Modeling of the Gas−Antisolvent (GAS) Process for Crystallization of Beclomethasone Dipropionate Using Carbon Dioxide
Bibliographic record
Abstract
The purpose of this work was to develop a mathematical model to describe the crystal size distributions (CSDs) produced from the gas−antisolvent (GAS) technique on the crystallization of beclomethasone-17,21-dipropionate (BDP), which is an anti-inflammatory corticosteroid commonly used to treat asthma. The solvent used was acetone, and the antisolvent was carbon dioxide (CO 2 ). The GAS technique was chosen for its ability to produce micrometer-sized particles of uniform size. A better understanding of how the GAS process affects the CSDs of BDP is desirable to optimize the GAS experimental conditions for the production of inhalable powders for next-generation dry-powder portable inhalers (DPIs). To describe the pressurization during the GAS process, a mass balance and a phase equilibrium model were required. A predictive relative partial molar volume fraction (RPMVF) equilibrium model was used in the absence of existing phase data for the BDP−acetone−CO 2 system. This model uses the binary two-phase solvent/CO 2 phase equilibrium, and then relates it to the solid concentration. The model was tested successfully with the phenanthrene−toluene−CO 2 model system, the naphthalene−toluene−CO 2 model system, and the more complex cholesterol−acetone−CO 2 model system before being used to predict the BDP−acetone−CO 2 system. A population balance was then used to model experimentally determined particle size distributions. Two models for secondary nucleation were used independently: (i) an empirical equation that is commonly used to model secondary nucleation, and (ii) a theory-based equation. The crystallization model was able to give good estimates of the cumulative volume (mass)-weighted size distributions metrics d p (10%), d p (50%), and d p (90%) of the experimental CSDs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".