Department of Environmental Science and Policy (ESP), Università degli Studi di Milano, Via Celoria 10, 20133, Milan, Italy.
Environment and Health Department, Istituto Superiore di Sanità, Viale Regina Elena, 299, 00161, Rome, Italy.
Arch Toxicol. 2022 Jul;96(7):1935-1950. doi: 10.1007/s00204-022-03299-x. Epub 2022 May 3.
Alternative methods to animal use in toxicology are evolving with new advanced tools and multilevel approaches, to answer from one side to 3Rs requirements, and on the other side offering relevant and valid tests for drugs and chemicals, considering also their combination in test strategies, for a proper risk assessment.While stand-alone methods, have demonstrated to be applicable for some specific toxicological predictions with some limitations, the new strategy for the application of New Approach Methods (NAM), to solve complex toxicological endpoints is addressed by Integrated Approaches for Testing and Assessment (IATA), aka Integrated Testing Strategies (ITS) or Defined Approaches for Testing and Assessment (DA). The central challenge of evidence integration is shared with the needs of risk assessment and systematic reviews of an evidence-based Toxicology. Increasingly, machine learning (aka Artificial Intelligence, AI) lends itself to integrate diverse evidence streams.In this article, we give an overview of the state of the art of alternative methods and IATA in toxicology for regulatory use for various hazards, outlining future orientation and perspectives. We call on leveraging the synergies of integrated approaches and evidence integration from in vivo, in vitro and in silico as true in vivitrosi.
替代动物使用的方法在毒理学领域不断发展,出现了新的先进工具和多层次方法,从一方面满足 3R 要求,另一方面为药物和化学物质提供相关和有效的测试,同时考虑到它们在测试策略中的组合,以进行适当的风险评估。虽然独立的方法已经证明适用于一些具有一定局限性的特定毒理学预测,但新的应用新方法途径(NAM)的策略,通过综合测试和评估方法(IATA),即综合测试策略(ITS)或定义的测试和评估方法(DA),来解决复杂的毒理学终点问题。证据综合的核心挑战与风险评估和基于证据的毒理学系统评价的需求是共享的。机器学习(又名人工智能,AI)越来越多地被用于整合不同的证据流。在本文中,我们概述了替代方法和毒理学中 IATA 的最新技术,用于各种危害的监管用途,概述了未来的方向和前景。我们呼吁利用综合方法和证据整合的协同作用,从体内、体外和计算模型中获得真正的体内体外综合。