Abstract. This study investigated the effects of genetic polymorphisms in organic cation transporter (OCT) genes, such as OCT1-3, OCTN1, MATE1, and MATE2-K, on metformin pharmacokinetics. Of particular interest was the influence of genetic polymorphisms as covariates on the variability in the population pharmacokinetics (PPK) of metformin using nonlinear mixed effects modeling (NONMEM). In a retrospective data analysis, data on subjects from five independent metformin bioequivalence studies that used the same protocol were assembled and compared with 96 healthy control subjects who were administered a single oral 500 mg dose of metformin. Genetic polymorphisms of OCT2-808 G>T and OCTN1-917C>T had a significant (P<0.05) effect on metformin pharmacokinetics, yielding a higher peak concentration with a larger area under the serum time-concentration curve. The values obtained were 102± 34.5 L/h for apparent oral clearance (CL/F), 447±214 L for volume of distribution (V d /F), and 3.1±0.9 h for terminal half-life (mean±SD) by non-compartmental analysis. The NONMEM method gives similar results. The metformin serum levels were obtained by setting the one-compartment model to a first-order absorption and lag time. In the PPK model, the effects of OCT2-808 G>T and OCTN1-917C>T variants on the CL/F were significant (P<0.001 and P<0.05, respectively). Thus, genetic variants of OCTN1-917C>T, along with OCT2-808 G>T genetic polymorphisms, could be useful in titrating the optimal metformin dose.

The comprehensive panel of two biomarkers and seven response measures were well captured by the population PK/PD models. The subjects were more sensitive to the CNS (lower EC 50 values) than cardiovascular effects of dexmedetomidine.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• The interindividual variability of the pharmacokinetic parameters of cilostazol is relatively large.• Cilostazol undergoes extensive hepatic metabolism via the P450 enzymes, primarily CYP3A and, to a lesser extent, CYP2C19.• Indeed, <1% of the administered dose of cilostazol is excreted unchanged in the urine. WHAT THIS STUDY ADDS• A population pharmacokinetic analysis of cilostazol was conducted to evaluate the impact of CYP3A, CYP2C19 and ABCB1 polymorphisms on cilostazol disposition in vivo.• Genetic polymorphisms of CYP3A5 and CYP2C19 explain the substantial interindividual variability in the pharmacokinetics of cilostazol.• ABCB1 genotypes do not to appear to be associated with the disposition of cilostazol. AIMSTo investigate the influence of genetic polymorphisms in the CYP3A5, CYP2C19 and ABCB1 genes on the population pharmacokinetics of cilostazol in healthy subjects. METHODSSubjects who participated in four separate cilostazol bioequivalence studies with the same protocols were included in this retrospective analysis. One hundred and four healthy Korean volunteers were orally administered a single 50-or 100-mg dose of cilostazol. We estimated the population pharmacokinetics of cilostazol using a nonlinear mixed effects modelling (NONMEM) method and explored the possible influence of genetic polymorphisms in CYP3A (CYP3A5*3), CYP2C19 (CYP2C19*2 and CYP2C19*3) and ABCB1 (C1236T, G2677T/A and C3435T) on the population pharmacokinetics of cilostazol. RESULTSA two-compartment model with a first-order absorption and lag time described the cilostazol serum concentrations well. The apparent oral clearance (CL/F) was estimated to be 12.8 l h . Absorption rate constant was estimated at 0.24 h -1 and lag time was predicted at 0.57 h. The genetic polymorphisms of CYP3A5 had a significant (P < 0.001) influence on the CL/F of cilostazol. When CYP2C19 was evaluated, a significant difference (P < 0.01) was observed among the three genotypes (extensive metabolizers, intermediate metabolizers and poor metabolizers) for the CL/F. In addition, a combination of CYP3A5 and CYP2C19 genotypes was found to be associated with a significant difference (P < 0.005) in the CL/F. When including these genotypes, the interindividual variability of the CL/F was reduced from 34.1% in the base model to 27.3% in the final model. However, no significant differences between the ABCB1 genotypes and cilostazol pharmacokinetic parameters were observed. CONCLUSIONSThe results of the present study indicate that CYP3A5 and CYP2C19 polymorphisms explain the substantial interindividual variability that occurs in the metabolism of cilostazol.

BACKGROUND AND PURPOSEThe objective of this study was to investigate the combined influence of genetic polymorphisms in ABCB1 and CYP2D6 genes on risperidone pharmacokinetics. EXPERIMENTAL APPROACHSeventy-two healthy Korean volunteers receiving a single oral dose of 2 mg risperidone were included in this study. KEY RESULTSSignificant differences were observed between the ABCB1 3435C>T genotypes for the pharmacokinetic parameters (peak serum concentration) of risperidone and the active moiety (risperidone and its main metabolite, 9-hydroxyrisperidone). There were no significant differences in the area under the serum concentration-time curves of risperidone and the active moiety among the ABCB1 2677G>T/A and 3435C>T genotypes. However, the peak serum concentration and area under the serum concentration-time curves were significantly different among the ABCB1 3435C>T genotypes in CYP2D6*10/*10. CONCLUSIONS AND IMPLICATIONSThese findings indicate that polymorphisms of ABCB1 3435C>T in individuals with CYP2D6*10/*10, which has low metabolic activity, could play an important role in the potential adverse effects or toxicity of risperidone.

Aim: Tramadol is widely used to treat acute, chronic, and neuropathic pain. Its primary active metabolite, O- desmethyltramadol (M1), is mainly responsible for its µ-opioid receptor-related analgesic effect. Tramadol is metabolized to M1 mainly by the cytochrome P450 (CYP) 2D6 enzyme, and to other metabolites by CYP3A4 and CYP2B6. The aim of this study was to develop a population pharmacokinetic (PK) model of tramadol and its metabolite using healthy Korean subjects. Methods: Data on plasma concentrations of tramadol and M1 were obtained from 23 healthy Korean male subjects after a twice-daily oral dose of 100 mg of tramadol, every 12 hrs, for a total of 5 times. Blood samples were collected at 0 (pre-dose), 0.5, 1, 1.5, 2, 2.5, 3, 4, 6, 8, 10, 12, 24, 48 and 72 hrs after last administration. Plasma tramadol concentrations were then analyzed using LC/MS. Population PK analysis of tramadol and its metabolite was performed using a nonlinear mixed-effects modeling (NONMEM). Results: A one-compartment model with combined first-order and zero-order absorption was well fitted to the concentration–time curve of tramadol. M1 was well described by the one-compartment model as an extension of the parent drug (tramadol) model. Genetic polymorphisms of CYP2D6 correlated with the clearance of tramadol, and clearance from the central compartment to the metabolite compartment. Conclusion: The parent-metabolite model successfully characterized the PK of tramadol and its metabolite M1 in healthy Korean male subjects. These results could be applied to evaluate plasma tramadol concentrations after various dosing regimens.