Influence of solvent selection and RESS processing conditions on formation of a praziquantel-malonic acid cocrystal in supercritical CO2
Bibliographic record
Abstract
Praziquantel (PZQ) is an anthelmintic drug with low solubility, therefore cocrystallization and particle size reduction is desirable to improve bioavailability. In this study, a PZQ-malonic acid cocrystal was micronized by rapid expansion of supercritical solution (RESS). Due to low solubility in scCO 2 , four cosolvents were screened as RESS modifiers. While addition of acetone or THF yielded mixtures of PZQ and its cocrystal, MeOH and EtOH produced pure cocrystal. Impact of pressure (15–30 MPa), temperature (35–55 °C), and cosolvent loading (3–10 volumes) on phase-purity, yield, and particle size were investigated. Adding cosolvent to RESS facilitated dissolution of cocrystal formers in scCO 2 and crystallization of the cocrystal with yields up to 68.5 wt% and particle size as low as 600 nm. Results show that for APIs with low solubility in scCO 2 , cosolvent-modified RESS is a suitable approach for simultaneous crystallization and micronization. • A praziquantel-malonic acid cocrystal was made by RESS and reactive crystallization. • The API solubility in pure scCO 2 was too low for using the RESS process. • Four cosolvents with different properties were screened for RESS. • Effect of pressure, temperature, solvent amount was studied with MeOH cosolvent. • Yields up to 69 % were obtained from RESS with MeOH cosolvent.
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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) | 0.000 | 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".