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The GARD platform for potency assessment of skin sensitizing chemicals

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Kathrin S. Zeller 1, Andy Forreryd 1, Tim Lindberg 1, Robin Gradin 1,2, Aakash Chawade 3 and Malin Lindstedt 1
1 Department of Immunotechnology, Medicon Village, Lund, Sweden
2 SenzaGen AB, Medicon Village, Lund, Sweden
3 Swedish University of Agricultural Sciences, Alnarp, Sweden

Summary

Contact allergy induced by certain chemicals is a common health concern, and several alternative methods have been developed to fulfill the requirements of European legislation with regard to hazard assessment of potential skin sensiĀ­tizers. However, validated methods, which provide information about the potency of skin sensitizers, are still lacking. The cell-based assay Genomic Allergen Rapid Detection (GARD), targeting key event 3, dendritic cell activation, of the skin sensitization AOP, uses gene expression profiling and a machine learning approach for the prediction of chemicals as sensitizers or non-sensitizers. Based on the GARD platform, we here expanded the assay to predict three sensitizer potency classes according to the European Classification, Labelling and Packaging (CLP) Regulation, targeting cateĀ­gories 1A (strong), 1B (weak) and no cat (non-sensitizer). Using a random forest approach and 70 training samples, a potential biomarker signature of 52 transcripts was identified. The resulting model could predict an independent test set consisting of 18 chemicals, six from each CLP category and all previously unseen to the model, with an overall accuracy of 78%. Importantly, the model was shown to be conservative and only underestimated the class label of one chemical. Furthermore, an association of defined chemical protein reactivity with distinct biological pathways illustrates that our transcriptional approach can reveal information contributing to the understanding of underlying mechanisms in sensitization.

 

Keywords: in vitro assay, sensitization, potency, biomarkers, random forest


Supplementary file (.pdf)


 

ALTEX 34(4), 2017: 539-559

doi: 10.14573/altex.1701101


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