RESUMO
BACKGROUND AND THE PURPOSE OF THE STUDY: Solubility of pharmaceuticals is still a challenging subject and solubilization using cosolvents is the most common technique used in the pharmaceutical industry. The purpose of this study was reporting and modeling the experimental molar solubility of pioglitazone hydrochloride (PGZ-HCl) in binary and ternary mixtures of ethanol (EtOH), N-methyl pyrrolidone (NMP), polyethylene glycols (PEGs) 200, 400, 600 and water along with the density of saturated solutions at 298.2 °K. METHODS: To provide a computational method, the Jouyban-Acree model was fitted to the solubilities of the binary solvents, and solubilities of the ternary solvents were back-calculated by employing the solubility data in mono-solvents. In the next step, the ternary interaction terms were added to the model and the prediction overall mean percentage deviation (MPD) of the ternary data was reduced. Also a previously proposed version of the model was used to predict the solubility of PGZ-HCl in binary and ternary mixtures employing the experimental solubility data in mono-solvents. RESULTS: The overall MPD of the model for fitting the binary data and predicted data of ternary solvents were 2.0 % and 50.5 %, respectively. The overall MPD of the predicted solubilities in ternary solvents using the ternary interaction terms in the model was 34.2 %, and by using the proposed version of the Jouyban-Acree model for binary and ternary data the overall correlation and prediction errors were 18.0 and 15.0 %, respectively. CONCLUSION: The solubility of PGZ-HCl was increased by addition of EtOH, NMP, PEGs 200, 400 and 600 to aqueous solutions. The reported data extended the available solubility data of pharmaceuticals which are crucial in formulation of liquid dosage forms. The constants of the Jouyban-Acree model using the generated data are also reported which provides the possibility of solubility prediction in other solvent mixtures and temperatures.
RESUMO
PURPOSE: To provide predictive cosolvency models, the Abraham solvation parameters of solutes and the solvent coefficients were combined with the Jouyban-Acree and the log-linear models. These models require two and one solubility data points to predict the solubility of drugs in water-cosolvent mixtures. Ab initio prediction methods also were employed and the results were discussed. METHOD: The Jouyban-Acree model constants were correlated with variables derived from the Abraham solvation parameters of solutes and the solvent coefficients to present quantitative structure property relationship (QSPR) models. The calculated model constants using the QSPR models were used to predict the solubility in water-cosolvent mixtures. The mean percentage deviation (MPD), average absolute error (AAE) and root mean square error (RMSE) criteria were calculated to show the accuracy of the predictions. RESULTS: The overall MPD (+/-SD) of the proposed method employing solubility data in mono-solvents, i.e. two data points for each set, was 18.5+/-12.0 which indicates an acceptable prediction error from the practical point of view. The best cosolvency model employing aqueous solubility data was produced overall MPD of 75.2+/-72.6. The overall MPD of the proposed ab initio method was 74.9+/-19.3%. The models produced the same accuracy pattern considering MPD, AAE and RMSE criteria. CONCLUSION: The proposed model employing two solubility data points for each set produced acceptable prediction error (>>18%) and could be recommended for practical applications in pharmaceutical industry. MPD, AAE and RMSE criteria produced similar results considering various models. However, MPD criterion was preferred since its numerical values could be compared with experimental relative standard deviations for repeated experiments.