The oral bioavailability, excretion and cytochrome P450 inhibition properties of epiberberine: an in vivo and in vitro evaluation.

Epiberberine (EPI) is a novel and potentially effective therapeutic and preventive agent for diabetes and cardiovascular disease. To evaluate its potential value for drug development, a specific, sensitive and robust high-performance liquid chromatography-tandem mass spectrometry assay for the determination of EPI in rat biological samples was established. This assay was used to study the pharmacokinetics, bioavailability and excretion of EPI in rats after oral administration. In addition, a cocktail method was used to compare the inhibition characteristics of EPI on cytochrome P450 (CYP450) isoforms in human liver microsomes (HLMs) and rat liver microsomes (RLMs). The results demonstrated that EPI was rapidly absorbed and metabolized after oral administration (10, 54 or 81 mg/kg) in rats, with Tmax of 0.37-0.42 h and T1/2 of 0.49-2.73 h. The Cmax and area under the curve values for EPI increased proportionally with the dose, and the oral absolute bioavailability was 14.46%. EPI was excreted mainly in bile and feces, and after its oral administration to rats, EPI was eliminated predominantly by the kidneys. A comparison of the current half-maximal inhibitory concentration and Ki values revealed that EPI demonstrated an obvious inhibitory effect on CYP2C9 and CYP2D6. Furthermore, its effect was stronger in HLM than in RLM, more likely to be a result of noncompetitive inhibition.

Efavirenz clearances in vitro and in vivo in six cynomolgus monkeys associated with polymorphic cytochrome P450 2C9 and simulated by individual physiologically based pharmacokinetic models.

Cynomolgus monkey cytochrome P450 2C9 (formerly known as P450 2C43) variation was reportedly associated with metabolic clearance of the antiretroviral drug efavirenz in vivo (in three wild-type, one heterozygote and two homozygote animals), being unlikely in the case of human P450 2B6-dependent efavirenz clearance. In this study, the liver microsomal elimination rates of efavirenz for the same individual animals previously treated with intravenous/oral administrations of efavirenz showed significant reductions associated with the P450 2C9 p.[(I112L)] genotype (p < 0.05). Simulations of efavirenz clearance after oral administrations in individual cynomolgus monkeys were performed using individual simplified physiologically based pharmacokinetic (PBPK) modeling consisting of gut, liver and central compartments. The modeled hepatic intrinsic clearances were also significantly associated with the P450 2C9 genotypes, however, absorption rate constants or volumes of the systemic circulation were not likely determining factors for the individual efavirenz clearance variations in the six cynomolgus monkeys. This study provides important information to help simulate the clearances of efavirenz and related medicines associated with polymorphic P450 2C9 in individual cynomolgus monkeys, thereby facilitating the calculation of the fraction of liver microsomal clearance for estimating in vivo drug clearance with simplified PBPK modeling.

Use of Human Plasma Samples to Identify Circulating Drug Metabolites that Inhibit Cytochrome P450 Enzymes.

Drug interactions elicited through inhibition of cytochrome P450 (P450) enzymes are important in pharmacotherapy. Recently, greater attention has been focused on not only parent drugs inhibiting P450 enzymes but also on possible inhibition of these enzymes by circulating metabolites. In this report, an ex vivo method whereby the potential for circulating metabolites to be inhibitors of P450 enzymes is described. To test this method, seven drugs and their known plasma metabolites were added to control human plasma at concentrations previously reported to occur in humans after administration of the parent drug. A volume of plasma for each drug based on the known inhibitory potency and time-averaged concentration of the parent drug was extracted and fractionated by high-pressure liquid chromatography-mass spectrometry, and the fractions were tested for inhibition of six human P450 enzyme activities (CYP1A2, CYP2C8, CYP2C9, CYP2C19, CYP2D6, and CYP3A4). Observation of inhibition in fractions that correspond to the retention times of metabolites indicates that the metabolite has the potential to contribute to P450 inhibition in vivo. Using this approach, norfluoxetine, hydroxyitraconazole, desmethyldiltiazem, desacetyldiltiazem, desethylamiodarone, hydroxybupropion, erythro-dihydrobupropion, and threo-dihydrobupropion were identified as circulating metabolites that inhibit P450 activities at a similar or greater extent as the parent drug. A decision tree is presented outlining how this method can be used to determine when a deeper investigation of the P450 inhibition properties of a drug metabolite is warranted.

Physiologically based pharmacokinetic modeling to predict drug-drug interactions involving inhibitory metabolite: a case study of amiodarone.

Evaluation of drug-drug interaction (DDI) involving circulating inhibitory metabolites of perpetrator drugs has recently drawn more attention from regulatory agencies and pharmaceutical companies. Here, using amiodarone (AMIO) as an example, we demonstrate the use of physiologically based pharmacokinetic (PBPK) modeling to assess how a potential inhibitory metabolite can contribute to clinically significant DDIs. Amiodarone was reported to increase the exposure of simvastatin, dextromethorphan, and warfarin by 1.2- to 2-fold, which was not expected based on its weak inhibition observed in vitro. The major circulating metabolite, mono-desethyl-amiodarone (MDEA), was later identified to have a more potent inhibitory effect. Using a combined "bottom-up" and "top-down" approach, a PBPK model was built to successfully simulate the pharmacokinetic profile of AMIO and MDEA, particularly their accumulation in plasma and liver after a long-term treatment. The clinical AMIO DDIs were predicted using the verified PBPK model with incorporation of cytochrome P450 inhibition from both AMIO and MDEA. The closest prediction was obtained for CYP3A (simvastatin) DDI when the competitive inhibition from both AMIO and MDEA was considered, for CYP2D6 (dextromethorphan) DDI when the competitive inhibition from AMIO and the competitive plus time-dependent inhibition from MDEA were incorporated, and for CYP2C9 (warfarin) DDI when the competitive plus time-dependent inhibition from AMIO and the competitive inhibition from MDEA were considered. The PBPK model with the ability to simulate DDI by considering dynamic change and accumulation of inhibitor (parent and metabolite) concentration in plasma and liver provides advantages in understanding the possible mechanism of clinical DDIs involving inhibitory metabolites.