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基于药效动力学模型的乳腺癌患者艾日布林骨髓抑制的安全性管理。

Pharmacodynamic Model-Based Safety Management of Eribulin-Induced Myelosuppression in Patients With Breast Cancer.

机构信息

Laboratory of Pharmacometrics and Systems Pharmacology, Keio Frontier Research and Education Collaboration Square (K-FRECS) at Tonomachi, Keio University, Kawasaki, Kanagawa, Japan.

Keio University School of Medicine, Tokyo, Japan.

出版信息

Ther Drug Monit. 2023 Jun 1;45(3):318-326. doi: 10.1097/FTD.0000000000001036.

Abstract

BACKGROUND

Neutropenia is a major dose-limiting toxicity of cancer chemotherapy. Semimechanistic mathematical models have been applied to describe the time course of neutrophil counts. The objectives of this study were to develop a mathematical model describing changes in neutrophil counts during eribulin treatment, to apply the empirical Bayes method to predict the probability of developing neutropenia ≥ grade 3 during eribulin treatment in each patient, and to propose the implementation of this mathematical tool in clinical practice for individual safety management.

METHODS

The present model analysis and subsequent external evaluation were performed using the data of 481 patients with breast cancer, previously obtained from a postmarketing surveillance (training set) and a phase 2 clinical study (validation set). The model we previously reported (Kawamura et al 2018) was modified to improve its predictive capability. The individual time course of neutrophil changes during the treatment period was predicted by the empirical Bayes method using the observed neutrophil counts at baseline and the first measurement after the first eribulin dose. To evaluate the predictability of this method, the predicted neutrophil counts were compared with those of the observed values.

RESULTS

The developed model provided good individual predictions, as indicated by the goodness-of-fit plots between the predicted and observed neutrophil counts, especially for a lower neutrophil count range. Days required to reach the nadir after the dose were also well-predicted. The sensitivity, specificity, and accuracy of the prediction of neutropenia grade ≥3 were 76%, 53%, and 71%, respectively.

CONCLUSIONS

We developed a mathematical method for predicting and managing the risk of neutropenia during eribulin treatment. This method is generally applicable to other cases of chemotherapy-induced neutropenia and can be a new practical tool for individual safety management.

摘要

背景

中性粒细胞减少是癌症化疗的主要剂量限制毒性。半机械论数学模型已被应用于描述中性粒细胞计数的时间过程。本研究的目的是开发一种描述艾立布林治疗期间中性粒细胞计数变化的数学模型,应用经验贝叶斯方法预测每位患者在艾立布林治疗期间发生中性粒细胞减少≥3 级的概率,并提出将该数学工具应用于临床实践以进行个体化安全管理。

方法

本模型分析及随后的外部评估使用了来自上市后监测(训练集)和 2 期临床研究(验证集)的 481 例乳腺癌患者的数据。我们之前报道的模型(Kawamura 等人,2018 年)进行了修改,以提高其预测能力。使用基线和首次艾立布林剂量后第一次测量的观察中性粒细胞计数,通过经验贝叶斯方法预测治疗期间个体中性粒细胞变化的个体时间过程。为了评估该方法的可预测性,将预测的中性粒细胞计数与观察值进行了比较。

结果

所开发的模型提供了良好的个体预测,这可以从预测和观察到的中性粒细胞计数之间的拟合优度图看出,特别是在较低的中性粒细胞计数范围内。达到剂量后达到最低点所需的天数也得到了很好的预测。预测中性粒细胞减少≥3 级的敏感性、特异性和准确性分别为 76%、53%和 71%。

结论

我们开发了一种预测和管理艾立布林治疗期间中性粒细胞减少风险的数学方法。该方法通常适用于其他化疗引起的中性粒细胞减少情况,并且可以成为个体安全管理的新实用工具。

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