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J Pharm Sci. 2017 Jul;106(7):1752-1759. doi: 10.1016/j.xphs.2017.03.004. Epub 2017 Mar 16.

In Silico Prediction of hPXR Activators Using Structure-Based Pharmacophore Modeling.

Author information

1
Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850; Sohyaku Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, Toda, Japan. Electronic address: Torimoto.Nao@ma.mt-pharma.co.jp.
2
Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850.
3
Sohyaku Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, Toda, Japan.

Abstract

The activation of pregnane X receptor (PXR), a member of the nuclear receptor superfamily, can mediate potential drug-drug interactions by regulating the expression of several drug-mediated enzymes and transporters, resulting in reduced therapeutic efficacy or increased toxicity by producing reactive metabolites. Therefore, in the early stage of drug development, it is important to predict these risks using an in silico approach. We constructed a human PXR (hPXR) pharmacophore model based on known structural information of compounds that activate PXR. We evaluated the prediction accuracy of the model using data sets generated on 68 original synthetic compounds from the Mitsubishi Tanabe Pharma Corporation and over 2500 drugs from the National Institutes of Health Chemical Genomics Center Pharmaceutical Collection for their ability to activate hPXR. The prediction accuracies of the PXR pharmacophore model were 0.78 and 0.86 for the Mitsubishi Tanabe Pharma Corporation and National Institutes of Health Chemical Genomics Center Pharmaceutical Collection, respectively. The compounds resulting in the smallest root-mean square deviation hit by pharmacophore search were the well-known PXR inducers such as Bosentan. These results suggest that using the in silico approach developed in this study is useful to identify potential hPXR activators and modify the drug design during the early stage of drug development.

KEYWORDS:

PXR; computational ADME; cytochrome P450; in silico modeling; induction; molecular modeling

PMID:
28315689
DOI:
10.1016/j.xphs.2017.03.004
[Indexed for MEDLINE]

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