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模拟表皮生长因子受体酪氨酸激酶抑制剂治疗非小细胞肺癌患者时暴露驱动的不良事件。

Modeling exposure-driven adverse events of EGFR TKIs in the treatment of patients with non-small cell lung cancer.

作者信息

Yong Ling, Liu Yan'e, Jian Wei-Zhe, Cai Lei, Bao Tian-Yu, Liu Chen, Gan En-Ze, Wang Tian-Yu, Luo Ping-Yao, Cao Bao-Shan, Liu Wei, Zhou Tian-Yan

机构信息

Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System, Department of Pharmaceutics, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China.

Department of Pharmacy, Peking University Third Hospital, Beijing, 100191, China.

出版信息

Acta Pharmacol Sin. 2025 Jun 6. doi: 10.1038/s41401-025-01573-z.

Abstract

The adverse events associated with antitumour drugs have recently emerged as an increasingly significant clinical concern. Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs) serve as pivotal therapeutic agents for non-small cell lung cancer (NSCLC). However, considerable interindividual variability exists in drug exposure, along with a high incidence and severity of adverse events. In this study, we quantitatively investigated the impacts of EGFR TKI exposure and other covariates on the severity of the maximum grade of drug-related adverse events (MDRAE) in NSCLC patients treated with EGFR TKIs. Data were collected from 277 patients treated with gefitinib, icotinib, afatinib or osimertinib. Population pharmacokinetic (PopPK) models were constructed for each drug, and individual exposure metrics were derived through model simulations. Normalized individual exposures to different EGFR TKIs based on their IC values and MDRAE data were integrated to develop an ordinal logistic regression model for an exposure-safety analysis. A user-friendly nomogram was subsequently designed. The probability of high-grade MDRAE was significantly associated with normalized exposure levels, a history of EGFR TKI treatment, sex and other factors. Model simulations revealed substantial interindividual variability in drug exposure and the probability of different grades of MDRAE for the same treatment regimen. This study quantitatively elucidates the influences of drug exposure and other critical factors on safety, thereby contributing to the formulation of individualized treatment strategies to prevent and promptly address drug safety-related issues.

摘要

与抗肿瘤药物相关的不良事件最近已成为一个日益重要的临床关注点。表皮生长因子受体酪氨酸激酶抑制剂(EGFR TKIs)是非小细胞肺癌(NSCLC)的关键治疗药物。然而,药物暴露存在相当大的个体间差异,不良事件的发生率和严重程度也很高。在本研究中,我们定量研究了EGFR TKI暴露及其他协变量对接受EGFR TKIs治疗的NSCLC患者药物相关不良事件最大等级(MDRAE)严重程度的影响。数据收集自277例接受吉非替尼、埃克替尼、阿法替尼或奥希替尼治疗的患者。为每种药物构建了群体药代动力学(PopPK)模型,并通过模型模拟得出个体暴露指标。基于不同EGFR TKIs的IC值和MDRAE数据对个体暴露进行标准化整合,以建立用于暴露-安全性分析的有序逻辑回归模型。随后设计了一个用户友好的列线图。高级别MDRAE的概率与标准化暴露水平、EGFR TKI治疗史、性别和其他因素显著相关。模型模拟显示,对于相同的治疗方案,药物暴露和不同等级MDRAE的概率存在很大的个体间差异。本研究定量阐明了药物暴露和其他关键因素对安全性的影响,从而有助于制定个性化治疗策略,以预防和及时解决药物安全相关问题。

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