一种基于机器学习的病理生理学的新方法方法,用于对危险化学混合物的剂量依赖性评估和实验验证
Sarita Limbu1, Eric Glasgow1, Tessa Block1
1Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3700 O St. NW, Washington, DC 20057, USA.
Toxics
|July 26, 2024
概括
一种新的混合方法,AI-CPTM,将人工智能与病理生理学相结合,以预测化学混合物的毒性和机制. 这种方法提高了识别有害环境化学品及其健康风险的准确性.
科学领域:
- 环境毒理学和计算化学.
- 开发用于化学安全评估的新型预测模型.
背景情况:
- 环境化学物质,包括per-和多基基物质 (PFAS),通常存在于不同度的混合物中,造成癌症等重大健康风险.
- 目前用于评估化学混合物的剂量依赖性毒性和潜在机制的方法不足以进行全面的健康风险评估.
- 迫切需要先进的方法来准确预测混合物毒性并阐明它们的作用机制.
研究的目的:
- 开发和验证一种全面的新方法方法 (NAM),用于预测化学混合物的剂量依赖性毒性.
- 将人工智能 (AI) 与基于病理生理学的毒理学模型 (CPTM) 集成,以创建一个混合框架 (AI-CPTM).
- 提高预测准确度,并更深入地了解驱动混合物毒性的机制.
主要方法:
- 第1阶段:基于AI的方法 (AI-HNN) 和CPTM用于毒性预测的评估.
- 第二阶段:将AI-HNN和CPTM整合到AI-CPTM框架中,使用实验和虚拟化学混合物.
- 第三阶段:使用斑马鱼胚胎毒性试验对AI-CPTM预测进行实验验证,并与其他机器学习模型进行比较 (RF,Bagging,AdaBoost,SVR,GB,KR,DT,KN,Consensus).
主要成果:
- AI-HNN模型实现了高预测性能,准确度超过80%,AUC超过90%.
- 与独立的人工智能模型相比,AI-CPTM框架展示了优越的预测能力,准确预测了包括PFAS在内的各种混合物的毒性和机制.
- 实验验证证证实了AI-CPTM方法在评估化学混合物毒性和相互作用方面提高了性能和可靠性.
结论:
- 人工智能-CPTM是第一个混合NAM,将人工智能与病理生理学集成为全面的化学混合物毒性预测,这是一个重大进步.
- 这种新的方法大大改善了有毒化学品,混合物及其机制的识别,解决了现有方法的局限性.
- AI-CPTM框架提供了一个强大的工具,用于对复杂的环境化学物质暴露的健康风险评估.
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