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Three in vitro methods for the prediction of the skin sensitization hazard have been validated. However, predicting sensitizer potency is a key requirement for risk assessment. Here, we report a database of 312 chemicals tested in the KeratinoSens™ assay and for kinetic peptide binding. These data were used in multiple regression analysis against potency in the local lymph node assay (LLNA). The dataset covers the majority of chemicals from the validation of the LLNA to predict human potency and this subset was analyzed for prediction of human sensitization potency by in vitro data. Global analysis yields a regression of in vitro data to LLNA pEC3 with an R(2) of 60% predicting LLNA EC3 with a mean error of 3.5-fold. The highest weight in the regression has the reaction rate with peptides, followed by Nrf2-induction and cytotoxicity in KeratinoSens™. The correlation of chemicals tested positive in vitro with human data has an R(2) of 49%, which is similar to the correlation between LLNA and human data. Chemicals were then grouped into mechanistic domains based on experimentally observed peptide-adduct formation and predictions from the TIMES SS software. Predictions within these domains with a leave-one-out approach were more accurate, and for several mechanistic domains LLNA EC3 can be predicted with an error of 2- to 3-fold. However, prediction accuracy differs between domains and domain assignment cannot be made for all chemicals. Thus, this comprehensive analysis indicates that combining global and domain models to assess sensitizer potency may be a practical way forward.

Predicting Skin Sensitizer Potency Based on In Vitro Data from KeratinoSens and Kinetic Peptide Binding: Global Versus Domain-Based Assessment