肝移植患者术后口服他克莫司群体药动学研究

目的:考察肝移植患者术后口服他克莫司的群体药动学模型,为临床个体化用药提供参考。方法:回顾性收集天津市第一中心医院18例肝移植患者术后口服他克莫司12 h全血药浓度监测数据145个。运用非线性混合效应模型(nonlinear mixed effect model,NONMEM)建立他克莫司群体药动学模型,并考察了年龄、性别、移植术后天数、血清肌酐等固定效应对药动学参数的影响,得到最终模型方程,最后利用Bayesian反馈得到的个体药动学参数值进行个体化给药方案设计。结果:本次研究建立起了口服他克莫司一级吸收和消除的二房室群体药动学模型,并通过NONMEM模拟程序为1例患者进行了个体化给药设计。结论:NONMEM法建立的模型能较好地估算他克莫司的个体及群体药动学参数,为临床合理使用他克莫司提供参考依据。     

成人慢性肾小球肾炎患者他克莫司群体药动学研究

目的:建立成人慢性肾小球肾炎患者他克莫司群体药动学(population pharmaco-kinetics,PPK)模型。方法:收集55例慢性肾小球肾炎患者的268个他克莫司血药浓度数据。采用非线性混合效应模型考察CYP3A5基因型、体质量、年龄、实验室指标、合并用药等对他克莫司药动学参数的影响,建立他克莫司PPK模型,并通过拟合优度诊断、Bootstrap自举法及正态预测分布误差法对模型进行验证。结果:他克莫司表观清除率及表观分布容积的群体典型值分别为13.8L·h-1和733L,CYP3A5基因型和合并用药五酯胶囊对他克莫司清除率具有显著影响。经验证他克莫司PPK模型稳定有效。结论:首次建立成人慢性肾小球肾炎患者他克莫司PPK模型,可为慢性肾小球肾炎患者的他克莫司个体化给药提供参考。     

中国特发性膜性肾病患者他克莫司群体药动学研究

本研究建立他克莫司在特发性膜性肾病(IMN)患者中的群体药代动力学(PPK)模型,并定量考察他克莫司药代动力学的影响因素。收集96名IMN患者的610个常规检测的他克莫司谷浓度数据,采用非线性混合效应模型(NONMEM)考察CYP3A5基因型、年龄、性别、体重、肝肾功能、合用药物等对他克莫司药动学参数的影响,并建立他克莫司群体药动学模型。应用拟合优度图(GOT)、自举法(Bootstrap)和预测值校准的直观预测检验(pc-VPC)对构建的模型进行评价。采用一房室模型描述他克莫司体内变化过程, CYP3A5*1/*3型和*1/*1型表观清除率分别是*3/*3型的1.57倍和1.86倍,合用五酯胶囊患者他克莫司清除率是未合用的73.6%,合用金水宝胶囊患者是未合用的1.2倍。模型评价显示构建的模型稳定,结果可靠。本文临床试验经滨州医学院附属医院伦理委员会批准并在滨州医学院附属医院进行。建立的群体药动学模型较好地描述他克莫司在中国IMN患者体内的药动学特征,为他克莫司的个体化治疗提供依据。     

调整给药方案对他克莫司低谷浓度肝移植患者的影响

目的:研究他克莫司在低谷浓度肝移植患者的药动学,并在每天总药量不增加的前提下,调整给药方案,使患者的谷浓度达到有效浓度范围。方法:对6例低谷浓度的肝移植患者进行药动学研究,将每12小时给药1次改为每8小时给药。采用微粒子酶免疫分析法(MEIA)测定血药浓度,比较给药方案调整前、后他克莫司谷浓度。结果:调整后的他克莫司平均谷浓度高于调整前的谷浓度(P<0.05),他克莫司的平均谷浓度由调整前的5.8μg.L-1升至调整后的7.8μg.L-1,升高了23.3%。结论:对于他克莫司低谷浓度的肝移植患者,在不增加日给药剂量的前提下,可以通过缩短给药间隔增加谷浓度,使血药浓度更加平稳。     

他克莫司在肝移植患者中群体药动学研究进展

新型强效免疫抑制剂广泛应用于肝移植术后受体患者。其中,他克莫司在临床的应用不仅预防和治疗了肝移植患者术后抗移植排斥反应,提高移植器官的存活率,并已成为肝移植术后抗排异反应的一线用药。本文从他克莫司及其在肝移植患者中的应用、群体药动学的基本认识,以及口服他克莫司在肝移植患者的群体药动学研究等方面做一综述。     

他克莫司群体药动学在儿童肝移植中的研究进展

他克莫司治疗窗窄，药动学个体差异大，临床难以建立儿童受者的个体化治疗方案。群体药动学（PPK）在个体化给药研究方面有巨大优势。为了实现他克莫司在儿童肝移植受者中的个体化治疗，国内外学者致力于儿童肝移植受者的PPK研究，但各研究的结果存在差异。本文通过检索PubMed、Web of Science及Scopus数据库中的相关文献，着重分析了既往他克莫司PPK在儿童肝移植受者中的研究，总结影响他克莫司PPK参数的主要因素，期望应用PPK方法为构建儿童肝移植受者的个体化治疗方案奠定基础。     

最优子集回归模型在预测肝移植受者他克莫司血药浓度中的应用

目的：建立肝移植受者他克莫司血药浓度简易估算方法。方法：收集37例肝移植受者口服他克莫司的176份稳态全血浓度数据,采用最优子集回归法建立他克莫司稳态血药浓度简易估算公式。结果：以浓度测定前4日他克莫司累积剂量预测他克莫司血药浓度的准确性及精密度较好,平均预测误差（0.04±2.5）ng/ml,平均绝对误差（2.00±1.45）ng/ml,80.8%的血药浓度数据绝对预测误差≤3.0ng/ml。结论：本方法预测他克莫司血药浓度准确性和精密度较好,简便迅捷。     

肾移植患者西罗莫司的群体药动学研究

目的:建立中国肾移植患者西罗莫司的群体药动学模型,为实施个体化用药提供理论支持。方法:选择47名肾移植术后采用西罗莫司+泼尼松+环孢素或他克莫司或霉酚酸酯(MMF)三联免疫抑制治疗的患者为研究对象,回顾性收集47名患者服药后的101个西罗莫司稳态血药浓度及相应的试验室检查数据,运用Winnonmix药动学软件,采用非线性混合效应模型(NONMEM)分析体重、年龄、性别、给药剂量、合并用药、肌酐清除率等对药动学参数的影响。最终模型的验证采用Jackknife法进行内部验证。结果:西罗莫司符合无滞后时间的一级消除动力学一室模型。固定效应结果量子,合用MMF和体重可影响药物清除率。最终模型公式为:CL/F(L·h-1)=11.01×0.14MMF+0.089×W。CL/F和Vd/F的群体典型值分别是11.01L·h-1和3616L,个体间变异分别为62.82%和85.07%。观测值和预测值间的残差(SD)和相关系数(r)分别是1.0ng·mL-1和0.94。结论:所建立的群体药动学模型能较好地估算服用西罗莫司的肾移植患者的个体及群体药动学参数,对指导临床个体化用药具有重要意义。     

微粒子酶免疫法监测肝移植术后他克莫司血药浓度

目的:为了避免他克莫司的不良反应,对使用他克莫司的肝移植患者实施治疗药物监测。方法:用微粒子酶免疫法测定全血他克莫司谷浓度,并对他克莫司谷浓度的监测结果进行回顾性分析。结果:当肝移植患者被给予他克莫司、泼尼松和硫唑嘌呤时,他克莫司谷浓度与剂量之间存在正相相关。为了得到理想的效果和最小的毒性,他克莫司的全血药物浓度在肝移植后的90d内应维持在10～20μg·L~(-1),90d后在5～15μg·L~(-1)。结论:对于肝移植患者,他克莫司的全血药物浓度监测是很必要的,而且对于减少毒性和排斥反应的危险性是很有帮助的。     

CYP3A5和MDR1基因多态性对中国肝移植患者他克莫司药动学的影响

目的探讨细胞色素P450酶3A5（CYP3A5）基因和多药耐药基因（MDR1）C1236T、G2677T／A、C3435T多态性对肝移植患者口服他克莫司（TAC）后体内药动学参数的影响。方法采集28例肝移植患者手术后第1周和第3周血标本,采用LC—MS／MS法检测TAC血药浓度,计算主要药动学参数。采用聚合酶链反应结合基因测序分析28例肝移植患者CYP3A5＊3和MDR1主要基因型。结果携带MDR1 3435T基因型的肝移植患者口服TAC后,药动学参数AUC0→1和ρmax明显高于3435CC型患者,而CYP3A5＊3、MDR1 C1236T和G2677T／A基因多态性对TAC的药动学参数无明显影响。结论携带MDR1 3435T基因型肝移植患者比3435CC型患者需要较高剂量才能达到目标浓度。     

他克莫司在中国肾移植患者中的群体药物动力学研究

本研究旨在考察口服他克莫司(tacrolimus)在中国肾移植患者中的群体药物动力学特征并探讨群体药物动力学参数和相关因素间的关系。研究中回顾性搜集了58例肾移植患者的802份他克莫司稳态全血样本资料。患者随机分为模型建立组(41例)和模型验证组(17例)。用非线性混合效应模型(NONMEM)程序中的一级评估法(first-order estimation，FO)对模型建立组的数据进行分析。计算清除率(CL/F)、表观分布容积(V/F)的群体典型值，定量评价人口统计学指标、生化指标和合并用药等固定效应因素对药物动力学参数的影响。单室一级吸收和消除模型能够较好地拟合数据。最终模型包含了移植术后时间(POD)、红细胞压积(HCT)、谷草转氨酶(AST)、合并使用佩尔地平(NICA)和地尔硫(DIL)等对CL/F的影响。用模型验证组数据进行验证的结果表明观测值和模型预测值之间没有明显的偏倚，模型的稳定性和准确度较好。CL/F和V/F的群体典型值分别为21.7 L·h^-1和241 L；相应的个体间变异分别为41.6%和49.7%。观测值与预测值之间的残差SD为2.19 μg·L^-1。本文建立的模型可以为临床他克莫司剂量选择提供一定参考。     

Dosing recommendations based on population pharmacokinetics of tacrolimus in Mexican adult patients with kidney transplant

The aim of this study was to perform a population pharmacokinetic analysis of tacrolimus in Mexican adult kidney transplant patients to analyse the influence of clinical and genetic covariates to propose a dosage regimen. Kidney transplant patients (>18 years old) receiving oral tacrolimus treatment were included in the current study. The population pharmacokinetic model was built using a one‐compartment model and the First Order Conditional Estimation method with Interaction (FOCEI via NONMEM v.7.3.). A total of 600 tacrolimus trough blood concentrations from 52 kidney transplant patients were analysed. Tacrolimus clearances were 26, 18.8 and 12.3 L/h, for patients with genetic polymorphisms CYP3A5*1*1, *1*3 and *3*3, respectively. The influence of haematocrit was inversely related to tacrolimus clearance, following an allometric power function. Total volume of distribution was 604 L. Interindividual variability associated with tacrolimus clearance and distribution volume for the final model was 33 and 63%, respectively, with a residual error of 2.5 ng/mL. Relative bioavailability was calculated between generic formulations A (0.53) and B (1) of tacrolimus. Internal validation was performed through bootstrap analysis to evaluate the stability of the final model; external validation was performed in a new group of patients (n = 13) to estimate residual errors on basic (57.8%) and final (34.8%) models. Finally, stochastic simulations were performed to propose a dosage regimen based on haematocrit, CYP3A5 genotype and generic formulation of tacrolimus. A stable and predictive population pharmacokinetic model of tacrolimus was developed for Mexican adult kidney transplant patients; additionally, the proposed dosage regimen of tacrolimus should be prospectively validated.     

A population pharmacokinetic study of tacrolimus in healthy Chinese volunteers and liver transplant patients

Aim:

To develop a population pharmacokinetic (PopPK) model of tacrolimus in healthy Chinese volunteers and liver transplant recipients for investigating the difference between the populations, and for potential individualized medication.

Methods:

A set of 1100 sparse trough concentration data points from 112 orthotopic liver transplant recipients, as well as 851 dense data points from 40 healthy volunteers receiving a single dose of tacrolimus (2 mg, po) were collected. PopPK model of tacrolimus was constructed using the program NONMEM. Related covariates such as age, hepatic and renal functions that were potentially associated with tacrolimus disposition were evaluated. The final model was validated using bootstrapping and a visual predictive check.

Results:

A two-compartment model of tacrolimus could best describe the data from the two populations. The final model including two covariates, population (liver transplant recipients or volunteers) and serum ALT (alanine aminotransferase) level, was verified and adequately described the pharmacokinetic characteristics of tacrolimus. The estimates of V2/F, Q/F and V3/F were 22.7 L, 76.3 L/h and 916 L, respectively. The estimated CL/F in the volunteers and liver transplant recipients was 32.8 and 18.4 L/h, respectively. Serum ALT level was inversely related to CL/F, whereas age did not influence CL/F. Thus, the elderly (≥65 years) and adult (<65 years) groups in the liver transplant recipients showed no significant difference in the clearance of tacrolimus.

Conclusion:

Compared with using the sparse data only, the integrating modeling technique combining sparse data from the patients and dense data from the healthy volunteers improved the PopPK analysis of tacrolimus.     

Population pharmacokinetics of tacrolimus in whole blood and plasma in asian liver transplant patients

OBJECTIVES: The objectives of this study were to develop population pharmacokinetic models of tacrolimus in an Asian population with whole blood and plasma drug concentration data, to compare the variability of the pharmacokinetic parameters in these two matrices and to search for the main patient characteristics that explain the variability in pharmacokinetic parameters. STUDY DESIGN: Prospective pharmacokinetic assessment followed by model fitting. PATIENTS: Whole blood samples from 31 liver transplant patients in a local hospital receiving oral tacrolimus as part of their immunosuppressive therapy were assessed. Plasma samples from 29 of the 31 patients were also evaluated. Concentrations of tacrolimus in whole blood and plasma were determined by an electrospray high-performance liquid chromatography with tandem mass spectrometry. Two hundred and thirteen whole blood and 157 plasma tacrolimus concentrations were used for building two nonlinear mixed-effects population models to describe the disposition of tacrolimus in whole blood and plasma, respectively. Covariates that were investigated included demographic characteristics, biological markers of liver and renal functions, corticosteroid dose and haematological parameter. RESULTS: A one-compartment model was used to describe the whole blood and plasma concentration-time data of tacrolimus after oral administration. For the whole blood population model, the population estimates of the first-order absorption rate constant (k(a)), apparent clearance based on whole blood concentration after oral administration (CL(B)/F) and apparent volume of distribution based on whole blood concentrations after oral administration (V(d,B)/F) were 2.08h(-1), 14.1 L/h and 217L, respectively. The coefficient of variations (CVs) of interpatient variabilities in CL(B)/F and V(d,B)/F were 65.7% and 63.8%, respectively. Bodyweight, liver and renal function influenced CL(B)/F, while height and haematocrit influenced V(d,B)/F. The residual (unexplained) variability was 34.8%. For the plasma population model, the population estimates of the k(a), apparent clearance based on plasma concentrations after oral administration (CL(P)/F) and apparent volume of distribution based on plasma concentrations after oral administration (V(d,P)/F) were 5.21h(-1), 537 L/h and 563L, respectively. The CVs of interpatient variabilities in CL(P)/F and V(d,P)/F were 96.0% and 105.4%, respectively. Bodyweight was found to influence CL(P)/F, while the erythrocyte-to-plasma concentration ratio influenced V(d,P)/F. The residual (unexplained) variability was 49.8% at the mean plasma concentration of 1.1 ng/mL. CONCLUSIONS: Whole blood and plasma population pharmacokinetic models of tacrolimus in Asian adult and paediatric liver transplant patients were developed using prospective data in a clinical setting. This has identified and quantified sources of interindividual variability in CL(B)/F, V(d,B)/F, CL(P)/F and V(d,P)/F of tacrolimus in Asian liver transplant patients. Information derived from the whole blood population model may subsequently be used by clinicians for dosage individualisation through Bayesian forecasting.     

Factors affecting the apparent clearance of tacrolimus in Korean adult liver transplant recipients

STUDY OBJECTIVE: To identify the factors affecting tacrolimus apparent total body clearance (Cl/F [F = bioavailability]) in adult liver transplant recipients. DESIGN: Population pharmacokinetic analysis using data from a retrospective chart review. SETTING: University-affiliated hospital in Seoul, South Korea. PATIENTS: Fifty-one adult liver transplant recipients who had received tacrolimus after transplantation. MEASUREMENTS AND MAIN RESULTS: Data on 35 adult liver transplant recipients for model building and 16 patients for model validation were obtained retrospectively. Population average parameter estimates of Cl/F and apparent volume of distribution (V/F) were sought by using the nonlinear mixed-effect model (NONMEM) program. A number of clinical covariates were screened for their influence on these pharmacokinetic parameters. The final optimal population model related Cl/F to total bilirubin, early (< or = 3 days) and late (> 35 days) postoperative days, international normalized ratio (INR), and graft:recipient weight ratio (GRWR). The NONMEM estimates indicated that the Cl/F of tacrolimus was decreased in patients with a small graft, hyperbilirubinemia, and a high INR. In addition, the Cl/F of tacrolimus almost doubled 4 days after transplantation, but decreased with an increase in duration of therapy after day 35. Mean prediction error and mean absolute prediction error were 0.26 and 3.78 ng/ml, respectively, for the validation sample. A final analysis in all 51 patients, which consisted of 1775 blood samples for concentration measurements, identified the following regression model: Cl/F (L/hr) = (0.36 + 2.01/POD * L) * TBIL(-0.23 (TBIL = 1 if TBIL level < or = 1.2 mg/dl, otherwise TBIL = TBIL level)) *49((if POD < or = 3 days)) * 0.75((if INR > 1.4)) * 0.86((if GRWR < or = 1.25%)) * WT, where L was 1 if postoperative day (POD) was greater than 35 days, otherwise L was 0; V/F was 568 L, TBIL was total bilirubin, and WT was body weight. The interindividual variabilities (coefficients of variation) in Cl/F and V/F were 35.35% and 68.12%, respectively. The residual variability was 3.14 ng/ml. CONCLUSION: These findings could be useful to the health care provider for adjustment of tacrolimus dosage in adult liver transplant recipients with various clinical factors.     

Population pharmacokinetics of tacrolimus in Asian paediatric liver transplant patients

AIMS: The purpose of this study was to describe the population pharmacokinetics of intravenous and oral tacrolimus (FK506) in 20 Asian paediatric patients, aged 1-14 years, following liver transplantation and to identify possible relationships between clinical covariates and population parameter estimates. METHODS: Details of drug dosage histories, sampling times and concentrations were collected retrospectively from routine therapeutic drug monitoring data accumulated for at least 4 days after surgery. Before analysis, patients were randomly allocated to either the population data set (n = 16) or a validation data set (n = 4). The population data set was comprised of 771 concentration measurements of patients admitted over the last 3 years. Population modelling using the nonlinear mixed-effects model (NONMEM) program was performed on the population data set, using a one-compartment model with first-order absorption and elimination. Population average parameter estimates of clearance (CL), volume of distribution (V) and oral bioavailability (F) were sought; a number of clinical and demographic variables were tested for their influence on these parameters. RESULTS: The final optimal population models related clearance to age, volume of distribution to body surface area and bioavailability to body weight and total bilirubin concentration. Predictive performance of this model evaluated using the validation data set, which comprised 86 concentrations, showed insignificant bias between observed and model-predicted blood tacrolimus concentrations. A final analysis performed in all 20 patients identified the following relationships: CL (l h-1) = 1.46 *[1 + 0. 339 * (AGE (years) -2.25)]; V (l) = 39.1 *[1 + 4.57 * (BSA (m2)-0. 49)]; F = 0.197 *[1 + 0.0887 * (WT (kg) -11.4)] and F = 0.197 *[1 + 0.0887 * (WT (kg) -11.4)] * [1.61], if the total bilirubin > or = 200 micromol l-1. The interpatient variabilities (CV%) in CL, V and F were 33.5%, 33.0% and 24.1%, respectively. The intrapatient variability (s.d.) among observed and model-predicted blood concentrations was 5.79 ng ml-1. CONCLUSIONS: In this study, the estimates of the pharmacokinetic parameters of tacrolimus agreed with those obtained from conventional pharmacokinetic studies. It also identified significant relationships in Asian paediatric liver transplant patients between the pharmacokinetics of tacrolimus and developmental characteristics of the patients.     

Extrapolation of physiologically based pharmacokinetic model for tacrolimus from renal to liver transplant patients

Physiologically based pharmacokinetic (PBPK) modeling is useful for evaluating differences in drug exposure among special populations, but it has not yet been employed to evaluate the absorption process of tacrolimus. In this study, we developed a minimal PBPK model with a compartmental absorption and transit model for renal transplant patients using available data in the literature and clinical data from our hospital. The effective permeability value of tacrolimus absorption and parameters for the single adjusting compartment were optimized via sensitivity analyses, generating a PBPK model of tacrolimus for renal transplant patients with good predictability. Next, we extrapolated the pharmacokinetics of tacrolimus for liver transplant patients by changing the population demographic parameters of the model. When the physiological parameters of a population with normal liver function were changed to those of a population with impaired hepatic function (Child-Pugh class A) in the constructed renal transplant PBPK model, the predicted tacrolimus concentrations were consistent with the observed concentrations in liver transplant patients. In conclusion, the constructed tacrolimus PBPK model for renal transplant patients could predict the pharmacokinetics in liver transplant patients by slightly reducing the hepatic function, even at three weeks post-transplantation.     

Population pharmacokinetic estimation of tacrolimus apparent clearance in adult liver transplant recipients

The goal was to study the factors affecting tacrolimus apparent clearance (CL/F) in adult liver transplant recipients. Tacrolimus dose and concentration data (n = 694) were obtained from 67 liver transplant recipients (22 female and 45 male), and the data were analyzed using a nonlinear mixed-effect modeling (NONMEM) method. A 1-compartment pharmacokinetic model with first-order elimination, an absorption rate constant fixed at 4.5 hours, and first-order conditional estimation was used to describe tacrolimus disposition. The predictive performance of the final model was evaluated using data splitting and assessing bias and precision of the estimates. The population estimate of tacrolimus CL/F and apparent volume of distribution (V/F) were found to be 21.3 L/h (95% confidence interval, CI, 18.0-24.6 L/h) and 316.1 L (95% CI 133-495 L), respectively. Neither patient's age, weight, gender, nor markers of liver function influenced tacrolimus CL/F. The final model was TVCL = 21.3 + 9.8 x (1 - HEM) + 3.4 x (1 - ALB) - 2.1 x (1 - DIL) - 7.4 x (1 - FLU), where TVCL, typical estimate of apparent clearance, HEM = 0 if hematocrit <35%, otherwise 1; ALB = 0 if albumin <3.5 g/dL, otherwise 1; DIL = 0 if diltiazem is coadministered, otherwise 1; FLU = 0 if fluconazole is coadministered, otherwise 1. This study identified the factors that significantly affect tacrolimus disposition in adult liver transplant recipients during the early posttransplantation period. This information will be helpful to clinicians for dose individualization of tacrolimus in liver transplant recipients with different clinical conditions including anemia or hypoalbuminemia or in those patients receiving diltiazem or fluconazole.     

Failure of traditional trough levels to predict tacrolimus concentrations

The objective of this study was to estimate tacrolimus population parameter values and to evaluate the ability of the maximum a posteriori probability (MAP) Bayesian fitting procedure to predict tacrolimus blood levels, using the traditional strategy of monitoring only trough levels, for dosage individualization in liver transplant patients. Forty patients treated with tacrolimus after liver transplantation were studied during the early posttransplant phase (first 2 weeks). This phase was divided into four time periods (1-4 days, 5-7 days, 8-11 days, 12-14 days). Tacrolimus was administered twice daily. Approximately one determination of a tacrolimus trough level on whole blood was performed each day. The NPEM2 program was used to obtain population pharmacokinetic parameter values. With each individual pharmacokinetic parameter estimated by the MAP Bayesian method for a given period, the authors evaluated the prediction of future levels of tacrolimus for that patient for the next period. This evaluation of Bayesian fitting predictive performance was performed using the USC*PACK clinical software. Mean pharmacokinetic parameter values were in the same general range as previously published values obtained with richer data sets. However, during each period, the percentage of blood levels predicted within 20% did not exceed 40%. The traditional strategy of obtaining only trough whole blood levels does not provide enough dynamic information for the MAP Bayesian fitting procedure (the best method currently available) to be used for adaptive control of drug dosage regimens for oral tacrolimus. The authors suggest modifying the blood concentration monitoring scheme to add at least one other concentration measured during the absorptive or distributive phase to obtain more information about the behavior of the drug. D-Optimal design and similar strategies should be considered.