Towards integrated ADME prediction: past, present and future directions for modelling metabolism by UDP-glucuronosyltransferases

Abstract

Undesirable absorption, distribution, metabolism, excretion (ADME) properties are the cause of many drug development failures and this has led to the need to identify such problems earlier in the development process. This review highlights computational (in silico) approaches that have been used to identify the characteristics of ligands influencing molecular recognition and/or metabolism by the drug-metabolising enzyme UDP-gucuronosyltransferase (UGT). Current studies applying pharmacophore elucidation, 2D-quantitative structure metabolism relationships (2D-QSMR), 3D-quantitative structure metabolism relationships (3D-QSMR), and non-linear pattern recognition techniques such as artificial neural networks and support vector machines for modelling metabolism by UGT are reported. An assessment of the utility of in silico approaches for the qualitative and quantitative prediction of drug glucuronidation parameters highlights the benefit of using multiple pharmacophores and also non-linear techniques for classification. Some of the challenges facing the development of generalisable models for predicting metabolism by UGT, including the need for screening of more diverse structures, are also outlined.

Three-Dimensional Quantitative Structure-Activity Relationship Studies on UGT1A9-Mediated 3-O-Glucuronidation of Natural Flavonols Using a Pharmacophore-Based Comparative Molecular Field Analysis Model

“…2D/3D QSAR models developed for UGTs (Sorich et al, 2002(Sorich et al, , 2008Smith et al, 2004) have predicted the substrate selectivity and/or binding affinity of inhibitors [as reflected by low apparent inhibitor constant (K i,app )]. However, V max or CL int is more relevant for defining the susceptibility of chemicals to be metabolized and reflect the in vivo biotransformation efficiency by individual human UGT isoforms (at low physiological concentrations at or below 1 M), because higher substrate affinity toward UGTs does not always translate to faster glucuronidation rates.…”

Homology modeling and metabolism prediction of human carboxylesterase-2 using docking analyses by GriDock: a parallelized tool based on AutoDock 4.0

“…The metabolizing activity of the most relevant redox enzymes [6] (i.e., cytochromes P450 [7], monoamine oxidases [8], and alcohol dehydrogenases [9]) and those of some conjugating enzymes [10] (e.g., UDP-glucuronosyltransferases [11], sulfotransferases [12], methyltransferases [13] and glutathione-S-transferases [14]) have been investigated in great detail by computational techniques and can be successfully predicted by several approaches. Apart from our previous studies focused on hCES1 [15,16], the hydrolyzing activity of the human hydrolases has been scantly analyzed in silico [17], although they play key roles in the hydrolytic metabolism of xenobiotics as well as in the activation of most carrierlinked prodrugs [18][19][20][21].…”

Prediction models of human plasma protein binding rate and oral bioavailability derived by using GA–CG–SVM method

“…Currently in silico prediction of pharmacokinetic and pharmacodynamic properties, including absorption, distribution, metabolism, excretion and toxicity (ADMET), in the early stage of drug discovery has been thought to be an efficient way to help to reduce the cost of drug development [1,2]. Up to now, many prediction models of ADMET properties have been established [3,4], but some of these models are still not competent for the prediction purpose [5], such as, typically, oral bioavailability (BIO) [6][7][8] and human plasma protein binding rate (PPBR) [8].…”