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使用机器学习方法来估计在老鼠中使用可卡因的病理生物学影响

Daniel Vitor de Souza¹, Barbara Dos Anjos Rosario¹, Gabriel Carvalhal de Aguiar¹

¹Departments of Biosciences and Federal University of São Paulo, UNIFESP, Santos, SP, Brazil.

Xenobiotica; the fate of foreign compounds in biological systems

|November 2, 2025

概括

机器学习可以准确地预测裂可卡因的剂量和服用途径. 肝脏焦化和脏Ki-67表达等关键生物标志物被确定为药物效应的关键预测因素.

科学领域:

毒理学毒理学毒理学
药理学药理学是指药理学的学科.
计算生物学计算生物学

背景情况:

裂可卡因的服用途径和剂量显著影响其系统和行为效应.
准确预测暴露参数对于理解毒性和制定干预措施至关重要.

研究的目的:

开发一种机器学习 (ML) 工作流程,用于预测可卡因剂量和注射途径.
用ML识别与使用可卡因暴露相关的关键生物参数.

主要方法:

随机森林 (RF) 分析被用来构建预测模型.
用特征重要性和相互作用依赖性分析来确定关键变量.
评估了17个生物变量,这些变量是通过腹腔内注射或吸入暴露于不同剂量的老鼠.

主要成果:

机器学习模型在训练中达到85%的准确性,在测试中达到100%.
发现的关键预测因素包括肝脏焦化,脏Ki-67表达,肝脏双核和逃生行为.
该模型在预测剂量和路线方面表现出高准确性,在某些暴露组中发现了特定的错误分类.

结论:

机器学习提供了一个准确的方法来预测裂可卡因暴露参数.
已识别的生物标志物提供了关于可卡因的系统和行为影响的见解.
这种方法可以帮助理解药物毒性,并为未来的研究提供信息.

关键词:

克拉克可卡因是一种可卡因.实验模型是一个实验模型.整体的方法整体的方法鼠标鼠标鼠标鼠标鼠标

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