano-emulsifying drug delivery system (SNEDDS) to enhance the oral bioavailability of a BCS class IV drug, etoposide (VP-16). METHOD: A series of SNEDDS formulations with VP-16 were prepared consisting of medium chain triglycerides, polysorbate 80, diethylene glycol monoethyl ether and propylene glycol monolaurate type-1. Based on an obtained ternary phase diagram, an optimum formulation was selected and characterized in terms of size, zeta potential, loading, morphology and in vitro drug release. The pharmacokinetic parameters and oral bioavailability of VP-16 suspension and VP-16 in SNEDDS was assessed using 30 Male Sprague-Dawley rats and compared with the commercial product (VePesid®). RESULTS: Pharmacokinetic data showed that the mean values for AUC0-t of VP-16 in SNEDDS was 6.4 fold higher compared to a drug suspension and 2.4-folds higher than VePesid®. Similarly, the mean value for Cmax of VP-16 in SNEDDS (1.13± 0.07 µg/ml µg.h/mL) was higher than VePesid® (0.62± 0.09 µg/mL) and drug suspension (0.13± 0.07 µg/mL). CONCLUSION: The SNEDDS formulation was able to enhance the oral bioavailability of the BCS Class IV chemotherapeutic agent VP-16 by increasing the dissolution and absorption of the drug. A good in vitro in vivo correlation was found between the in vitro dissolution and in vivo absorption data of VP-16 SNEDDS preparation. Therefore, SNEDDS formulations might be a very promising approach for BCS Class IV drugs.
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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".