Ako Roland, Dong Dong, Wu Baojian
Department of Pharmacological and Pharmaceutical Sciences, College of Pharmacy, University of Houston, Houston, TX 77030, USA.
Xenobiotica. 2012 Sep;42(9):891-900. doi: 10.3109/00498254.2012.675094. Epub 2012 Apr 12.
UDP-glucuronosyltransferase 2B7 (UGT2B7) is an important enzyme responsible for clearance of many drugs. Here, we report two 3D quantitative structure-activity relationship (QSAR) models for UGT2B7 using the pharmacophore and VolSurf approach, respectively. The dataset included 53 structurally diverse UGT2B7 substrates, 36 of which were used for the training set and 17 of which for the external test set. Pharmacophore-based 3D-QSAR model (or hypothesis) was developed using the Discovery Studio program. A user-defined "glucuronidation site" feature was forcefully included in a pharmacophore hypothesis. VolSurf-based 3D-QSAR model was generated using the VolSurf program. This involves calculation of VolSurf descriptors, variable selection with the FFD algorithm, and partial least squares (PLS) analyses. The best pharmacophore model (r(2) = 0.736) consists of one glucuronidation site, one hydrogen bond acceptor, and three hydrophobic regions. Using this model, K(m) values for 14 of 17 test substrates were predicted within one log unit. The yielded VolSurf (PLS) model with two components shows statistical significance in both fitting and internal predicting (r(2) = 0.866, q(2) = 0.728). Further, the K(m) values for all test substrates were predicted within one log unit. In addition, the VolSurf model reveals an overlay of chemical features influencing the enzyme-substrate binding. Those include molecular size and shape, integy moments, capacity factors, best volumes of DRY probe, H-bonding, and log P. In conclusion, the pharmacophore and VolSurf approaches are successfully utilized to establish predictive models for UGT2B7. The derived models should be an efficient tool for high throughput prediction of UGT2B7 metabolism.
尿苷二磷酸葡萄糖醛酸基转移酶2B7（UGT2B7）是一种负责多种药物清除的重要酶。在此，我们分别使用药效团和VolSurf方法报告了两种针对UGT2B7的三维定量构效关系（QSAR）模型。数据集包括53种结构多样的UGT2B7底物，其中36种用于训练集，17种用于外部测试集。基于药效团的三维定量构效关系模型（或假设）使用Discovery Studio程序开发。在药效团假设中强制纳入了用户定义的“葡萄糖醛酸化位点”特征。基于VolSurf的三维定量构效关系模型使用VolSurf程序生成。这涉及VolSurf描述符的计算、使用FFD算法进行变量选择以及偏最小二乘法（PLS）分析。最佳药效团模型（r(2) = 0.736）由一个葡萄糖醛酸化位点、一个氢键受体和三个疏水区域组成。使用该模型，17种测试底物中的14种的K(m)值在一个对数单位内被预测。生成的具有两个成分的VolSurf（PLS）模型在拟合和内部预测方面均具有统计学意义（r(2) = 0.866，q(2) = 0.728）。此外，所有测试底物的K(m)值在一个对数单位内被预测。此外，VolSurf模型揭示了影响酶 - 底物结合的化学特征叠加。这些特征包括分子大小和形状、积分矩、容量因子、DRY探针的最佳体积、氢键以及log P。总之，药效团和VolSurf方法成功用于建立UGT2B7的预测模型。所推导的模型应是用于高通量预测UGT2B7代谢的有效工具。
