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Precise prediction of activators for the human constitutive androstane receptor using structure-based three-dimensional quantitative structure-activity relationship methods.

Authors
  • Kato, Harutoshi1
  • Yamaotsu, Noriyuki2
  • Iwazaki, Norihiko3
  • Okamura, Shigeaki3
  • Kume, Toshiyuki4
  • Hirono, Shuichi5
  • 1 School of Pharmacy, Kitasato University, Minato-ku, Tokyo 108-8641, Japan; DMPK Research Laboratories, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama 335-8505, Japan. Electronic address: [email protected] , (Japan)
  • 2 School of Pharmacy, Kitasato University, Minato-ku, Tokyo 108-8641, Japan. , (Japan)
  • 3 DMPK Research Laboratories, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama 335-8505, Japan. , (Japan)
  • 4 Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama 335-8505, Japan. , (Japan)
  • 5 School of Pharmacy, Kitasato University, Minato-ku, Tokyo 108-8641, Japan. Electronic address: [email protected] , (Japan)
Type
Published Article
Journal
Drug metabolism and pharmacokinetics
Publication Date
Jun 01, 2017
Volume
32
Issue
3
Pages
179–188
Identifiers
DOI: 10.1016/j.dmpk.2017.02.001
PMID: 28412023
Source
Medline
Keywords
License
Unknown

Abstract

The constitutive androstane receptor (CAR, NR1I3) regulates the expression of numerous drug-metabolizing enzymes and transporters. The upregulation of various enzymes, including CYP2B6, by CAR activators is a critical problem leading to clinically severe drug-drug interactions (DDIs). To date, however, few effective computational approaches for identifying CAR activators exist. In this study, we aimed to develop three-dimensional quantitative structure-activity relationship (3D-QSAR) models to predict the CAR activating potency of compounds emerging in the drug-discovery process. Models were constructed using comparative molecular field analysis (CoMFA) based on the molecular alignments of ligands binding to CAR, which were obtained from ensemble ligand-docking using 28 compounds as a training set. The CoMFA model, modified by adding a lipophilic parameter with calculated logD7.4 (S+logD7.4), demonstrated statistically good predictive ability (r2 = 0.99, q2 = 0.74). We also confirmed the excellent predictability of the 3D-QSAR model for CAR activation (r2pred = 0.71) using seven compounds as a test set for external validation. Collectively, our results indicate that the 3D-QSAR model developed in this study provides precise prediction of CAR activating potency and, thus, should be useful for selecting drug candidates with minimized DDI risk related to enzyme-induction in the early drug-discovery stage.

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