• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.511-520
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• 2010

The Pharmacokinetic model is a complex nonlinear model with pharmacokinetic parameters that is some-times represented by a complex form of differential equations. A population pharmacokinetic model adds individual variability using the random effects to the pharmacokinetic model. It amounts to the nonlinear mixed effect model. This paper, reviews the population pharmacokinetic model from a statistical viewpoint; in addition, a population pharmacokinetic model is also applied to the real clinical data along with a review of the statistical meaning of this model.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.139-152
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• 2012

The result of the analysis of a population pharmacokinetic model can directly influence the decision of the dose level applied to the targeted patients. Therefore the validation procedure of the final model is very important in this area. This paper reviews the validation methods of population pharmacokinetic models from a statistical viewpoint. In addition, the whole procedure of the analysis of population pharmacokinetics, from the base model to the final model (that includes various validation procedures for the final model) is tested with real clinical data.

• 대한임상약리학회:학술대회논문집
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• 2007.11a
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• pp.84-84
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• 2007

• Korean Journal of Clinical Pharmacy
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• v.20 no.1
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• pp.9-16
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• 2010

Purpose: 본 연구는 한국인 성인 조혈모세포이식환자를 대상으로 경구용 사이클로스포린의 집단약동학 분석을 통하여 사이클로스포린의 약동학적 파라미터에 영향을 미치는 요인 분석을 실시하고자 하였다. Methods: 2000년 12월부터 2006년 8월까지 서울대학교병원에서 동종조혈모세포이식을 받고 면역억제제로 사이클로스포린을 복용한 성인 백혈병환자를 대상으로 후향적으로 자료를 수집하였다. 사이클로스포린의 약동학에 영향을 미치는 인자로는 연령, 성별, 이식 후 날짜, 신기능, 공여자와의 관계, 질병의 종류, 혈중 빌리루빈 농도, 사이클로스포린의 대사를 유도하는 프레드니솔론의 투여량, 헤마토크리트, 사이클로스포린의 대사를 저해하는 약물의 병용여부 등을 검토하였다. 분석은 NONMEM$^{(R)}$ VI 프로그램을 이용하였으며, 변수를 추가하지 않은 기본 모형을 만든 후에 단계적인 요인의 추가와 제거를 통해 최종모형을 제작하였다. Results: 최종 상관 모형은 다음과 같다; CL/F (L/h) = $85.6{\times}e^{(0.646\;{\times}\;HCT/28.9\;+\;0.0464\;{\times}\;Gender)}$. 사이클로스포린의 겉보기 클리어런스는 환자의 성별이 남자일 때 또는 헤마토크릿이 감소할수록 증가하였다. 그 외 파라미터는 다음과 같이 계산되었다; $K_{\alpha}=0.0787\;(h^{-1})$; Q=57.1(L/kg/h); $V_{d-central\;compartment}$=1,100 (L); $V_{d-peripheral\;compartment}$ = 213,000(L). 개체간 편차는 40% 미만이었으며, 개체내 편차를 포함하는 잔차는 24.02%였다. Conclusions: 사이클로스포린의 약동학적 특징과 그 클리어런스에 영향을 끼칠 수 있는 임상적 요인을 이해하는 것은 환자 개개인의 용량과 용법의 결정 및 이상반응 발생의 예방에 유용할 수 있다. 한국인 조혈모세포이식환자에서 사이클로스포린의 약동학에 영향을 미치는 최종 파라미터를 구한 본 연구의 결과는 조혈모세포이식을 받은 한국인 성인환자에서 사이클로스포린의 모니터링 및 용량조절에 유용할 것으로 전망된다.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.175-188
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• 2015

This study is about estimation methods for the population pharmacokinetic and pharmacodymic model. This is a nonlinear mixed effect model, and it is difficult to find estimates of parameters because of nonlinearity. In this study, we examined theoretical background of various estimation methods provided by NONMEM, which is the most widely used software in the pharmacometrics area. We focused on estimation methods using a stochastic sampling approach - IMP, IMPMAP, SAEM and BAYES. The SAEM method showed the best performance among methods, and IMPMAP and BAYES methods showed slightly less performance than SAEM. The major obstacle to a stochastic sampling approach is the running time to find solution. We propose new approach to find more precise initial values using an ITS method to shorten the running time.

• Korean Journal of Clinical Pharmacy
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• v.28 no.1
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• pp.24-29
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• 2018

Background: Cyclosporine is an immunosuppressive agent used to treat and prevent graft versus host reaction (GVHR)-a complication associated with stem cell transplantation. This study aimed to develop a population pharmacokinetic model of cyclosporine and investigate factors affecting cyclosporine clearance in pediatric hematopoietic stem cell transplant patients. Methods: A total of 650 cyclosporine concentrations recorded in 65 patients who underwent hematopoietic stem cell transplantation were used. Data including age, sex, weight, height, body surface area (BSA), type of disease, chemotherapy before stem cell transplantation, type of donor, serum creatinine levels, total bilirubin concentration, hematocrit value, and type of concomitant anti-fungal agents and methylprednisolone used were retrospectively collected. Data related to cyclosporine dosage, administration time, and blood concentration were also collected. All data were analyzed using the non-linear mixed effect model; a two-compartment model with first-order elimination was used. Results: The population pharmacokinetic model of cyclosporine using the NONMEM program was as follows: $CL(L/h)=5.9{\times}(BSA/1.2)^{0.9}$, V2 (L) = 54.5, Q (L/h) = 3.5, V3 (L) = 1080.0, $k_a(h^{-1})=0.000377$. BSA was selected as a covariate of cyclosporine clearance, which increased with an increase in BSA. Conclusion: A population pharmacokinetic model for Korean pediatric hematopoietic stem cell transplant patients was developed, and the important factor affecting cyclosporine clearance was found to be BSA. The model might contribute to the development of the most appropriate dosing regimen for cyclosporine. Further studies on population pharmacokinetics should be carried out, prospectively targeting pediatric patients.

• Korean Journal of Clinical Pharmacy
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• v.18 no.1
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• pp.60-67
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• 2008

This study aimed to compare a microparticle enzyme immunoassay (MEIA) with a liquid chromatography-tandem mass spectrometry (LC/MS/MS) technique for the measurement of tacrolimus concentrations in adult liver transplant recipients, to investigate how the assay choice influenced the population pharmacokinetics of tacrolimus and to identify patient characteristics that affected pharmacokinetic parameters in each assay. Tacrolimus concentrations from 29 liver (n=52 paired-samples) transplant recipients measured by both MEIA and LC/MS/MS were used to evaluate the performance of these methods in the clinical setting. Tacrolimus pharmacokinetics was studied independently using MEIA and LC/MS/MS data in 70 adult patients using a population approach performed with NONMEM. Patient characteristics which influenced pharmacokinetic parameters in each assay were compared. The relation between LC/MS/MS and MEIA measurements was best described by the regression equation MEIA=1.465*LC/MS/MS-1.336 (r=0.91). Multiple linear regression analysis showed significant inverse relationships between assay difference and hematocrit (Hct) (p<0.025) in liver graft recipients. In MEIA, the population estimate of tacrolimus CL/F and apparent volume of distribution (Vd/F) were found to be 10.1 L/h and 226 L, and in LC/MS/MS, 13 L/h and 305 L respectively. Neither patient's age, weight, gender, grafted hepatic weight, albumin concentration, nor markers of liver function influenced tacrolimus CL/F The final model of CL/F was found to be 10.1+(Hct/Hct mean)$^{12.0}$ in MEIA and 13+(1+Hct/578) in LC/MS/MS indicating that CL/F was influenced by hematocrit.

• Korean Journal of Clinical Pharmacy
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• v.26 no.4
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• pp.312-317
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• 2016

Objective: Midazolam is mainly metabolized by cytochrome P450 (CYP) 3A. Inhibition or induction of CYP3A can affect the pharmacological activity of midazolam. The aims of this study were to develop a population pharmacokinetic (PK) model and evaluate the effect of CYP3A-mediated interactions among ketoconazole, rifampicin, and midazolam. Methods: Three-treatment, three-period, crossover study was conducted in 24 healthy male subjects. Each subject received 1 mg midazolam (control), 1 mg midazolam after pretreatment with 400 mg ketoconazole once daily for 4 days (CYP3A inhibition phase), and 2.5 mg midazolam after pretreatment with 600 mg rifampicin once daily for 10 days (CYP3A induction phase). The population PK analysis was performed using a nonlinear mixed effect model ($NONMEM^{(R)}$ 7.2) based on plasma midazolam concentrations. The PK model was developed, and the first-order conditional estimation with interaction was applied for the model run. A three-compartment model with first-order elimination described the PK. The influence of ketoconazole and rifampicin, CYP3A5 genotype, and demographic characteristics on PK parameters was examined. Goodness-of-fit (GOF) diagnostics and visual predictive checks, as well as bootstrap were used to evaluate the adequacy of the model fit and predictions. Results: Twenty-four subjects contributed to 900 midazolam concentrations. The final parameter estimates (% relative standard error, RSE) were as follows; clearance (CL), 31.8 L/h (6.0%); inter-compartmental clearance (Q) 2, 36.4 L/h (9.7%); Q3, 7.37 L/h (12.0%), volume of distribution (V) 1, 70.7 L (3.6%), V2, 32.9 L (8.8%); and V3, 44.4 L (6.7%). The midazolam CL decreased and increased to 32.5 and 199.9% in the inhibition and induction phases, respectively, compared to that in control phase. Conclusion: A PK model for midazolam co-treatment with ketoconazole and rifampicin was developed using data of healthy volunteers, and the subject's CYP3A status influenced the midazolam PK parameters. Therefore, a population PK model with enzyme-mediated drug interactions may be useful for quantitatively predicting PK alterations.