Suppr超能文献

六项临床研究的综合数据分析指向他莫昔芬的模型指导精准给药。

Integrated Data Analysis of Six Clinical Studies Points Toward Model-Informed Precision Dosing of Tamoxifen.

作者信息

Klopp-Schulze Lena, Mueller-Schoell Anna, Neven Patrick, Koolen Stijn L W, Mathijssen Ron H J, Joerger Markus, Kloft Charlotte

机构信息

Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Free University of Berlin, Berlin, Germany.

Graduate Research Training Program PharMetrX, Berlin, Germany.

出版信息

Front Pharmacol. 2020 Mar 31;11:283. doi: 10.3389/fphar.2020.00283. eCollection 2020.

Abstract

INTRODUCTION

At tamoxifen standard dosing, ∼20% of breast cancer patients do not reach proposed target endoxifen concentrations >5.97 ng/mL. Thus, better understanding the large interindividual variability in tamoxifen pharmacokinetics (PK) is crucial. By applying non-linear mixed-effects (NLME) modeling to a pooled 'real-world' clinical PK database, we aimed to (i) dissect several levels of variability and identify factors predictive for endoxifen exposure and (ii) assess different tamoxifen dosing strategies for their potential to increase the number of patients reaching target endoxifen concentrations.

METHODS

Tamoxifen and endoxifen concentrations with genetic and demographic data of 468 breast cancer patients from six reported studies were used to develop a NLME parent-metabolite PK model. Different levels of variability on model parameters or measurements were investigated and the impact of covariates thereupon explored. The model was subsequently applied in a simulation-based comparison of three dosing strategies with increasing degree of dose individualization for a large virtual breast cancer population. Interindividual variability of endoxifen concentrations and the fraction of patients at risk for not reaching target concentrations were assessed for each dosing strategy.

RESULTS AND CONCLUSIONS

The integrated NLME model enabled to differentiate and quantify four levels of variability (interstudy, interindividual, interoccasion, and intraindividual). Strong influential factors, i.e., CYP2D6 activity score, drug-drug interactions with CYP3A and CYP2D6 inducers/inhibitors and age, were reliably identified, reducing interoccasion variability to <20% CV. Yet, unexplained interindividual variability in endoxifen formation remained large (47.2% CV). Hence, therapeutic drug monitoring seems promising for achieving endoxifen target concentrations. Three tamoxifen dosing strategies [standard dosing (20 mg QD), CYP2D6-guided dosing (20, 40, and 60 mg QD) and individual model-informed precision dosing (MIPD)] using three therapeutic drug monitoring samples (5-120 mg QD) were compared, leveraging the model. The proportion of patients at risk for not reaching target concentrations was 22.2% in standard dosing, 16.0% in CYP2D6-guided dosing and 7.19% in MIPD. While in CYP2D6-guided- and standard dosing interindividual variability in endoxifen concentrations was high (64.0% CV and 68.1% CV, respectively), it was considerably reduced in MIPD (24.0% CV). Hence, MIPD demonstrated to be the most promising strategy for achieving target endoxifen concentrations.

摘要

引言

在他莫昔芬标准剂量治疗时,约20%的乳腺癌患者无法达到建议的目标4-羟基他莫昔芬浓度>5.97 ng/mL。因此,更好地理解他莫昔芬药代动力学(PK)中个体间的巨大差异至关重要。通过将非线性混合效应(NLME)模型应用于汇总的“真实世界”临床PK数据库,我们旨在(i)剖析几个变异水平并确定预测4-羟基他莫昔芬暴露的因素,以及(ii)评估不同的他莫昔芬给药策略增加达到目标4-羟基他莫昔芬浓度患者数量的潜力。

方法

使用来自六项已报道研究的468例乳腺癌患者的他莫昔芬和4-羟基他莫昔芬浓度以及遗传和人口统计学数据,开发NLME母体-代谢物PK模型。研究了模型参数或测量值的不同变异水平,并探讨了协变量对其的影响。随后,该模型被应用于对大量虚拟乳腺癌人群的三种给药策略进行基于模拟的比较,这三种给药策略的剂量个体化程度逐渐增加。评估了每种给药策略下4-羟基他莫昔芬浓度的个体间变异以及未达到目标浓度风险患者的比例。

结果与结论

整合的NLME模型能够区分和量化四个变异水平(研究间、个体间、给药间隔间和个体内)。可靠地识别出了强影响因素,即CYP2D6活性评分、与CYP3A和CYP2D6诱导剂/抑制剂的药物相互作用以及年龄,将给药间隔间变异降低至<20%CV。然而,4-羟基他莫昔芬形成中无法解释的个体间变异仍然很大(47.2%CV)。因此,治疗药物监测对于实现4-羟基他莫昔芬目标浓度似乎很有前景。利用该模型比较了三种他莫昔芬给药策略[标准给药(20 mg每日一次)、CYP2D6指导给药(20、40和60 mg每日一次)和个体模型指导的精准给药(MIPD)],使用三个治疗药物监测样本(5-120 mg每日一次)。标准给药中未达到目标浓度风险患者的比例为22.2%,CYP2D6指导给药中为16.0%,MIPD中为7.19%。虽然在CYP2D6指导给药和标准给药中,4-羟基他莫昔芬浓度的个体间变异较高(分别为64.0%CV和68.1%CV),但在MIPD中显著降低(24.0%CV)。因此,MIPD被证明是实现目标4-羟基他莫昔芬浓度最有前景的策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e668/7136483/7d8200ba7135/fphar-11-00283-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验