Cytochrome P450 (CYP) and UDP-glucuronosyltransferase (UGT), which both exist as enzyme "superfamilies," are together responsible for the metabolism of most hepatically cleared drugs. There is currently intense interest in the development of techniques that permit identification of the CYP and UGT isoform(s) involved in the metabolism of a newly discovered drug, and hence prediction of factors likely to alter elimination in vivo. In addition, the quantitative scaling of kinetic parameters for a metabolic pathway assumes importance for identifying newly discovered drugs with undesirable in vivo pharmacokinetic properties. Although qualitative and quantitative in vitro-in vivo correlation based on data generated using human liver tissue or recombinant enzymes have been applied successfully to many drugs eliminated by CYP, these strategies have proved less definitive for glucuronidated compounds. Computational (in silico) modeling techniques that potentially provide a facile and economic alternative to the in vitro methods are now emerging. This review assesses the utility of in vitro and in silico approaches for the qualitative and quantitative prediction of drug glucuronidation parameters and the challenges facing the development of generalizable models.