Johnson Stephen R, Chen Xue-Qing, Murphy Denette, Gudmundsson Olafur
Bristol-Myers Squibb, Co., Princeton, New Jersey 08543, USA.
Mol Pharm. 2007 Jul-Aug;4(4):513-23. doi: 10.1021/mp070030+. Epub 2007 Jun 1.
The optimization of aqueous solubility is an important step along the route to bringing a new therapeutic to market. We describe the development of an empirical computational model to rank the pH-dependent aqueous solubility of drug candidates. The model consists of three core components to describe aqueous solubility. The first is a multivariate QSAR model for the prediction of the intrinsic solubility of the neutral solute. The second facet of the approach is the consideration of ionization using a predicted pKa and the Henderson-Hasselbalch equation. The third aspect of the model is a novel method for assessing the effects of crystal packing on solubility through a series of short molecular dynamics simulations of an actual or hypothetical small molecule crystal structure at escalating temperatures. The model also includes a Monte Carlo error function that considers the variability of each of the underlying components of the model to estimate the 90% confidence interval of estimation.
优化水溶性是将一种新疗法推向市场过程中的重要一步。我们描述了一种经验计算模型的开发，用于对候选药物的pH依赖性水溶性进行排名。该模型由三个描述水溶性的核心组件组成。第一个是用于预测中性溶质固有溶解度的多变量QSAR模型。该方法的第二个方面是使用预测的pKa和亨德森-哈塞尔巴尔赫方程来考虑离子化。该模型的第三个方面是一种新颖的方法，通过在不断升高的温度下对实际或假设的小分子晶体结构进行一系列短分子动力学模拟，来评估晶体堆积对溶解度的影响。该模型还包括一个蒙特卡罗误差函数，该函数考虑模型每个基础组件的变异性，以估计估计的90%置信区间。
