High throughput research and evaporation rate modeling for solvent screening for ethylcellulose barrier membranes in pharmaceutical applications
Bibliographic record
Abstract
CONTEXT: Ethylcellulose is commonly dissolved in a solvent or formed into an aqueous dispersion and sprayed onto various dosage forms to form a barrier membrane to provide controlled release in pharmaceutical formulations. Due to the variety of solvents utilized in the pharmaceutical industry and the importance solvent can play on film formation and film strength it is critical to understand how solvent can influence these parameters. OBJECTIVE: To systematically study a variety of solvent blends and how these solvent blends influence ethylcellulose film formation, physical and mechanical film properties and solution properties such as clarity and viscosity. MATERIALS AND METHODS: Using high throughput capabilities and evaporation rate modeling, thirty-one different solvent blends composed of ethanol, isopropanol, acetone, methanol, and/or water were formulated, analyzed for viscosity and clarity, and narrowed down to four solvent blends. Brookfield viscosity, film casting, mechanical film testing and water permeation were also completed. RESULTS AND DISCUSSION: High throughput analysis identified isopropanol/water, ethanol, ethanol/water and methanol/acetone/water as solvent blends with unique clarity and viscosity values. Evaporation rate modeling further rank ordered these candidates from excellent to poor interaction with ethylcellulose. Isopropanol/water was identified as the most suitable solvent blend for ethylcellulose due to azeotrope formation during evaporation, which resulted in a solvent-rich phase allowing the ethylcellulose polymer chains to remain maximally extended during film formation. Consequently, the highest clarity and most ductile films were formed. CONCLUSION: Employing high throughput capabilities paired with evaporation rate modeling allowed strong predictions between solvent interaction with ethylcellulose and mechanical film properties.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".