Different effects of fluvoxamine on rabeprazole pharmacokinetics in relation to CYP2C19 genotype status

Insight into Risk Factors, Pharmacogenetics/Genomics, and Management of Adverse Drug Reactions in Elderly: A Narrative Review

The European Medicine Agency (EMA) has defined Adverse Drug Reactions (ADRs) as "a noxious and unintended response to a medicine", not including poisoning, accidental, or intentional overdoses. The ADR occurrence differs based on the approach adopted for defining and detecting them, the characteristics of the population under study, and the research setting. ADRs have a significant impact on morbidity and mortality, particularly among older adults, and represent a financial burden for health services. Between 30% and 60% of ADRs might be predictable and preventable, emerging as a result of inappropriate prescription, drug chemistry inherent toxicity, cell-specific drug toxicity, age- and sex-related anomalies in drug absorption, distribution, metabolism, and elimination (ADME), and drug-drug interactions (DDIs) in combination therapies or when a patient is treated with different drugs for concomitant disorders. This is particularly important in chronic diseases which require long-term treatments. Rapid developments in pharmacogenetics/genomics have improved the understanding of ADRs accompanied by more accurate prescriptions and reduction in unnecessary costs. To alleviate the burden of ADRs, especially in the elderly, interventions focused on pharmaceutical principles, such as medication review and reconciliation, should be integrated into a broader assessment of patients' characteristics, needs, and health priorities. Digital health interventions could offer valuable solutions to assist healthcare professionals in identifying inappropriate prescriptions and promoting patient adherence to pharmacotherapies.

The investigation of the complex population-drug-drug interaction between ritonavir-boosted lopinavir and chloroquine or ivermectin using physiologically-based pharmacokinetic modeling

OBJECTIVES

Therapy failure caused by complex population-drug-drug (PDDI) interactions including CYP3A4 can be predicted using mechanistic physiologically-based pharmacokinetic (PBPK) modeling. A synergy between ritonavir-boosted lopinavir (LPVr), ivermectin, and chloroquine was suggested to improve COVID-19 treatment. This work aimed to study the PDDI of the two CYP3A4 substrates (ivermectin and chloroquine) with LPVr in mild-to-moderate COVID-19 adults, geriatrics, and pregnancy populations.

METHODS

The PDDI of LPVr with ivermectin or chloroquine was investigated. Pearson's correlations between plasma, saliva, and lung interstitial fluid (ISF) levels were evaluated. Target site (lung epithelial lining fluid [ELF]) levels of ivermectin and chloroquine were estimated.

RESULTS

Upon LPVr coadministration, while the chloroquine plasma levels were reduced by 30, 40, and 20%, the ivermectin plasma levels were increased by a minimum of 425, 234, and 453% in adults, geriatrics, and pregnancy populations, respectively. The established correlation equations can be useful in therapeutic drug monitoring (TDM) and dosing regimen optimization.

CONCLUSIONS

Neither chloroquine nor ivermectin reached therapeutic ELF levels in the presence of LPVr despite reaching toxic ivermectin plasma levels. PBPK modeling, guided with TDM in saliva, can be advantageous to evaluate the probability of reaching therapeutic ELF levels in the presence of PDDI, especially in home-treated patients.

Rates of Divergent Pharmacogenes in a Psychiatric Cohort of Inpatients with Depression-Arguments for Preemptive Testing

Background: The international drug agencies annotate pharmacogenes for many years. Pharmacogenetic testing is thus far only established in few settings, assuming that only few patients are actually affected by drug-gene interactions. Methods: 108 hospitalized patients with major depressive disorder were genotyped for CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, NAT2, DPYD; VKORC1 and TMTP. Results: We found 583 (mean 5.4, median 5) divergent phenotypes (i.e., divergent from the common phenotypes considered normal, e.g., extensive metabolizer) in the 12 analyzed pharmacokinetic genes. The rate for at least one divergent phenotype was 100% in our cohort for CYP, but also for all 12 important pharmacogenes: patients had at least two divergent phenotypes. Compared to a large Danish cohort, CYP2C9 NM and IM status, CYP2C19 UM, CYP2D6 UM and DYPD (GAS 0, 1, 2) genotypes differed statistical significantly. For CYP2D6 and CYP2C19, 13% of the patients were normal metabolizers for both enzymes in our cohort, but this value was 27.3% in the Danish cohort, which is a highly significant difference (p < 0.0001). Conclusion: Divergent phenotypes in pharmacogenes are not the exception, but the rule. Patients with divergent phenotypes seem more prone for hospitalization, emphasizing the need for pre-emptive testing to avoid inefficacy and adverse drug effects in all patients.

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.

Qualitative and quantitative determination of anaprazole and its major metabolites in human plasma

Anaprazole is a novel proton pump inhibitor under development for the treatment of gastric and duodenal ulcers. In the present study, an ultra-performance liquid chromatography-ultraviolet detector/quadrupole time-of-flight mass spectrometry method was developed for the metabolic profiling of human plasma after an oral administration of 40 mg anaprazole. The principal metabolic pathways were identified as sulfoxide reduction to thioether (M8-1), dehydrogenation (M21-1), sulfoxide oxidation to sulfone (M16-3), and sulfoxide reduction with O-demethylation to form carboxylic acid (M7-1). Anaprazole, M8-1, M16-3, M21-1, and M7-1 were selected and further quantified in human plasma by using a rapid and sensitive liquid chromatography-tandem mass spectrometry method. Anaprazole and its four metabolites were extracted from 50 of μL plasma by acetonitrile protein precipitation. Chromatographic retention and separation were achieved on an Kinetex XB-C₁₈ column (50 mm × 4.6 mm i.d., 5 μm) under gradient elution using 5 mM ammonium acetate with 0.005 % ammonium hydroxide and methanol with 0.005 % ammonium hydroxide as the mobile phase. Positive electrospray ionization was performed using multiple reaction monitoring with transitions of m/z 402.2→242.2, 386.2→226.2, 400.2→242.2, 418.2→282.2, and 386.2→161.2 for anaprazole, M8-1, M21-1, M16-3, and M7-1, respectively. This method was linear in the range of 5.00-3000 ng/mL for anaprazole and 1.00-600 ng/mL for the four metabolites. The lower limit of quantitation was established at 5.00 ng/mL for anaprazole and 1.00 ng/mL for the metabolites. The quantitative method was used to evaluate the pharmacokinetics of anaprazole in phase I clinical trials.

Drug-drug-gene interactions and adverse drug reactions

The economic and health burden caused by adverse drug reactions has increased dramatically in the last few years. This is likely to be mediated by increasing polypharmacy, which increases the likelihood for drug–drug interactions. Tools utilized by healthcare practitioners to flag potential adverse drug reactions secondary to drug–drug interactions ignore individual genetic variation, which has the potential to markedly alter the severity of these interactions. To date there have been limited published studies on impact of genetic variation on drug–drug interactions. In this review, we establish a detailed classification for pharmacokinetic drug–drug–gene interactions, and give examples from the literature that support this approach. The increasing availability of real-world drug outcome data linked to genetic bioresources is likely to enable the discovery of previously unrecognized, clinically important drug–drug–gene interactions.

Mechanism of Reductive Metabolism and Chiral Inversion of Proton Pump Inhibitors

Racemic proton pump inhibitors (PPIs) have been developed into pure enantiomers given superior pharmacokinetic profiles. However, after doses of single enantiomer PPIs, different degrees of chiral inversion were observed. We investigated the relationship between chiral inversion and reductive metabolism of PPIs, as well as the mechanism of reductive metabolism. In liver microsomes and Sprague-Dawley rats, PPI thioethers were stereoselectively oxidized to (R)- and (S)-PPIs, indicating that thioethers could be the intermediates of chiral inversion. By comparing the area under the plasma concentration-time curve ratios of thioether to rabeprazole under different routes of administration and blood sampling site, it was determined that thioether was mainly formed in the liver rather than the intestine. The formation rate of PPI thioethers in liver subcellular fractions was significantly higher than that in buffers. Sulfhydryl-blocking agents, such as N-ethylmaleimide, menadione, and ethacrynic acid, inhibited the reductive metabolism of PPIs in vitro, and their corresponding glutathione conjugates were observed. Similar amounts of thioethers were formed in glutathione solutions as in liver subcellular fractions, indicating that biologic reducing agents, instead of reductases, accelerated the reductive metabolism of PPIs. The reduction rates in glutathione solutions were ordered as follows: rabeprazole > omeprazole > lansoprazole > pantoprazole, which was consistent with the natural bond orbital charges of sulfur atoms in these compounds. In conclusion, PPIs were transformed into thioethers by biologic reducing agents in liver, and thioethers continued to be oxidized to two enantiomers, leading to chiral inversion. Furthermore, inhibiting oxidative metabolism of PPIs enhanced reductive metabolism and chiral inversion.

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.

Pharmacokinetic drug interaction profiles of proton pump inhibitors: an update

Proton pump inhibitors (PPIs) are used extensively for the treatment of gastric acid-related disorders, often over the long term, which raises the potential for clinically significant drug interactions in patients receiving concomitant medications. These drug–drug interactions have been previously reviewed. However, the current knowledge is likely to have advanced, so a thorough review of the literature published since 2006 was conducted. This identified new studies of drug interactions that are modulated by gastric pH. These studies showed the effect of a PPI-induced increase in intragastric pH on mycophenolate mofetil pharmacokinetics, which were characterised by a decrease in the maximum exposure and availability of mycophenolic acid, at least at early time points. Post-2006 data were also available outlining the altered pharmacokinetics of protease inhibitors with concomitant PPI exposure. New data for the more recently marketed dexlansoprazole suggest it has no impact on the pharmacokinetics of diazepam, phenytoin, theophylline and warfarin. The CYP2C19-mediated interaction that seems to exist between clopidogrel and omeprazole or esomeprazole has been shown to be clinically important in research published since the 2006 review; this effect is not seen as a class effect of PPIs. Finally, data suggest that coadministration of PPIs with methotrexate may affect methotrexate pharmacokinetics, although the mechanism of interaction is not well understood. As was shown in the previous review, individual PPIs differ in their propensities to interact with other drugs and the extent to which their interaction profiles have been defined. The interaction profiles of omeprazole and pantoprazole sodium (pantoprazole-Na) have been studied most extensively. Several studies have shown that omeprazole carries a considerable potential for drug interactions because of its high affinity for CYP2C19 and moderate affinity for CYP3A4. In contrast, pantoprazole-Na appears to have lower potential for interactions with other medications. Lansoprazole and rabeprazole also seem to have a weaker potential for interactions than omeprazole, although their interaction profiles, along with those of esomeprazole and dexlansoprazole, have been less extensively investigated. Only a few drug interactions involving PPIs are of clinical significance. Nonetheless, the potential for drug interactions should be considered when choosing a PPI to manage gastric acid-related disorders. This is particularly relevant for elderly patients taking multiple medications, or for those receiving a concomitant medication with a narrow therapeutic index.

Inhibition of baicalin on metabolism of phenacetin, a probe of CYP1A2, in human liver microsomes and in rats

Baicalin has been used as mainly bioactive constituent of about 100 kinds of traditional Chinese medicines in Chinese pharmacopoeia. The effect of baicalin on cytochrome P450 should be paid more attention because baicalin was used widely. The aim of this study was to investigate whether baicalin could inhibit CYP1A2 in pooled human liver microsomes (HLMs) and in rats in vivo and the gene polymorphisms could affect inter-individual variation in IC50 in 28 human livers. Phenacetin was used as probe of CYP1A2. Kinetic parameter of CYP1A2 and IC50 of baicalin on CYP1A2 to each sample were measured and the common CYP1A2 polymorphisms (-3860G>A and -163C>A) were genotyped. The results showed that baicalin exhibited a mixed-type inhibition in pooled HLMs, with a Ki value of 25.4 µM. There was substantial variation in Km, Vmax, CLint of CYP1A2 and IC50 of baicalin on CYP1A2 (3∼10-fold). The range was from 26.6 to 114.8 µM for Km, from 333 to 1330 pmol·min(-1)·mg(-1)protein for Vmax and from 3.8 to 45.3 µL·min(-1)·mg(-1) protein for CLint in HLMs (n = 28). The Mean (range) value of IC50 in 28 HLMs was 36.3 (18.9 to 56.1) µM. The genotypes of -3860G>A and -163C>A had no significant effect on the inhibition of baicalin on CYP1A2. The animal experiment results showed that baicalin (450 mg/kg, i.v.) significantly decreased the Cmax and CL of phenacetin, and increased C(60 min), t1/2, Vd and AUC (P<0.05). There were significant correlations between percentage of control in C(60 min), t1/2, CL, AUC of phenacetin and Cmax of baicalin in 11 rats (P<0.05). Protein binding experiments in vitro showed that baicalin (0-2000 mg/L) increased the unbound phenacetin from 14.5% to 28.3%. In conclusion, baicalin can inhibit the activity of CYP1A2 in HLMs and exhibit large inter-individual variation that has no relationship with gene polymorphism. Baicalin can change the pharmacokinetics of phenacetin in rats.

Impact of genetic polymorphism on drug-drug interactions mediated by cytochromes: a general approach

Currently, quantitative prediction of the impact of genetic polymorphism and drug-drug interactions mediated by cytochromes, based on in vivo data, is made by two separate methods and restricted to a single cytochrome. We propose a unified approach for describing the combined impact of drug-drug interactions and genetic polymorphism on drug exposure. It relies on in vivo data and uses the following three characteristic parameters: one for the victim drug, one for the interacting drug, and another for the genotype. These parameters are known for a wide range of drugs and genotypes. The metrics of interest are the ratio of victim drug area under the curve (AUC) in patients with genetic variants taking both drugs, to the AUC in patients with either variant or wild-type genotype taking the victim drug alone. The approach was evaluated by external validation, comparing predicted and observed AUC ratios found in the literature. Data were found for 22 substrates, 30 interacting drugs, and 38 substrate-interacting drug couples. The mean prediction error of AUC ratios was 0.02, and the mean prediction absolute error was 0.38 and 1.34, respectively. The model may be used to predict the variations in exposure resulting from a number of drug-drug-genotype combinations. The proposed approach will help (1) to identify comedications and population at risk, (2) to adapt dosing regimens, and (3) to prioritize the clinical pharmacokinetic studies to be done.

In vivo quantitative prediction of the effect of gene polymorphisms and drug interactions on drug exposure for CYP2C19 substrates

We present a unified quantitative approach to predict the in vivo alteration in drug exposure caused by either cytochrome P450 (CYP) gene polymorphisms or CYP-mediated drug-drug interactions (DDI). An application to drugs metabolized by CYP2C19 is presented. The metrics used is the ratio of altered drug area under the curve (AUC) to the AUC in extensive metabolizers with no mutation or no interaction. Data from 42 pharmacokinetic studies performed in CYP2C19 genetic subgroups and 18 DDI studies were used to estimate model parameters and predicted AUC ratios by using Bayesian approach. Pharmacogenetic information was used to estimate a parameter of the model which was then used to predict DDI. The method adequately predicted the AUC ratios published in the literature, with mean errors of -0.15 and -0.62 and mean absolute errors of 0.62 and 1.05 for genotype and DDI data, respectively. The approach provides quantitative prediction of the effect of five genotype variants and 10 inhibitors on the exposure to 25 CYP2C19 substrates, including a number of unobserved cases. A quantitative approach for predicting the effect of gene polymorphisms and drug interactions on drug exposure has been successfully applied for CYP2C19 substrates. This study shows that pharmacogenetic information can be used to predict DDI. This may have important implications for the development of personalized medicine and drug development.

Effect of CYP2C19 genotypes on the pharmacokinetic/pharmacodynamic relationship of rabeprazole after a single oral dose in healthy Chinese volunteers

AIMS

To explore the pharmacokinetic/pharmacodynamic relationship of rabeprazole and the role of CYP2C19 genotypes after a single oral dose in healthy Chinese volunteers by a population approach.

METHODS

Plasma concentration time profile data and intragastric pH values of 19 genotyped healthy male adults after a single oral dose of rabeprazole in an open label randomized fashion were used for this population analysis. Simulation technology was performed to examine the rabeprazole response in subjects with different CYP2C19 genotypes to further investigate the effect of acid inhibition.

RESULTS

The pharmacokinetics of rabeprazole was characterized by a two-compartment model with first order absorption and with an absorption lag-time. The results show that clearance of rabeprazole was affected by CYP2C19 genotypes (average clearances of homEM, hetEM, and PM were 13.9, 11.5, and 8.74 L·h(-1) respectively). An effect compartment with a sigmoidal Emax model was considered more rational for analyzing the relationship between rabeprazole concentrations and intragastric pH values. Simulated results suggest that rabeprazole 20 mg once daily for PMs is sufficient, but might be administered more frequently for other genotypes in treating gastro-esophageal reflux disease.

CONCLUSION

The CYP2C19 genotype played a considerable role in the pharmacokinetic characteristics of rabeprazole, and this might need to be taken into account for clinical use.

Effects of diltiazem on pharmacokinetics of tacrolimus in relation to CYP3A5 genotype status in renal recipients: from retrospective to prospective

The impact of CYP3A5*3, a CYP3A5 nonexpresser genotype, on inhibitory effects of diltiazem on tacrolimus metabolism has not been assessed. In retrospective study, when coadministered with diltiazem, mean increments in dose-adjusted C(0D7), C(max) and AUC(0-12 h) for tacrolimus were larger in CYP3A5 expressers than in CYP3A5 nonexpressers (48.7 vs 3.7%, 31.7 vs 17.2% and 38.2 vs 18.5%, respectively). Subsequently, a prospective study was carried out, patients were randomized to algorithm-predicted dosing or standard dosing. For CYP3A5 expressers, an algorithm guided by CYP3A5 and diltiazem significantly reduced tacrolimus maintenance dosage (P=0.009) and improved the accuracy of tacrolimus initial dose, resulting in reduction in out-of-range C(0) after initial dose (P=0.002) and dose adjustments (P=0.004). However, for CYP3A5 nonexpressers, primary end points were not achieved, and tacrolimus-sparing effect of diltiazem was not remarkable. Our study results show that CYP3A5 genotype-guided tacrolimus-diltiazem combination is a promising therapy in renal transplant recipients in the early postoperative stage.

Prediction of pharmacokinetic drug-drug interaction caused by changes in cytochrome P450 activity using in vivo information

The aim of the present paper was to present an overview of the current status of the methods used to predict the magnitude of pharmacokinetic drug-drug interactions (DDIs) which are caused by apparent changes in cytochrome P450 (CYP) activity with an emphasis on a method using in vivo information. In addition, more than a hundred representative CYP substrates, inhibitor and inducer drugs involved in significant pharmacokinetic DDIs were selected from the literature and are listed. Although the magnitude of DDIs has been conventionally predicted based on in vitro experiments, their predictability is restricted occasionally due to several difficulties, including a precise determination of the unbound inhibitor concentrations at the enzyme site and a reliable in vitro measurement of the inhibition constant (K(i)). Alternatively, a simple method has been recently proposed for the prediction of the magnitude of DDIs based on information fully available from in vivo clinical studies. The new in vivo-based method would be applicable to the adjustment of dose regimens in actual pharmacotherapy situations although it requires a prior clinical study for the prediction. In this review, theoretical and quantitative relationships between the in vivo- and the in vitro-based prediction methods are considered. One of the interesting outcomes of the consideration is that the K(i)-normalized dose (dose/in vitro K(i)) of larger than approximately 20L (2-200L, when variability is considered) may be a pragmatic index which predicts significant in vivo DDIs. In the last part of the article, the relevance of the inclusion of the in vivo-based method into the process of new drug development is discussed for good prediction of in vivo DDIs.

Proton pump inhibitors: an update of their clinical use and pharmacokinetics

BACKGROUND

Proton pump inhibitors (PPIs) represent drugs of first choice for treating peptic ulcer, Helicobacter pylori infection, gastrooesophageal reflux disease, nonsteroidal anti-inflammatory drug (NSAID)-induced gastrointestinal lesions (complications), and Zollinger-Ellison syndrome.

RESULTS

The available agents (omeprazole/esomeprazole, lansoprazole, pantoprazole, and rabeprazole) differ somewhat in their pharmacokinetic properties (e.g., time-/dose-dependent bioavailability, metabolic pattern, interaction potential, genetic variability). For all PPIs, there is a clear relationship between drug exposure (area under the plasma concentration/time curve) and the pharmacodynamic response (inhibition of acid secretion). Furthermore, clinical outcome (e.g., healing and eradication rates) depends on maintaining intragastric pH values above certain threshold levels. Thus, any changes in drug disposition will subsequently be translated directly into clinical efficiency so that extensive metabolizers of CYP2C19 will demonstrate a higher rate of therapeutic nonresponse.

CONCLUSIONS

This update of pharmacokinetic, pharmacodynamic, and clinical data will provide the necessary guide by which to select between the various PPIs that differ-based on pharmacodynamic assessments-in their relative potencies (e.g., higher doses are needed for pantoprazole and lansoprazole compared with rabeprazole). Despite their well-documented clinical efficacy and safety, there is still a certain number of patients who are refractory to treatment with PPIs (nonresponder), which will leave sufficient space for future drug development and clinical research.

Differential genotype dependent inhibition of CYP2C9 in humans

The effects of genetic polymorphisms in drug-metabolizing enzymes (e.g., CYP2C9(*)3) on drug clearance have been well characterized but much less is known about whether these polymorphisms alter susceptibility to drug-drug interactions. Previous in vitro work has demonstrated that genotype-dependent inhibition of CYP2C9 mediated flurbiprofen metabolism, suggesting the possibility of genotype-dependent inhibition interactions in vivo. In the current study, flurbiprofen was used as a probe substrate and fluconazole as a prototypical inhibitor to investigate whether genotype-dependent inhibition of CYP2C9 occurs in vivo. From 189 healthy volunteers who were genotyped for CYP2C9 polymorphisms, 11 control subjects (CYP2C9(*)1/(*)1), 9 heterozygous and 2 homozygous for the CYP2C9(*)3 allele participated in the pharmacokinetic drug interaction study. Subjects received a single 50-mg oral dose of flurbiprofen alone or after administration of either 200 or 400 mg of fluconazole for 7 days using an open, randomized, crossover design. Flurbiprofen and fluconazole plasma concentrations along with flurbiprofen and 4'-hydroxyflurbiprofen urinary excretion were monitored. Flurbiprofen apparent oral clearance differed significantly among the three genotype groups (p < 0.05) at baseline but not after pretreatment with 400 mg of fluconazole for 7 days. Changes in flurbiprofen apparent oral clearance after fluconazole coadministration were gene dose-dependent, with virtually no change occurring in (*)3/(*)3 subjects. Analysis of fractional clearances suggested that the fraction metabolized by CYP2C9, as influenced by genotype, determined the degree of drug interaction observed. In summary, the presence of CYP2C9(*)3 alleles (either one or two alleles) can alter the degree of drug interaction observed upon coadministration of inhibitors.

Influence of lansoprazole and rabeprazole on mycophenolic acid pharmacokinetics one year after renal transplantation

Peptic ulcer disease is a common complication after organ transplantation, and long-term administration of antiulcer agents is needed in many renal transplant recipients. Although several drug interactions with mycophenolic acid (MPA), the active metabolite of the prodrug mycophenolate mofetil (MMF), have been reported, little is known about the interaction between MPA and proton pump inhibitors (PPIs). The present study investigated the drug interaction between MMF and lansoprazole or rabeprazole and the impact of cytochrome (CYP) 2C19, and multidrug resistance (MDR)1 C3435T polymorphisms on these drug interactions at 1 year after renal transplantation. Retrospectively, 61 recipients were divided into 3 groups: MMF and tacrolimus as combination immunosuppressive therapy, together with either 30 mg lansoprazole (n = 22) or 10 mg rabeprazole (n = 17), or without PPI (n = 22). One year after transplantation, plasma concentrations of MPA were measured by high-performance liquid chromatography. The mean dose-unadjusted and -adjusted Cmax of MPA with 30 mg lansoprazole were significantly lower than those without PPI (11.8 vs. 17.8 microg/mL, P = 0.0197, and 22.6 vs. 33.1 ng/mL/mg MMF, P = 0.0222, respectively). In recipients having the CYP2C19 *1/*2+*1/*3 or MDR1 C3435T CC genotype, the mean dose-adjusted AUC0-12 of MPA with 30 mg lansoprazole was significantly smaller than that with 10 mg rabeprazole or without PPI. The plasma concentration of MPA was influenced by 30 mg lansoprazole but not 10 mg rabeprazole. Because of the greater gastric acid secretion-inhibitory effect of 30 mg lansoprazole in recipients having the CYP2C19 *1/*2+*1/*3 (intermediate metabolizer) or MDR1 C3435T CC genotype, the elution and hydrolysis of MMF might be decreased. Although the clinical relevance might be minor, the fact that administration of 30 mg lansoprazole in patients having the CYP2C19 *2 or *3 allele or the MDR1 C3435T CC genotype diminishes the absorption of MPA in the maintenance stage after renal transplantation should be taken into consideration with regard to the MPA pharmacokinetics.

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.

Effects of clarithromycin and verapamil on rabeprazole pharmacokinetics between CYP2C19 genotypes

OBJECTIVE

Rabeprazole as a proton pump inhibitor (PPI) is mainly reduced to rabeprazole thioether via a nonenzymatic pathway, with minor CYP2C19 and CYP3A4 involvement. The aim of this study was to compare possible effects of clarithromycin and verapamil as inhibitors of CYP3A4 on the pharmacokinetics of rabeprazole among CYP2C19 genotypes.

METHODS

A three-way randomized, double-blind, placebo-controlled crossover study was performed. Nineteen volunteers, of whom six were homozygous extensive metabolizers (EMs), eight were heterozygous EMs, and five were poor metabolizers (PMs) for CYP2C19, received three 6-day courses of either daily 800 mg clarithromycin, 240 mg verapamil, or placebo in a randomized fashion, with a single oral dose of 20 mg rabeprazole on day 6 in all cases. Plasma concentrations of rabeprazole and rabeprazole thioether were monitored up to 24 h after the dosing.

RESULTS

In the control phase, the AUC(0-infinity) values for rabeprazole and rabeprazole thioether were 1,005+/-366 and 412+/-149 ng.h/ml in homozygous EMs, 1,108+/-340 and 491+/-245 ng.h/ml in heterozygous EMs, and 2,697+/-364 and 2,116+/-373 ng.h/ml in PMs, respectively. There were significant differences (p<0.001) in the AUC(0-infinity) of rabeprazole and rabeprazole thioether among three different CYP2C19 genotypes. In the clarithromycin and verapamil phases, no significant differences were found in the pharmacokinetic parameters of rabeprazole compared with those in the control phase irrespective of CYP2C19 genotypes, whereas the AUC(0-infinity) of rabeprazole thioether was significantly increased 2.8-fold and 2.3-fold in homozygous EMs (p<0.01), 2.0-fold and 2.0-fold in heterozygous EMs (p<0.05), and 1.6-fold and 1.9-fold in PMs (p<0.05), respectively. In each genotype group for CYP2C19, there were no statistical differences in the percent increase in those pharmacokinetic parameters between the clarithromycin and verapamil pretreatment phases.

CONCLUSION

The pharmacokinetic parameters of rabeprazole were not altered by clarithromycin or verapamil irrespective of the CYP2C19 genotypes. However, this result shows that both clarithromycin and verapamil significantly influence the disposition of rabeprazole by inhibiting the oxidation of the thioether, since the AUC(0-infinity) of rabeprazole thioether that has no effect on acid secretion increased. Therefore, the pharmacokinetic interactions between rabeprazole and CYP3A4 or P-glycoprotein inhibitors have limited clinical significance.