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

P.A. Smith, M.J. Sorich, L.S.C. Low, R.A. McKinnon, J.O. Miners

    Research output: Contribution to journalArticlepeer-review

    60 Citations (Scopus)


    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.
    Original languageEnglish
    Pages (from-to)507-517
    Number of pages11
    JournalJournal of Molecular Graphics and Modelling
    Issue number6
    Publication statusPublished - Jul 2004


    • UDP-glucuronosyltransferase
    • UGT
    • ADME
    • QSAR
    • QSMR
    • Pharmacophore
    • Metabolism
    • Support vector machine
    • Modelling


    Dive into the research topics of 'Towards integrated ADME prediction: past, present and future directions for modelling metabolism by UDP-glucoronosyltransferases'. Together they form a unique fingerprint.

    Cite this