Numerical analysis of time-dependent inhibition kinetics: comparison between rat liver microsomes and rat hepatocyte data for mechanistic model fitting. (1st November 2020)

• Record Type: Journal Article
• Authors: Pham, Chuong; Nagar, Swati; Korzekwa, Ken
• Is Part Of: Xenobiotica. Volume 50:Number 11(2020:Nov.)
• Page Start: 1301
• Page End: 1310
• Publication Date: 2020-11-01
• Subjects: Cytochrome P450 -- hepatocytes -- mechanism-based inhibition -- microsomes -- time-dependent inhibition; Metabolism -- Periodicals; Drugs -- Physiological effect -- Periodicals; Food additives -- Periodicals; Chemicals -- Physiological effect -- Periodicals; Biochemistry -- Periodicals; Pharmaceutical Preparations -- metabolism -- Periodicals; Metabolism -- Periodicals; 574.133
• DOI: 10.1080/00498254.2017.1345020
• Languages: English
• ISSNs: 0049-8254

Abstract

Abstract: Time-dependent inhibition (TDI) may confound drug interaction predictions. Recently, models were generated for an array of TDI kinetic schemes using numerical analysis of microsomal assays. Additionally, a distinct terminal inactivation step was identified for certain mechanism based inhibitors (MBI) following reversible metabolite intermediate complex (MIC) formation. Longer hepatocyte incubations potentially allow analysis of slow TDI and terminal inactivation. In the experiments presented here, we compared the quality of TDI parameterization by numerical analysis between hepatocyte and microsomal data. Rat liver microsomes (RLM), suspended rat hepatocytes (SRH) and sandwich-cultured rat hepatocytes (SCRH) were incubated with the prototypical CYP3A MBI troleandomycin and the substrate midazolam. Data from RLM provided a better model fit as compared to SRH. Increased CYP3A expression after dexamethasone (DEX) induction improved the fit for RLM and SRH. A novel sequential kinetic scheme, defining inhibitor metabolite production prior to MIC formation, improved the fit compared to direct MIC formation. Furthermore, terminal inactivation rate constants were parameterized for RLM and SRH samples with DEX-induced CYP3A. The low expression of CYP3A and experimental error in SCRH resulted in poor data for model fitting. Overall, RLM generated data better suited for elucidation of TDI mechanisms by numerical analysis.