Determination of the solubility of anticancer drugs in supercritical carbon dioxide using empirical models and artificial neural network

 Low solubility of pharmaceutical compounds leads to increasing the required drug dosage and their side effects as well as reducing their therapeutic efficiency. Producing pharmaceutical micro/nanoparticles with homogenous morphology and narrow size distribution is one of the confirmed approaches for their solubility enhancement. So, selection and designing an appropriate method for this purpose is one of the most important research fields of pharmaceutical industries. Over the past three decades, supercritical carbon dioxide (sc-CO2) based methods as a clean and green technologies have been received much attention in various fields of pharmaceutical industries. However, in order to design and development of these methods for producing micro/nanoparticles, determination of the compounds solubility in sc-CO2 is essential.

 In this research, well known empirical models (Adachi and Lu, Ch and Madras, Hozahzbr et al., Bian et al., Mendez-Santiago-Teja), as well as the artificial neural network model were applied for prediction the solubility of six anticancer drugs (Aprepitant, 5-Fluorouracil, Imatinib mesylate, Capecitabine, Letrozole, Docetaxel) in sc-CO2.  In order to evaluate the accuracy of these models, a comparison was made between the calculated solubility values and the available experimental data, based on several statistical criteria, such as the average absolute relative deviation (AARD%), adjusted correlation coefficient (Radj) and F-value.

According to obtained results, Adachi and Lu model with AARD% value of 12.12% and Radj value of 0.97 provided acceptable results for solubility of mentioned drugs in sc-CO2. Also, in comparison between empirical and artificial neural network models, the latter one with AARD% value of 1.65% and Radj value of 0.9960 was appointed as the most appropriate model for correlation of drugs solubility data.