Application of Defined Approaches for Skin Sensitization to Agrochemical Products

Abstract

Skin sensitization testing is a regulatory requirement for safety evaluations of pesticides in multiple countries. Globally harmonized test guidelines that include in chemico and in vitro 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 in vivo 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 in silico 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 in vivo 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.

The GARD™skin assay: Investigation of the applicability domain for metals

“…For this reason, human data were available in the literature only for a limited number of the substances in this study (n=5). The limited availability of human reference data is far from unique to the present study, and a recent example includes the assessment of agrochemical formulations (Strickland et al, 2022), for which the authors reported all performance metrics of evaluated NAMs exclusively versus available animal data. In addition, animal data from the assay or the GPMT were available for a total of eight and three compounds, respectively.…”

From Pipeline to Plant Protection Products: Using New Approach Methodologies (NAMs) in Agrochemical Safety Assessment

“…The first three key events have in chemico and in vitro assays outlined in OECD 442A-E. These assays are routinely utilized for the assessment of agrochemical active ingredients , and formulations , and can be adapted into non-GLP studies for early stage screening. Finally, the data interpretation of in chemico , in vitro , and in silico assessments of sensitization potential are outlined in OECD 497, which includes interpretation for a “2 out of 3” and “integrated testing strategy” (ITS) approaches for data interpretation.…”

SkinSensPred as a Promising in Silico Tool for Integrated Testing Strategy on Skin Sensitization

“…In addition, Strickland et al extended the applicability of DA to 27 mixtures of agrochemicals [18], considering only active ingredients. In their study, the animal assay data without human evidence were compared with the adjusted DA approach, and individual methods include the DPRA assay, KeratinoSens assay, h-CLAT assay, and QSAR Toolbox.…”

“…The commercial software Derek and the corresponding ITSv1 approach were not included in the comparison due to the tool being commercial. To predict the mixture, Strickland et al [18] followed OECD guidelines [4][5][6] with minor modifications regarding conducting the in chemico and in vitro experiments. In addition, they followed the GHS guidance for in silico results, which classify a sensitizer mixture by at least one sensitizer ingredient whose concentration is more than 0.1%.…”

“…In a comparison with ITS prediction and the Globally Harmonized System of Classification and Labeling of Chemicals (GHS) [17], both ITSv1 and ITSv2 showed promising results for classifying skin sensitizers. The DAs were also useful for classifying mixtures consisting of agrochemicals [18].…”