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Yadav J , Paragas E , Korzekwa K , Nagar S
Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions
Pharmacol Ther. 2020 Feb;206 :107449
PMID: 31836452 PMCID: PMC6995442 URL: https://www.ncbi.nlm.nih.gov/pubmed/31836452
AbstractCytochrome P450 (CYP) enzyme kinetics often do not conform to Michaelis-Menten assumptions, and time-dependent inactivation (TDI) of CYPs displays complexities such as multiple substrate binding, partial inactivation, quasi-irreversible inactivation, and sequential metabolism. Additionally, in vitro experimental issues such as lipid partitioning, enzyme concentrations, and inactivator depletion can further complicate the parameterization of in vitro TDI. The traditional replot method used to analyze in vitro TDI datasets is unable to handle complexities in CYP kinetics, and numerical approaches using ordinary differential equations of the kinetic schemes offer several advantages. Improvement in the parameterization of CYP in vitro kinetics has the potential to improve prediction of clinical drug-drug interactions (DDIs). This manuscript discusses various complexities in TDI kinetics of CYPs, and numerical approaches to model these complexities. The extrapolation of CYP in vitro TDI parameters to predict in vivo DDIs with static and dynamic modeling is discussed, along with a discussion on current gaps in knowledge and future directions to improve the prediction of DDI with in vitro data for CYP catalyzed drug metabolism.
Notes1879-016x Yadav, Jaydeep Paragas, Erickson Korzekwa, Ken Nagar, Swati Journal Article Review England Pharmacol Ther. 2020 Feb;206:107449. doi: 10.1016/j.pharmthera.2019.107449. Epub 2019 Dec 11.