Assessing skin sensitization hazard in mice and men using non-animal test methods

An in vitro skin sensitization assay termed EpiSensA for broad sets of chemicals including lipophilic chemicals and pre/pro-haptens

To evaluate chemicals (e.g. lipophilic chemicals, pre/pro-haptens) that are difficult to correctly evaluate using in vitro skin sensitization tests (e.g. DPRA, KeratinoSens or h-CLAT), we developed a novel in vitro test termed "Epidermal Sensitization Assay: EpiSensA" that uses reconstructed human epidermis. This assay is based on the induction of multiple marker genes (ATF3, IL-8, DNAJB4 and GCLM) related to two keratinocyte responses (inflammatory or cytoprotective) in the induction of skin sensitization. Here, we first confirmed the mechanistic relevance of these marker genes by focusing on key molecules that regulate keratinocyte responses in vivo (P2X₇ for inflammatory and Nrf2 for cytoprotective responses). The up-regulation of ATF3 and IL-8, or DNAJB4 and GCLM induced by the representative sensitizer 2,4-dinitrochlorobenzene in human keratinocytes was significantly suppressed by a P2X₇ specific antagonist KN-62, or by Nrf2 siRNA, respectively, which supported mechanistic relevance of marker genes. Moreover, the EpiSensA had sensitivity, specificity and accuracy of 93%, 100% and 93% for 29 lipophilic chemicals (logKow≥3.5), and of 96%, 75% and 88% for 43 hydrophilic chemicals including 11 pre/pro-haptens, compared with the LLNA. These results suggested that the EpiSensA could be a mechanism-based test applicable to broad sets of chemicals including lipophilic chemicals and pre/pro-haptens.

Skin Sensitisation, Adverse Outcome Pathways and Alternatives

For toxicologists who are in any way associated with skin sensitisation, the last two decades have seen a series of fundamental changes. We have migrated from old-style guinea-pig assays, via the refined and reduced Local Lymph Node Assay (LLNA), to witness the imminent dominance of in vitro and in silico methods. Yet, over the same period, the use of the output data for human safety assurance has evolved from ‘black box’ risk assessment, via the quantitative risk assessment enabled by the LLNA measurement of potency, to a new period of relative uncertainty. This short review will endeavour to address these topics, all the while keeping a focus on three essential principles: a) that skin sensitisation potential is intrinsic in the molecular structure of the chemical; b) that test methods should have a mechanistic foundation; and finally c) that the only reason for undertaking any skin sensitisation work has to be the protection of human health.

Nrf2 Activation as a Key Event Triggered by Skin Sensitisers: The Development of the Stable KeratinoSens Reporter Gene Assay

The 21st century paradigm for toxicology and the adverse outcome pathway concept envisage a future toxicology largely based on mechanistic in vitro assays and relying mainly on cellular models. In the skin sensitisation field, this concept was not intuitive at the beginning. Given the high structural diversity of skin sensitising molecules, classical receptor binding as the molecular initiating event in a cell-based assay could be excluded from the start, leaving the question of how cells could sense potential skin sensitising chemicals and be able to differentiate them from non-sensitisers. When we entered this field in 2006, we realised that, in another emerging field of toxicology, detailed work on the antioxidant/electrophile sensing pathway Keap1/Nrf2/ARE was being performed. We postulated that, based on their intrinsic electrophilicity, a large structural variety of skin sensitisers would activate this pathway. This was demonstrated in a preliminary pilot study with an existing, breast cancer-derived reporter cell line. Broader confirmation of this initial hypothesis then came from a multitude of genome-wide studies, in which sensitiser-induced changes to the transcriptome were investigated. The results showed that this regulatory pathway is indeed the most common regulatory pathway activated by sensitisers at the gene expression level, and the underlying event in keratinocytes has become formalised as a Key Event in the Organisation for Economic Co-operation and Development (OECD) Adverse Outcome Pathway for sensitisation. These studies led to the development of the KeratinoSens® assay, which became the first cell-based in vitro test for skin sensitisation to be endorsed by a European Union Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM) statement and an OECD Test Guideline. More recently, a number of studies have further developed this approach into 3-D skin models. Here, we review the underlying mechanism and the development of the KeratinoSens assay. We also present data on the stability of the assay over time, which is a key requirement for a cell-based biological assay to be endorsed in a regulatory context.

State of the art in non-animal approaches for skin sensitization testing: from individual test methods towards testing strategies

The hazard assessment of skin sensitizers relies mainly on animal testing, but much progress is made in the development, validation and regulatory acceptance and implementation of non-animal predictive approaches. In this review, we provide an update on the available computational tools and animal-free test methods for the prediction of skin sensitization hazard. These individual test methods address mostly one mechanistic step of the process of skin sensitization induction. The adverse outcome pathway (AOP) for skin sensitization describes the key events (KEs) that lead to skin sensitization. In our review, we have clustered the available test methods according to the KE they inform: the molecular initiating event (MIE/KE1)-protein binding, KE2-keratinocyte activation, KE3-dendritic cell activation and KE4-T cell activation and proliferation. In recent years, most progress has been made in the development and validation of in vitro assays that address KE2 and KE3. No standardized in vitro assays for T cell activation are available; thus, KE4 cannot be measured in vitro. Three non-animal test methods, addressing either the MIE, KE2 or KE3, are accepted as OECD test guidelines, and this has accelerated the development of integrated or defined approaches for testing and assessment (e.g. testing strategies). The majority of these approaches are mechanism-based, since they combine results from multiple test methods and/or computational tools that address different KEs of the AOP to estimate skin sensitization potential and sometimes potency. Other approaches are based on statistical tools. Until now, eleven different testing strategies have been published, the majority using the same individual information sources. Our review shows that some of the defined approaches to testing and assessment are able to accurately predict skin sensitization hazard, sometimes even more accurate than the currently used animal test. A few defined approaches are developed to provide an estimate of the potency sub-category of a skin sensitizer as well, but these approaches need further independent evaluation with a new dataset of chemicals. To conclude, this update shows that the field of non-animal approaches for skin sensitization has evolved greatly in recent years and that it is possible to predict skin sensitization hazard without animal testing.

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.

Preliminary performance data of the RHE/IL-18 assay performed on SkinEthic™ RHE for the identification of contact sensitizers

OBJECTIVE

The purpose of this study was to evaluate the performances of the RHE/IL-18 assay using the SkinEthic^™ RHE model for the identification of contact sensitizers.

METHODS

A set of 18 substances and mixtures was tested on this epidermal model, following the RHE/IL-18 protocol. The final results of the assay were obtained following 5 interpretation schemes, to determine the optimal prediction model for this assay with this specific test system. The data were analysed with a special focus on the basal level of IL-18 release and on the performance obtained with respect to three different gold standards: LLNA, HRIPT and an integrated reference, constructed from all available results.

RESULTS

No important differences were found in the performance levels depending on the three gold standards. The performances obtained with the SkinEthic^™ RHE model support that this model may be considered as an alternative to different reconstructed epidermis models (EpiDERM^™ , EpiCS^™ and VUMC-EE) for the performance of RHE/IL-18 assays.

CONCLUSION

The prediction model to be used was refined, and more substances have to be tested in order to gather enough data for this evaluation and to determine the right criteria applicable for this assay using the SkinEthic^™ RHE test system.

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.

Chemical applicability domain of the Local Lymph Node Assay (LLNA) for skin sensitisation potency. Part 2. The biological variability of the murine Local Lymph Node Assay (LLNA) for skin sensitisation

The Local Lymph Node Assay (LLNA) is the most common in vivo regulatory toxicology test for skin sensitisation, quantifying potency as the EC3, the concentration of chemical giving a threefold increase in thymidine uptake in the local lymph node. Existing LLNA data can, along with clinical data, provide useful comparator information on the potency of sensitisers. Understanding of the biological variability of data from LLNA studies is important for those developing non-animal based risk assessment approaches for skin allergy. Here an existing set of 94 EC3 values for 12 chemicals, all tested at least three times in the same vehicle have been analysed by calculating standard deviations (SD) for logEC3 values. The SDs range from 0.08 to 0.22. The overall SD for the 94 logEC3 values is 0.147. Thus the 95% confidence limits (2xSD) for LLNA EC3 values are within a factor of 2, comparable to those for physico-chemical measurements such as partition coefficients and solubility. The residual SDs of Quantitative Mechanistic Models (QMMs) based on physical organic chemistry parameters are similar to the overall SD of the LLNA, indicating that QMMs of this type are unlikely to be bettered for predictive accuracy.

In vitro Models to Evaluate Drug-Induced Hypersensitivity: Potential Test Based on Activation of Dendritic Cells

Hypersensitivity drug reactions (HDRs) are the adverse effect of pharmaceuticals that clinically resemble allergy. HDRs account for approximately 1/6 of drug-induced adverse effects, and include immune-mediated ("allergic") and non-immune-mediated ("pseudo allergic") reactions. In recent years, the severe and unpredicted drug adverse events clearly indicate that the immune system can be a critical target of drugs. Enhanced prediction in preclinical safety evaluation is, therefore, crucial. Nowadays, there are no validated in vitro or in vivo methods to screen the sensitizing potential of drugs in the pre-clinical phase. The problem of non-predictability of immunologically-based hypersensitivity reactions is related to the lack of appropriate experimental models rather than to the lack of -understanding of the adverse phenomenon. We recently established experimental conditions and markers to correctly identify drug associated with in vivo hypersensitivity reactions using THP-1 cells and IL-8 production, CD86 and CD54 expression. The proposed in vitro method benefits from a rationalistic approach with the idea that allergenic drugs share with chemical allergens common mechanisms of cell activation. This assay can be easily incorporated into drug development for hazard identification of drugs, which may have the potential to cause in vivo hypersensitivity reactions. The purpose of this review is to assess the state of the art of in vitro models to assess the allergenic potential of drugs based on the activation of dendritic cells.

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).

Immune-competent human skin disease models

All skin diseases have an underlying immune component. Owing to differences in animal and human immunology, the majority of drugs fail in the preclinical or clinical testing phases. Therefore animal alternative methods that incorporate human immunology into in vitro skin disease models are required to move the field forward. This review summarizes the progress, using examples from fibrosis, autoimmune diseases, psoriasis, cancer and contact allergy. The emphasis is on co-cultures and 3D organotypic models. Our conclusion is that current models are inadequate and future developments with immune-competent skin-on-chip models based on induced pluripotent stem cells could provide a next generation of skin models for drug discovery and testing.

Analysis of the Local Lymph Node Assay (LLNA) variability for assessing the prediction of skin sensitisation potential and potency of chemicals with non-animal approaches

The knowledge of the biological mechanisms leading to the induction of skin sensitisation has favoured in recent years the development of alternative non-animal methods. During the formal validation process, results from the Local Lymph Node Assay (LLNA) are generally used as reference data to assess the predictive capacity of the non-animal tests. This study reports an analysis of the variability of the LLNA for a set of chemicals for which multiple studies are available and considers three hazard classification schemes: POS/NEG, GHS/CLP and ECETOC. As the type of vehicle used in a LLNA study is known to influence to some extent the results, two analyses were performed: considering the solvent used to test the chemicals and without considering the solvent. The results show that the number of discordant classifications increases when a chemical is tested in more than one solvent. Moreover, it can be concluded that study results leading to classification in the strongest classes (1A and EXT) seem to be more reliable than those in the weakest classes. This study highlights the importance of considering the variability of the reference data when evaluating non-animal tests.

Evaluation of a High-Throughput Peptide Reactivity Format Assay for Assessment of the Skin Sensitization Potential of Chemicals

The direct peptide reactivity assay (DPRA) is a validated method for in vitro assessment of the skin sensitization potential of chemicals. In the present work, we describe a peptide reactivity assay using 96-well plate format and systematically identified the optimal assay conditions for accurate and reproducible classification of chemicals with known sensitizing capacity. The aim of the research is to ensure that the analytical component of the peptide reactivity assay is robust, accurate, and reproducible in accordance with criteria that are used for the validation of bioanalytical methods. Analytical performance was evaluated using quality control samples (QCs; heptapeptides at low, medium, and high concentrations) and incubation of control chemicals (chemicals with known sensitization capacity, weak, moderate, strong, extreme, and non-sensitizers) with each of three synthetic heptapeptides, viz Cor1-C420 (Ac-NKKCDLF), cysteine- (Ac-RFAACAA), and lysine- (Ac-RFAAKAA) containing heptapeptides. The optimal incubation temperature for all three heptapeptides was 25°C. Apparent heptapeptide depletion was affected by vial material composition. Incubation of test chemicals with Cor1-C420, showed that peptide depletion was unchanged in polypropylene vials over 3-days storage in an autosampler but this was not the case for borosilicate glass vials. For cysteine-containing heptapeptide, the concentration was not stable by day 3 post-incubation in borosilicate glass vials. Although the lysine-containing heptapeptide concentration was unchanged in both polypropylene and borosilicate glass vials, the apparent extent of lysine-containing heptapeptide depletion by ethyl acrylate, differed between polypropylene (24.7%) and glass (47.3%) vials. Additionally, the peptide-chemical complexes for Cor1-C420-cinnamaldehyde and cysteine-containing heptapeptide-2, 4-dinitrochlorobenzene were partially reversible during 3-days of autosampler storage. These observations further highlight the difficulty in adapting in vitro methods to high-throughput format for screening the skin sensitization potential of large numbers of chemicals whilst ensuring that the data produced are both accurate and reproducible.

Analysis of publically available skin sensitization data from REACH registrations 2008-2014

The public data on skin sensitization from REACH registrations already included 19,111 studies on skin sensitization in December 2014, making it the largest repository of such data so far (1,470 substances with mouse LLNA, 2,787 with GPMT, 762 with both in vivo and in vitro and 139 with only in vitro data). 21% were classified as sensitizers. The extracted skin sensitization data was analyzed to identify relationships in skin sensitization guidelines, visualize structural relationships of sensitizers, and build models to predict sensitization.

A chemical with molecular weight > 500 Da is generally considered non-sensitizing owing to low bioavailability, but 49 sensitizing chemicals with a molecular weight > 500 Da were found.

A chemical similarity map was produced using PubChem’s 2D Tanimoto similarity metric and Gephi force layout visualization. Nine clusters of chemicals were identified by Blondel’s module recognition algorithm revealing wide module-dependent variation.

Approximately 31% of mapped chemicals are Michael’s acceptors but alone this does not imply skin sensitization. A simple sensitization model using molecular weight and five ToxTree structural alerts showed a balanced accuracy of 65.8% (specificity 80.4%, sensitivity 51.4%), demonstrating that structural alerts have information value.

A simple variant of k-nearest neighbors outperformed the ToxTree approach even at 75% similarity threshold (82% balanced accuracy at 0.95 threshold). At higher thresholds, the balanced accuracy increased. Lower similarity thresholds decrease sensitivity faster than specificity.

This analysis scopes the landscape of chemical skin sensitization, demonstrating the value of large public datasets for health hazard prediction.

Predicting skin sensitisation using a decision tree integrated testing strategy with an in silico model and in chemico/in vitro assays

There is a pressing need for non-animal methods to predict skin sensitisation potential and a number of in chemico and in vitro assays have been designed with this in mind. However, some compounds can fall outside the applicability domain of these in chemico/in vitro assays and may not be predicted accurately. Rule-based in silico models such as Derek Nexus are expert-derived from animal and/or human data and the mechanism-based alert domain can take a number of factors into account (e.g. abiotic/biotic activation). Therefore, Derek Nexus may be able to predict for compounds outside the applicability domain of in chemico/in vitro assays. To this end, an integrated testing strategy (ITS) decision tree using Derek Nexus and a maximum of two assays (from DPRA, KeratinoSens, LuSens, h-CLAT and U-SENS) was developed. Generally, the decision tree improved upon other ITS evaluated in this study with positive and negative predictivity calculated as 86% and 81%, respectively. Our results demonstrate that an ITS using an in silico model such as Derek Nexus with a maximum of two in chemico/in vitro assays can predict the sensitising potential of a number of chemicals, including those outside the applicability domain of existing non-animal assays.

Intra- and inter-laboratory reproducibility and accuracy of the LuSens assay: A reporter gene-cell line to detect keratinocyte activation by skin sensitizers

Several non-animal methods are now available to address the key events leading to skin sensitization as defined by the adverse outcome pathway. The KeratinoSens assay addresses the cellular event of keratinocyte activation and is a method accepted under OECD TG 442D. In this study, the results of an inter-laboratory evaluation of the "me-too" LuSens assay, a bioassay that uses a human keratinocyte cell line harboring a reporter gene construct composed of the rat antioxidant response element (ARE) of the NADPH:quinone oxidoreductase 1 gene and the luciferase gene, are described. Earlier in-house validation with 74 substances showed an accuracy of 82% in comparison to human data. When used in a battery of non-animal methods, even higher predictivity is achieved. To meet European validation criteria, a multicenter study was conducted in 5 laboratories. The study was divided into two phases, to assess 1) transferability of the method, and 2) reproducibility and accuracy. Phase I was performed by testing 8 non-coded test substances; the results showed a good transferability to naïve laboratories even without on-site training. Phase II was performed with 20 coded test substances (performance standards recommended by OECD, 2015). In this phase, the intra- and inter-laboratory reproducibility as well as accuracy of the method was evaluated. The data demonstrate a remarkable reproducibility of 100% and an accuracy of over 80% in identifying skin sensitizers, indicating a good concordance with in vivo data. These results demonstrate good transferability, reliability and accuracy of the method thereby achieving the standards necessary for use in a regulatory setting to detect skin sensitizers.

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.

Probabilistic hazard assessment for skin sensitization potency by dose-response modeling using feature elimination instead of quantitative structure-activity relationships

Supervised learning methods promise to improve integrated testing strategies (ITS), but must be adjusted to handle high dimensionality and dose-response data. ITS approaches are currently fueled by the increasing mechanistic understanding of adverse outcome pathways (AOP) and the development of tests reflecting these mechanisms. Simple approaches to combine skin sensitization data sets, such as weight of evidence, fail due to problems in information redundancy and high dimensionality. The problem is further amplified when potency information (dose/response) of hazards would be estimated. Skin sensitization currently serves as the foster child for AOP and ITS development, as legislative pressures combined with a very good mechanistic understanding of contact dermatitis have led to test development and relatively large high-quality data sets. We curated such a data set and combined a recursive variable selection algorithm to evaluate the information available through in silico, in chemico and in vitro assays. Chemical similarity alone could not cluster chemicals' potency, and in vitro models consistently ranked high in recursive feature elimination. This allows reducing the number of tests included in an ITS. Next, we analyzed with a hidden Markov model that takes advantage of an intrinsic inter-relationship among the local lymph node assay classes, i.e. the monotonous connection between local lymph node assay and dose. The dose-informed random forest/hidden Markov model was superior to the dose-naive random forest model on all data sets. Although balanced accuracy improvement may seem small, this obscures the actual improvement in misclassifications as the dose-informed hidden Markov model strongly reduced " false-negatives" (i.e. extreme sensitizers as non-sensitizer) on all data sets.

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.

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.

Proposing a scientific confidence framework to help support the application of adverse outcome pathways for regulatory purposes

An adverse outcome pathway (AOP) describes the causal linkage between initial molecular events and an adverse outcome at individual or population levels. Whilst there has been considerable momentum in AOP development, far less attention has been paid to how AOPs might be practically applied for different regulatory purposes. This paper proposes a scientific confidence framework (SCF) for evaluating and applying a given AOP for different regulatory purposes ranging from prioritizing chemicals for further evaluation, to hazard prediction, and ultimately, risk assessment. The framework is illustrated using three different AOPs for several typical regulatory applications. The AOPs chosen are ones that have been recently developed and/or published, namely those for estrogenic effects, skin sensitisation, and rodent liver tumor promotion. The examples confirm how critical the data-richness of an AOP is for driving its practical application. In terms of performing risk assessment, human dosimetry methods are necessary to inform meaningful comparisons with human exposures; dosimetry is applied to effect levels based on non-testing approaches and in vitro data. Such a comparison is presented in the form of an exposure:activity ratio (EAR) to interpret biological activity in the context of exposure and to provide a basis for product stewardship and regulatory decision making.

Alternative Toxicity Testing: Analyses on Skin Sensitization, ToxCast Phases I and II, and Carcinogenicity Provide Indications on How to Model Mechanisms Linked to Adverse Outcome Pathways

This article studies alternative toxicological approaches, with new (skin sensitization, ToxCast) and previous (carcinogenicity) analyses. Quantitative modeling of rate-limiting steps in skin sensitization and carcinogenicity predicts the majority of toxicants. Similarly, successful (Quantitative) Structure-Activity Relationships models exploit the quantification of only one, or few rate-limiting steps. High-throughput assays within ToxCast point to promising associations with endocrine disruption, whereas markers for pathways intermediate events have limited correlation with most endpoints. Since the pathways may be very different (often not simple linear chains of events), quantitative analysis is necessary to identify the type of mechanism and build the appropriate model.