Determination of 5-fluorouracil anticancer drug solubility in supercritical CO(2)using semi-empirical and machine learning models

利用半经验模型和机器学习模型测定5-氟尿嘧啶抗癌药物在超临界CO₂中的溶解度

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Abstract

In order to provide the facilities to design the supercritical fluid (SCF) processes for micro or nanosizing of solid solute compounds such as drugs, it is essential to obtain their solubility in green solvents like pressurized CO(2). This important role is the first stage for assessing each SCF technology. A statistical method was developed for the first time and employed to determine 5-fluorouracil (5-Fu) solubility. The measurements were performed at different pressures (120-270 bar) and temperatures (308-338 K) through UV-vis spectrophotometry, for the first time. The solubility was obtained between 0.0024 and 0.0176 g/L. The 5-Fu mole fraction at constant temperature, increases with an increase in pressure. Whereas, a crossover point has been seen. Three models with different approaches were applied to correlate and model the experimental data set: (i) seven density-based models, (ii) PR equations of state (vdW2 mixing rule), and (iii) machine learning-based models, namely non-linear regressions, Random Forest, Gradient Boosting, Decision Tree, and Kernel Ridge. All tested models successfully correlate and model the solubility data within an acceptable accuracy. Meanwhile, the empirical model suggested by Sodeifian model 2, is superior with the lowest AARD% (AARD = 4.12%). Finally, total, solvation, and vaporization enthalpies of the drug/Sc-CO(2) binary system were determined using semi-empirical correlations, for the first time.

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