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肿瘤生物治疗药物的半机制清除模型及研究设计的影响:以西妥昔单抗为例

Semimechanistic Clearance Models of Oncology Biotherapeutics and Impact of Study Design: Cetuximab as a Case Study.

作者信息

Grisic Ana-Marija, Khandelwal Akash, Bertolino Mauro, Huisinga Wilhelm, Girard Pascal, Kloft Charlotte

机构信息

Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, Berlin, Germany.

Graduate Research Training Program PharMetrX, Berlin, Germany.

出版信息

CPT Pharmacometrics Syst Pharmacol. 2020 Nov;9(11):628-638. doi: 10.1002/psp4.12558. Epub 2020 Oct 9.

Abstract

This study aimed to explore the currently competing and new semimechanistic clearance models for monoclonal antibodies and the impact of clearance model misspecification on exposure metrics under different study designs exemplified for cetuximab. Six clearance models were investigated under four different study designs (sampling density and single/multiple-dose levels) using a rich data set from two cetuximab clinical trials (226 patients with metastatic colorectal cancer) and using the nonlinear mixed-effects modeling approach. A two-compartment model with parallel Michaelis-Menten and time-decreasing linear clearance adequately described the data, the latter being related to post-treatment response. With respect to bias in exposure metrics, the simplified time-varying linear clearance (CL) model was the best alternative. Time-variance of the linear CL component should be considered for biotherapeutics if response impacts pharmacokinetics. Rich sampling at steady-state was crucial for unbiased estimation of Michaelis-Menten elimination in case of the reference (parallel Michaelis-Menten and time-varying linear CL) model.

摘要

本研究旨在探讨目前用于单克隆抗体的相互竞争的新半机制清除模型,以及清除模型设定错误对不同研究设计下暴露指标的影响,以西妥昔单抗为例进行说明。使用来自两项西妥昔单抗临床试验(226例转移性结直肠癌患者)的丰富数据集,采用非线性混合效应建模方法,在四种不同的研究设计(采样密度和单剂量/多剂量水平)下研究了六种清除模型。具有平行米氏动力学和时间递减线性清除的二室模型充分描述了数据,后者与治疗后反应相关。关于暴露指标的偏差,简化的时变线性清除(CL)模型是最佳选择。如果反应影响药代动力学,对于生物治疗药物应考虑线性CL成分的时间变化。在参考(平行米氏动力学和时变线性CL)模型的情况下,稳态时的丰富采样对于米氏消除的无偏估计至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/407d/7679074/2fad9466ada8/PSP4-9-628-g001.jpg

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