Abstract
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.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call