title: In silico investigation of agonist activity of a structurally diverse set of drugs to hPXR using HM-BSM and HM-PNN.
abstract: 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 hPXR. 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 LOO (2) =0.797, q EXT (2) =0.674; for HM-PNN, r (2)=0.882, q LOO (2) =0.856, q EXT (2) =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 hPXR.
authors.affiliation: School of Basic Medical Sciences, Nanjing Medical University, Nanjing, 210029, China.
authors.forename: Yi-Ming
authors.initials: YM
authors.lastname: Zhang
authors.affiliation: School of Pharmacy, Nanjing Medical University, Nanjing, 211166, China.
authors.forename: Mei-Jia
authors.initials: MJ
authors.lastname: Chang
authors.affiliation: School of Pharmacy, Nanjing Medical University, Nanjing, 211166, China. yangxushu@njmu.edu.cn.
authors.forename: Xu-Shu
authors.initials: XS
authors.lastname: Yang
authors.affiliation: School of Basic Medical Sciences, Nanjing Medical University, Nanjing, 210029, China. hanxiao@njmu.edu.cn.
authors.forename: Xiao
authors.initials: X
authors.lastname: Han
mesh_terms: D003198:Computer Simulation; D006801:Humans; D015233:Models, Statistical; D008970:Molecular Weight; D016571:Neural Networks, Computer; D000077297:Pregnane X Receptor; D021281:Quantitative Structure-Activity Relationship; D011987:Receptors, Steroid / Q000819:agonists* / Q000737:chemistry; D054852:Small Molecule Libraries / Q000737:chemistry*; D055672:Static Electricity
chemical_list: D000077297:Pregnane X Receptor; D011987:Receptors, Steroid; D054852:Small Molecule Libraries
keywords: agonist activity; heuristic method-Best Subset Modeling; heuristic method-Polynomial Neural Networks; human pregnane X receptor; quantitative structure-activity relationship; structural features