Identification of a Single Time-point for Plasma Lansoprazole Measurement That Adequately Reflects Area Under the Concentration-Time Curve

Exploring the impact of ethnicity on drug pharmacokinetics using PBPK models: A case study with lansoprazole in Japanese subjects

AIMS

The aim of this study is to demonstrate the use of PBPK modelling to explore the impact of ethnic differences on drug PK.

METHODS

A PBPK model developed for lansoprazole was used to predict the clinical PK of lansoprazole in Japanese subjects by incorporating the physiological parameters of a Japanese population into the model. Further verification of the developed Japanese population with clinical studies involving eight other CYP substrates-omeprazole, ticlopidine, alprazolam, midazolam, nifedipine, cinacalcet, paroxetine and dextromethorphan-was also carried out.

RESULTS

The PK of lansoprazole in both Caucasian and Japanese subjects was well predicted by the model as the observed data were within the 5th and 95th percentiles across all the clinical studies. In age- and sex-matched simulations in both the Caucasian and Japanese populations, the predicted PK (mean ± SD) of a single oral dose of 30-mg lansoprazole was higher in the Japanese population in all cases, with more than twofold higher AUC of 5.98 ± 6.43 mg/L.h (95% CI: 4.72, 7.24) vs. 2.46 ± 2.45 mg/L.h (95% CI: 1.98, 2.94) in one scenario. In addition, in two out of the nine clinical DDIs of lansoprazole and the additional CYP substrates simulated using the Japanese population, the predicted DDI in Japanese was more than 1.25-fold that in Caucasians, indicating an increased DDI liability.

CONCLUSIONS

By accounting for various physiological parameters that characterize a population in a PBPK model, the impact of the different identified interethnic differences on the drug's PK can be explored, which can inform the adoption of drugs from one region to another.

Optimising Seniors' Metabolism of Medications and Avoiding Adverse Drug Events Using Data on How Metabolism by Their P450 Enzymes Varies with Ancestry and Drug-Drug and Drug-Drug-Gene Interactions

Many individuals ≥65 have multiple illnesses and polypharmacy. Primary care physicians prescribe >70% of their medications and renew specialists’ prescriptions. Seventy-five percent of all medications are metabolised by P450 cytochrome enzymes. This article provides unique detailed tables how to avoid adverse drug events and optimise prescribing based on two key databases. DrugBank is a detailed database of 13,000 medications and both the P450 and other complex pathways that metabolise them. The Flockhart Tables are detailed lists of the P450 enzymes and also include all the medications which inhibit or induce metabolism by P450 cytochrome enzymes, which can result in undertreatment, overtreatment, or potentially toxic levels. Humans have used medications for a few decades and these enzymes have not been subject to evolutionary pressure. Thus, there is enormous variation in enzymatic functioning and by ancestry. Differences for ancestry groups in genetic metabolism based on a worldwide meta-analysis are discussed and this article provides advice how to prescribe for individuals of different ancestry. Prescribing advice from two key organisations, the Dutch Pharmacogenetics Working Group and the Clinical Pharmacogenetics Implementation Consortium is summarised. Currently, detailed pharmacogenomic advice is only available in some specialist clinics in major hospitals. However, this article provides detailed pharmacogenomic advice for primary care and other physicians and also physicians working in rural and remote areas worldwide. Physicians could quickly search the tables for the medications they intend to prescribe.

Pharmacogenetics of drug-drug interaction and drug-drug-gene interaction: a systematic review on CYP2C9, CYP2C19 and CYP2D6

Currently, most guidelines on drug-drug interaction (DDI) neither consider the potential effect of genetic polymorphism in the strength of the interaction nor do they account for the complex interaction caused by the combination of DDI and drug-gene interaction (DGI) where there are multiple biotransformation pathways, which is referred to as drug-drug-gene interaction (DDGI). In this systematic review, we report the impact of pharmacogenetics on DDI and DDGI in which three major drug-metabolizing enzymes - CYP2C9, CYP2C19 and CYP2D6 - are central. We observed that several DDI and DDGI are highly gene-dependent, leading to a different magnitude of interaction. Precision drug therapy should take pharmacogenetics into account when drug interactions in clinical practice are expected.

Pharmacokinetics of lansoprazole and its main metabolites after single intravenous doses in healthy Chinese subjects

The aim of the study was to evaluate the pharmacokinetics (PK) of lansoprazole (LPZ) and its main metabolites 5'-hydroxy lansoprazole (HLPZ) and lansoprazole sulphone (LPZS) after single intravenous (i.v.) doses of LPZ in healthy Chinese subjects, and the relationship between the cytochrome P450 (CYP) 2C19 phenotypes and the plasma concentrations of LPZS at the time-points in the elimination phase of LPZ. Twelve subjects were given lansoprazole by i.v. infusion. Blood samples were collected at designated time points up to 24 h. Plasma concentrations of LPZ, HLPZ and LPZS were quantified by a selective and sensitive liquid chromatography-tandem mass spectrometric (LC-MS/MS) method. After single i.v. doses of 15, 30 and 60 mg LPZ, C(max) and area under the plasma concentration-time curve (AUC(0-t)) of LPZ were 725 ± 151, 1480 ± 190, 3130 ± 480 µg · L(-1) and 1690 ± 1210, 3630 ± 2530, 8080 ± 4550 µg · h · L(-1), respectively. LPZ was generally well tolerated in healthy Chinese subjects, and displayed linear PK in the range of 15-60 mg. There were significant differences in the elimination of LPZ and the formation of LPZS between the single CYP2C19 poor metabolizer (PM) and the CYP2C19 extensive metabolizers (EM). The concentration of LPZS at the time-points in the elimination phase of LPZ could be monitored for CYP2C19 phenotyping. As a probe drug for CYP2C19 phenotyping, LPZ for injection might be more suitable than LPZ oral formulations.

Clinical usefulness of limited sampling strategies for estimating AUC of proton pump inhibitors

Cytochrome P450 (CYP) 2C19 (CYP2C19) genotype is regarded as a useful tool to predict area under the blood concentration-time curve (AUC) of proton pump inhibitors (PPIs). In our results, however, CYP2C19 genotypes had no influence on AUC of all PPIs during fluvoxamine treatment. These findings suggest that CYP2C19 genotyping is not always a good indicator for estimating AUC of PPIs. Limited sampling strategies (LSS) were developed to estimate AUC simply and accurately. It is important to minimize the number of blood samples because of patient's acceptance. This article reviewed the usefulness of LSS for estimating AUC of three PPIs (omeprazole: OPZ, lansoprazole: LPZ and rabeprazole: RPZ). The best prediction formulas in each PPI were AUC(OPZ)=9.24 x C(6h)+2638.03, AUC(LPZ)=12.32 x C(6h)+3276.09 and AUC(RPZ)=1.39 x C(3h)+7.17 x C(6h)+344.14, respectively. In order to optimize the sampling strategy of LPZ, we tried to establish LSS for LPZ using a time point within 3 hours through the property of pharmacokinetics of its enantiomers. The best prediction formula using the fewest sampling points (one point) was AUC(racemic LPZ)=6.5 x C(3h) of (R)-LPZ+13.7 x C(3h) of (S)-LPZ-9917.3 x G1-14387.2×G2+7103.6 (G1: homozygous extensive metabolizer is 1 and the other genotypes are 0; G2: heterozygous extensive metabolizer is 1 and the other genotypes are 0). Those strategies, plasma concentration monitoring at one or two time-points, might be more suitable for AUC estimation than reference to CYP2C19 genotypes, particularly in the case of coadministration of CYP mediators.

Identification of the time-point which gives a plasma rabeprazole concentration that adequately reflects the area under the concentration-time curve

OBJECTIVE

The purpose of this study is to evaluate whether a simple formula using limited blood samples can predict the area under the plasma rabeprazole concentration-time curve (AUC) in co-administration with CYP inhibitors.

METHODS

A randomized double-blind placebo-controlled crossover study design in three phases was conducted at intervals of 2 weeks. Twenty-one healthy Japanese volunteers, including three CYP2C19 genotype groups, took a single oral 20-mg dose of rabeprazole after three 6-day pretreatments, i.e., clarithromycin 800 mg/day, fluvoxamine 50 mg/day, and placebo. Prediction formulas of the AUC were derived from pharmacokinetics data of 21 subjects in three phases using multiple linear regression analysis. Ten blood samples were collected over 24 h to calculate AUC. Plasma concentrations of rabeprazole was measured by an HPLC-assay (l.l.q.=1 ng/ml).

RESULTS

The AUC was based on all the data sets (n=63). The linear regression using two points (C3 and C6) could predict AUC(0-infinity) precisely, irrespective of CYP2C19 genotypes and CYP inhibitors (AUC(0-infinity)=1.39xC3+7.17xC6+344.14, r (2)=0.825, p<0.001).

CONCLUSION

The present study demonstrated that the AUC of rabeprazole can be estimated by the simple formula using two-point concentrations. This formula can be more accurate for the prediction of AUC estimation than that reflected by CYP2C19 genotypes without any determination, even if there are significant differences for the CYP2C19 genotypes. Therefore, this prediction formula might be useful to evaluate whether CYP2C19 genotypes really reflects the curative effect of rabeprazole.