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, r²=0.881, q_LOO²=0.797, q_EXT²=0.674; for HM-PNN, r²=0.882, q_LOO²=0.856, q_EXT²=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.

Keywords

human pregnane X receptor / agonist activity / heuristic method-Best Subset Modeling / heuristic method-Polynomial Neural Networks / structural features / quantitative structure-activity relationship

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