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Background: Chemically induced skin sensitization, or allergic contact dermatitis, is a common occupational and public health issue. Regulatory authorities require an assessment of potential to cause skin sensitization for many chemical products. Defined approaches for skin sensitization (DASS) identify potential chemical skin sensitizers by integrating data from multiple non-animal tests based on human cells, molecular targets, and computational model predictions using standardized data interpretation procedures. While several DASS are internationally accepted by regulatory agencies, the data interpretation procedures vary in logical complexity, and manual application can be time-consuming or prone to error. Results: We developed the DASS App, an open-source web application, to facilitate user application of three regulatory testing strategies for skin sensitization assessment: the Two-out-of-Three (2o3), the Integrated Testing Strategy (ITS), and the Key Event 3/1 Sequential Testing Strategy (KE 3/1 STS) without the need for software downloads or computational expertise. The application supports upload and analysis of user-provided data, includes steps to identify inconsistencies and formatting issues, and provides predictions in a downloadable format. Conclusion: This open-access web-based implementation of internationally harmonized regulatory guidelines for an important public health endpoint is designed to support broad user uptake and consistent, reproducible application. The DASS App is freely accessible via https://ntp.niehs.nih.gov/go/952311 and all scripts are available on GitHub (https://github.com/NIEHS/DASS). [ABSTRACT FROM AUTHOR]

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Background: Allergic contact dermatitis caused by henna‐based hair‐colouring products has been associated with adulteration of henna with p‐phenylenediamine (PPD). Objectives: To develop a testing approach based on in vitro techniques that address key events within the skin sensitization adverse outcome pathway in order to evaluate the allergenic potential of hair‐colouring products. Methods: The following in vitro assays were used to test the sensitizing capacity of hair dye ingredients: the micro‐direct peptide reactivity assay (mDPRA); the HaCaT keratinocyte‐associated interleukin (IL)‐18 assay; the U937 cell line activation test (U‐SENS)/IL‐8 levels; the blood monocyte‐derived dendritic cell test; and genomic allergen rapid detection (GARD skin). Those techniques with better human concordance were selected to evaluate the allergenic potential of 10 hair‐colouring products. Results: In contrast to the information on the label, chromatographic analyses identified PPD in all products. The main henna biomarker, lawsone, was not detected in one of the 10 products. Among the techniques evaluated by testing hair dye ingredients, the mDPRA, the IL‐18 assay, GARD skin and the U‐SENS correlated better with human classification (concordances of 91.7%‐100%) and were superior to the animal testing (concordance of 78.5%). Thus, these assays were used to evaluate hair‐colouring products, which were classified as skin sensitizers by the use of different two‐of‐three approaches. Conclusions: Our findings highlight the toxicological consequences of, and risks associated with, the undisclosed use of PPD in henna‐based "natural" "real‐life" products. [ABSTRACT FROM AUTHOR]

Computational prioritization of chemicals for potential skin sensitization risks plays essential roles in the risk assessment of environmental chemicals and drug development. Given the huge number of chemicals for testing, computational methods enable the fast identification of high-risk chemicals for experimental validation and design of safer alternatives. However, the development of robust prediction model requires a large dataset of tested chemicals that is usually not available for most toxicological endpoints, especially for human data. A small training dataset makes the development of effective models difficult with insufficient coverage and accuracy. In this study, an ensemble tree-based multitask learning method was developed incorporating three relevant tasks in the well-defined adverse outcome pathway (AOP) of skin sensitization to transfer shared knowledge to the major task of human sensitizers. The results show both largely improved coverage and accuracy compared with three state-of-the-art methods. A user-friendly prediction server was available at https://cwtung.kmu.edu.tw/skinsensdb/predict. As AOPs for various toxicity endpoints are being actively developed, the proposed method can be applied to develop prediction models for other endpoints. [ABSTRACT FROM AUTHOR]

At the ESCD congress held in Manchester in 2016, a session was organized to encourage more dialogue between clinicians with expertise in skin sensitization and toxicologists seeking to provide effective risk assessment to prevent human health issues. That session focused on the remaining uncertainties regarding the induction and regulation of skin sensitization in humans, and the opportunities and challenges associated with the refinement and improvement of risk assessment methodologies. This short article, prompted by those discussions, debates what the authors regard as being among the most important and most intriguing uncertainties about skin sensitization and allergic contact dermatitis in humans, and the most significant opportunities for improving risk assessment. The aim has been to provide a basis for mapping out the areas that might benefit from a closer alignment between the relevant clinical community and toxicologists charged with the responsibility of ensuring that skin sensitization risks are understood and managed. [ABSTRACT FROM AUTHOR]

Drugs, cosmetics, preservatives, fragrances, pesticides, metals, and other chemicals can cause skin sensitization. The ability to predict the skin sensitization potential and potency of substances is therefore of enormous importance to a host of different industries, to customers' and workers' safety. Animal experiments have been the preferred testing method for most risk assessment and regulatory purposes but considerable efforts to replace them with non-animal models and in silico models are ongoing. This review provides a comprehensive overview of the computational approaches and models that have been developed for skin sensitization prediction over the last 10 years. The scope and limitations of rule-based approaches, read-across, linear and nonlinear (quantitative) structure-activity relationship ((Q)SAR) modeling, hybrid or combined approaches, and models integrating computational methods with experimental results are discussed followed by examples of relevant models. Emphasis is placed on models that are accessible to the scientific community, and on model validation. A dedicated section reports on comparative performance assessments of various approaches and models. The review also provides a concise overview of relevant data sources on skin sensitization. [ABSTRACT FROM AUTHOR]

Cosmetics Europe, the European Trade Association for the cosmetics and personal care industry, is conducting a multi-phase program to develop regulatory accepted, animal-free testing strategies enabling the cosmetics industry to conduct safety assessments. Based on a systematic evaluation of test methods for skin sensitization, five non-animal test methods (DPRA (Direct Peptide Reactivity Assay), KeratinoSensTM, h-CLAT (human cell line activation test), U-SENSTM, SENS-IS) were selected for inclusion in a comprehensive database of 128 substances. Existing data were compiled and completed with newly generated data, the latter amounting to one-third of all data. The database was complemented with human and local lymph node assay (LLNA) reference data, physicochemical properties and use categories, and thoroughly curated. Focused on the availability of human data, the substance selection resulted nevertheless resulted in a high diversity of chemistries in terms of physico-chemical property ranges and use categories. Predictivities of skin sensitization potential and potency, where applicable, were calculated for the LLNA as compared to human data and for the individual test methods compared to both human and LLNA reference data. In addition, various aspects of applicability of the test methods were analyzed. Due to its high level of curation, comprehensiveness, and completeness, we propose our database as a point of reference for the evaluation and development of testing strategies, as done for example in the associated work of Kleinstreuer et al. We encourage the community to use it to meet the challenge of conducting skin sensitization safety assessment without generating new animal data. [ABSTRACT FROM AUTHOR]

Skin sensitization is a toxicity endpoint of widespread concern, for which the mechanistic understanding and concurrent necessity for non-animal testing approaches have evolved to a critical juncture, with many available options for predicting sensitization without using animals. Cosmetics Europe and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods collaborated to analyze the performance of multiple non-animal data integration approaches for the skin sensitization safety assessment of cosmetics ingredients. The Cosmetics Europe Skin Tolerance Task Force (STTF) collected and generated data on 128 substances in multiple in vitro and in chemico skin sensitization assays selected based on a systematic assessment by the STTF. These assays, together with certain in silico predictions, are key components of various non-animal testing strategies that have been submitted to the Organization for Economic Cooperation and Development as case studies for skin sensitization. Curated murine local lymph node assay (LLNA) and human skin sensitization data were used to evaluate the performance of six defined approaches, comprising eight non-animal testing strategies, for both hazard and potency characterization. Defined approaches examined included consensus methods, artificial neural networks, support vector machine models, Bayesian networks, and decision trees, most of which were reproduced using open source software tools. Multiple non-animal testing strategies incorporating in vitro, in chemico, and in silico inputs demonstrated equivalent or superior performance to the LLNA when compared to both animal and human data for skin sensitization. [ABSTRACT FROM AUTHOR]

The replacement of animal use in testing for regulatory classification of skin sensitizers is a priority for US federal agencies that use data from such testing. Machine learning models that classify substances as sensitizers or non-sensitizers without using animal data have been developed and evaluated. Because some regulatory agencies require that sensitizers be further classified into potency categories, we developed statistical models to predict skin sensitization potency for murine local lymph node assay (LLNA) and human outcomes. Input variables for our models included six physicochemical properties and data from three non-animal test methods: direct peptide reactivity assay; human cell line activation test; and KeratinoSens™ assay. Models were built to predict three potency categories using four machine learning approaches and were validated using external test sets and leave-one-out cross-validation. A one-tiered strategy modeled all three categories of response together while a two-tiered strategy modeled sensitizer/non-sensitizer responses and then classified the sensitizers as strong or weak sensitizers. The two-tiered model using the support vector machine with all assay and physicochemical data inputs provided the best performance, yielding accuracy of 88% for prediction of LLNA outcomes (120 substances) and 81% for prediction of human test outcomes (87 substances). The best one-tiered model predicted LLNA outcomes with 78% accuracy and human outcomes with 75% accuracy. By comparison, the LLNA predicts human potency categories with 69% accuracy (60 of 87 substances correctly categorized). These results suggest that computational models using non-animal methods may provide valuable information for assessing skin sensitization potency. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Allergic contact dermatitis (ACD) is a delayed-type hypersensitivity (DTH) reaction induced by repeated contact with sensitizers. The ability of a chemical to act as a sensitizer has most frequently been tested in animals. As the use of animals for these purposes is gradually and globally being phased out, there is a need for reliable in vitro surrogate assays. Currently proposed in vitro assays are designed to test four key events of the adverse outcome pathway (AOP) involving covalent modification of self-proteins by sensitizers (haptenation) and presentation of new antigens (hapten/carrier complexes) to the immune system. There appears to be imperfect alignment of in vitro assays with clinical and/or animal data, suggesting possibly additional mechanisms of ACD development. Indeed, studies on allergies to small drugs, small chemical-induced HLA-peptide exchange for vaccination purposes and cosmetic ingredient-induced exposure of autoantigens suggest a possibility of DTH response promotion by hapten/carrier-independent mechanisms. Therefore, there is a need for additional appropriate in vitro assays, in order to achieve maximal concordance between clinical and/or animal data and in vitro assays. In this paper, we will review evidence supporting the idea of diverse mechanisms of ACD development. We will also discuss the impact of these multiple mechanisms, on the AOP and on the in vitro assays that should be used for allergen detection. We will propose alloreactivity-like reactions, aided by computer modeling and biochemical tests of compound-HLA binding, as additional tools for better prediction of DTH reactions, resulting from exposure to ingredients in cosmetic products. The combination of the proposed tests, along with the existing assays, should further enhance animal-free assessment of sensitizing potential of individual chemicals. [ABSTRACT FROM AUTHOR]

ABSTRACT The murine local lymph node assay (LLNA) is widely used to identify chemicals that may cause allergic contact dermatitis. Exposure to a dermal sensitizer results in proliferation of local lymph node T cells, which has traditionally been measured by in vivo incorporation of [3H]methyl thymidine. A more recent non-isotopic variation of the assay utilizes bromodeoxyuridine (BrdU) incorporation in vivo. To further improve the utility of this assay, we developed an ex vivo BrdU labeling procedure eliminating the need for in vivo injections. The results of this assay correctly identified a strong sensitizer (i.e., trimellitic anhydride) as well as weak/moderate sensitizers (i.e., eugenol, cinnamaldehyde and hexylcinnaminic aldehyde). As anticipated, neither non-sensitizers isopropanol and lactic acid nor the false negative chemical nickel II sulfate hexahydrate induced a positive threshold response in the assay. The results of this assay are in close agreement with those of the in vivo LLNA:BrdU-enzyme-linked immunosorbent assay labeling procedure. We also used the ex vivo BrdU LLNA procedure to evaluate ammonium hexachloroplatinate, ammonium tetrachloroplatinate and cis-diamminedichloroplatinum(II) and the assay correctly identified them as sensitizers based on the calculation of EC2 values. We conclude that this ex vivo BrdU labeling method offers predictive capacity comparable to previously established LLNA protocols while eliminating animal injections and the use of radioisotope. Published 2014. This article is a U.S. Government work and is in the public domain in the USA. [ABSTRACT FROM AUTHOR]