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.
Highlights
Skin sensitization and associated diseases such as contact allergy affect a substantial portion of the general population with an estimated prevalence of 15-20% in industrialized countries (Peiser et al, 2012)
The amount of chemical per exposed skin area that induces sensitization varies significantly (Basketter et al, 2014) among chemicals; skin sensitizer potency information is imperative for accurate risk assessment
Developers of alternative test methods should rely on human clinical data in order to achieve high predictivity of human sensitization, this type of data is rather scarce and most available data is derived from the Local Lymph Node Assay (LLNA) (Basketter et al, 2009)
Summary
Skin sensitization and associated diseases such as contact allergy affect a substantial portion of the general population with an estimated prevalence of 15-20% in industrialized countries (Peiser et al, 2012). Allergic contact dermatitis (ACD), a type IV hypersensitivity reaction, is common among certain occupational groups such as those regularly exposed to chemicals or involved in wet work (Behroozy and Keegel, 2014). The European Union has imposed requirements for testing of > 60,000 chemicals in the context of REACH (Hartung and Rovida, 2009), and prohibited animal testing for cosmetics and their ingredients (European Parliament, 2009). Information on both the skin sensitizing capacity and the potency of a chemical has to be provided to meet the regulatory requirements for classification and sub-categorization. Animal-free alternative assays that meet these requirements are urgently needed
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