Estudo termodinâmico utilizando a equação PC-SAFT para prever a solubilidade de compostos farmacêuticos

Abstract

The solubility of drugs in different solvents is a critical parameter for the development of effective pharmaceutical formulations, as well as for the optimization and understanding of crystallization processes. In the pharmaceutical industry, crystallization is one of the main unit operations for the purification of active pharmaceutical ingredients. The aim of this work is to study the application of the advanced PC-SAFT (Perturbed-Chain Statistical Associating Fluid Theory) equation of state in calculating the solubility of drugs in different solvents. The PCSAFT equation requires five parameters for each pure associative substance, and binary interaction parameters for mixtures, which were obtained from literature data. The methodology of the work consisted of implementing the equation in a code structured in Python, which has simple syntax and a large library of mathematical functions. After estimating the property using the equation, the predicted solubility results were compared with experimental data obtained from bibliographical references. As a result, it was possible to predict the solubility of drugs in different solvents and temperatures. However, they differed from the experimental data, ranging from 0.19% to 689.52%. These differences indicate the need to adjust the parameters of the equation according to each mixture, highlighting the complex molecular interactions of the compounds. In addition to the solubility prediction, solubility curves were generated for the drugs in solvents, which showed a similar trend to the experimental results, but with divergent values. Therefore, it can be concluded that the PCSAFT equation has the potential to predict the properties of complex substances, and also offers opportunities for improvement in its association parameters, which vary depending on the compounds, the mixture and their interactions. In addition, the tool developed proved to be functional in predicting the properties of solutions.