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A machine learning approach for thermodynamic modeling of the statically measured solubility of nilotinib hydrochloride monohydrate (anti-cancer drug) in supercritical CO₂.

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-CO₂ ranged from 0.1 × 10^-5 to 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 a

SUBMITTER: Nateghi H

PROVIDER: S-EPMC10412577 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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A machine learning approach for thermodynamic modeling of the statically measured solubility of nilotinib hydrochloride monohydrate (anti-cancer drug) in supercritical CO₂.

OmicsDI is part of the ELIXIR infrastructure

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Dataset Information

A machine learning approach for thermodynamic modeling of the statically measured solubility of nilotinib hydrochloride monohydrate (anti-cancer drug) in supercritical CO2.

OmicsDI is part of the ELIXIR infrastructure

Tweets

A machine learning approach for thermodynamic modeling of the statically measured solubility of nilotinib hydrochloride monohydrate (anti-cancer drug) in supercritical CO₂.