前瞻性对照、随机临床试验,采用基于模型的贝叶斯预测为肾移植受者制定个体化他克莫司剂量。

A prospective controlled, randomized clinical trial of kidney transplant recipients developed personalized tacrolimus dosing using model-based Bayesian Prediction.

机构信息

Nephrology Department, Hospital Universitari de Bellvitge-Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain.

Department of Clinical Sciences, Medicine Unit, University of Barcelona, Barcelona, Spain.

出版信息

Kidney Int. 2023 Oct;104(4):840-850. doi: 10.1016/j.kint.2023.06.021. Epub 2023 Jun 28.

Abstract

For three decades, tacrolimus (Tac) dose adjustment in clinical practice has been calculated empirically according to the manufacturer's labeling based on a patient's body weight. Here, we developed and validated a Population pharmacokinetic (PPK) model including pharmacogenetics (cluster CYP3A4/CYP3A5), age, and hematocrit. Our study aimed to assess the clinical applicability of this PPK model in the achievement of Tac Co (therapeutic trough Tac concentration) compared to the manufacturer's labelling dosage. A prospective two-arm, randomized, clinical trial was conducted to determine Tac starting and subsequent dose adjustments in 90 kidney transplant recipients. Patients were randomized to a control group with Tac adjustment according to the manufacturer's labeling or the PPK group adjusted to reach target Co (6-10 ng/ml) after the first steady state (primary endpoint) using a Bayesian prediction model (NONMEM). A significantly higher percentage of patients from the PPK group (54.8%) compared with the control group (20.8%) achieved the therapeutic target fulfilling 30% of the established superiority margin defined. Patients receiving PPK showed significantly less intra-patient variability compared to the control group, reached the Tac Co target sooner (5 days vs 10 days), and required significantly fewer Tac dose modifications compared to the control group within 90 days following kidney transplant. No statistically significant differences occurred in clinical outcomes. Thus, PPK-based Tac dosing offers significant superiority for starting Tac prescription over classical labeling-based dosing according to the body weight, which may optimize Tac-based therapy in the first days following transplantation.

摘要

三十年来,在临床实践中,他克莫司(Tac)剂量的调整一直是根据制造商的标签,根据患者的体重进行经验性计算。在这里,我们开发并验证了一个包含药代动力学(群体 CYP3A4/CYP3A5)、年龄和红细胞压积的群体药代动力学(PPK)模型。我们的研究旨在评估与制造商的标签剂量相比,该 PPK 模型在实现 Tac Co(治疗谷 Tac 浓度)方面的临床适用性。一项前瞻性、两臂、随机临床试验旨在确定 90 名肾移植受者的 Tac 起始剂量和随后的剂量调整。患者被随机分配到对照组,根据制造商的标签调整 Tac 剂量,或根据 PPK 组在达到第一个稳定状态后调整剂量,以达到目标 Co(6-10ng/ml)(主要终点),使用贝叶斯预测模型(NONMEM)。与对照组(20.8%)相比,PPK 组有更高比例的患者(54.8%)达到治疗目标,满足了 30%的既定优势边界定义。与对照组相比,接受 PPK 的患者显示出明显较低的个体内变异性,更早达到 Tac Co 目标(5 天与 10 天),并且在肾移植后 90 天内需要的 Tac 剂量调整明显少于对照组。在临床结果方面没有出现统计学上的显著差异。因此,与基于体重的经典标签剂量相比,基于 PPK 的 Tac 剂量设定在开始 Tac 处方方面具有显著优势,这可能在移植后最初几天优化 Tac 治疗。

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