Application of Defined Approaches for Skin Sensitization to Agrochemical Products.

Skin sensitization testing is a regulatory requirement for safety evaluations of pesticides in multiple countries. Globally harmonized test guidelines that include and methods reduce animal use, but no single assay is recommended as a complete replacement for animal tests. Defined approaches (DAs) that integrate data from multiple non-animal methods are accepted; however, the methods that comprise them have been evaluated using monoconstituent substances rather than mixtures or formulations. To address this data gap, we tested 27 agrochemical formulations in the direct peptide reactivity assay (DPRA), the KeratinoSens™ assay, and the human cell line activation test (h-CLAT). These data were used as inputs to evaluate three DAs for hazard classification of skin sensitization potential and two DAs for potency categorization. When compared to historical animal results, balanced accuracy for the DAs for predicting skin sensitization hazard (i.e., sensitizer vs. nonsensitizer) ranged from 56 to 78%. The best performing DA was the "2 out of 3 (2o3)" DA, in which the hazard classification was based on two concordant results from the DPRA, KeratinoSens, or h-CLAT. The KE 3/1 sequential testing strategy (STS), which uses h-CLAT and DPRA results, and the integrated testing strategy (ITSv2), which uses h-CLAT, DPRA, and an hazard prediction from OECD QSAR Toolbox, had balanced accuracies of 56-57% for hazard classification. Of the individual test methods, KeratinoSens had the best performance for predicting hazard outcomes. Its balanced accuracy of 81% was similar to that of the 2o3 DA (78%). For predicting potency categories defined by the United Nations Globally Harmonized System of Classification and Labelling of Chemicals (GHS), the correct classification rate of the STS was 52% and that of the ITSv2 was 43%. These results demonstrate that non-animal test methods have utility for evaluating the skin sensitization potential of agrochemical formulations as compared to animal reference data. While additional data generation is needed, testing strategies such as DAs anchored to human biology and mechanistic information provide a promising approach for agrochemical formulation testing.