PT  - JOURNAL ARTICLE
AU  - Erickson M. Paragas
AU  - Zeyuan Wang
AU  - Ken Korzekwa
AU  - Swati Nagar
TI  - Complex Cytochrome P450 Kinetics Due to Multisubstrate Binding and Sequential Metabolism. Part 2. Modeling of Experimental Data
AID  - 10.1124/dmd.121.000554
DP  - 2021 Dec 01
TA  - Drug Metabolism and Disposition
PG  - 1100--1108
VI  - 49
IP  - 12
4099  - http://dmd.aspetjournals.org/content/49/12/1100.short
4100  - http://dmd.aspetjournals.org/content/49/12/1100.full
SO  - Drug Metab Dispos2021 Dec 01; 49
AB  - 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.SIGNIFICANCE STATEMENT The metabolism of midazolam, ticlopidine, and diazepam by CYP3A4 results in multiple metabolites and sequential metabolism. This study evaluates the use of rate equations and numerical methods to characterize the in vitro enzyme kinetics. Use of complex cytochrome P450 kinetic models is necessary to obtain accurate parameter estimates for predicting in vivo disposition.