Amending the U-SENS™ skin sensitization test method for interfering auto-fluorescent chemicals.

Limitations of the applicability domain of new approach methodologies (NAM) present a major challenge for the testing of cosmetic ingredients in Europe, as the regulation does not allow to resort to in vivo test method. Therefore, research focused on overcoming such limitations of established in vitro test methods is frequently conducted. Here, we address a limitation of the U-SENS™, an in vitro skin sensitization test method that addresses the key event 3 on activation of dendritic cells of the adverse outcome pathway (AOP) for skin sensitization. The applicability domain of the U-SENS™ excludes autofluorescent substances that can interfere with the measurement of the expression of CD86, i.e., the primary readout. An evaluation of several fluorochromes identified APC as most suitable for testing auto-fluorescent chemicals. Acceptance criteria were reproducibly met when using the APC-labelled antibody. Equivalent performance in terms of reproducibility and skin sensitisation hazard assessment of the standard FITC-labelled antibodies and the APC-labelled antibodies was demonstrated by testing 40 substances. Finally, the value of the expanded technical applicability domain was highlighted with a case study using sulfuretin. In conclusion, we successfully demonstrated the expansion of the U-SENS™ applicability domain to interfering auto-fluorescent chemicals by using APC-labelled antibodies.

Skin sensitisation testing in practice: Applying a stacking meta model to cosmetic ingredients.

Recently, several non-animal approaches contributing to the identification of skin sensitisation hazard have been introduced. Their validation and acceptance has largely been directed towards regulatory classification. Considering the driving force for replacement of in vivo tests centred on cosmetics, it is reasonable to ask how well the new approaches perform in this respect. In the present study, 219 substances, largely cosmetic raw materials (including dyes, preservatives and fragrances), have been evaluated in our Defined Approach integrating a stacking meta model (version 5), incorporating the individual outcomes of 3 in vitro validated methods (Direct Peptide Reactivity Assay, Keratinosens™, U-SENS™), 2 in silico tools (TIMES SS, TOXTREE) and physicochemical parameters (volatility, pH). Stacking meta model outcomes were compared with existing local lymph node assay (LLNA) data. Non-sensitisers comprised 68/219; 86 were weak/moderate and 65 were stronger sensitisers. The model version revision demonstrate the gain to discriminate sensitizers to non-sensitiser when the in silico TIMES model is incorporated as input parameter. The 85% to 91% accuracy for the cosmetics categories, indicates the stacking meta model offers value for the next generation risk assessment framework. These results pinpoint the power of the stacking meta model relying on a confidence based on the probability given in any individual prediction.

Assessment of a defined approach based on a stacking prediction model to identify skin sensitization hazard.

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.

The Myeloid U937 Skin Sensitization Test (U-SENS) addresses the activation of dendritic cell event in the adverse outcome pathway for skin sensitization.

The U-SENS™ assay, formerly known as MUSST (Myeloid U937 Skin Sensitization Test), is an in vitro method to assess skin sensitization. Dendritic cell activation following exposure to sensitizers was modelled in the U937 human myeloid cell line by measuring the induction of the expression of CD86 by flow cytometry. The predictive performance of U-SENS™ was assessed via a comprehensive comparison analysis with the available human and LLNA data of 175 substances. U-SENS™ showed 79% specificity, 90% sensitivity and 88% accuracy. A four laboratory ring study demonstrated the transferability, reliability and reproducibility of U-SENS™, with a reproducibility of 95% within laboratories and 79% between-laboratories, showing that the U-SENS™ assay is a promising tool in a skin sensitization risk assessment testing strategy.