Population pharmacokinetics of doxorubicin in Indian cancer patients using NONMEM

Vijay Kumar, Harish K. Kaushik, Satish B. Kumar, N. Reddy, Narasimha Y. Reddy, T. Kumaraswamy, Praneet Kumar, K. Devarakonda
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引用次数: 2

Abstract

The population pharmacokinetics of doxorubicin were evaluated based on a mixed-effect model using the NONMEM (VI) program. Doxorubicin in plasma was measured using high-performance liquid chromatography. Plasma concentration measurements (85 plasma samples) of doxorubicin from 28 patients with cancer receiving doxorubicin (with other co-medication) ranging from 20–120 mg by infusion over 1–2 h were analyzed according to a two-compartment model both in FO and FOCE methods. Additive proportional error model was used to describe inter-individual and residual variability. The influence of covariates such as age, body surface area, gender, and clinical laboratory values (SGOT, SGPT) on total body clearance (CL) and volume of distribution (Vd) were examined. No covariate was found to affect the CL and Vd of unchanged doxorubicin. The CL and Vd estimated by FO method were 1.42 L/h and 51.1 L, respectively, and FOCE method are 1.43 L/h and 51.4 L, respectively. The inter-individual variability for CL and Vd and residual variability were 45.8%, 36%, and 12.6%, respectively. The population means and inter-individual and residual variability of pharmacokinetics of doxorubicin were evaluated using the NONMEM program. The results of this study show that the population pharmacokinetic approach could be useful to manage doxorubicin cardio toxicity using sparse data in a clinical setting.
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用NONMEM分析阿霉素在印度癌症患者中的群体药代动力学
使用NONMEM (VI)程序基于混合效应模型评估阿霉素的群体药代动力学。用高效液相色谱法测定血浆中阿霉素的含量。28例接受多柔比星(与其他联合用药)输注1-2小时20-120 mg范围内的癌症患者的血浆浓度(85份血浆样本),根据双室模型,采用FO和FOCE方法进行分析。使用加性比例误差模型来描述个体间和剩余变异。研究年龄、体表面积、性别、临床化验值(SGOT、SGPT)等协变量对总清除率(CL)和分布容积(Vd)的影响。未发现影响阿霉素原药CL和Vd的协变量。FO法估测的CL和Vd分别为1.42 L/h和51.1 L, FOCE法估测的CL和Vd分别为1.43 L/h和51.4 L。CL和Vd的个体间变异率和剩余变异率分别为45.8%、36%和12.6%。使用NONMEM程序评估阿霉素药代动力学的总体均值、个体间和剩余变异性。本研究结果表明,群体药代动力学方法可用于管理阿霉素心脏毒性使用稀疏的数据在临床设置。
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