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

详细信息    查看全文

• 作者：Gavin Maxwell ; Cameron MacKay ; Richard Cubberley ; Michael Davies ; Nichola Gellatly ; Stephen Glavin ; Todd Gouin ; S ; rine Jacquoilleot ; Craig Moore ; Ruth Pendlington ; Ouarda Saib ; David Sheffield ; Richard Stark ; Vicki Summerfield
• 关键词：Skin sensitisation ; Adverse outcome pathway ; Risk assessment ; Mathematical modelling
• 刊名：Toxicology in Vitro
• 出版年：February, 2014
• 年：2014
• 卷：28
• 期：1
• 页码：8-12
• 全文大小：735 K

文摘

As documented in the recent OECD report 鈥榯he adverse outcome pathway for skin sensitisation initiated by covalent binding to proteins鈥?(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; ); 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 (鈥榯otal haptenated protein鈥?model and 鈥楥D8⁺ T cell response鈥?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.