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将皮肤致敏不良结局途径(AOP)应用于定量风险评估。

Applying the skin sensitisation adverse outcome pathway (AOP) to quantitative risk assessment.

机构信息

Safety & Environmental Assurance Centre (SEAC) Colworth, Unilever, Colworth Science Park, Sharnbrook, Bedford MK44 1LQ, UK.

出版信息

Toxicol In Vitro. 2014 Feb;28(1):8-12. doi: 10.1016/j.tiv.2013.10.013. Epub 2013 Oct 31.

DOI:10.1016/j.tiv.2013.10.013
PMID:24184331
Abstract

As documented in the recent OECD report 'the adverse outcome pathway for skin sensitisation initiated by covalent binding to proteins' (OECD, 2012), the chemical and biological events driving the induction of human skin sensitisation have been investigated for many years and are now well understood. Several non-animal test methods have been developed to predict sensitiser potential by measuring the impact of chemical sensitisers on these key events (Adler et al., 2011; Maxwell et al., 2011); however our ability to use these non-animal datasets for risk assessment decision-making (i.e. to establish a safe level of human exposure for a sensitising chemical) remains limited and a more mechanistic approach to data integration is required to address this challenge. Informed by our previous efforts to model the induction of skin sensitisation (Maxwell and MacKay, 2008) we are now developing two mathematical models ('total haptenated protein' model and 'CD8(+) T cell response' model) that will be linked to provide predictions of the human CD8(+) T cell response for a defined skin exposure to a sensitising chemical. Mathematical model development is underpinned by focussed clinical or human-relevant research activities designed to inform/challenge model predictions whilst also increasing our fundamental understanding of human skin sensitisation. With this approach, we aim to quantify the relationship between the dose of sensitiser applied to the skin and the extent of the hapten-specific T cell response that would result. Furthermore, by benchmarking our mathematical model predictions against clinical datasets (e.g. human diagnostic patch test data), instead of animal test data, we propose that this approach could represent a new paradigm for mechanistic toxicology.

摘要

如经合组织最近的报告“由蛋白质共价结合引发的皮肤致敏的不良结局途径”(经合组织,2012 年)所述,多年来一直在研究驱动人类皮肤致敏的化学和生物学事件,并且现在已经得到了很好的理解。已经开发了几种非动物测试方法,通过测量化学敏化剂对这些关键事件的影响来预测敏化剂潜力(Adler 等人,2011 年;Maxwell 等人,2011 年);然而,我们使用这些非动物数据集进行风险评估决策(即确定致敏化学物质的人类暴露安全水平)的能力仍然有限,需要采用更具机制性的方法进行数据集成,以应对这一挑战。受我们以前对皮肤致敏诱导建模的努力的启发(Maxwell 和 MacKay,2008 年),我们现在正在开发两个数学模型(“总结合蛋白”模型和“CD8(+)T 细胞反应”模型),这两个模型将被链接起来,为定义的皮肤暴露于致敏化学物质的情况下的人类 CD8(+)T 细胞反应提供预测。数学模型的开发是基于重点临床或人类相关的研究活动,旨在为模型预测提供信息/挑战,同时也增加我们对人类皮肤致敏的基本理解。通过这种方法,我们旨在量化施用于皮肤的敏化剂剂量与由此产生的半抗原特异性 T 细胞反应的程度之间的关系。此外,通过将我们的数学模型预测与临床数据集(例如,人体诊断斑贴试验数据)而不是动物测试数据进行基准测试,我们提出这种方法可以代表机制毒理学的一种新范例。

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