Supercritical Carbon Dioxide for Pharmaceutical Co-Crystal Production
Bibliographic record
Abstract
Pharmaceuticals in Biopharmaceutics Classification System (BCS) Class II (low solubility, high permeability) are often modified to improve kinetic solubility. Co-crystallization and micronization are common methods for improving kinetic solubility. The basis of understanding co-crystallization processes is solubility and phase stability. In the majority of co-crystallizations, conventional solvents are utilized. Co-crystallization using supercritical carbon dioxide as a co-solvent and antisolvent can offer advantages over conventional co-crystallization including a greener solvent choice and the production of small, uniform particles without additional micronization. Gas antisolvent is the most widely reported supercritical fluid (SCF) co-crystallization process possibly due to its versatility in solvent selection and similarities to conventional antisolvent processes. This review focused on exploring critical co-crystallization parameters and feasibility of SCF techniques. In this review, it was identified that solvent choice proves to be one of the most critical parameters, impacting morphology, yield, phase purity, or polymorph to different extents. It was also identified that a systematic study of solubility to design co-crystallization processes is needed to optimize SCF co-crystallization yield and throughput. Furthermore, a focus on solubility and modeling of multicomponent systems and development of ternary phase diagrams can lead to robust, tailored co-crystallization processes in SCF systems, transitioning this technology to become more common in industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".