Design space approach in the development of esculetin nanocrystals by a small-scale wet-bead milling process
Bibliographic record
Abstract
Esculetin, a natural coumarin derived from herbs, has shown different potential pharmacological activities. However, the poor aqueous solubility of esculetin may limit its therapeutic efficacy. Nanocrystal is a promising dosage form to overcome this drawback by enhancing drug saturation solubility. Hence, a laboratory-scale wet-bead milling approach was employed to develop an esculetin nanocrystal formulation. Povacoat™ was selected as a stabilizer after screening different surfactants and polymers. Design of experiments was applied to identify and understand the relationship between process parameters and formulation compositions at the stage of formulation optimization. Furthermore, the optimized formulation was solidified using two different approaches (spray-drying and freeze-drying) and characterized by dynamic light scattering, laser diffraction, transmission electron microscopy, scanning electron microscopy, thermal analysis, and X-ray powder diffraction. An esculetin-Povacoat™ nanocrystal formulation that maintained a 200 nm particle size for 180 days was successfully obtained with specific critical process parameters and formulation composition within the design space.
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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".