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Assessment of multiple cytochrome P450 activities in metabolically inactivated human liver microsomes and roles of P450 2C isoforms in reaction phenotyping studies.

Norie MurayamaKanako YajimaMikiko HikawaKanami ShimuraYu IshiiMasaki TakadaYasuhiro UnoMasahiro UtohKazuhide IwasakiHiroshi Yamazaki
Published in: Biopharmaceutics & drug disposition (2017)
The fraction of substrate metabolized (fm ) can be used to estimate drug interactions and can be determined by comparison of the intrinsic clearances (CLint ) of victim drugs obtained from inhibited and uninhibited hepatic enzymes. Commercially available human liver microsomes were recently developed in which one cytochrome P450 (P450) isoform is selectively inactivated. These inactivated liver microsomes were used to evaluate the roles of P450 2C isoforms in the depletion and oxidation of probe substrates. Determination of CLint with sets of control and P450 2C9-inactivated liver microsomes yielded fm,P450 2C9 values of 0.69-1.0 for celecoxib, diclofenac and warfarin. Apparent minor contributions of P450 1A2/2C8/3A4 were seen in depletion assays, yielding ~1 for the sum of the fm values. Selectively inactivated liver microsomes were thereby shown to be potentially useful for determining the in vitro fm values for major P450 2C9 contributions to substrate oxidations. Metabolite formations from diclofenac and warfarin were suppressed by 62-84% by the replacement of control liver microsomes with P450 2C9-inactivated liver microsomes. R-, S- and racemic omeprazole and troglitazone oxidation activities by liver microsomes at multiple substrate concentrations were suppressed by 26-36% and 22-50%, respectively, when P450 2C19- and 2C8-inactivated liver microsomes were used in place of control liver microsomes. This study provides important information to help elucidate the different roles of P450 isoforms in metabolite formation at different substrate concentrations. The data obtained allow the fractions metabolized to be calculated for victim drugs.
Keyphrases
  • computed tomography
  • machine learning
  • mass spectrometry
  • artificial intelligence
  • electronic health record
  • drug induced