External evaluation of the predictive performance of seven population pharmacokinetic models for phenobarbital in neonates.

AIM: Several studies have reported population pharmacokinetic models for phenobarbital (PB), but the predictive performance of these models has not been well documented. This study aims to do external evaluation of the predictive performance in published pharmacokinetic models. METHODS: Therapeutic drug monitoring data collected in neonates and young infants treated with PB for seizure control was used for external evaluation. A literature review was conducted through PubMed to identify population pharmacokinetic models. Prediction- and simulation-based diagnostics, and Bayesian forecasting were performed for external evaluation. The incorporation of allometric scaling for body size and maturation factors into the published models was also tested for prediction improvement. RESULTS: A total of 79 serum concentrations from 28 subjects were included in the external dataset. Seven population pharmacokinetic studies of PB were identified as relevant in the literature search and included for our evaluation. The model by Voller et al showed the best performance concerning prediction-based evaluation. In simulation-based analyses, the normalized prediction distribution error of two models (those of Shellhaas et al and Marsot et al) obeyed a normal distribution. Bayesian forecasting with more than one observation improved predictive capability. Incorporation of both allometric size scaling and maturation function generally enhanced the predictive performance, with improvement as observed in the model of Vucicevic et al. CONCLUSIONS: The predictive performance of published pharmacokinetic models of PB was diverse. Bayesian forecasting and incorporation of both size and maturation factors could improve the predictability of the models for neonates.

Influence of CYP2D6 Polymorphism on the Pharmacokinetic/Pharmacodynamic Characteristics of Carvedilol in Healthy Korean Volunteers.

BACKGROUND: Carvedilol is commonly used to treat hypertension as a ß- and α1-adrenoreceptor blocker, but it is metabolized by CYP2D6, and CYP2D6*10 allele is dominant in Asian population. The objective of this study was to assess the influence of CYP2D6 polymorphisms on the pharmacokinetic (PK) and pharmacodynamic (PD) characteristics of carvedilol in healthy Korean volunteers. METHODS: A PK/PD study for a single and multiple dosing of carvedilol were conducted. All volunteers in 3 genotypic groups received single oral dose of carvedilol 12.5 mg for 3 days, then 25 mg QD for 5 days, and 12.5 mg QD for another 3 days. PK parameters for carvedilol and its three metabolites were determined using non-compartmental analysis. For PD properties, blood pressure, heart rate, and the chronotropic dose 25 (CD25) value were obtained. RESULTS: The IM_2 group with two *10 alleles (intermediate metabolizers) exhibited lower clearance of carvedilol as well as higher area under the curve (AUC) for O-desmethyl carvedilol. The ratio of CD25 to baseline at multiple dosing was significantly higher in the combined IM group (IM_1 and IM_2) than in the EM group, however, the ratio of CD25 after single and multiple dosing and the other PD markers were not significantly different between the 3 genotypic groups compared with the baseline. CONCLUSION: These findings showed that CYP2D6 genotype influenced the PK characteristics of carvedilol and no differences in PD response were observed in Korean healthy volunteers. Registered at the ClinicalTrials.gov, NCT02286934.

Effects of CYP2C19 Genetic Polymorphisms on PK/PD Responses of Omeprazole in Korean Healthy Volunteers.

The aim of this study was to examine the effects of CYP2C19*2 and *3 genetic polymorphisms on omeprazole pharmacokinetic (PK) and pharmacodynamic (PD) responses. Twenty-four healthy Korean volunteers were enrolled and given 20 mg omeprazole orally once daily for 8 days. The genotypes of CYP2C19 single nucleotide polymorphisms (SNPs) (*2, *3, and *17) were screened. The plasma concentrations of omeprazole, omeprazole sulfone, and 5-hydroxy (5-OH) omeprazole were determined by liquid chromatography with tandem mass spectrometry (LC-MS/MS). The noncompartmental method was used for the determination of PK parameters. Change of mean pH and proportion (%) of time of gastric pH above 4.0 were estimated. The poor metabolizer (PM) group had the lowest metabolic ratio and exhibited the highest area under the curve (AUC) for omeprazole among the CYP2C19 phenotype groups. The PM group showed the greatest change of mean pH and the highest % time of gastric pH above 4.0. The relationship between AUC of omeprazole and % time of gastric pH above 4.0 was confirmed. The study demonstrates that CYP2C19*2 and *3 influence the PKs and PDs of omeprazole in Korean healthy volunteers.

Quantitative Modeling Analysis Demonstrates the Impact of CYP2C19 and CYP2D6 Genetic Polymorphisms on the Pharmacokinetics of Amitriptyline and Its Metabolite, Nortriptyline.

Amitriptyline is a tricyclic antidepressant that is metabolized mainly by CYP2C19 and CYP2D6 enzymes. Higher plasma levels of amitriptyline and its active metabolite, nortriptyline, are associated with an increased risk of adverse events including anticholinergic effects. The aim of this study was to evaluate the effects of CYP2C19 and CYP2D6 genetic polymorphisms on amitriptyline and nortriptyline pharmacokinetics. Twenty-four Korean healthy adult male volunteers were enrolled in the study after stratification by their CYP2C19 and CYP2D6 genotypes. Serial blood draws for pharmacokinetic analysis were made after a single oral 25-mg dose of amitriptyline was administered. Plasma amitriptyline and nortriptyline concentrations were measured by a validated liquid chromatography with tandem mass spectrometry. Population pharmacokinetic modeling analysis was conducted using NONMEM, which evaluated the effects of CYP2C19 and CYP2D6 genotypes on amitriptyline and nortriptyline pharmacokinetics. The biotransformation of amitriptyline into nortriptyline was significantly different between subjects with the CYP2C19*2/*2, *2/*3, and *3/*3 genotypes and those with the other genotypes, with an estimated metabolic clearance of 17 and 61.5 L/h, respectively. Clearance of amitriptyline through pathways other than biotransformation into nortriptyline was estimated as 18.8 and 30.6 L/h for subjects with the CYP2D6*10/*10 and *10/*5 genotypes and those with the other genotypes, respectively. This study demonstrated a quantitative effect of the CYP2C19 and CYP2D6 genotypes on amitriptyline and nortriptyline pharmacokinetics. Production of nortriptyline from amitriptyline was associated with CYP2C19 genotypes, and clearance of amitriptyline through pathways other than biotransformation into nortriptyline was associated with CYP2D6 genotypes. These observations may be useful in developing individualized, optimal therapy with amitriptyline.

The influences of SLCO1B1 and ABCB1 genotypes on the pharmacokinetics of simvastatin, in relation to CYP3A4 inhibition.

AIM: To investigate the combined effects of SLCO1B1 and ABCB1 genotypes on the pharmacokinetics of simvastatin and its active metabolite simvastatin acid, in relation to CYP3A4 inhibition. METHODS: We conducted a single-dose pharmacokinetic study of simvastatin in 26 healthy volunteers screened for their SLCO1B1 c.521T>C and ABCB1 c.1236C>T-2677G>T-3435C>T genotypes, with and without amlodipine pretreatment. The genetic effects and drug-interaction effect on simvastatin pharmacokinetic parameters were analyzed using a linear-mixed model. RESULTS: The SLCO1B1 c.521T>C variant significantly increased exposure to simvastatin acid by around 40% (p < 0.05), similar to that caused by the amlodipine pretreatment. The ABCB1 gene showed no influence on exposure to simvastatin or simvastatin acid. CONCLUSION: Only SLCO1B1, not ABCB1 genotype, is likely to be associated with simvastatin-induced myopathy. SLCO1B1 genotyping may be particularly beneficial in simvastatin users who are co-administered CYP3A4 inhibitors.