A machine learning approach for thermodynamic modeling of the statically measured solubility of nilotinib hydrochloride monohydrate (anti-cancer drug) in supercritical CO2
Year: 2023
Type: article
Abstract: Nilotinib hydrochloride monohydrate (NHM) is an anti-cancer drug whose solubility was statically determined in supercritical carbon dioxide (SC-CO2) for the first time at various temperatures (308–338 K) and pressures (120–270 bar). The mole fraction of the drug dissolved in SC-CO2ranged from 0.1 × 10–5to 0.59 × 10–5, corresponding to the solubility range of 0.016–0.094 g/L. Four sets of models were employed to evaluate the correlation of experimental data; (1) ten empirical and semi-empirical models with three to six adjustable parameters, such as Chrastil, Bartle, Sparks, Sodeifian, Mendez-Santiago and Teja (MST), Bian, Jouyban, Garlapati-Madras, Gordillo, and Jafari-Nejad; (2) Peng-Robinson equation of state (Van der Waals mixing rule, had anAARD%of 10.73); (3) expanded liquid theory (modified Wilson model, on average, theAARDof this model was 11.28%); and (4) machine learning (ML) algorithms (random forest, decision trees, multilayer perceptron, and deep neural network with respective R2values of 0.9933, 0.9799, 0.9724 and 0.9701). All the models showed an acceptable agreement with the experimental data, among them, the Bian model exhibited excellent performance with anAARD%of 8.11. Finally, the vaporization (73.49 kJ/mol) and solvation (− 21.14 kJ/mol) enthalpies were also calculated for the first time.
Source: Scientific Reports
Institutions: University of Kashan, Islamic Azad University Quchan
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,390
Funder: University of Kashan
OpenAlex ID: W4385704081