Integrated skin sensitization assessment based on OECD methods (III): Adding human data to the assessment
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Abstract
Skin sensitizer potency assessment based on new approach methodologies is key to deriving a point of departure (PoD) for risk assessment. Regression models to predict a PoD based on OECD-validated in vitro tests and trained on local lymph node assay (LLNA) data were previously presented, and results from human tests were recently compiled. To integrate both data sources, the Reference Chemical Potency List (RCPL), which provides potency values (PV) for 33 chemicals integrating LLNA and human data in a structured weight-of-evidence approach, was developed. When calculating regression models vs PV or LLNA data, different weights for the input parameters were noted. As the RCPL is based on too few chemicals to train robust statistical models, the list of human data was extended to a larger set of PV (n = 139) with associated in vitro data. This database was used to retrain the regression models and to compare regression models trained vs (i) LLNA, (ii) PV or (iii) human DSA04 values. Using the PV as a target, predictive models of similar predictivity to the LLNA-based models were obtained, which mainly differ in a lesser weight of cytotoxicity and a higher weight of cell activation and reactivity parameters. Analysis of the human DSA04 dataset indicates a similar pattern but also shows that the human dataset is too small and biased to be a key dataset for potency prediction. Hence, an enlarged set of PV values appears to be a complementary tool to train predictive models next to an LLNA-only database.
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