Universal data-driven models to estimate the solubility of anti-cancer drugs in supercritical carbon dioxide: Correlation development and machine learning modeling

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The current study aims at modeling the solubility of anti-cancer agents in supercritical carbon dioxide (SC-CO 2 ). An extensive databank, including 893 measured samples for 33 anti-cancer agents were collected from the literature, covering extensive ranges of operating conditions. Eight density-based empirical models were firstly employed to correlate the collected data. After adjusting their constant coefficients, four of them provided satisfactory estimations, with total average absolute relative errors (AAREs) below 10 %. A novel six-parameter empirical correlation was also proposed, with input factors optimized based on the Pearson coefficient analysis. This correlation produced satisfactory results for the analyzed drugs, achieving a total AARE of 7.71 %. Afterward, a generalized and unified model was built using the intelligent method of gaussian process regression (GPR). For the testing data, this model showed excellent results with AARE and R 2 values of 2.90 % and 99.87 %, respectively. Furthermore, its estimations for all anti-cancer agents outperformed the empirical correlations significantly. Both empirical and intelligent models accurately described the physical behavior of anti-cancer agents’ solubility in SC-CO 2 under various conditions. Subsequently, the most effective factors on the performances of the models were recognized through a sensitivity analysis. • 893 experimental data for solubility of 33 anti-cancer agents in SC-CO 2 were analyzed. • Eight density-based empirical models were assayed to predict the solubility. • A novel six-parameter correlation was proposed by optimizing the input factors. • A generalized machine learning model was designed based on the GPR method. • The most effective factors on solubility were identified by a sensitivity analysis.

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