Correlations of the structural features (including conformational flexibility) in the active sites with the substrate profiles of several SULTs have been well established. One is encouraged to closely integrate in silico approaches with the structural knowledge of the active sites for development of a rationalized and accurate tool that is able to predict metabolism of SULTs toward chemicals and drug candidates.
The UDP-glucuronosyltransferase (UGT) enzyme catalyzes the glucuronidation reaction which is a major metabolic and detoxification pathway in humans. Understanding the mechanisms for substrate recognition by UGT assumes great importance in an attempt to predict its contribution to xenobiotic/drug disposition in vivo. Spurred on by this interest, 2D/3D-quantitative structure activity relationships (QSAR) and pharmacophore models have been established in the absence of a complete mammalian UGT crystal structure. This review discusses the recent progress in modeling human UGT substrates including those with multiple sites of glucuronidation. A better understanding of UGT active site contributing to substrate selectivity (and regioselectivity) from the homologous enzymes (i.e., plant and bacterial UGTs, all belong to family 1 of glycosyltransferase (GT1)) is also highlighted, as these enzymes share a common catalytic mechanism and/or overlapping substrate selectivity.
In vitro metabolism and inhibition studies, which serve as the basis of predicting pharmacokinetic events in vivo, are an essential part of pharmaceutical development and research. With the increasing occurrences of a typical kinetic profiles, modeling of enzyme kinetics is no longer a one-step operation of fitting classical Michaelis-Menten equation to the data. It involves considerable computational works regarding model selection and discrimination. This study presented XL Kinetics, a free Microsoft Excel add-in program written in Visual Basic for Application (VBA), for enzyme kinetic analysis. The program provides 11 most frequently used enzyme (stead-state) kinetic models including the models describing atypical kinetics (i.e., substrate inhibition, sigmoidal and biphasic models), a bisubstrate compulsory ordered model, and four reversible inhibition models. To evaluate the program, modeling results from XL_Kinetics and the commercial software packages (i.e., GraphPad Prism and Sigma Plot) were systematically compared. The results show that the kinetic parameters and their respective standard errors derived using XL_Kinetics are essentially the same as those obtained with the commercial software's. In conclusion, XL_ Kinetics automates enzyme kinetic analysis in MS Excel, and may provide drug researchers and students with a fast, reliable and easy-to-use tool for routine analysis of enzyme kinetic data.
UDP-glucuronosyltransferase 2B7 (UGT2B7) is an important enzyme responsible for clearance of many drugs. Here, we report two 3D quantitative structure-activity relationship (QSAR) models for UGT2B7 using the pharmacophore and VolSurf approach, respectively. The dataset included 53 structurally diverse UGT2B7 substrates, 36 of which were used for the training set and 17 of which for the external test set. Pharmacophore-based 3D-QSAR model (or hypothesis) was developed using the Discovery Studio program. A user-defined "glucuronidation site" feature was forcefully included in a pharmacophore hypothesis. VolSurf-based 3D-QSAR model was generated using the VolSurf program. This involves calculation of VolSurf descriptors, variable selection with the FFD algorithm, and partial least squares (PLS) analyses. The best pharmacophore model (r(2) = 0.736) consists of one glucuronidation site, one hydrogen bond acceptor, and three hydrophobic regions. Using this model, K(m) values for 14 of 17 test substrates were predicted within one log unit. The yielded VolSurf (PLS) model with two components shows statistical significance in both fitting and internal predicting (r(2) = 0.866, q(2) = 0.728). Further, the K(m) values for all test substrates were predicted within one log unit. In addition, the VolSurf model reveals an overlay of chemical features influencing the enzyme-substrate binding. Those include molecular size and shape, integy moments, capacity factors, best volumes of DRY probe, H-bonding, and log P. In conclusion, the pharmacophore and VolSurf approaches are successfully utilized to establish predictive models for UGT2B7. The derived models should be an efficient tool for high throughput prediction of UGT2B7 metabolism.
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