In vitro models of drug metabolism are being increasingly applied in the drug discovery and development process as tools for predicting human pharmacokinetics and for the prediction of drug-drug interaction risks associated with new chemical entities. The use of in vitro predictive approaches offers several advantages including minimization of compound attrition during development, with associated cost and time savings, as well as minimization of human risk due to the rational design of clinical drug-drug interaction studies. This article reviews the principles underlying the various mathematical models used to scale in vitro drug metabolism data to predict in vivo clearance and the magnitude of drug-drug interactions resulting from reversible as well as mechanism-based metabolic inhibition. Examples illustrating the predictive utility of specific in vitro approaches are critically reviewed. Commonly encountered uncertainties and sources of bias and error in the in vitro determination of intrinsic clearance and metabolic inhibitory potency, including nonspecific microsomal binding, solvent effects on enzyme activities, and uncertainties in estimating enzyme-available inhibitor concentrations are reviewed. In addition, the impact and clinical relevance of complexities such as dosing route-dependent effects, atypical multi-site kinetics of drug-metabolizing enzymes, non-cytochrome P450 determinants of metabolic clearance, and concurrent inhibition and induction, on the applicability and predictive accuracy of current in vitro models are discussed.