Afzelius L, Zamora I, Ridderström M, Andersson T B, Karlén A, Masimirembwa C M
Department of Drug Metabolism and Pharmacokinetics & Bioanalytical Chemistry, AstraZeneca R&D, Mölndal, Sweden.
Mol Pharmacol. 2001 Apr;59(4):909-19. doi: 10.1124/mol.59.4.909.
This study describes the generation of a three-dimensional quantitative structure activity relationship (3D-QSAR) model for 29 structurally diverse, competitive CYP2C9 inhibitors defined experimentally from an initial data set of 73 compounds. In parallel, a homology model for CYP2C9 using the rabbit CYP2C5 coordinates was built. For molecules with a known interaction mode with CYP2C9, this homology model, in combination with the docking program GOLD, was used to select conformers to use in the 3D-QSAR analysis. The remaining molecules were docked, and the GRID interaction energies for all conformers proposed by GOLD were calculated. This was followed by a principal component analysis (PCA) of the GRID energies for all conformers of all compounds. Based on the similarity in the PCA plot to the inhibitors with a known interaction mode, the conformer to be used in the 3D-QSAR analysis was selected. The compounds were randomly divided into two groups, the training data set (n = 21) to build the model and the external validation set (n = 8). The PLS (partial least-squares) analysis of the interaction energies against the K(i) values generated a model with r(2) = 0.947 and a cross-validation of q(2) = 0.730. The model was able to predict the entire external data set within 0.5 log units of the experimental K(i) values. The amino acids in the active site showed complementary features to the grid interaction energies in the 3D-QSAR model and were also in agreement with mutagenesis studies.
本研究描述了针对29种结构各异的竞争性CYP2C9抑制剂生成三维定量构效关系(3D-QSAR)模型的过程,这些抑制剂是从73种化合物的初始数据集中通过实验确定的。同时,利用兔CYP2C5的坐标构建了CYP2C9的同源模型。对于与CYP2C9具有已知相互作用模式的分子,该同源模型与对接程序GOLD相结合,用于选择在3D-QSAR分析中使用的构象异构体。其余分子进行对接,并计算GOLD提出的所有构象异构体的GRID相互作用能。随后对所有化合物的所有构象异构体的GRID能量进行主成分分析(PCA)。根据PCA图中与具有已知相互作用模式的抑制剂的相似性,选择用于3D-QSAR分析的构象异构体。将这些化合物随机分为两组,训练数据集(n = 21)用于构建模型,外部验证集(n = 8)。对相互作用能与K(i)值进行偏最小二乘法(PLS)分析,生成了一个r(2) = 0.947且交叉验证q(2) = 0.730的模型。该模型能够在实验K(i)值的0.5对数单位内预测整个外部数据集。活性位点中的氨基酸与3D-QSAR模型中的网格相互作用能表现出互补特征,并且也与诱变研究结果一致。