3D-QSAR studies on UDP-glucuronosyltransferase 2B7 substrates using the pharmacophore and VolSurf approaches

Ako, Roland; Dong, Dong; Wu, Baojian

doi:10.3109/00498254.2012.675094

Cited by 10 publications

(3 citation statements)

References 41 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: 90%

<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: 90%

<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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“…However, such models are uncommon for UGTs due to the lack of UGT crystal structures and challenges in protein expression. In the past, molecular modeling techniques, including two-dimensional (2D)- and three-dimensional (3D)- quantitative structure–activity relationship and pharmacophore modeling, were employed to understand molecular features of UGT1A1 and UGT2B7 substrates. , Here, for the first time, we are using a structure-based pharmacophore modeling approach to understand the molecular features determining UGT2B17–substrate interactions.…”

Section: Discussionmentioning

confidence: 99%

Promiscuity and Quantitative Contribution of UGT2B17 in Drug and Steroid Metabolism Determined by Experimental and Computational Approaches

Ahire,

Mariasoosai,

Naji-Talakar

et al. 2024

J. Chem. Inf. Model.

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Uridine 5′-diphospho-glulcuronosyltransferase 2B17 (UGT2B17) is important in the metabolism of steroids and orally administered drugs due to its high interindividual variability. However, the structural basis governing the substrate selectivity or inhibition of UGT2B17 remains poorly understood. This study investigated 76 FDA-approved drugs and 20 steroids known to undergo glucuronidation for their metabolism by UGT2B17. Specifically, we assessed the substrate selectivity for UGT2B17 over other UGT enzymes using recombinant human UGT2B17 (rUGT2B17), human intestinal microsomes, and human liver microsomes. The quantitative contribution of intestinal UGT2B17 in the glucuronidation of these compounds was characterized using intestinal microsomes isolated from UGT2B17 expressors and nonexpressors. In addition, a structure-based pharmacophore model for UGT2B17 substrates was built and validated using the studied pool of substrates and nonsubstrates. The results show that UGT2B17 could metabolize 23 out of 96 compounds from various chemical classes, including alcohols and carboxylic acids, particularly in the intestine. Interestingly, amines were less susceptible to UGT2B17 metabolism, though they could inhibit the enzyme. Three main pharmacophoric features of UGT2B17 substrates include (1) the presence of an accessible −OH or −COOH group near His35 residue, (2) a hydrophobic functional group at ∼4.5−5 Å from feature 1, and (3) an aromatic ring ∼5−7 Å from feature 2. Most of the studied compounds inhibited UGT2B17 activity irrespective of their substrate potential, indicating the possibility of multiple mechanisms. These data suggest that UGT2B17 is promiscuous in substrate selectivity and inhibition and has a high potential to produce significant variability in the absorption and disposition of orally administered drugs.

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“…A similar, pharmacophore-3D QSAR and VolSurf-3D QSAR approach was employed by Ako et al for the prediction of K m values for UGT2B7 substrates. 140 Modeling UGT2B7 substrate selectivity is inherently difficult, especially in the absence of crystallographic data, as it can accept both phenolic, alcoholic hydroxyl (e.g., estrogen and lipids), 141 carboxylic acid (e.g., ibuprofen), 142 and amino (e.g., carbamazepine) groups as SOG. For 53 substrates (36 in training and 17 in test set), experimental binding affinities were measured using recombinant enzyme systems.…”

Section: Udp-glucuronosyltransferase (Ugt)mentioning

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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3D-QSAR studies on UDP-glucuronosyltransferase 2B7 substrates using the pharmacophore and VolSurf approaches

Cited by 10 publications

References 41 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>

Promiscuity and Quantitative Contribution of UGT2B17 in Drug and Steroid Metabolism Determined by Experimental and Computational Approaches

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

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