Eiji Yukawa

The Effects of Genetic Polymorphisms of CYP2C9 and CYP2C 19 on Phenytoin Metabolism in Japanese Adult Patients with Epilepsy: Studies in Stereoselective Hydroxylation and Population Pharmacokinetics

Summary: Purpose: The aim of this study was to clarify the effects of genetic polymorphisms of cytochrome P450 (CYP) 2C9 and 2C19 on the metabolism of phenytoin (PHT). In addition, a population pharmacokinetic analysis was performed.

CYP2C19 polymorphism effect on phenobarbitone

AbstractObjective: The aim of this study was to clarify the effect of genetic polymorphisms of CYP2C19 on the pharmacokinetics of phenobarbitone (PB) using a nonlinear mixed-effects model (NONMEM) analysis in Japanese adults with epilepsy. Methods: A total of 144 serum PB concentrations were obtained from 74 subjects treated with both PB and phenytoin but without valproic acid. All patients were classified into three groups by CYP2C19 genotyping: G1, G2 and G3 were homozygous for the wild type of CYP2C19 (*1/*1), heterozygous extensive metabolizers (EMs), (*1/*2 or *1/*3), and poor metabolizers (PMs), (*2/*2, *2/*3), respectively. All data were analyzed using NONMEM to estimate pharmacokinetic parameters of PB with respect to the CYP2C19 genotype. Results: Thirty-three patients belonged to G1 (44.6%), 35 to G2 (47.3%), and 6 to G3 (8.1%). The total clearance (CL) of PB significantly decreased by 18.8% in PMs (G3) relative to EMs (G1 and G2). The CL tended to be lower in G2 than in G1. Conclusion: In this study, we first demonstrated the effect of the CYP2C19 polymorphism on pharmacokinetics of PB by genotyping. The contribution of other metabolic enzymes in the metabolism of PB in humans remains to be elucidated; however, it appears that the disposition of PB is mediated in part by this enzyme. The estimated population clearance values in the three genotype groups can be used to predict the PB dose required to achieve an appropriate serum concentration in an individual patient.

Digoxin Population Pharmacokinetics from Routine Clinical Data: Role of Patient Characteristics for Estimating Dosing Regimens

Abstract— Routine clinical pharmacokinetic data collected from patients receiving digoxin have been analysed to evaluate the role of patient characteristics for estimating dosing regimens. The data were analysed using NONMEM, a computer program designed for population pharmacokinetic analysis that allows pooling of data. The pharmacokinetic model of digoxin was described using a one‐compartment steady‐state model. The effect of a variety of developmental and demographic factors on clearance was investigated. NONMEM estimates indicate that digoxin clearance was influenced by the demographic variables of age, total body weight, serum creatinine and sex. The interindividual variability in digoxin clearance was modelled with additive error with an estimated standard deviation of 46·15 L day−1 and the intraindividual variability, or residual error was 0·209 ng mL−1. The dosing method based on clearance values obtained by NONMEM analysis allowed the prediction of the steady‐state concentration as a function of maintenance dose with acceptable error for therapeutic drug monitoring.

Population-Based Investigation of Valproic Acid Relative Clearance Using Nonlinear Mixed Effects Modeling: Influence of Drug-Drug Interaction and Patient Characteristics

Nonlinear mixed effects modeling (NONMEM) was used to estimate the effects of drug‐drug interaction on valproic acid relative clearance values using 792 serum levels gathered from 400 pediatric and adult patients with epilepsy (age range, 0.3–54.8 years) during their clinical routine care. Patients received valproic acid as monopharmacy or in combination with either the antiepileptic drugs, phenobarbital, or carbamazepine. The final model describing valproic acid relative clearance was CL (mL/hr/kg) = 15.6 · TBW (kg)−0.252 · DOSE (mg/kg/day)0.183 · 0.898GEN · COPB · COCBZ, where COPB equals 1.10 if the patient is treated with phenobarbital, a value of unity otherwise, and COCBZ equals 0.769 · DOSE (mg/kg/day)0.179 if the patient is treated with carbamazepine, a value of unity otherwise. Valproic acid relative clearance was highest in the very young and decreased in a weight‐related fashion in children, with minimal changes observed in adults. This pattern was consistent whether valproic acid was administered alone or coadministered with phenobarbital or carbamazepine. When valproic acid was coadministered with phenobarbital or carbamazepine, valproic acid relative clearance increased as compared with that in monopharmacy. Its magnitude in the presence of carbamazepine increased in a valproic acid daily dose‐related fashion. Concomitant administration of phenobarbital and valproic acid resulted in a 10% increase on valproic acid relative clearance. The clearance in female patients was approximately 10% less than that in male patients.

Optimisation of antiepileptic drug therapy : The importance of serum drug concentration monitoring

SummaryThe ability to measure the serum concentrations of antiepileptic drugs, and the widespread use of this procedure, has markedly improved the treatment given to patients with epilepsy during the past 3 decades. The monitoring of antiepileptic drug concentrations in serum is necessary for the optimal drug therapy of seizures, because the therapeutic and toxic effects of these drugs are better related to serum concentration than to administered dosage. Monitoring appeared to have a major impact on improving the effectiveness and safety of antiepileptic drug therapy.The age-related variability of pharmacokinetic parameters may also require the individualisation of therapy, with subsequent re-evaluation as the person grows older. Monitoring serum concentrations of antiepileptic drugs may help to optimise the dose. A drug concentration, however, can only be regarded as a guide around which to alter the dosage according to the patients clinical condition.Serum drug concentration monitoring is particularly useful to ensure compliance and in helping to manage combinations of antiepileptic drugs that invariably interact. The addition or deletion of other antiepileptic drugs may change dosage requirements. Therefore, routine monitoring of antiepileptic drug serum concentrations would be extremely useful, especially in the paediatric population, and in patients who require associated antiepileptic medication.

Population‐Based Investigation of Relative Clearance of Digoxin in Japanese Patients by Multiple Trough Screen Analysis: An Update

The steady‐state concentrations of digoxin at trough levels were studied to reestablish the role of patient characteristics in estimating doses for digoxin using routine therapeutic drug‐monitoring data. The data (n = 548) showing steady‐state serum concentrations of digoxin after repetitive oral administration in 385 hospitalized patients were analyzed using NONMEM, a computer program designed to analyze pharmacokinetics in study populations by allowing pooling of data. Analysis of the pharmacokinetics of digoxin was accomplished with a simple steady‐state pharmacokinetic model. The effect of a variety of developmental and demographic factors on the clearance of digoxin was investigated. Estimates generated by NONMEM indicated that clearance of digoxin was influenced by the demographic variables of age, total body weight, serum creatinine, estimated creatinine clearance, gender, the coadministration of spironolactone, the presence or absence of congestive heart failure, and the administration of a half‐tablet. The interindividual variability in the clearance of digoxin was modeled with proportional error with an estimated coefficient of variation of ∼22%; the residual variability was ∼25.0%. An a priori method, based on the value for clearance of digoxin obtained by NONMEM analysis, was proposed as a useful adjunct for the prediction of the steady‐state concentration of digoxin at trough level as a function of the maintenance dose of digoxin.

Detection of Carbamazepine Drug Interaction by Multiple Peak Approach Screening using Routine Clinical Pharmacokinetic Data

Multiple peak approach screening was used to detect the effects of other antiepileptic drugs on the population estimates of relative clearance of carbamazepine. Routine clinical pharmacokinetic data (N = 1,010) collected from 466 patients receiving carbamazepine were analyzed according to a simple steady‐state pharmacokinetic model with the use of NONMEM, a computer program designed for population pharmacokinetic analysis that allows pooling of data. NONMEM estimates indicated that carbamazepine clearance decreased nonlinearly with increasing total body weight (TBW) in the maturation process (over an age range of 5 months to 15 years) and increased nonlinearly with increasing daily dose (mg/kg) of carbamazepine. Concomitant administration of other antiepileptic drugs resulted in an increase in clearance of carbamazepine as follows: valproic acid alone (VPA), 7%; phenobarbital alone (PB), 16%; more than two antiepileptic drugs (POLY), 27%. Final regression model of relative clearance (Cl) for all data was: Cl (mL/hr/kg) = 64.9 · TBW (kg)−0.336. DOSE (mg/kg/day)0.465 1.07VPA 1.16PB 1.27POLY

Dosing time dependency of doxorubicin-induced cardiotoxicity and bone marrow toxicity in rats.

Cardiac toxicity caused by doxorubicin (adriamycin) is a serious dose‐limiting factor in the clinical situation. However, the influence of doxorubicin dosing time has not been clarified from the viewpoints of cardiotoxic development and its mechanism. In this study, we have investigated the dosing time dependency of doxorubicin‐induced cardiotoxicity and bone marrow toxicity after repeated administration of doxorubicin in rats. When doxorubicin (5 mg kg−1, i.p.) was administered every seven days (total of 30 mg kg−1) at 3, 9, 15 or 21h after the light was turned on (HALO), toxic death was significantly higher in the 9 HALO treated group than the other groups. When doxorubicin was injected every seven days for 28 days at 9 or 21 HALO, we measured the levels of creatine kinase, malondialdehyde (MDA; an index of lipid peroxide), and glutathione peroxidase (GPx) as markers of cardiotoxicity. On days 14 and 28, creatine kinase levels were significantly higher in the 9‐HALO group compared with the 21‐HALO group (P < 0.01, respectively). On day 14, MDA levels increased significantly in the 9 HALO group compared with the 21 HALO group (P < 0.01). A single dose of doxorubicin was administered at 9‐h or 21‐h after the light was turned on to investigate the dosing‐time‐dependent difference of the pharmacokinetics. The area under the plasma time‐concentration curve showed a significant increase at 9 HALO compared with 21 HALO (P < 0.05). These results suggested that the dosing‐time‐dependent difference of cardiotoxicity induced by doxorubicin was closely related to the daily variation of doxorubicin pharmacokinetics. In conclusion, the choice of optimal dosing time based on the chronopharmacokinetics of doxorubicin may decrease the cardio‐toxicity and enable the practice of effective and safe chemotherapy of doxorubicin.

Phenytoin intoxication induced by fluvoxamine.

A patient had phenytoin intoxication after administration of fluvoxamine, a selective serotonin reuptake inhibitor. The serum concentration of phenytoin increased dramatically from 16.6 to 49.1 microg/mL when fluvoxamine was coadministered, although the daily dosage of phenytoin and other drugs had not changed. During phenytoin and fluvoxamine treatment, ataxia, a typical side effect of phenytoin, was observed. The genotypes of CYP2C9 and 2C19, the enzymes responsible for phenytoin metabolism, were homozygous for the wild-type alleles (CYP2C9*1/*1 and 2C19*1/ *1). The interaction may be a result of inhibition of both CYP2C9 and 2C19 by fluvoxamine.

Pharmacokinetic and Pharmacodynamic Analysis of Subcutaneous Recombinant Human Granulocyte Colony Stimulating Factor (Lenograstim) Administration

A total of 72 adult healthy volunteers were administered 1 μg/kg of rhG‐CSF. There was no correlation between Cmax and an increase in peripheral neutrophil count, and there was a negative correlation between AUC and this increase. The mechanism of this is probably based on the correlation between the elimination rate constant (ke) and neutrophil increase. The ke probably has a close relationship with uptake by neutrophil and its progenitor via the G‐CSF receptor. An individual with higher ke should therefore show a greater increase in neutrophil count. Therefore, AUC is proportional to the rhG‐CSF remainder, that is, the proportion that is not consumed in the course of increasing the neutrophil count. In such a situation, the bioavailability calculated from the AUC is unlikely to indicate the absorbed amount. The authors also analyzed the pharmacokinetics using a two‐compartment model with zero‐order absorption and first‐order elimination. This model was sufficient to obtain a good curve fit, and this demonstrates that the absorption process is not a first‐order but a zero‐order process. Therefore, there might be an upper limit to the rhG‐CSF transfer rate from subcutaneous tissue to blood.