在新生儿中使用机器学习对万科米辛剂量进行个性化
Bo-Hao Tang1, Jin-Yuan Zhang2, Karel Allegaert3,4,5
1Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
Clinical pharmacokinetics
|June 10, 2023
概括
机器学习模型可以准确预测新生儿的万科米辛水平,从而改善个性化剂量. 与标准剂量方案相比,这种方法提高了治疗目标的实现.
科学领域:
- 药理学 药理学是指药理学的学科.
- 新生儿医学 新生儿医学
- 机器学习应用 机器学习应用
背景情况:
- 新生儿万科米辛的剂量表现出很高的变化,需要个性化方案.
- 达到目标最低度 (C0) 和曲线下面积 (AUC0-24) 对于有效的万科米辛治疗至关重要.
- 在新生儿中优化万科米辛剂量需要先进的预测策略.
研究的目的:
- 评估机器学习 (ML) 模型在预测新生儿中万科米辛治疗目标 (C0和AUC0-24) 的有效性.
- 评估ML是否可以促进计算最佳的个人万科米辛剂量方案.
- 为了比较基于ML的预测与新生儿万科米辛治疗的传统药理动力学模型.
主要方法:
- 利用大量新生儿万科米辛数据集,开发了用于C0和AUC0-24预测的ML模型.
- 采用贝叶斯后期估计用于个别AUC0-24计算.
- 使用外部数据集和各种ML算法 (包括Catboost) 验证预测性能.
主要成果:
- 基于Catboost的C0-ML模型使用剂量方案和共变量准确预测了治疗前的C0,比人口药理动力学模型提高了42.5%的预测准确度.
- 在80.3%的虚拟新生儿中,ML优化的剂量实现了药学动力学目标C0,明显高于标准剂量.
- 在获得初始C0测量后,基于Catboost的AUC-ML模型预测了AUC0-24的准确率为80.3%.
结论:
- 开发了精确的ML模型,用于预测新生儿万科米辛治疗中的C0和AUC0-24.
- 这些模型可以在治疗开始前准确地推单个的万科米辛剂量.
- ML模型在第一个治疗药物监测 (TDM) 结果后便于调整剂量,优化新生儿万科米辛治疗.
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