Department of Clinical Pharmacology and Pharmacy, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands.
Service of Clinical Pharmacology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
CPT Pharmacometrics Syst Pharmacol. 2021 Dec;10(12):1497-1511. doi: 10.1002/psp4.12718. Epub 2021 Oct 24.
This study aimed to determine whether published pharmacokinetic (PK) models can adequately predict the PK profile of imatinib in a new indication, such as coronavirus disease 2019 (COVID-19). Total (bound + unbound) and unbound imatinib plasma concentrations obtained from 134 patients with COVID-19 participating in the CounterCovid study and from an historical dataset of 20 patients with gastrointestinal stromal tumor (GIST) and 85 patients with chronic myeloid leukemia (CML) were compared. Total imatinib area under the concentration time curve (AUC), maximum concentration (C ) and trough concentration (C ) were 2.32-fold (95% confidence interval [CI] 1.34-3.29), 2.31-fold (95% CI 1.33-3.29), and 2.32-fold (95% CI 1.11-3.53) lower, respectively, for patients with CML/GIST compared with patients with COVID-19, whereas unbound concentrations were comparable among groups. Inclusion of alpha1-acid glycoprotein (AAG) concentrations measured in patients with COVID-19 into a previously published model developed to predict free imatinib concentrations in patients with GIST using total imatinib and plasma AAG concentration measurements (AAG-PK-Model) gave an estimated mean (SD) prediction error (PE) of -20% (31%) for total and -7.0% (56%) for unbound concentrations. Further covariate modeling with this combined dataset showed that in addition to AAG; age, bodyweight, albumin, CRP, and intensive care unit admission were predictive of total imatinib oral clearance. In conclusion, high total and unaltered unbound concentrations of imatinib in COVID-19 compared to CML/GIST were a result of variability in acute phase proteins. This is a textbook example of how failure to take into account differences in plasma protein binding and the unbound fraction when interpreting PK of highly protein bound drugs, such as imatinib, could lead to selection of a dose with suboptimal efficacy in patients with COVID-19.
本研究旨在确定已发表的药代动力学（PK）模型是否能充分预测新型适应证（如 2019 年冠状病毒病（COVID-19））中伊马替尼的 PK 特征。来自参与 CounterCovid 研究的 134 例 COVID-19 患者和来自 20 例胃肠道间质瘤（GIST）患者及 85 例慢性髓性白血病（CML）患者的历史数据集的总（结合+游离）和游离伊马替尼血浆浓度进行了比较。CML/GIST 患者的总伊马替尼浓度-时间曲线下面积（AUC）、最大浓度（C ）和谷浓度（C ）分别低 2.32 倍（95%置信区间[CI] 1.34-3.29）、2.31 倍（95%CI 1.33-3.29）和 2.32 倍（95%CI 1.11-3.53），而游离浓度在各组间无差异。将 COVID-19 患者的α1-酸性糖蛋白（AAG）浓度纳入之前发表的模型中，该模型旨在通过总伊马替尼和血浆 AAG 浓度测量（AAG-PK 模型）预测 GIST 患者游离伊马替尼浓度，结果估算的平均（SD）预测误差（PE）为总浓度-20%（31%），游离浓度-7.0%（56%）。使用该综合数据集进一步进行协变量建模表明，除 AAG 外，年龄、体重、白蛋白、C 反应蛋白和重症监护病房入院与总伊马替尼口服清除率相关。总之，COVID-19 患者的总伊马替尼浓度高且游离伊马替尼浓度不变，这是急性期蛋白变异性的结果。这是一个典型的例子，说明了在解释高度蛋白结合药物（如伊马替尼）的 PK 时，如果不考虑血浆蛋白结合和游离分数的差异，可能会导致在 COVID-19 患者中选择疗效不佳的剂量。
