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  • Title: Applying the skin sensitisation adverse outcome pathway (AOP) to quantitative risk assessment.
    Author: Maxwell G, MacKay C, Cubberley R, Davies M, Gellatly N, Glavin S, Gouin T, Jacquoilleot S, Moore C, Pendlington R, Saib O, Sheffield D, Stark R, Summerfield V.
    Journal: Toxicol In Vitro; 2014 Feb; 28(1):8-12. PubMed ID: 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.
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