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A Population Pharmacokinetic Analysis for Enteric-coated Mycophenolate Sodium Using Non-linear Mixed Effect Model in Korean Kidney Transplant Recipients
비선형 혼합효과 모형을 이용한 신이식 후 장용정 마이코페놀레이트의 집단약동학

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Authors
한나영
Advisor
오정미
Major
약학대학 약학과
Issue Date
2014-08
Publisher
서울대학교 대학원
Keywords
EC-MPSpopulation pharmacokineticsflip-flop phenomenonnonlinear mixed-effect modeling (NONMEM)renal transplantationgenetic polymorphism
Description
학위논문 (박사)-- 서울대학교 대학원 : 약학과, 2014. 8. 오정미.
Abstract
Mycophenolic acid (MPA) has been used as an immunosuppressive agent to prevent rejection events as a combination with calcineurin inhibitor (CNI) and corticosteroids in renal transplantation. Since the first original mycophenolate mofetil (MMF) was introduced in 1995, the post-transplant rates of rejection decreased from ~40% to 20%. However, gastrointestinal adverse events are frequently observed in renal transplant recipients treated with MMF. To abrogate these adverse drug events and improve the clinical outcome, enteric-coated mycophenolate sodium (EC-MPS, Myfortic®) was developed.
Although EC-MPS and MMF showed similar efficacy and safety profiles, the MPA pharmacokinetic (PK) parameters after administration of EC-MPS and MMF were different as a result of the enteric-coating formulation. For instance, the lag-time varied much more in EC-MPS treated patients, resulting in unpredictable PK profiles. Moreover, ethnic differences in the prevalence of the metabolic enzymes and transporters may affect PK alterations. Nevertheless, fixed doses have been used empirically according to patients body weight and concurrent medications in the clinical setting because there are no dosing strategies and prediction model established. In some studies, therapeutic drug monitoring (TDM) is recommended as a tool to optimize MPA treatment in renal transplant recipients. The MPA area under the total plasma concentration time curve (AUC) is a better predictor of the risk of rejection than trough concentration of MPA. But, AUC measurements have limitations to perform for practical reasons which the variability in MPA exposure is wide compared to the therapeutic window and is influenced by many factors.
The objective of this study was to develop a population PK model and to evaluate the influence of genetic and clinical factors on the MPA PK of EC-MPS in Korean renal transplant recipients. And we aimed to design the optimum dosing strategies for this population, or individual patient considering the variability issues discussed in our population PK model.
Patients over 18 years of age, who were primary recipient of cadaveric or living-related kidney transplants, and who were maintained on 180 to 720 mg twice daily EC-MPS as part of a double or a triple immunosuppressive regimen with stable serum creatinine values, were asked to give informed consent to be qualified for inclusion in this study. Assuming the usual 80% power and 5% type I error, the range of sample size was 34-43.
For PK analysis, plasma samples were taken pre-dose, 0.5, 1, 2, 3, 4, and 6 hour after morning EC-MPS administration. Total MPA concentrations were measured using a validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) method in Chemical Laboratory of Seoul National University Hospital. Demographic and laboratory data, including renal function, weight, and genotypes of transporter and metabolic enzyme, were collected from electronic medical records. All patients were genotyped for UGT1A7-9, UGT2B7, ABCC2, and SLCO1B.
Population PK analysis of EC-MPS was performed using non-linear mixed-effects modeling (NONMEM). 1 and 2 compartment models with first-order or non-linear elimination were tested to fit the MPA concentration-time data. The data were best described using a 2-compartment model with a lag-time, first-order absorption and first-order elimination. The entero-hepatic recirculation was described with a separate metabolic compartment.
Fixed effects of body weight, age, gender, donor type, graft weight, concomitant medications, biochemistry parameters as well as genotypes, which may influence the PK of MPA, were investigated. All covariates were incorporated into the model in such a way as to preserve positive PK parameters using additive, proportional, exponential, and/or power approaches. After clinical and genetic factors were evaluated using a stepwise covariate method, we selected clinically relevant covariates considering covariate effects. Population PK data were analyzed with NONMEM 7.2 using first-order conditional estimation with interaction (FOCE+I). In modeling, the minimum value of objective function (OFV) can be used as a criterion for model selection. A decrease in the OFV >3.84 or an increase in the OFV >6.64 shows a significant improvement of a nested model with one degree of freedom of p <0.05 and p <0.01, respectively. Model adequacy was further evaluated by using goodness-of-fit (GOF) plots through the use of Xpose 4 in R software ver. 3.0.2.
The final model was validated to accuracy and robustness by non-parametric bootstrap with resampling and prediction corrected visual predictive check (pcVPC). Resampling was repeated 2,000 times and the 5th and 95th percentile values of estimated parameters from the bootstrap procedure were compared with those estimated from the original data set. And we plotted the observed concentrations and 90% prediction intervals of simulated concentrations versus time from 200 pcVPC results. The entire procedure was performed using Perl-speaks-NONMEM (PsN) ver. 3.6.2. And next, we performed a Monte Carlo simulation to explore the optimal dose in several simulation scenarios. The AUCs according to the several virtual scenarios were simulated based on a sample of 1,000 patients and compared with recommended AUC range from previous studies.
As a result, total 34 patients and 166 MPA plasma concentrations were included. A time lagged 2-compartment (central and metabolic compartment) with a flip-flop model best describes the PK of MPA. Population parameters of apparent clearance (CL/F), central and metabolic compartment volume of distribution (Vc/F and Vp/F), and absorption rate constant were estimated as 9.3 L/h, 42 and 60.3 L, and 1.24 hr-1, respectively. The covariate analysis identified lower creatinine clearance (CLcr) and SLCO1B1 388A>G variant genotype were correlated with lower MPA clearance, on the contrary, UGT1A9 -118dT variant had decreased distribution of MPA, contributing to lower absorption. The median estimates resulting from the bootstrap procedure are similar to the population estimates of the final model. This validation method showed a good agreement between the simulated and observed concentrations at all sampling time points.
Finally, we performed the model-based simulations in order to define optimal dose to achieve target AUC(0-12h). A simulation of each 1,000 patients showed that the new dosing strategy resulted in a higher success of achieving the target AUC(0-12h) in the 30-60 mg.h/L. Furthermore, in most of cases, when considering to UGT1A9, SLCO1B1 genotype, and renal function, MPA 540 mg twice daily is the optimum dose to reach a target AUC(0-12h).
In conclusion, CLcr, UGT1A9 and SLCO1B1 genotypes seem to be promising parameters to predict the pharmacokinetics with flip-flop phenomenon of EC-MPS in transplant recipient having stable renal function. This model on clinical practice may help prevent overexposure and to achieve a proper AUC in Korean population.
Language
English
URI
https://hdl.handle.net/10371/120087
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College of Pharmacy (약학대학)Dept. of Pharmacy (약학과)Theses (Ph.D. / Sc.D._약학과)
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