ArticleXenobiotica; the fate of foreign compounds in biological systems2020
Numerical analysis of time-dependent inhibition kinetics: comparison between rat liver microsomes and rat hepatocyte data for mechanistic model fitting.
Article in Xenobiotica; the fate of foreign compounds in biological systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Article
- Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions.Pharmacology & therapeutics · 2020Review
- Impact of Lipid Partitioning on the Design, Analysis, and Interpretation of Microsomal Time-Dependent Inactivation.Drug metabolism and disposition: the biological fate of chemicals · 2019Article
- Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A.Molecular pharmaceutics · 2018Article
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3 authors at 1 institution in 1 country.
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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.
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