Solubility Prediction of Pharmaceutical and Chemical Compounds in Pure and Mixed Solvents Using Predictive Models
Bibliographic record
Abstract
Thermodynamic models offer a fast, reliable, and cost-effective method to select the best solvent or solvent mixtures for crystallization of solid components. To optimize the performance of the unit operations which produce active pharmaceutical ingredients (APIs), the physical properties of the solute and solvent must be known. Solubility prediction is very crucial in the fine and specialty chemical industries, as the total cost of production is high in most cases. In this study, the solubility of three chemical compounds, 3-pentadecylphenol, lovastatin, and valsartan, in different solvents and solvent mixtures were studied experimentally and theoretically. The thermodynamic models of the UNIFAC and the NRTL-SAC model were used for prediction. The results of the prediction from the two models and their average relative deviation for the three model compounds showed a better performance for the NRTL-SAC model compared to the UNIFAC. For the case of lovastatin and valsartan, the NRTL-SAC model gives the average relative deviation of 0.2401 and 0.3843, respectively. Because of the flexibility of the NRTL-SAC program code that is written for the phase behavior prediction, it can be used for further analysis and optimization of the performance of crystallization processes (i.e., solvent screening and yield of the process). This study shows that the NRTL-SAC model can be used effectively in pharmaceutical industry, especially for solvent screening purposes.
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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 | 0.001 |
| 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 | 0.000 |
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".