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Background: This study investigates the pharmacokinetics and pharmacodynamics of tacrolimus using the once-daily (OD) formulation in the early stage after living donor liver transplantation (LDLT) in comparison with those using the twice-daily (TD) formulation. Methods: Nine patients undergoing primary LDLT and treated with the OD tacrolimus formulation were included. The trough blood concentration (C0) of tacrolimus was monitored every day for 3 weeks after LDLT. A time course study of the blood tacrolimus concentrations and calcineurin (CN) phosphatase activity in peripheral blood mononuclear cells was performed 3 weeks after LDLT. Pharmacokinetic and pharmacodynamic parameters were compared with previously reported data using the TD formulation. Results: The interindividual variability in the daily dose of tacrolimus was significantly larger in the OD formulation than in the TD formulation (P < 0.001). In the time course study, the tacrolimus blood concentrations at 4, 8, and 12 hours after administration and the area under the concentration-time curve from 0 to 24 hours (AUC(0-24)) in the OD group were significantly higher than in the TD group, although the C0 was equivalent. In addition, the C0 was not significantly correlated with the AUC(0-24) in the OD formulation. The apparent clearance and the pharmacodynamic parameters examined were not significantly different between the OD and TD groups. Conclusions: The C0 monitoring of the OD formulation may not be optimal in patients at the early stage after LDLT because the C0 was not correlated with the AUC(0-24). If clinicians target the same C0 using the OD and TD formulations, the exposure of tacrolimus can be higher in the OD formulation, and excessive immunosuppression should be noted. Particular attention should be paid to the patients in the early stage after LDLT in the use of the OD oral formulation of tacrolimus.

Background: Topiramate is a second-generation antiepileptic drug used as monotherapy and adjunctive therapy in adults and children with partial seizures. A population pharmacokinetic analysis was performed to improve the topiramate dosage adjustment for individualized treatment. Methods: Patients whose steady-state serum concentration of topiramate was routinely monitored at Kyoto University Hospital from April 2012 to March 2013 were included in the model-building data. A nonlinear mixed effects modeling program (NONMEM) was used to evaluate the influence of covariates on topiramate pharmacokinetics. The obtained population pharmacokinetic model was evaluated by internal model validations, including goodness-of-fit plots and prediction-corrected visual predictive checks, and was externally confirmed using the validation data from January 2015 to December 2015. Results: A total of 177 steady-state serum concentrations from 93 patients were used for the model-building analysis. The patients’ age ranged from 2 to 68 years, and body weight ranged from 8.6 to 105 kg. The median serum concentration of topiramate was 1.7 μg/mL, and half of the patients received carbamazepine co-administration. Based on a one-compartment model with first order absorption and elimination, the apparent volume of distribution was 105 L/70 kg, and the apparent clearance was allometrically related to the body weight as 2.25 L/h/70 kg without carbamazepine or phenytoin. Combination treatment with carbamazepine or phenytoin increased the apparent clearance to 3.51 L/h/70 kg. Goodness-of-fit plots, prediction corrected visual predictive check, and external validation using the validation data from 43 patients confirmed an appropriateness of the final model. Simulations based on the final model showed that dosage adjustments allometrically scaling to body weight can equalize the serum concentrations in children of various ages as well as adults. Conclusion: The population pharmacokinetic model, using the power scaling of body weight, effectively elucidated the topiramate serum concentration profile ranging from pediatric to adult patients. Dosage adjustments based on body weight and concomitant antiepileptic drug help obtain the dosage of topiramate necessary to reach an effective concentration in each individual.

Background: Levetiracetam, a second-generation antiepileptic drug, is frequently used for managing partial-onset seizures. About 70% of the administered dose is excreted in urine unchanged, and dosage adjustment is recommended based on the individual's renal function. In this study, a population pharmacokinetic model of levetiracetam was developed using routinely monitored serum concentration data for individualized levetiracetam therapy. Methods: Patients whose serum concentrations of levetiracetam at steady-state were routinely monitored at Kyoto University Hospital from April 2012 to March 2013 were enrolled. The influence of patient characteristics on levetiracetam pharmacokinetics was evaluated using the nonlinear mixed-effects modeling (NONMEM) program. Results: A total of 583 steady-state concentrations from 225 patients were used for the analysis. The median patient age and estimated glomerular filtration rate (eGFR) were 38 (range: 1–89) years and 98 (15–189) mL·min−1·1.73 m−2, respectively. Serum concentration–time data of levetiracetam were well described by a 1-compartment model with first-order absorption. Oral clearance was allometrically related to the individual body weight and eGFR. An increase in the dose significantly increased oral clearance. No improvement in model fit was observed by including the covariate of any concomitant antiepileptic drugs. The population mean clearance for an adult weighing 70 kg and with a normal renal function was 4.8 and 5.9 L/h for 500 mg bis in die (bid) and 1500 mg bid, respectively. Conclusions: Oral clearance allometrically related with body weight and eGFR can well predict the routine therapeutic drug monitoring data from pediatric to aged patients with varying renal function. Dosage adjustments based on renal function are effective in controlling the trough and peak concentrations in similar ranges.

Sunitinib, a multitargeted tyrosine kinase inhibitor, offers favorable therapeutic outcomes to patients with advanced renal cell carcinoma. However, to maximize the clinical benefits, an effective therapeutic management strategy with dose optimization is essential. The objectives of this analysis were to describe the pharmacokinetics (PK) of sunitinib by a population PK approach and to quantitatively evaluate the effect of potential predictive factors including ABCG2 genotype on the PK of sunitinib.Plasma concentration-time profiles at 3 consecutive days including a total of 245 sunitinib plasma concentrations were available from 19 Japanese patients with renal cell carcinoma. Blood samples were collected on days 2, 8, and 15 after the start of the therapy. Population PK analysis was performed using NONMEM 7.2. Body weight, gender, and genotype of ABCG2 421CA were evaluated as potential covariates. Interoccasion variability (IOV) among the 3 sampling days was also assessed as a random effect parameter.The sunitinib PK profiles were best described by a 1-compartment model with first-order absorption. The ABCG2 421CA genotype was identified as a significant covariate for the prediction of oral clearance (CL/F). No significant improvement in model fit was observed by including body weight and/or gender. A systematic difference in estimated population CL/F was observed between days 2 and 8, which was quantified as approximately 30% decrease over time. This difference was described as a covariate for CL/F in the model. IOV included as a random effect parameter significantly improved the model fit.This analysis provides a population PK model of sunitinib with the ABCG2 421CA genotype as a predictive covariate for CL/F. It also suggests that IOV and change of CL/F over time need to be considered to predict the sunitinib PK more accurately. These findings will be implemented to optimize the pharmacotherapy of sunitinib.

BACKGROUND Everolimus has been used for the treatment of unresectable or metastatic renal cell carcinoma (RCC). Here, we measured blood concentrations of everolimus to obtain the population pharmacokinetic parameters and to examine the relationship between blood concentration and clinical outcomes. METHODS Twenty-two Japanese patients were enrolled. Blood samples were collected before and 2, 4, 8, and 24 hours after drug administration on days 1 and 8 of everolimus therapy (5 or 10 mg) from inpatients; occasional samples were collected from outpatients. Blood concentrations of everolimus were measured by high-performance liquid chromatography with tandem mass spectrometry. Population pharmacokinetic analysis was conducted using the NONMEM software. RESULTS Everolimus pharmacokinetics was best described by a 2-compartment model with population mean estimates of apparent oral clearance of 10.0 L/h and an interindividual variability of 42.4%. There was no relationship between overall best responses and the predicted trough concentrations at day 8. The predicted trough concentration in patients who terminated everolimus treatment owing to adverse drug reactions (ADRs) was significantly higher than in patients who stopped the treatment owing to disease progression or other reasons (27.6 ± 3.1 versus 15.7 ± 2.3 ng/mL; mean ± SEM). Patients who terminated the treatment owing to ADRs had significantly shorter time-to-treatment failure than other patients (112 versus 187 days, median). CONCLUSIONS This study reports the first population pharmacokinetic parameters of everolimus in patients with RCC. Individual dose adjustment based on everolimus blood concentrations helps to avoid early drug cessation due to ADRs.