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Int. J. Mol. Sci. 2019, 20(3), 666; https://doi.org/10.3390/ijms20030666

Skin Sensitization Testing—What’s Next?

1
SenzaGen AB, Medicon Village, S-223 81 Lund, Sweden
2
Department of Immunotechnology, Lund University, Medicon Village (bldg 406), S-223 81 Lund, Sweden
*
Author to whom correspondence should be addressed.
Received: 29 December 2018 / Revised: 29 January 2019 / Accepted: 1 February 2019 / Published: 4 February 2019
(This article belongs to the Special Issue Inflammatory Skin Conditions 2018)
Full-Text   |   PDF [173 KB, uploaded 4 February 2019]

Abstract

There is an increasing demand for alternative in vitro methods to replace animal testing, and, to succeed, new methods are required to be at least as accurate as existing in vivo tests. However, skin sensitization is a complex process requiring coordinated and tightly regulated interactions between a variety of cells and molecules. Consequently, there is considerable difficulty in reproducing this level of biological complexity in vitro, and as a result the development of non-animal methods has posed a major challenge. However, with the use of a relevant biological system, the high information content of whole genome expression, and comprehensive bioinformatics, assays for most complex biological processes can be achieved. We propose that the Genomic Allergen Rapid Detection (GARD™) assay, developed to create a holistic data-driven in vitro model with high informational content, could be such an example. Based on the genomic expression of a mature human dendritic cell line and state-of-the-art machine learning techniques, GARD™ can today accurately predict skin sensitizers and correctly categorize skin sensitizing potency. Consequently, by utilizing advanced processing tools in combination with high information genomic or proteomic data, we can take the next step toward alternative methods with the same predictive accuracy as today’s in vivo methods—and beyond. View Full-Text
Keywords: genomics; machine learning; skin sensitization; adverse outcome pathways; next generation in vitro tests genomics; machine learning; skin sensitization; adverse outcome pathways; next generation in vitro tests
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Grundström, G.; Borrebaeck, C.A. Skin Sensitization Testing—What’s Next? Int. J. Mol. Sci. 2019, 20, 666.

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