Department of Hospital Pharmacy, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
Department of Internal Medicine, Division of Nephrology & Transplantation, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Br J Clin Pharmacol. 2019 Mar;85(3):601-615. doi: 10.1111/bcp.13838. Epub 2019 Jan 17.
The aims of this study were to describe the pharmacokinetics of tacrolimus immediately after kidney transplantation, and to develop a clinical tool for selecting the best starting dose for each patient.
Data on tacrolimus exposure were collected for the first 3 months following renal transplantation. A population pharmacokinetic analysis was conducted using nonlinear mixed-effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates.
A total of 4527 tacrolimus blood samples collected from 337 kidney transplant recipients were available. Data were best described using a two-compartment model. The mean absorption rate was 3.6 h , clearance was 23.0 l h (39% interindividual variability, IIV), central volume of distribution was 692 l (49% IIV) and the peripheral volume of distribution 5340 l (53% IIV). Interoccasion variability was added to clearance (14%). Higher body surface area (BSA), lower serum creatinine, younger age, higher albumin and lower haematocrit levels were identified as covariates enhancing tacrolimus clearance. Cytochrome P450 (CYP) 3A5 expressers had a significantly higher tacrolimus clearance (160%), whereas CYP3A4*22 carriers had a significantly lower clearance (80%). From these significant covariates, age, BSA, CYP3A4 and CYP3A5 genotype were incorporated in a second model to individualize the tacrolimus starting dose: [Formula: see text] Both models were successfully internally and externally validated. A clinical trial was simulated to demonstrate the added value of the starting dose model.
For a good prediction of tacrolimus pharmacokinetics, age, BSA, CYP3A4 and CYP3A5 genotype are important covariates. These covariates explained 30% of the variability in CL/F. The model proved effective in calculating the optimal tacrolimus dose based on these parameters and can be used to individualize the tacrolimus dose in the early period after transplantation.
本研究旨在描述肾移植后即刻他克莫司的药代动力学，并开发一种用于为每位患者选择最佳起始剂量的临床工具。
收集了肾移植后 3 个月内的他克莫司暴露数据。采用非线性混合效应模型进行群体药代动力学分析。评估了人口统计学、临床和遗传参数作为协变量。
共获得 337 例肾移植受者的 4527 份他克莫司血样数据。数据最好用两室模型来描述。平均吸收速率为 3.6 h，清除率为 23.0 l h（个体间差异 39%，IIV），中央分布容积为 692 l（49%IIV），外周分布容积为 5340 l（53%IIV）。清除率增加了批间变异性（14%）。较高的体表面积（BSA）、较低的血清肌酐、较年轻的年龄、较高的白蛋白和较低的血细胞比容水平被确定为增加他克莫司清除率的协变量。细胞色素 P450（CYP）3A5 表达者的他克莫司清除率显著升高（160%），而 CYP3A4*22 携带者的清除率显著降低（80%）。从这些显著的协变量中，年龄、BSA、CYP3A4 和 CYP3A5 基因型被纳入第二个模型，以个体化他克莫司的起始剂量：[公式：见正文]两个模型均成功进行了内部和外部验证。进行了一项临床试验模拟，以证明起始剂量模型的附加价值。
为了更好地预测他克莫司的药代动力学，年龄、BSA、CYP3A4 和 CYP3A5 基因型是重要的协变量。这些协变量解释了 CL/F 中 30%的变异性。该模型在基于这些参数计算最佳他克莫司剂量方面效果良好，可以用于移植后早期个体化他克莫司剂量。
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