Genomic Allergen Rapid Detection In-House Validation—A Proof of Concept
Author(s) -
Henrik Johansson,
Frida Rydnert,
Jochen Kühnl,
Andreas Schepky,
Carl Borrebaeck,
Malin Lindstedt
Publication year - 2014
Publication title -
toxicological sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.352
H-Index - 183
eISSN - 1096-6080
pISSN - 1096-0929
DOI - 10.1093/toxsci/kfu046
Subject(s) - computational biology , chemical compound , in vivo , sensitization , biomarker , chemistry , chemical safety , biology , immunology , medicine , biochemistry , toxicity , genetics , risk analysis (engineering) , organic chemistry
Chemical sensitization is an adverse immunologic response to chemical substances, inducing hypersensitivity in exposed individuals. Identifying chemical sensitizers is of great importance for chemical, pharmaceutical, and cosmetic industries, in order to prevent the use of sensitizers in consumer products. Historically, chemical sensitizers have been assessed mainly by in vivo methods, however, recently enforced European legislations urge and promote the development of animal-free test methods able to predict chemical sensitizers. Recently, we presented a predictive biomarker signature in the myeloid cell line MUTZ-3, for assessment of skin sensitizers. The identified genomic biomarkers were found to be involved in immunologically relevant pathways, induced by recognition of foreign substances and regulating dendritic cell maturation and cytoprotective mechanisms. We have developed the usage of this biomarker signature into a novel in vitro assay for assessment of chemical sensitizers, called Genomic Allergen Rapid Detection (GARD). The assay is based on chemical stimulation of MUTZ-3 cultures, using the compounds to be assayed as stimulatory agents. The readout of the assay is a transcriptional quantification of the genomic predictors, collectively termed the GARD Prediction Signature (GPS), using a complete genome expression array. Compounds are predicted as either sensitizers or nonsensitizers by a Support Vector Machine model. In this report, we provide a proof of concept for the functionality of the GARD assay by describing the classification of 26 blinded and 11 nonblinded chemicals as sensitizers or nonsensitizers. Based on these classifications, the accuracy, sensitivity, and specificity of the assay were estimated to 89, 89, and 88%, respectively.
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