Characterization of intestinal and hepatic P450 enzymes in cynomolgus monkeys with typical substrates and inhibitors for human P450 enzymes

Department of Drug Metabolism, Drug Safety Research Center, Tokushima Research Institute, Japan.
Xenobiotica (Impact Factor: 2.2). 02/2012; 42(8):719-30. DOI: 10.3109/00498254.2012.656732
Source: PubMed


Cynomolgus monkeys are widely used to predict human pharmacokinetic and/or toxic profiles in the drug developmental stage. Characterization of cynomolgus monkey P450s such as the mRNA expression level, substrate specificity, and inhibitor selectivity were conducted to provide helpful information in designing monkey in vivo studies and monkey-to-human extrapolation. The expression levels of 12 monkey P450 mRNAs, which are considered to be important P450 subfamilies in drug metabolism, were investigated in the liver, small intestine (duodenum, jejunum, and ileum), and colon of individual monkeys. 3. In vitro activities and intrinsic clearance values were determined in monkey intestinal and liver microsomes (MIM and MLM, respectively) using nine typical oxidative reactions for human P450s. Paclitaxel 6α-hydroxylation, diclofenac 4′-hydroxylation, and S-mephenytoin 4′-hydroxylation showed low activities in MIM and MLM. IC₅₀ values of eight selective inhibitors of human P450s were determined in MIM and MLM. Inhibitory effects of furafylline and sulfaphenazole were weak in monkeys on phenacetin O-deethylation and diclofenac 4′-hydroxylation, respectively. These results show profiles of monkey P450s in both the intestine and liver in detail and contribute to a better understanding of the species difference in substrate specificity and inhibitor selectivity between cynomolgus monkeys and humans.

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Available from: Masateru Miyake, Jan 08, 2015
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    • "Pharmaceutical development is benefited by identifying what differences exist in drug metabolism between primate species; indeed, it is these very differences that have driven the use of nonhuman primates in coordination with other animal models, and there is a great potential to advance their utility through resolving and modeling genetic influence on drug metabolism (Emoto et al. 2013; Nishimuta et al. 2011; Yoda et al. 2012 "
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