Assessment of a defined approach based on a stacking prediction model to identify skin sensitization hazard. (October 2019) - Tourneix, Fleur; Alépée, Nathalie; Detroyer, Ann; Eilstein, Joan; Martinozzi Teissier, Silvia; Nardelli, Laurent; Noçairi, Hicham; Pauloin, Thierry; Piroird, Cécile; Del Bufalo, Aurélia
Abstract: Skin sensitization is an important toxicological endpoint in the safety assessment of chemicals and cosmetic ingredients. Driven by ethical considerations and European Union (EU) legislation, its assessment has progressed from the reliance on traditional animal models to the use of non-animal test methods. It is generally accepted that the assessment of skin sensitization requires the integration of various non-animal test methods in defined approaches (DAs), to cover the mechanistic key events of the adverse outcomes pathway (AOP) (OECD, 2014 ). Several case studies for DAs predicting skin sensitization hazard or potency have been submitted to the OECD, including a stacking meta-model developed by L'Oréal Research & Innovation (OECD, 2017b ; Del Bufalo et al., 2018 ; Noçairi et al., 2016 ). The present study evaluated the predictive performance of the defined approach integrating a stacking meta-model incorporating in silico, in chemico and in vitro assays, using the Cosmetics Europe (CE) skin sensitization database. Based on the optimized prediction cut-offs, the defined approach provided a hazard prediction for 97 chemicals with a sensitivity of 91%, a specificity of 76% and accuracy of 86% (kappa of 0.67) against human skin sensitization hazard data and a sensitivity of 85%, specificity of 91% and accuracy of 87% (kappa of 0.67) against Local Lymph Node Assay (LLNA) hazard data. A comparison of the in vivo LLNA with human hazard data for the same 97 chemicals showed a sensitivity of 92%, specificity of 51% and accuracy of 78% (kappa of 0.48). Thus, the defined approach showed a higher degree of concordance, as compared to the LLNA for predicting human skin sensitization hazard. Moreover, a comparison with the six DAs selected for evaluation of their predictivity in the study by Kleinstreuer et al. (2018) showed a similar high accuracy of 86% for 97 overlapping chemicals. The next step will be an independent evaluation of the DA for its integration in the performances based test guidelines (PBTG) for skin sensitization. Highlights: A stacking defined approach (DA) is assessed to predict skin sensitization hazard. The DA predicts human skin sensitization hazard better compared to the LLNA data. No human category 1 & 2 sensitizers nor cat. 6 non-sensitizers are mispredicted. The DA shows a similar high accuracy as the best performing DAs submitted to OECD.