The prediction of metabolic drug-drug interactions should include quantitative attributes, such as variability in the study populations, and the results should be presented in terms of probability and uncertainty. The simple algebraic equations used to calculate one mean value for the extent of drug-drug interaction are adequate for qualitative or semi-quantitative risk assessment. However, truly quantitative predictions continue to fail. The success of drug-drug interaction predictions requires understanding of the relationship between drug disposition and quantifiable influential factors on the change in systemic exposure. The complex interplay of influential factors, including variability estimates, on successful prediction of drug interaction have not been systematically examined. Therefore, physiologically relevant models of metabolic drug-drug interaction will likely play increasingly important roles in improving quantitative predictions and in the assessment of the influential factors underlying the interactions. The physiologically-based approach, with stochastic considerations, offers a powerful alternative to the empirical calculation of mean values. In addition to quantitative estimation of the interaction for assessing probability of risk, a reasonably validated predictive model is useful for prospective optimization of study designs. As a consequence, the definitive clinical trial would yield more meaningful information to support dosing recommendations. This review focuses on illustrating the importance of an integrated approach to building useful models for prediction of metabolism-based drug-drug interactions in human subjects.