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International Journal of Pharmacology
  Year: 2018 | Volume: 14 | Issue: 3 | Page No.: 320-328
DOI: 10.3923/ijp.2018.320.328
Substrate Selectivity for UDP-glucuronosyltransferase1A8 using the Pharmacophore Approach
Xiaokang Wang, Caiyun Jiang, Xiaosong Wu, Peng Zou and Zhufeng Wu

Abstract:
Background and Objective: UDP-glucuronosyltransferases 1A8 (UGT1A8) is an important enzyme responsible for glucuronidation of numerous xenobiotic/drugs. The objective of this study was to establish a substrate selectivity model through pharmacophore approach. Methodology: Thirty-six substrates of UGT1A8 collected from the literature were divided into training (n = 24) and test sets (n = 12). The Discovery Studio 2.5 (DS) software was utilized to establish the pharmacophore model. The HypoGen algorithm that was available in 3D QSAR Pharmacophore Generation protocol was applied to construct pharmacophore hypotheses. Correlation analyses were performed between the predicted activity and the experimental activity of the training and test sets. Results: The established pharmacophore model consisted of 2 hydrogen-bonding acceptors and one aromatic ring. The best pharmacophore model (hypothesis 1) was statistically significant with high value of correlation coefficient and low value of difference between the null cost and the total cost. Besides, the predicted catalysis activities were within one log residual of experimental value for substrates in the test set. Conclusion: Pharmacophore model for UGT1A8 was successfully constructed for the first time in this study. The established model contributed to an improved understanding of the UGT1A8’s substrate selectivity. Besides, this model would be an efficient tool for high-throughput prediction of UGT1A8 metabolism.
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How to cite this article:

Xiaokang Wang, Caiyun Jiang, Xiaosong Wu, Peng Zou and Zhufeng Wu, 2018. Substrate Selectivity for UDP-glucuronosyltransferase1A8 using the Pharmacophore Approach. International Journal of Pharmacology, 14: 320-328.

DOI: 10.3923/ijp.2018.320.328

URL: https://scialert.net/abstract/?doi=ijp.2018.320.328

 
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