Establishment of pharmacophore and VolSurf models to predict the substrates of UDP-glucuronosyltransferase1A3

Wu, Zhufeng; Zhang, Qizhou; Ma, Zhiguo; Wu, Baojian

doi:10.3109/00498254.2015.1016136

Cited by 3 publications

(3 citation statements)

References 47 publications

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“…The pharmacophore model indicated that hydrogen-bonding acceptor played a significant role for the molecules glucuronidation by UGT1A8, consisting with the results of pharmacophore analysis for UGT1A3 and UGT2B7 [19,20]. Hydrogen-bond acceptor was regarded as a hydrophilic group.…”

Section: Discussionmentioning

confidence: 92%

<span>A Study of Substrate Selectivity for UDP-Glucuronosyltransferase1A8 Using the Pharmacophore Approach</span>

Wang¹,

Jiang²,

Zou³

et al. 2017

Preprint

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UDP-glucuronosyltransferases 1A8 (UGT1A8) is an important enzyme responsible for glucuronidation of numerous xenobiotic/drugs. The objective of this study is to establish a substrate selectivity model through pharmacophore approach. 2. Thirty-six substrates of UGT1A8 collated 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. The established pharmacophore model consisted of two hydrogenbonding acceptors and one ring aromatic. 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. In summary, a 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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Section: Discussionmentioning

confidence: 92%

<span>A Study of Substrate Selectivity for UDP-Glucuronosyltransferase1A8 Using the Pharmacophore Approach</span>

Wang¹,

Jiang²,

Zou³

et al. 2017

Preprint

Self Cite

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“…The same group have recently developed very similar VolSurf‐3D QSAR and Pharmacophore‐3D QSAR models for estimating clearance of UGT1A3 substrates . This study used a dataset of 34 compounds in the training set and 16 compounds in the test set for which clearance data were collected from the literature reports on recombinant enzyme.…”

Section: Prediction Of Phase II (Ugt and Non‐ugt) Drug Metabolismmentioning

confidence: 99%

“…The same group have recently developed very similar VolSurf-3D QSAR and Pharmacophore-3D QSAR models for estimating clearance of UGT1A3 substrates. 143 This study used a dataset of 34 compounds in the training set and 16 compounds in the test set for which clearance data were collected from the literature reports on recombinant enzyme. Using a very similar methodology, they found that the test set prediction accuracies for 81% of the UGT1A3 substrates prediction were within one log unit for a four-log unit range.…”

Section: Ugt2b7mentioning

confidence: 99%

Recent advances in the prediction of non‐CYP450‐mediated drug metabolism

Dixit

Lal

Agrawal

2017

WIREs Comput Mol Sci

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Computational models of drug metabolism prediction have focused mainly on cytochrome P450 enzymes, because drug–drug interactions, reactive metabolite formation, hepatotoxicity, idiosyncratic adverse drug interactions, and/or loss of efficacy of many drugs were the results of interactions with CYP450s. Metabolic regioselectivity and isoform specificity prediction models for CYP450‐catalyzed reactions have reached approximately 95% accuracy. Thus, a new drug candidate is less likely to show unexpected metabolic profile due to metabolism via CYP450 pathways. For such candidates, secondary metabolic Phase I and II enzymes are likely to play an expected (or unexpected) role in drug metabolism. The importance of flavin monooxygenases (FMOs), aldehyde and alcohol dehydrogenase, monoamine oxidase from the Phase I and UDP‐glucuronosyltransferase (UGT), sulfotransferase, glutathione S‐transferase, and methyltransferase from Phase II has increased and United States Food and Drug Administration guidelines on NDA have specific recommendations for in vitro and in vivo testing against these enzymes. Thus, there is an urgent requirement of reliable predictive models for drug metabolism catalyzed by these enzymes. In this review, we have classified drug metabolism prediction models (site of metabolism, isoform specificity, and kinetic parameter) for these enzymes into Phase I and II. When such models are unavailable, we discuss the Quantitative Structure Activity Relationship (QSAR), pharmacophore, docking, dynamics, and reactivity studies performed for the prediction of substrates and inhibitors. Recently published models for FMO and UGT are discussed. The need for comprehensive, widely applicable, sequential primary and secondary metabolite prediction is highlighted. Potential difficulties and future prospectives in the development of such models are discussed. WIREs Comput Mol Sci 2017, 7:e1323. doi: 10.1002/wcms.1323 This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Computer and Information Science > Chemoinformatics Software > Molecular Modeling

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In silico models to predict tubular secretion or reabsorption clearance pathway using physicochemical properties and structural characteristics

2022

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Establishment of pharmacophore and VolSurf models to predict the substrates of UDP-glucuronosyltransferase1A3

Cited by 3 publications

References 47 publications

<span>A Study of Substrate Selectivity for UDP-Glucuronosyltransferase1A8 Using the Pharmacophore Approach</span>

<span>A Study of Substrate Selectivity for UDP-Glucuronosyltransferase1A8 Using the Pharmacophore Approach</span>

Recent advances in the prediction of non‐CYP450‐mediated drug metabolism

In silico models to predict tubular secretion or reabsorption clearance pathway using physicochemical properties and structural characteristics

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