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.

译文

:水溶性的优化是将新疗法推向市场的重要一步。我们描述了经验计算模型的发展,以对候选药物的pH依赖性水溶解度进行排名。该模型由三个核心成分组成,用于描述水溶性。第一个是用于预测中性溶质固有溶解度的多元QSAR模型。该方法的第二个方面是考虑使用预测的pKa和Henderson-Hasselbalch方程进行电离。该模型的第三方面是一种新颖的方法,可通过在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。该模型还包括蒙特卡洛误差函数,该函数考虑模型的每个基本组件的可变性以估计90%的估计置信区间。

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