Non-animal test methods for predicting skin sensitization potentials

The dimer effect: A refinement approach towards skin sensitization assessment in-chemico using Amino acid Derivative Reactivity Assay

Skin sensitization is a key endpoint for safety assessment, especially for cosmetics and personal care products. The adverse outcome pathway for skin sensitization and the chemical and biological events driving the induction of human skin sensitization are now well understood. Several non-animal test methods have been developed to predict sensitizer potential by measuring the impact of chemical sensitizers on these key events. In this work, we have focused on Key Event 1 (the molecular initiating step), which is based on formation of a covalent adduct between skin sensitizers and endogenous proteins and/or peptides in the skin. There exists three in-chemico assays approved by the Organization for Economic Co-operation and Development-(1) Direct Peptide Reactivity Assay (DPRA), (2) Amino Acid Derivative Reactivity Assay (ADRA), and (3) Kinetic Direct Peptide Reactivity Assay (kDPRA) to quantify peptide/amino acid derivative depletion after incubation with test chemicals. However, overestimated depletion of the cysteine-based peptide/amino acid derivatives is known in such assays because of the dimerization of the thiol group. In this present work, we report the synthesis and structural confirmation of the dimer of N-(2-[1-naphthyl]acetyl)-L-cysteine (NAC) from the ADRA assay to allow simultaneous determination of (a) peptide depletion by quantifying NAC monomer and (b) peptide dimerization by quantifying NAC dimer thereby eliminating the overestimation. We present a case study with three chemicals to demonstrate the importance of this approach. Thus, this simultaneous assay gives a more informed view of the peptide reactivity of chemicals to better identify skin sensitizers.

The BMDC model, a performant cell-based test to assess the sensitizing potential and potency of chemicals including pre/pro-haptens

BACKGROUND

Chemical-induced allergies at workplace represent a significant occupational health issue. These substances must be properly identified as sensitizers. In previous studies, an original model using mouse bone marrow-derived dendritic cells (BMDC) was developed for this purpose.

OBJECTIVES

The aim of this study was to evaluate the predictive capacity of the BMDC model with a large panel of sensitizers (including pre- and pro-haptens) and non-sensitizers.

METHODS

The readout from the BMDC model is based on expression levels of six phenotypic markers measured by flow cytometry.

RESULTS

The results indicate that 29 of the 37 non-sensitizers, and 81 of the 86 sensitizers were correctly classified compared to the Local Lymph Node Assay (LLNA). Statistical analysis revealed the BMDC model to have a sensitivity of 94%, a specificity of 78%, and an accuracy of 89%. The EC2 (Effective Concentration) values calculated with this model allow sensitizers to be categorized into four classes: extreme, strong, moderate and weak.

CONCLUSIONS

These excellent predictive performances show that the BMDC model discriminates between sensitizers and non-sensitizers with outstanding precision equal to or better than existing validated alternative models. Moreover, this model allows to predict sensitization potency of chemicals. The BMDC test could therefore be proposed as an additional tool to assess the sensitizing potential and potency of chemicals.

Application of Defined Approaches to Assess Skin Sensitization Potency of Isothiazolinone Compounds

INTRODUCTION

Isothiazolinones (ITs) are widely used as antimicrobial preservatives in cosmetics and as additives for preservation of consumer and industrial products to control bacteria, fungi, and algae. Although they are effective biocides, they have the potential to produce skin irritation and sensitization, which poses a human health hazard. In this project, we evaluated nonanimal defined approaches (DAs) for skin sensitization that can provide point-of-departure estimates for use in quantitative risk assessment for ITs.

MATERIALS AND METHODS

The skin sensitization potential of six ITs was evaluated using three internationally harmonized nonanimal test methods: the direct peptide reactivity assay, KeratinoSens™, and the human cell line activation test. Results from these test methods were then applied to two versions of the Shiseido Artificial Neural Network DA.

RESULTS

Sensitization hazard or potency predictions were compared with those of the in vivo murine local lymph node assay (LLNA). The nonanimal methods produced skin sensitization hazard and potency classifications concordant with those of the LLNA. EC3 values (the estimated concentration needed to produce a stimulation index of three, the threshold positive response) generated by the DAs had less variability than LLNA EC3 values, and confidence limits from the DAs overlapped those of the LLNA EC3 for most substances.

CONCLUSION

The application of in silico models to in chemico and in vitro skin sensitization data is a promising data integration procedure for DAs to support hazard and potency classification and quantitative risk assessment.

Reconfiguring the online tool of SkinSensPred for predicting skin sensitization of pesticides

Adverse outcome pathway (AOP)-based computational models provide state-of-the-art prediction for human skin sensitizers and are promising alternatives to animal testing. However, little is known about their applicability to pesticides due to scarce pesticide data for evaluation. Moreover, pesticides traditionally have been tested on animals without human data, making validation difficult. Direct application of AOP-based models to pesticides may be inappropriate since their original applicability domains were designed to maximize reliability for human response prediction on diverse chemicals but not pesticides. This study proposed to identify a consensus chemical space with concordant human responses predicted by the SkinSensPred online tool and animal testing data to reduce animal testing. The identified consensus chemical space for non-sensitizers achieved high concordance of 85% and 100% for the cross-validation and independent test, respectively. The reconfigured SkinSensPred can be applied as the first-tier tool for identifying non-sensitizers to reduce. animal testing for pesticides by 19.6%.

Proof-of-Concept Organ-on-Chip Study: Topical Cinnamaldehyde Exposure of Reconstructed Human Skin with Integrated Neopapillae Cultured under Dynamic Flow

Pharmaceutical and personal care industries require human representative models for testing to ensure the safety of their products. A major route of penetration into our body after substance exposure is via the skin. Our aim was to generate robust culture conditions for a next generation human skin-on-chip model containing neopapillae and to establish proof-of-concept testing with the sensitizer, cinnamaldehyde. Reconstructed human skin consisting of a stratified and differentiated epidermis on a fibroblast populated hydrogel containing neopapillae spheroids (RhS-NP), were cultured air-exposed and under dynamic flow for 10 days. The robustness of three independent experiments, each with up to 21 intra-experiment replicates, was investigated. The epidermis was seen to invaginate into the hydrogel towards the neopapille spheroids. Daily measurements of lactate dehydrogenase (LDH) and glucose levels within the culture medium demonstrated high viability and stable metabolic activity throughout the culture period in all three independent experiments and in the replicates within an experiment. Topical cinnamaldehyde exposure to RhS-NP resulted in dose-dependent cytotoxicity (increased LDH release) and elevated cytokine secretion of contact sensitizer specific IL-18, pro-inflammatory IL-1β, inflammatory IL-23 and IFN-γ, as well as anti-inflammatory IL-10 and IL-12p70. This study demonstrates the robustness and feasibility of complex next generation skin models for investigating skin immunotoxicity.

Models of Dendritic Cells to Assess Skin Sensitization

Allergic contact dermatitis (ACD) is a complex skin pathology occurring in reaction against environmental substances found in the workplace (cement, hair dyes, textile dyes), in the private environment (e.g., household products, cosmetic ingredients), or following skin exposure to drugs. Many cells are involved in the initiation of ACD during the sensitization phase. The four key events (KE) of skin sensitization AOP are covalent binding to skin proteins (KE1), keratinocyte activation (KE2), activation of DCs (KE3), and T-cell activation and proliferation (KE4), leading to the adverse outcome of ACD. Dendritic cells (DCs) are thus playing a key role in ACD pathophysiology. Indeed, in the presence of chemical sensitizers, DCs migrate from the skin to the draining lymph nodes and present peptide-chemical conjugates to T cells, leading to their activation and proliferation. In vitro methods have been actively developed to assess the activation of DCs by chemicals to establish a reliable in vitro sensitization test. Therefore, this review will detail the most used methods and protocols to develop DC models in vitro. Three different models of DCs will be addressed: 1) DCs derived from Cord Blood (CD34-DCs), 2) DCs derived from Monocytes (Mo-DCs), and 3) DCs derived from mice Bone-Marrow (BM-DCs). In addition, a model of exposition to contact sensitizers to assess KE3 of skin sensitization will be detailed for each of the models presented.

True Grit: A Story of Perseverance Making Two out of Three the First Non-Animal Testing Strategy (Adopted as OECD Guideline No. 497)

In the last two decades, great strides have been made in developing alternative methods to animal testing for regulatory and safety testing. In 2021, a breakthrough in regulatory testing was achieved in that the first test strategies employing non-animal test methods for skin sensitization have been accepted as OECD guideline 497, which falls under the mutual acceptance of data (MAD) by OECD member states. Achieving this goal was a story of hard work and perseverance of the many people involved. This review gives an overview of some of the many aspects and timelines this entailed—just from the perspective of one stakeholder. In the end, the true grit of all involved allowed us to achieve not only a way forward in using test strategies for skin sensitization, but also a new approach to address other complex toxicological effects without the use of animals in the future.

Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors

In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints and other non-intuitive descriptors, the interpretability of the existing models is limited. The aim of this work is to develop a strategy to replace the non-intuitive features by predicted outcomes of bioassays. We show that such replacement is indeed possible and that as few as ten interpretable, predicted bioactivities are sufficient to reach competitive performance. On a holdout data set of 257 compounds, the best model (“Skin Doctor CP:Bio”) obtained an efficiency of 0.82 and an MCC of 0.52 (at the significance level of 0.20). Skin Doctor CP:Bio is available free of charge for academic research. The modeling strategies explored in this work are easily transferable and could be adopted for the development of more interpretable machine learning models for the prediction of the bioactivity and toxicity of small organic compounds.

Development of quantitative model of a local lymph node assay for evaluating skin sensitization potency applying machine learning CatBoost

The estimated concentrations for a stimulation index of 3 (EC3) in murine local lymph node assay (LLNA) is an important quantitative value for determining the strength of skin sensitization to chemicals, including cosmetic ingredients. However, animal testing bans on cosmetics in Europe necessitate the development of alternative testing methods to LLNA. A machine learning-based prediction method can predict complex toxicity risks from multiple variables. Therefore, we developed an LLNA EC3 regression model using CatBoost, a new gradient boosting decision tree, based on the reliable Cosmetics Europe database which included data for 119 substances. We found that a model using in chemico/in vitro tests, physical properties, and chemical information associated with key events of skin sensitization adverse outcome pathway as variables showed the best performance with a coefficient of determination (R²) of 0.75. In addition, this model can indicate the variable importance as the interpretation of the model, and the most important variable was associated with the human cell line activation test that evaluate dendritic cell activation. The good performance and interpretability of our LLNA EC3 predictable regression model suggests that it could serve as a useful approach for quantitative assessment of skin sensitization.

Skin Doctor CP: Conformal Prediction of the Skin Sensitization Potential of Small Organic Molecules

Skin sensitization potential or potency is an important end point in the safety assessment of new chemicals and new chemical mixtures. Formerly, animal experiments such as the local lymph node assay (LLNA) were the main form of assessment. Today, however, the focus lies on the development of nonanimal testing approaches (i.e., in vitro and in chemico assays) and computational models. In this work, we investigate, based on publicly available LLNA data, the ability of aggregated, Mondrian conformal prediction classifiers to differentiate between non- sensitizing and sensitizing compounds as well as between two levels of skin sensitization potential (weak to moderate sensitizers, and strong to extreme sensitizers). The advantage of the conformal prediction framework over other modeling approaches is that it assigns compounds to activity classes only if a defined minimum level of confidence is reached for the individual predictions. This eliminates the need for applicability domain criteria that often are arbitrary in their nature and less flexible. Our new binary classifier, named Skin Doctor CP, differentiates nonsensitizers from sensitizers with a higher reliability-to-efficiency ratio than the corresponding nonconformal prediction workflow that we presented earlier. When tested on a set of 257 compounds at the significance levels of 0.10 and 0.30, the model reached an efficiency of 0.49 and 0.92, and an accuracy of 0.83 and 0.75, respectively. In addition, we developed a ternary classification workflow to differentiate nonsensitizers, weak to moderate sensitizers, and strong to extreme sensitizers. Although this model achieved satisfactory overall performance (accuracies of 0.90 and 0.73, and efficiencies of 0.42 and 0.90, at significance levels 0.10 and 0.30, respectively), it did not obtain satisfying class-wise results (at a significance level of 0.30, the validities obtained for nonsensitizers, weak to moderate sensitizers, and strong to extreme sensitizers were 0.70, 0.58, and 0.63, respectively). We argue that the model is, in consequence, unable to reliably identify strong to extreme sensitizers and suggest that other ternary models derived from the currently accessible LLNA data might suffer from the same problem. Skin Doctor CP is available via a public web service at https://nerdd.zbh.uni-hamburg.de/skinDoctorII/.

The Overt and Hidden Use of Animal-Derived Products in Alternative Methods for Skin Sensitisation: A Systematic Review

In vitro methods that can replace animal testing in the identification of skin sensitisers are now a reality. However, as cell culture and related techniques usually rely on animal-derived products, these methods may be failing to address the complete replacement of animals in safety assessment. The objective of this study was to identify the animal-derived products that are used as part of in vitro methods for skin sensitisation testing. Thus, a systematic review of 156 articles featuring 83 different in vitro methods was carried out and, from this review, the use of several animal-derived products from different species was identified, with the use of fetal bovine serum being cited in most of the methods (78%). The use of sera from other animals, monoclonal antibodies and animal proteins were also variously mentioned. While non-animal alternatives are available and methods free of animal-derived products are emerging, most of the current methods reported used at least one animal-derived product, which raises ethical and technical concerns. Therefore, to deliver technically and ethically better in vitro methods for the safety assessment of chemicals, more effort should be made to replace products of animal origin in existing methods and to avoid their use in the development of new method protocols.

Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability

The ability to predict the skin sensitization potential of small organic molecules is of high importance to the development and safe application of cosmetics, drugs and pesticides. One of the most widely accepted methods for predicting this hazard is the local lymph node assay (LLNA). The goal of this work was to develop in silico models for the prediction of the skin sensitization potential of small molecules that go beyond the state of the art, with larger LLNA data sets and, most importantly, a robust and intuitive definition of the applicability domain, paired with additional indicators of the reliability of predictions. We explored a large variety of molecular descriptors and fingerprints in combination with random forest and support vector machine classifiers. The most suitable models were tested on holdout data, on which they yielded competitive performance (Matthews correlation coefficients up to 0.52; accuracies up to 0.76; areas under the receiver operating characteristic curves up to 0.83). The most favorable models are available via a public web service that, in addition to predictions, provides assessments of the applicability domain and indicators of the reliability of the individual predictions.

Prediction of the skin sensitising potential and potency of compounds via mechanism-based binary and ternary classification models

Skin sensitisation, one of the most frequent forms of human immune toxicity, is authenticated to be a significant endpoint in the field of drug discovery and cosmetics. Due to the drawbacks of traditional animal testing methods, in silico methods have advanced to study skin sensitisation. In this study, mechanism-based binary and ternary classification models were constructed with a comprehensive data set. 1007 compounds were collected to develop five series of local and global models based on mechanisms. In each series, compounds were classified into five groups according to EC3 values, and applied as training sets, test sets and external validation sets. For each of the five series, 81 binary classification models and 81 ternary classification models were acquired via 9 molecular fingerprints and 9 machine learning methods using a novel KNIME workflow. Meanwhile, the applicability domains for the best 10 models were figured out to certify the rationality of prediction effect. In addition, 8 toxic substructures probably causing skin sensitisation were identified to speculate whether a compound is a skin sensitiser. The mechanism-based prediction models and the toxic substructures can be applied to predict the skin sensitising potential and potency of compounds.

Non-animal methods to predict skin sensitization (II): an assessment of defined approaches *

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.

Green Toxicology: a strategy for sustainable chemical and material development

Green Toxicology refers to the application of predictive toxicology in the sustainable development and production of new less harmful materials and chemicals, subsequently reducing waste and exposure. Built upon the foundation of "Green Chemistry" and "Green Engineering", "Green Toxicology" aims to shape future manufacturing processes and safe synthesis of chemicals in terms of environmental and human health impacts. Being an integral part of Green Chemistry, the principles of Green Toxicology amplify the role of health-related aspects for the benefit of consumers and the environment, in addition to being economical for manufacturing companies. Due to the costly development and preparation of new materials and chemicals for market entry, it is no longer practical to ignore the safety and environmental status of new products during product development stages. However, this is only possible if toxicologists and chemists work together early on in the development of materials and chemicals to utilize safe design strategies and innovative in vitro and in silico tools. This paper discusses some of the most relevant aspects, advances and limitations of the emergence of Green Toxicology from the perspective of different industry and research groups. The integration of new testing methods and strategies in product development, testing and regulation stages are presented with examples of the application of in silico, omics and in vitro methods. Other tools for Green Toxicology, including the reduction of animal testing, alternative test methods, and read-across approaches are also discussed.

SkinSensDB: a curated database for skin sensitization assays

Skin sensitization is an important toxicological endpoint for chemical hazard determination and safety assessment. Prediction of chemical skin sensitizer had traditionally relied on data from rodent models. The development of the adverse outcome pathway (AOP) and associated alternative in vitro assays have reshaped the assessment of skin sensitizers. The integration of multiple assays as key events in the AOP has been shown to have improved prediction performance. Current computational models to predict skin sensitization mainly based on in vivo assays without incorporating alternative in vitro assays. However, there are few freely available databases integrating both the in vivo and the in vitro skin sensitization assays for development of AOP-based skin sensitization prediction models. To facilitate the development of AOP-based prediction models, a skin sensitization database named SkinSensDB has been constructed by curating data from published AOP-related assays. In addition to providing datasets for developing computational models, SkinSensDB is equipped with browsing and search tools which enable the assessment of new compounds for their skin sensitization potentials based on data from structurally similar compounds. SkinSensDB is publicly available at http://cwtung.kmu.edu.tw/skinsensdb.

Prediction of skin sensitization potency using machine learning approaches

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.

Local tolerance testing under REACH: Accepted non-animal methods are not on equal footing with animal tests

In general, no single non-animal method can cover the complexity of any given animal test. Therefore, fixed sets of in vitro (and in chemico) methods have been combined into testing strategies for skin and eye irritation and skin sensitisation testing, with pre-defined prediction models for substance classification. Many of these methods have been adopted as OECD test guidelines. Various testing strategies have been successfully validated in extensive in-house and inter-laboratory studies, but they have not yet received formal acceptance for substance classification. Therefore, under the European REACH Regulation, data from testing strategies can, in general, only be used in so-called weight-of-evidence approaches. While animal testing data generated under the specific REACH information requirements are per se sufficient, the sufficiency of weight-of-evidence approaches can be questioned under the REACH system, and further animal testing can be required. This constitutes an imbalance between the regulatory acceptance of data from approved non-animal methods and animal tests that is not justified on scientific grounds. To ensure that testing strategies for local tolerance testing truly serve to replace animal testing for the REACH registration 2018 deadline (when the majority of existing chemicals have to be registered), clarity on their regulatory acceptance as complete replacements is urgently required.

Integrated decision strategies for skin sensitization hazard

One of the top priorities of the Interagency Coordinating Committee for the Validation of Alternative Methods (ICCVAM) is the identification and evaluation of non-animal alternatives for skin sensitization testing. Although skin sensitization is a complex process, the key biological events of the process have been well characterized in an adverse outcome pathway (AOP) proposed by the Organisation for Economic Co-operation and Development (OECD). Accordingly, ICCVAM is working to develop integrated decision strategies based on the AOP using in vitro, in chemico and in silico information. Data were compiled for 120 substances tested in the murine local lymph node assay (LLNA), direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT) and KeratinoSens assay. Data for six physicochemical properties, which may affect skin penetration, were also collected, and skin sensitization read-across predictions were performed using OECD QSAR Toolbox. All data were combined into a variety of potential integrated decision strategies to predict LLNA outcomes using a training set of 94 substances and an external test set of 26 substances. Fifty-four models were built using multiple combinations of machine learning approaches and predictor variables. The seven models with the highest accuracy (89-96% for the test set and 96-99% for the training set) for predicting LLNA outcomes used a support vector machine (SVM) approach with different combinations of predictor variables. The performance statistics of the SVM models were higher than any of the non-animal tests alone and higher than simple test battery approaches using these methods. These data suggest that computational approaches are promising tools to effectively integrate data sources to identify potential skin sensitizers without animal testing. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Evaluating Biomaterial- and Microfluidic-Based 3D Tumor Models

Cancer is a major cause of morbidity and mortality worldwide, with a disease burden estimated to increase over the coming decades. Disease heterogeneity and limited information on cancer biology and disease mechanisms are aspects that 2D cell cultures fail to address. Here, we review the current ‘state-of-the-art’ in 3D tissue-engineering (TE) models developed for, and used in, cancer research. We assess the potential for scaffold-based TE models and microfluidics to fill the gap between 2D models and clinical application. We also discuss recent advances in combining the principles of 3D TE models and microfluidics, with a special focus on biomaterials and the most promising chip-based 3D models.

Multivariate models for prediction of human skin sensitization hazard

One of the Interagency Coordinating Committee on the Validation of Alternative Method's (ICCVAM) top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays - the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT) and KeratinoSens™ assay - six physicochemical properties and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches, logistic regression and support vector machine, to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three logistic regression and three support vector machine) with the highest accuracy (92%) used: (1) DPRA, h-CLAT and read-across; (2) DPRA, h-CLAT, read-across and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens and log P. The models performed better at predicting human skin sensitization hazard than the murine local lymph node assay (accuracy 88%), any of the alternative methods alone (accuracy 63-79%) or test batteries combining data from the individual methods (accuracy 75%). These results suggest that computational methods are promising tools to identify effectively the potential human skin sensitizers without animal testing. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Evaluation of Non-animal Methods for Assessing Skin Sensitisation Hazard: A Bayesian Value-of-Information Analysis

This paper offers a Bayesian Value-of-Information (VOI) analysis for guiding the development of non-animal testing strategies, balancing information gains from testing with the expected social gains and costs from the adoption of regulatory decisions. Testing is assumed to have value, if, and only if, the information revealed from testing triggers a welfare-improving decision on the use (or non-use) of a substance. As an illustration, our VOI model is applied to a set of five individual non-animal prediction methods used for skin sensitisation hazard assessment, seven battery combinations of these methods, and 236 sequential 2-test and 3-test strategies. Their expected values are quantified and compared to the expected value of the local lymph node assay (LLNA) as the animal method. We find that battery and sequential combinations of non-animal prediction methods reveal a significantly higher expected value than the LLNA. This holds for the entire range of prior beliefs. Furthermore, our results illustrate that the testing strategy with the highest expected value does not necessarily have to follow the order of key events in the sensitisation adverse outcome pathway (AOP).

Phospholipidomic Profile Variation on THP-1 Cells Exposed to Skin or Respiratory Sensitizers and Respiratory Irritant

Occupational exposure to low molecular weight reactive chemicals often leads to development of allergic reactions such as allergic contact dermatitis and respiratory allergies. Further insights into the interaction of these chemicals with physiopathological relevant cellular models might provide the foundations for novel non-animal approaches to safety assessment. In this work we used the human THP-1 cell line to determine phospholipidome changes induced by the skin sensitizer 1-fluoro-2,4-dinitrobenzene (DNFB), the respiratory allergen hexamethylene diisocyanate (HDI), and the irritant methyl salicylate (MESA). We detected that these chemicals differently induce lipid peroxidation and modulate THP-1 IL-1β, IL-12B, IL-8, CD86, and HMOX1 transcription. Decreased phosphatidylethanolamine content was detected in cells exposed to MESA, while profound alterations in the relative abundance of cardiolipin species were observed in cells exposed to DNFB. All chemicals tested induced a decrease in the relative abundance of plasmanyl phosphatidylcholine species PC (O-16:0e/18:1) and phosphatidylinositol species PI (34:1), while increasing PI (38:4). An increased abundance of oleic acid was observed in the phospholipids of cells exposed to DNFB while a decreased abundance of palmitic acid was detected in cells treated with MESA or DNFB. We conclude that both specific and common alterations at phospholipidome levels are triggered by the different chemicals, while not allowing a complete distinction between them using a Canonical Analysis of Principal Coordinates (CAP). The common effects observed at phospholipids level with all the chemicals tested might be related to unspecific cell cytotoxic mechanisms that nevertheless may contribute to the elicitation of specific immune responses. J. Cell. Physiol. 231: 2639-2651, 2016. © 2016 Wiley Periodicals, Inc.

Bayesian integrated testing strategy (ITS) for skin sensitization potency assessment: a decision support system for quantitative weight of evidence and adaptive testing strategy

The presented Bayesian network Integrated Testing Strategy (ITS-3) for skin sensitization potency assessment is a decision support system for a risk assessor that provides quantitative weight of evidence, leading to a mechanistically interpretable potency hypothesis, and formulates adaptive testing strategy for a chemical. The system was constructed with an aim to improve precision and accuracy for predicting LLNA potency beyond ITS-2 (Jaworska et al., J Appl Toxicol 33(11):1353-1364, 2013) by improving representation of chemistry and biology. Among novel elements are corrections for bioavailability both in vivo and in vitro as well as consideration of the individual assays' applicability domains in the prediction process. In ITS-3 structure, three validated alternative assays, DPRA, KeratinoSens and h-CLAT, represent first three key events of the adverse outcome pathway for skin sensitization. The skin sensitization potency prediction is provided as a probability distribution over four potency classes. The probability distribution is converted to Bayes factors to: 1) remove prediction bias introduced by the training set potency distribution and 2) express uncertainty in a quantitative manner, allowing transparent and consistent criteria to accept a prediction. The novel ITS-3 database includes 207 chemicals with a full set of in vivo and in vitro data. The accuracy for predicting LLNA outcomes on the external test set (n = 60) was as follows: hazard (two classes)-100 %, GHS potency classification (three classes)-96 %, potency (four classes)-89 %. This work demonstrates that skin sensitization potency prediction based on data from three key events, and often less, is possible, reliable over broad chemical classes and ready for practical applications.

Toxicology: a discipline in need of academic anchoring--the point of view of the German Society of Toxicology

The paper describes the importance of toxicology as a discipline, its past achievements, current scientific challenges, and future development. Toxicological expertise is instrumental in the reduction of human health risks arising from chemicals and drugs. Toxicological assessment is needed to evaluate evidence and arguments, whether or not there is a scientific base for concern. The immense success already achieved by toxicological work is exemplified by reduced pollution of air, soil, water, and safer working places. Predominantly predictive toxicological testing is derived from the findings to assess risks to humans and the environment. Assessment of the adversity of molecular effects (including epigenetic effects), the effects of mixtures, and integration of exposure and biokinetics into in vitro testing are emerging challenges for toxicology. Toxicology is a translational science with its base in fundamental science. Academic institutions play an essential part by providing scientific innovation and education of young scientists.

A systems-biological study on the identification of safe and effective molecular targets for the reduction of ultraviolet B-induced skin pigmentation

Melanogenesis is the process of melanin synthesis through keratinocytes-melanocytes interaction, which is triggered by the damaging effect of ultraviolet-B (UVB) rays. It is known that melanogenesis influences diverse cellular responses, including cell survival and apoptosis, via complex mechanisms of feedback and crosstalk. Therefore, an attempt to suppress melanin production by modulating the melanogenesis pathway may induce perturbations in the apoptotic balance of the cells in response to UVB irradiation, which results in various skin diseases such as melasma, vitiligo, and skin cancer. To identify such appropriate target strategies for the reduction of UVB-induced melanin synthesis, we reconstructed the melanogenesis signaling network and developed a Boolean network model. Mathematical simulations of the melanogenesis network model revealed that the inhibition of beta-catenin in the melanocytes effectively reduce melanin production while having minimal influence on the apoptotic balance of the cells. Exposing cells to a beta-catenin inhibitor decreased pigmentation but did not significantly change the B-cell Chronic lymphocytic leukemia/lymphoma 2 expression, a potent regulator of apoptotic balance. Thus, our systems analysis suggests that the inhibition of beta-catenin may be the most appropriate target strategy for the reduction of UVB-induced skin pigmentation.

Unsaturated compounds induce up-regulation of CD86 on dendritic cells in the in vitro sensitization assay LCSA

Unsaturated compounds are known to cause false-positive reactions in the local lymph node assay (LLNA) but not in the guinea pig maximization test. We have tested a panel of substances (succinic acid, undecylenic acid, 1-octyn-3-ol, fumaric acid, maleic acid, linoleic acid, oleic acid, alpha-linolenic acid, squalene, and arachidonic acid) in the loose-fit coculture-based sensitization assay (LCSA) to evaluate whether unspecific activation of dendritic cells is a confounder for sensitization testing in vitro. Eight out of 10 tested substances caused significant up-regulation of CD86 on dendritic cells cocultured with keratinocytes and would have been classified as sensitizers; only succinic acid was tested negative, and squalene had to be excluded from data analysis due to poor solubility in cell culture medium. Based on human data, only undecylenic acid can be considered a true sensitizer. The true sensitizing potential of 1-octyn-3-ol is uncertain. Fumaric acid and its isomer maleic acid are not known as sensitizers, but their esters are contact allergens. A group of 18- to 20-carbon chain unsaturated fatty acids (linoleic acid, oleic acid, alpha-linolenic acid, and arachidonic acid) elicited the strongest reaction in vitro. This is possibly due to the formation of pro-inflammatory lipid mediators in the cell culture causing nonspecific activation of dendritic cells. In conclusion, both the LLNA and the LCSA seem to provide false-positive results for unsaturated fatty acids. The inclusion of T cells in dendritic cell-based in vitro sensitization assays may help to eliminate false-positive results due to nonspecific dendritic cell activation. This would lead to more accurate prediction of sensitizers, which is paramount for consumer health protection and occupational safety.

In vitro methods for hazard assessment of industrial chemicals - opportunities and challenges

Allergic contact dermatitis (ACD) is a delayed-type hypersensitivity immune reaction mediated by T-lymphocytes as a result of repeated exposure of an allergen primarily on skin. ACD accounts for up to 95% of occupational skin diseases, with epoxy resins implicated as one of the most common causes of ACD. Efficient high-throughput in vitro screening for accurate identification of compounds and materials that may pose hazardous risks in the workplace is crucial. At present, the murine local lymph node assay is the 'method of choice' for predicting the sensitizing potency of contact allergens. As the 3Rs principles of reduction, refinement, and replacement in animal testing has gained political and economic momentum, several in vitro screening methods have been developed for identifying potential contact allergens. To date, these latter methods have been utilized primarily to assess the skin sensitizing potential of the chemical components of cosmetic products with scant research attention as to the applicability of these methods to industrial chemicals, particularly epoxy resins. Herein we review the currently utilized in vitro methods and identify the knowledge gaps with regard to assessing the generalizability of in vitro screening methods for assessing the skin sensitizing potential of industrial chemicals.

Prediction of chemical respiratory sensitizers using GARD, a novel in vitro assay based on a genomic biomarker signature

Background

Repeated exposure to certain low molecular weight (LMW) chemical compounds may result in development of allergic reactions in the skin or in the respiratory tract. In most cases, a certain LMW compound selectively sensitize the skin, giving rise to allergic contact dermatitis (ACD), or the respiratory tract, giving rise to occupational asthma (OA). To limit occurrence of allergic diseases, efforts are currently being made to develop predictive assays that accurately identify chemicals capable of inducing such reactions. However, while a few promising methods for prediction of skin sensitization have been described, to date no validated method, in vitro or in vivo, exists that is able to accurately classify chemicals as respiratory sensitizers.

Results

Recently, we presented the in vitro based Genomic Allergen Rapid Detection (GARD) assay as a novel testing strategy for classification of skin sensitizing chemicals based on measurement of a genomic biomarker signature. We have expanded the applicability domain of the GARD assay to classify also respiratory sensitizers by identifying a separate biomarker signature containing 389 differentially regulated genes for respiratory sensitizers in comparison to non-respiratory sensitizers. By using an independent data set in combination with supervised machine learning, we validated the assay, showing that the identified genomic biomarker is able to accurately classify respiratory sensitizers.

Conclusions

We have identified a genomic biomarker signature for classification of respiratory sensitizers. Combining this newly identified biomarker signature with our previously identified biomarker signature for classification of skin sensitizers, we have developed a novel in vitro testing strategy with a potent ability to predict both skin and respiratory sensitization in the same sample.