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通过将机器学习和代谢学与人口药理动力学相结合,预测新生儿和婴儿中的万科米辛清除率
Hui Yu1, Jingcheng Xiao1, Hao-Jie Zhu2
1Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, Michigan, USA.
婴儿的万科米辛剂量由于高可变性而复杂. 机器学习模型,特别是渐变增强回归器,可以有效地预测使用血清肌素和月经后年龄等临床数据的万科米辛清除.
科学领域:
- 药理学 药理学是指药理学的学科.
- 临床药房 临床药房
- 计算生物学 计算生物学
背景情况:
- 新生儿和婴儿中的万科米辛的药理动力学显示出显著的变化,使治疗药物监测复杂化.
- 由于药物清除的个体间差异,达到康胺暴露的目标具有挑战性.
- 患者特异性因素对于优化易受伤害的儿科群体中的万科米辛剂量至关重要.
研究的目的:
- 调查患者特异性共变量对新生儿和婴儿中万科米辛清除的影响.
- 通过使用临床和代谢学数据,评估机器学习 (ML) 方法对万科米辛清除的预测性能.
- 为了确定关键的临床和代谢学预测器的万科米辛清除.
主要方法:
- 对42名新生儿和婴儿进行回顾性人群药动力学 (PK) 分析.
- 静脉注射万科米辛,分析了214个度测量结果.
- 在血样本上基于LC-MS/MS的非向代谢学测定.
- 一个隔间的PK模型具有第一阶淘汰,识别显著的共变量.
- 评估各种ML方法,包括梯度增强回归器 (GBR).
主要成果:
- 一个单间模型确定了血清肌素 (SCr),月经后年龄 (PMA) 和体重作为影响万素清除的显著共变量.
- 渐变增强回归器 (GBR) 使用临床共变量 (MSE:0.0033;R2:0.830) 显示了最高的预测性能.
- 对大多数模型来说,代谢学数据并没有显著提高预测准确性,尽管一些代谢物是顶级预测因素. 在PK和ML模型中,SCr和PMA都是关键预测因素.
结论:
- 整体ML方法,特别是GBR,是利用现有临床共变量预测菌素清除的有价值工具.
- 像SCr和PMA这样的临床因素是新生儿和婴儿中万科米辛清除变化的主要驱动因素.
- 虽然代谢学在清除预测方面提供了有限的附加值,但综合方法突出了探索复杂药物PK的潜力.
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