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Precise prediction of activators for the human constitutive androstane receptor using structure-based
Harutoshi Kato1, Noriyuki Yamaotsu2, Norihiko Iwazaki3
1School of Pharmacy, Kitasato University, Minato-ku, Tokyo 108-8641, Japan; DMPK Research Laboratories, Mitsubishi Tanabe Pharma Corporation, Toda-shi, Saitama 335-8505, Japan.
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.
Insights
We developed a 3D-QSAR model to predict constitutive androstane receptor (CAR) activators, crucial for minimizing drug-drug interaction risks. This computational approach aids in selecting safer drug candidates early in development.
Area of Science:
- Pharmacology
- Computational Chemistry
- Drug Discovery
Background:
- Constitutive androstane receptor (CAR) activation upregulates drug-metabolizing enzymes, causing significant drug-drug interactions (DDIs).
- Predicting CAR activators is challenging, hindering early-stage drug development and safety assessments.
- Effective computational tools are needed to identify potential CAR activators and mitigate DDI risks.
Purpose of the Study:
- To develop and validate a 3D-QSAR model for predicting the CAR activating potency of small molecules.
- To aid in the selection of drug candidates with a reduced risk of enzyme induction-related DDIs.
Main Methods:
- Comparative Molecular Field Analysis (CoMFA) was employed to build 3D-QSAR models.
- Ligand docking and molecular alignment were used to generate models based on 28 training compounds.
- A lipophilic parameter (S+logD7.4) was incorporated to enhance model performance.
Main Results:
- The developed CoMFA model showed high statistical significance (r² = 0.99, q² = 0.74).
- External validation with a test set of seven compounds confirmed excellent predictive ability (r²pred = 0.71).
- The model accurately predicts CAR activating potency.
Conclusions:
- The 3D-QSAR model provides a precise method for predicting CAR activating potency.
- This tool can assist in selecting drug candidates with minimized DDI risk during early drug discovery.
- The model facilitates the development of safer pharmaceuticals by addressing enzyme induction concerns.