Review of the Cosolvency Models for Predicting Drug Solubility in Solvent Mixtures: An Update
Bibliographic record
Abstract
The cosolvency models frequently used in solubility data modeling of drugs in mixed solvents were reviewed and their accuracies for calculating the solubility of solutes were briefly discussed. The models could be used either for correlation of the generated solubility data with temperature, solvent composition etc or for prediction of unmeasured solubility data using interpolation/extrapolation technique. Concerning the correlation results employing a given number of independent variables, the accuracies of the investigated models were comparable, since they could be converted to a single mathematical form, however, the accuracies were decreased when models emplyed more independent variables. The accurate correlative models could be employed for prediction purpose and/or screening the experimental solubility data to detect possible outliers. With regard to prediction results, the best predictions were made using the cosolvency models trained by a minimum number of experimental data points and an ab initio accurate prediction is not possible so far and further mathematical efforts are needed to provide such a tool. To connect this gap between available accurate correlative models with the ab initio predictive model, the generally trained models for calculating the solubility of various drugs in different binary mixtures, various drugs in a given binary solvent and also a given drug in various binary solvents at isothermal condition and/or different temperatures were reported. Available accuracy criteria used in the recent publications were reviewed including mean percentage deviation (MPD). The MPD for correlative models is 1-10% whereas the corresponding range for predictive models is 10-80% depend on the model capability and the number of independent variables employed by the model. This is an update for a review article published in this journal in 2008.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".