机器学习方法用于评估药物转移到人类母乳中的方法
Zhongyuan Zhao1,2, Peng Zou3, Yuan Fang1
1School of Pharmacy and Pharmaceutical Sciences, SUNY-Binghamton University, PO Box 6000, Binghamton, NY, 13902, USA.
Journal of pharmacokinetics and pharmacodynamics
|April 16, 2025
概括
机器学习模型准确预测人乳/血 (M/P) 药物比率,这对婴儿安全至关重要. 神经网络显示出最高的准确性,有助于对母乳养母亲的风险评估.
科学领域:
- 药理学和毒理学 药理学和毒理学
- 计算生物学和生物信息学
- 孕产妇和儿童的健康
背景情况:
- 人类乳/血 (M/P) 药物度比对评估药物转移到母乳中的情况至关重要,这会影响婴儿的安全.
- 预测这种比率对于管理哺乳期母亲的药物使用至关重要.
- 现有的M/P比率确定方法可能是资源密集的.
研究的目的:
- 评估各种机器学习 (ML) 算法的有效性,以预测M/P药物度比.
- 为了比较K-最近邻居 (KNN),随机森林,支持矢量机 (SVM) 和神经网络的M/P比率预测的性能.
- 为估计药物转移到母乳中提供数据驱动的工具.
主要方法:
- 使用了162种药物和11种预测变量的数据集.
- 采用二进制 (0, 1 和 ≥1) 和三进制 (0 至 <0.5, 0.5 至 <1, 和 ≥1) 划分为M/P比率.
- 应用了五倍交叉验证,主要组件分析 (PCA) 用于可视化,以及贝叶斯信息标准 (BIC) 用于KNN模型选择.
主要成果:
- 神经网络实现了最高的平均准确率:两类系统的82%和三类系统的76%.
- K-最近邻居 (KNN) 的准确率分别为79%和60%,随机森林77%和64%,支持矢量机 (SVM) 分别为78%和67%.
- 所有评估的ML模型都在预测M/P比率方面表现出显著的潜力.
结论:
- 机器学习技术在预测M/P药物比率方面显示出相当大的前景,有助于药物开发和风险评估.
- 这些预测模型可以为临床决策提供有关哺乳期患者药物安全性的信息.
- 建议对更大的数据集进行进一步的研究,以提高这些ML模型的可靠性和适用性.
相关概念视频
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