Comprehensive study of medications solubility in supercritical CO2 with and without co-solvent; Laboratory, theoretical, and intelligent approaches

• Published in: Journal of molecular liquids Vol. 411; p. 125765
• Main Authors: Hsu, Chou-Yi, Jasim, Dheyaa J., Bansal, Pooja, Juraev, Nizomiddin, Bokov, Dmitry Olegovich, Al-Rubaye, Ameer H., Mohmmed, Karrar Hatif, Redhee, Ahmed Huseen, Kumar, Abhinav, Mustafa, Yasser Fakri
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2024
• Subjects: Empirical model; Equation of state; Machine learning; Solubility; Supercritical CO2; Empirical model; Supercritical CO2; Equation of state; Solubility; Machine learning
• ISSN: 0167-7322
• DOI: 10.1016/j.molliq.2024.125765

•Solubility of Topiramate, Meclizine, and Dimenhydrinate in supercritical CO2 was measured at T=308–348 K and P=170–410 bar.•The addition of 5% mole ethanol to supercritical CO2 significantly enhance the supercritical solubility of these drugs.•Some empirical models and thermodynamic models (PR, SRK, PC-SAFT, UNIQUAC, and Wilson) were used for theoretical modelling.•The critical properties of Topiramate, Meclizine, and Dimenhydrinate were estimated using approved group contribution methods.•A machine learning approach was used to predict the solubility of Topiramate, Meclizine, and Dimenhydrinate in supercritical CO2 with/without ethanol. Determining the dissolution characteristics of medicines in supercritical CO2 is vital for formulating innovative drug delivery systems through an efficient supercritical process. This study investigates the solubility of three poorly bioavailable drugs −Topiramate, Meclizine, and Dimenhydrinate- in supercritical CO2, both with and without ethanol co-solvent, over a temperature range of 308 K to 348 K and pressures from 17 MPa to 41 MPa. The solubility of these medicines in supercritical CO2 (binary system) is notably low, ranging from 2.5 × 10-6 − 4.54 × 10-6, 0.26 × 10-5 − 2.3 × 10-5, and 0.20 × 10-5 − 1.91 × 10-5 in mole fraction, respectively. However, in the presence of ethanol (ternary system), their supercritical solubility significantly increases by factors of 2.75–5.84, 1.40–3.20, and 2.04–4.85, respectively. The supercritical solubility of the mentioned compounds are theoretically evaluated using several approaches, including empirical models, a machine learning methodology employing a multilayer perceptron neural network, thermodynamic models based on two cubic equations of state (Peng-Robinson (PR) and Soave–Redlich–Kwong (SRK)), and a non-cubic equation of state (perturbed chain-statistical associating fluid theory (PC-SAFT)), as well as two expanded liquid models (UNIQUAC and Wilson). The findings revealed that all the specified models demonstrate acceptable accuracy in correlating the experimental data of the specified drugs in both binary and ternary systems. Among these, the PR and SRK thermodynamic models, along with some empirical models, show the best results. Furthermore, the machine learning model exhibited outstanding accuracy in forecasting the supercritical solubility of the desired drugs, with over 99.9% alignment between their predicted and experimental data.