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Updated: Aug 16, 2026

Mass Spectrometry and Luminogenic-based Approaches to Characterize Phase I Metabolic Competency of In Vitro Cell Cultures
Published on: March 28, 2017
Towards integrated ADME prediction: past, present and future directions for modelling metabolism by
P A Smith1, M J Sorich, L S C Low
1Department of Clinical Pharmacology, Flinders University and Flinders Medical Centre, South Australia, Australia. paul.smith@flinders.edu.au
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.
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