Shen Min, Xiao Yunde, Golbraikh Alexander, Gombar Vijay K, Tropsha Alexander
Division of Medicinal Chemistry and Natural Products, School of Pharmacy, CB# 7360, University of North Carolina, Chapel Hill, North Carolina 27599-7360, USA.
J Med Chem. 2003 Jul 3;46(14):3013-20. doi: 10.1021/jm020491t.
Computational ADME (absorption, distribution, metabolism, and excretion) models may be used early in the drug discovery process in order to flag drug candidates with potentially problematic ADME profiles. We report the development, validation, and application of quantitative structure-property relationship (QSPR) models of metabolic turnover rate for compounds in human S9 homogenate. Biological data were obtained from uniform bioassays of 631 diverse chemicals proprietary to GlaxoSmithKline (GSK). The models were built with topological molecular descriptors such as molecular connectivity indices or atom pairs using the k-nearest neighbor variable selection optimization method developed at the University of North Carolina (Zheng, W.; Tropsha, A. A novel variable selection QSAR approach based on the k-nearest neighbor principle. J. Chem. Inf. Comput. Sci., 2000, 40, 185-194.). For the purpose of validation, the whole data set was divided into training and test sets. The training set QSPR models were characterized by high internal accuracy with leave-one-out cross-validated R(2) (q(2)) values ranging between 0.5 and 0.6. The test set compounds were correctly classified as stable or unstable in S9 assay with an accuracy above 85%. These models were additionally validated by in silico metabolic stability screening of 107 new chemicals under development in several drug discovery programs at GSK. One representative model generated with MolConnZ descriptors predicted 40 compounds to be metabolically stable (turnover rate less than 25%), and 33 of them were indeed found to be stable experimentally. This success (83% concordance) in correctly picking chemicals that are metabolically stable in the human S9 homogenate spells a rapid, computational screen for generating components of the ADME profile in a drug discovery process.
计算ADME（吸收、分布、代谢和排泄）模型可在药物发现过程的早期使用，以便标记具有潜在问题ADME特征的候选药物。我们报告了人S9匀浆中化合物代谢周转率的定量结构-性质关系（QSPR）模型的开发、验证和应用。生物数据来自葛兰素史克公司（GSK）专有的631种不同化学品的统一生物测定。使用北卡罗来纳大学开发的k近邻变量选择优化方法，用拓扑分子描述符（如分子连接性指数或原子对）构建模型（郑，W.；特罗普沙，A.一种基于k近邻原理的新型变量选择QSAR方法。《化学信息与计算机科学杂志》，2000年，40卷，185 - 194页）。为了进行验证，将整个数据集分为训练集和测试集。训练集QSPR模型的特点是具有较高的内部准确性，留一法交叉验证的R(2)（q(2)）值在0.5至0.6之间。测试集化合物在S9测定中被正确分类为稳定或不稳定，准确率超过85%。这些模型还通过对GSK几个药物发现项目中正在开发的107种新化学品进行计算机代谢稳定性筛选进行了验证。用MolConnZ描述符生成的一个代表性模型预测40种化合物代谢稳定（周转率小于25%），其中33种在实验中确实被发现是稳定的。在正确挑选出人S9匀浆中代谢稳定的化学品方面的这一成功（83%的一致性）意味着在药物发现过程中可以快速进行计算筛选以生成ADME特征的组成部分。
