Optimization of a solidified micelle formulation for enhanced oral bioavailability of atorvastatin calcium using statistical experimental design
Bibliographic record
Abstract
To enhance the oral bioavailability of atorvastatin calcium (ATV), a novel solidified micelle (S-micelle) was developed. Two surfactants, Gelucire 48/16 (G48) and Tween 20 (T20), were employed for micelle formation, and two solid carriers (SC), Florite PS-10 (FLO) and Vivapur 105 (VP105), were selected as solid carriers. The S-micelle was optimized using a Box-Behnken design with three independent variables, including G48:T20 (X1, 1.8:1), SC:G48 + T20 (X2, 0.65:1), and FLO:VP105 (X3, 1.4:0.6), resulting in a droplet size (Y1) of 198.4 nm, dissolution efficiency at 15 min in the pH 1.2 medium (Y2) of 47.6%, Carr’s index (Y3) of 16.9, and total quantity (Y4) of 562.5 mg. The optimized S-micelle resulted in good correlation showing percentage prediction values less than 10%. The optimized S-micelle formed a nanosized dispersion in the aqueous phase, with a higher dissolution rate than raw ATV and crushed Lipitor®. The optimized S-micelle improved the relative bioavailability of oral ATV (25 mg equivalent/kg) in rats by approximately 509 and 271% compared to raw ATV and crushed Lipitor®, respectively. In conclusion, the optimized S-micelle possesses great potential for the development of solidified formulations for improved oral absorption of poorly water-soluble 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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".