13 October 2016

Optimised strategies for the identification of skin sensitising chemicals

Image Source: European Commission

JRC scientists have been developing and optimising animal-free testing strategies for the identification of chemicals with the potential to cause skin allergy, as a contribution towards international efforts aimed at fulfilling regulatory information requirements on chemical safety while at the same time reducing or avoiding the use of vertebrate animals.

Traditionally, this information has been obtained by performing an animal test, such as the Local Lymph Node Assay (LLNA) in mice. However, for scientific and animal welfare reasons, regulatory bodies are increasingly requiring that information on skin sensitisation potential is provided instead by mechanistic information generated by using alternative (non-animal) test methods.

At present, EU legislation does not provide detailed and explicit guidance on how the different pieces of mechanistic information, generated by non-animal methods, should be combined in order to predict the potential for chemically induced skin sensitisation. Therefore, in line with the EURL ECVAM skin sensitisation strategy, JRC scientists have been exploring the development of these integrated prediction models.

A detailed analysis of a high quality skin sensitisation dataset by JRC scientists (Asturiol et al, 2016) has shown that sensitising and non-sensitising chemicals can be identified with a high level of overall predictive accuracy (approximately 93%) by using decision trees based on easily computable properties of chemicals, such as their reactivity towards proteins. This is therefore an efficient and cost-effective strategy forhazard assessment. In a separate study, JRC scientists contributed to an investigation led by researchers at the University of Wagenigen (Leontaridou et al, 2016). This study provides a novel illustration of how a decision theory approach can be applied to address cost-benefit questions related to the marketing and use of chemical products.

Read more in:

Asturiol et al (2016), "Consensus of classification trees for skin sensitisation hazard prediction", Toxicology in Vitro 36, 197–209, doi:dx.doi.org/10.1016/j.tiv.2016.07.014

Leontaridou et al (2016), "Evaluation of Non-animal Methods for Assessing Skin Sensitisation Hazard: A Bayesian Value-of-Information Analysis", ATLA 44, 255–269.

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