Jouyban Abolghasem
Faculty of Pharmacy and Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
J Pharm Pharm Sci. 2008;11(1):32-58. doi: 10.18433/j3pp4k.
The cosolvency models presented from 1960 to 2007 were reviewed and their accuracies for correlating and/or predicting the solubility of drugs in water-cosolvent mixtures were discussed. The cosolvency models could be divided into theoretical, semi-empirical and empirical models, the first group of models provide basic information from the solution, while the last group of models are good suitable for solubility correlation studies. The simplest cosolvency model, i.e. the log-linear model of Yalkowsky, provides an estimate of drug solubility in water-cosolvent mixtures using aqueous solubility of the drug, whereas the Jouyban-Acree model predicts the solubility with an acceptable error with the cost of one more data point (the solubility in neat cosolvent) which is required as input value in the prediction process. A number of error terms used in the literature was also discussed with a brief comments on the acceptable prediction error for pharmaceutical applications.
回顾了1960年至2007年提出的共溶剂模型，并讨论了它们在关联和/或预测药物在水-共溶剂混合物中溶解度方面的准确性。共溶剂模型可分为理论模型、半经验模型和经验模型，第一组模型从溶液中提供基本信息，而最后一组模型非常适合溶解度关联研究。最简单的共溶剂模型，即Yalkowsky的对数线性模型，使用药物的水溶性来估计药物在水-共溶剂混合物中的溶解度，而Jouyban-Acree模型以多一个数据点（纯共溶剂中的溶解度）为代价，以可接受的误差预测溶解度，该数据点是预测过程中所需的输入值。还讨论了文献中使用的一些误差项，并对药物应用中可接受的预测误差作了简要评论。
