Role of Surfactant Micellization for Enhanced Dissolution of Poorly Water-Soluble Cilostazol Using Poloxamer 407-Based Solid Dispersion via the Anti-Solvent Method
Bibliographic record
Abstract
This study aimed to investigate the role of micellization of sodium lauryl sulfate (SLS) in poloxamer 407 (POX)-based solid dispersions (POX-based SDs) using the anti-solvent method in enhancing the dissolution rate of practically water-insoluble cilostazol (CLT). Herein, SLS was incorporated into CLT-loaded SDs, at a weight ratio of 50:50:10 of CLT, POX, and SLS by three different methods: anti-solvent, fusion (60 °C), and solvent (ethanol) evaporation. The SDs containing micellar SLS in the anti-solvent method were superior in the transformation of the crystalline form of the drug into a partial amorphous state. It was notable that there was an existence of a hydrophobic interaction between the surfactant and the hydrophobic regions of polymer chain via non-covalent bonding and the adsorption of micellar SLS to the POX-based SDs matrix. Moreover, SLS micellization via the anti-solvent method was effectively interleaved in SDs and adhered by the dissolved CLT, which precluded drug particles from aggregation and recrystallization, resulting in improved SD wettability (lower contact angle) and reduced particle size and dissolution rate. In contrast, SDs without micellar SLS prepared by the solvent method exerted drug recrystallization and an increase of particle size, resulting in decreased dissolution. Incorporation of surfactant below or above critical micellar concentration (CMC) in SDs using the anti-solvent method should be considered in advance. Dissolution results showed that the pre-added incorporation of micellar SLS into POX-based SDs using the anti-solvent method could provide a way of a solubilization mechanism to enhance the dissolution rate 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 | 0.000 | 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) | 0.000 | 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".