Skin sensitisation prediction using read-across, an illustrative next generation risk assessment (NGRA) case study for vanillin.

Skin sensitisation is a key adverse human health effect to be addressed in the safety assessment of cosmetic ingredients. Regulatory demands and scientific progress have led to the development of a Next Generation Risk Assessment (NGRA) framework, relying on the use of New Approach Methodologies (NAM) Defined Approaches (DA) and read-across instead of generating animal data. This case study illustrates the application of read-across for the prediction of the skin sensitisation potential of vanillin at the hypothetical use concentration of 0.5% in a shower gel and face cream. A three-step process was applied to select the most suitable analogues based on their protein reactivity, structural characteristics, physicochemical properties, skin metabolism profile and availability of skin sensitisation data. The applied read-across approach predicted a weak skin sensitiser potential for vanillin corresponding with a Local Lymph Node Assay EC3 value of 10%. Based on this EC3 value a point of departure of 2500 µg/cm2 was derived, resulting in an acceptable exposure level (AEL) of 25 µg/cm2. Because the consumer exposure levels (CEL) for the face cream (13.5 µg/cm2) and shower gel (0.05 µg/cm2) scenarios were lower than the AEL, the NGRA concluded both uses as safe.

Off to a good start? Review of the predictivity of reactivity methods modelling the molecular initiating event of skin sensitization.

The assessment of skin sensitizing properties of chemicals has moved away from animal methods to new approach methodologies (NAM), guided by qualitative mechanistic understanding operationalized in an adverse outcome pathway (AOP). As with any AOP, the molecular initiating event (MIE) of covalent binding of a chemical to skin proteins is particularly important. This MIE has been modelled by several test methods by measuring the reaction of a test chemical with model peptides in chemico. To better understand the similarities and differences, a data repository with publicly available data for the direct peptide reactivity assay (DPRA), amino acid derivative reactivity assay (ADRA) and kinetic DPRA (kDPRA), as well as the peroxidase peptide reactivity assay (PPRA) was assembled. The repository comprises 260 chemicals with animal and human reference data, data on four relevant physicochemical properties, and between 161 to 242 test chemical results per test method. First, an overview of the experimental conditions of the four test methods was compiled allowing to readily compare them. Second, data analyses demonstrated that the test methods' predictivity was consistently reduced for poorly watersoluble chemicals and that the DPRA and ADRA can be used interchangeably. It also revealed new categorization thresholds for the DPRA and ADRA that are potentially relevant for strategic uses. In summary, a detailed assessment of reactivity test methods is provided, highlighting their potential and limitations. The results presented are intended to stimulate scientific discussion around test methods modelling the MIE of the skin sensitization AOP.

Next generation risk assessment for skin sensitisation: A case study with propyl paraben.

Skin sensitisation is a key adverse health effect to be addressed in the safety assessment of cosmetic ingredients. Regulatory demands have urged the development of Next Generation Risk Assessment (NGRA) using New Approach Methodologies (NAM) and Defined Approaches (DA) instead of animal models. An illustrative NGRA case study shall demonstrate if the use of propyl paraben at 0.2% in a face cream was safe for consumers. A sequential stacking tier testing DA based on NAM data predicted propyl paraben to be a non-sensitiser, while some NAM input data showed positive results. To increase confidence, structurally related parabens were considered, which revealed NAM and DA hazard predictions similar to those of propyl paraben, non-sensitiser classifications in animal models and very rare cases of human skin allergy. Based on a weight of evidence it was decided that propyl paraben should be considered a non-sensitiser leading to a favourable NGRA conclusion, in line with traditional risk assessment. Examination of an ab initio NGRA based on NAM and metabolism data resulted in a more conservative weak sensitiser consideration as point of departure, which still led to a favourable conclusion.

Read-across can increase confidence in the Next Generation Risk Assessment for skin sensitisation: A case study with resorcinol.

Historically skin sensitisation risk assessment for cosmetic ingredients was based on animal models, however regulatory demands have led to Next Generation Risk Assessment (NGRA), using data from New Approach Methodologies (NAM) and Defined Approaches (DA). This case study was meant to investigate if the use of resorcinol at 0.2% in a face cream was safe and a maximum use concentration could be defined. The NAM data and DA predictions could not provide sufficient confidence to determine a point of departure (POD). Therefore, the application of read-across was explored to increase the level of confidence. Analogue searches in various tools and databases using "mode of action" and "chemical structural features" retrieved 535 analogues. After refinement by excluding analogues without a defined structure, similar reactivity profile and skin sensitisation data, 39 analogues remained. A final selection was made based on three approaches: expert judgment, chemical similarity or Local Lymph Node Assay data (LLNA). All read-across approaches supported a moderate potency. A POD derived from the LLNA EC3 of 3.6% was determined leading to a favourable NGRA conclusion and a maximum use concentration of 0.36%. This was supported by a traditional risk assessment based on the available animal data for resorcinol.

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.

Modifications induced by chemical skin allergens on the metabolome of reconstructed human epidermis: A pilot high-resolution magic angle spinning nuclear magnetic resonance study.

BACKGROUND: High-resolution magic angle spinning (HRMAS) is a nuclear magnetic resonance (NMR) technique that enables the characterization of metabolic phenotypes/metabolite profiles of cells, tissues, and organs, under both normal and pathological conditions, without resorting to time-consuming extraction techniques. OBJECTIVES: To assess the impact of chemical skin sensitizers vs non-sensitizers on the metabolome of three-dimensional reconstructed human epidermis (RHE) by HRMAS NMR. METHODS: Based on the SENS-IS assay, 12 skin sensitizers and five non-sensitizing chemicals were investigated and applied on EpiSkin RHE at the published maximal non-irritating concentrations under the conditions of the test. The metabolome of RHE samples was then analyzed by HRMAS NMR. RESULTS: A total of 32 different metabolites were identified; 20 of these were quantified for all samples. Statistical univariate analysis showed that the tissue content of most measured metabolites (with the exception of acetate and glucose) was different in the untreated, treated with non-sensitizers, and treated with sensitizers samples. In RHE samples in contact with sensitizing chemicals, concentrations of 18 metabolites were significantly decreased. Alanine and tyrosine could not discriminate between sensitizer- and non-sensitizer-treated groups. A multivariate partial least-squares-discriminant analysis was performed on the two treated groups, discriminating sensitizing and non-sensitizing chemicals with a very good R2Y value of 0.87 and a good Q2Y value of 0.70. CONCLUSIONS: Data suggest that HRMAS NMR could be used to monitor the impact of chemicals, skin allergens vs non-sensitizers, on the metabolome of three-dimensional RHE.

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.