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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".