使用ToxCast生物试验数据和机器学习模型,识别消费品中生物制剂的发育和生殖毒性
Donghyeon Kim1, Siyeol Ahn1, Jinhee Choi1
1School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea.
Environment international
|June 25, 2025
概括
这项研究开发了使用ToxCast生物测试的机器学习模型,以预测生物杀菌剂的发育和生殖毒性 (DART). 该方法成功地确定了优先级高的生物制剂,减少了动物试验,并解决了消费者产品安全方面的数据缺口.
科学领域:
- 毒理学 毒理学 毒理学
- 计算毒理学计算毒理学
- 化学安全评估 化学安全评估
背景情况:
- 发育和生殖毒性 (DART) 对人类健康风险评估至关重要.
- 目前的DART评估很大程度上依赖于动物试验,这导致许多化学品,特别是消费品中的生物杀菌剂的数据缺口很大.
- 需要采用替代方法来有效地选化学品的DART潜力.
研究的目的:
- 为了确定与DART相关的ToxCast高通量查 (HTS) 生物测试.
- 开发和验证机器学习 (ML) 模型,用于预测生物制剂的DART潜力.
- 在进一步的DART评估中,优先考虑消费品中的生物制品.
主要方法:
- 从综合化学环境 (ICE) 数据库中编制了201个与DART相关的生物测试.
- 从ToxRefDB中将体外ToxCast生物测试数据 (AC50,热门呼叫) 与体内DART最低效应水平 (LEL) 相关联,以确定25个相关测试.
- 训练有素的ML模型 (随机森林,XGBoost等) 使用生物活性数据和分子指纹来预测DART潜力.
主要成果:
- 确定了25个ToxCast生物测试与体内DART数据显著相关.
- 在现有DART数据的基础上,实现了可接受的预测性 (F1得分为76.2%).
- 确定了100种缺乏体内数据的生物制剂,作为DART评估的高优先级候选药物.
结论:
- 托克斯卡斯特生物测试和ML模型对预测DART潜力显示出希望.
- 这种方法可以有效地解决消费产品中生物制剂的数据缺口.
- 开发的模型提供了一种具有成本效益和效率的策略,用于优先考虑安全评估的化学品,减少对动物试验的依赖.
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