Solubility Prediction of Pharmaceutical and Chemical Compounds in Pure and Mixed Solvents Using Predictive Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| 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) | 0.000 | 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 it