Abstract

Three CYP3A4 substrates, midazolam, ticlopidine, and diazepam, display non–Michaelis-Menten kinetics, form multiple primary metabolites, and are sequentially metabolized to secondary metabolites. We generated saturation curves for these compounds and analyzed the resulting datasets using a number of single-substrate and multisubstrate binding models. These models were parameterized using rate equations and numerical solutions of the ordinary differential equations. Multisubstrate binding models provided results superior to single-substrate models, and simultaneous modeling of multiple metabolites provided better results than fitting the individual datasets independently. Although midazolam datasets could be represented using standard two-substrate models, more complex models that include explicit enzyme-product complexes were needed to model the datasets for ticlopidine and diazepam. In vivo clearance predictions improved markedly with the use of in vitro parameters from the complex models versus the Michaelis-Menten equation. The results highlight the need to use sufficiently complex kinetic schemes instead of the Michaelis-Menten equation to generate accurate kinetic parameters.