在重症儿童中优化万科米辛治疗:一项人口药动力学研究,以利用新生物标志物在曲线估计下告知万科米辛区域
Kevin J Downes1,2,3,4, Athena F Zuppa1,4, Anna Sharova1,2
1The Center for Clinical Pharmacology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
在重症儿童中估计贝叶斯范科米辛AUC具有挑战性. 使用脏生物标记物的新模型,如基于cystatin C的eGFR,可以准确预测万科米辛AUC,改善治疗药物监测.
科学领域:
- 药理学 药理学是指药理学的学科.
- 儿科重症监护 儿科重症监护
- 生物标志物 生物标志物
背景情况:
- 建议以曲线下的面积 (AUC) 为指导的万科米辛治疗,以提高疗效.
- 在危急病患儿童中估计功能对于万科米辛剂量是具有挑战性的.
- 目前估计功能的方法可能不足以优化万科米辛治疗.
研究的目的:
- 开发和验证人口药理动力学 (PK) 模型,以估计危急儿童中的万科米辛AUC.
- 评估脏新型生物标志物作为万科米辛清除的共变量.
- 为了确定最佳的采样时间,以准确的贝叶斯AUC估计.
主要方法:
- 预计将50名重症儿童接受万科米治疗.
- 使用Pmetrics与脏生物标志物 (基于cystatin C的eGFR,尿液NGAL) 作为共变量进行非参数人群PK建模.
- 多模型优化用于定义最佳采样时间.
- 贝叶斯后部AUC与非分区分析AUC的比较.
主要成果:
- 一个双隔间模型最好地描述了万科素PK.
- 基于cystatin C的eGFR和尿液NGAL改善了万胺清除模型的可能性.
- 使用基于囊素C的eGFR或基于肌素的eGFR的模型促进了准确和精确的万科米辛AUC估计.
- 在测试模型中,AUC预测的偏差和不精确性很低.
结论:
- 纳入脏生物标志物的种群PK模型能够在重症儿童中准确估计万科米辛AUC.
- 基于cystatin C的eGFR是用于范胺清除模型的有价值的共变量.
- 这些发现支持改善治疗药物监测,用于儿童重症监护中的万科米辛.
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