Abstract

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 sensitizers. 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 categories 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

Published in

Altex
2017, Volume: 34, number: 4, pages: 539-559

SLU Authors

• Chawade, Aakash

  • Department of Plant Breeding, Swedish University of Agricultural Sciences
    

Sustainable Development Goals

SDG3 Ensure healthy lives and promote well-being for all at all ages


UKÄ Subject classification

Bioinformatics and Systems Biology
Cell and Molecular Biology


Publication identifier

DOI: https://doi.org/10.14573/altex.1701101

Permanent link to this page (URI)

https://res.slu.se/id/publ/85396