In Silico Investigation of Agonist Activity of a Structurally Diverse Set of Drugs to hPXR Using HM-BSM and HM-PNN

• Published in: Journal of Huazhong University of Science and Technology. Medical sciences 2016-06, Vol.36 (3), p.463-468
• Main Author: 张一鸣 常美佳 杨旭曙 韩晓
• Format: Article
• Language: English
• Subjects: Computer Simulation; Humans; Medicine; Medicine & Public Health; Models, Statistical; Molecular Weight; Neural Networks (Computer); Quantitative Structure-Activity Relationship; Receptors, Steroid - agonists; Receptors, Steroid - chemistry; Small Molecule Libraries - chemistry; Static Electricity; 关系模型; 启发式方法; 活性硅; 神经网络; 结构特征; 药物使用; 药物活性; 药物相互作用
• ISSN: 1672-0733; 1993-1352
• DOI: 10.1007/s11596-016-1609-4

The human pregnane X receptor（hPXR） plays a critical role in the metabolism, transport and clearance of xenobiotics in the liver and intestine. The hPXR can be activated by a structurally diverse of drugs to initiate clinically relevant drug-drug interactions. In this article, in silico investigation was performed on a structurally diverse set of drugs to identify critical structural features greatly related to their agonist activity towards h PXR. Heuristic method（HM）-Best Subset Modeling（BSM） and HM-Polynomial Neural Networks（PNN） were utilized to develop the linear and non-linear quantitative structure-activity relationship models. The applicability domain（AD） of the models was assessed by Williams plot. Statistically reliable models with good predictive power and explain were achieved（for HM-BSM, r~2=0.881, q~2_（LOO）=0.797, q~2_（EXT）=0.674; for HM-PNN, r~2=0.882, q~2_（LOO）=0.856, q~2_（EXT）=0.655）. The developed models indicated that molecular aromatic and electric property, molecular weight and complexity may govern agonist activity of a structurally diverse set of drugs to h PXR.