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Proactive identification and characterization of hazards attributable to chemicals are central aspects of risk assessments. Current legislations and trends in predictive toxicology advocate a transition from in vivo methods to nonanimal alternatives. For skin sensitization assessment, several OECD validated alternatives exist for hazard identification, but nonanimal methods capable of accurately characterizing the risks associated with sensitizing potency are still lacking. The GARD (Genomic Allergen Rapid Detection) platform utilizes exposure-induced gene expression profiles of a dendritic-like cell line in combination with machine learning to provide hazard classifications for different immunotoxicity endpoints. Recently, a novel genomic biomarker signature displaying promising potency-associated discrimination between weak and strong skin sensitizers was proposed. Here, we present the adaptation of the defined biomarker signature on a gene expression analysis platform suited for routine acquisition, confirm the validity of the proposed biomarkers, and define the GARDpotency assay for prediction of skin sensitizer potency. The performance of GARDpotency was validated in a blinded ring trial, in accordance with OECD guidance documents. The cumulative accuracy was estimated to 88.0% across 3 laboratories and 9 independent experiments. The within-laboratory reproducibility measures ranged between 62.5% and 88.9%, and the between-laboratory reproducibility was estimated to 61.1%. Currently, no direct or systematic cause for the observed inconsistencies between the laboratories has been identified. Further investigations into the sources of introduced variability will potentially allow for increased reproducibility. In conclusion, the in vitro GARDpotency assay constitutes a step forward for development of nonanimal alternatives for hazard characterization of skin sensitizers.

toxicological sciences

The GARDpotency Assay for Potency-Associated Subclassification of Chemical Skin Sensitizers—Rationale, Method Development, and Ring Trial Results of Predictive Performance and Reproducibility