Evaluating the performance of integrated approaches for hazard identification of skin sensitizing chemicals

Biological aspects of modern dental composites

Biological evaluation of resin-based dental composites has traditionally been based on in vitro endpoint tests with different methods to determine loss of cell viability and cell morphology changes after exposure to the material or monomer constituents. The data reveals a potential for biological effects, but clinical relevance of such data is limited. Positive allergy tests and allergic clinical reactions to dental monomers are observed in dental personnel and patients. The aim of this review is to address newer research on molecular events caused by exposure to resin-based composites to have a better understanding of the potential for clinical adverse effects. A more accurate understanding of the biological aspects of dental composite materials has been found after studying parameters like glutathione depletion, oxidative stress, genotoxicity, and immunomodulatory key effects in various cell culture models. Using omics-based approaches allow for a broader and non-specified search of changes caused by methacrylate exposure. Defense mechanisms and adaption are observed in cells exposed to monomer concentrations relevant to clinical exposure. The above-mentioned methods are the foundations for modified testing strategies. The clinical relevance of most available in vitro endpoint tests is of limited relevance for the patient. Research focusing on molecular mechanisms has given new insight into methacrylate toxicity in exposed cells. Using this knowledge from mechanistic studies to develop standardized in vitro biocompatibility tests will likely improve their clinical relevance.

Alternative Methods for Skin-Sensitization Assessment

Skin sensitization is a term used to refer to the regulatory hazard known as allergic contact dermatitis (ACD) in humans or contact hypersensitivity in rodents, an important health endpoint considered in chemical hazard and risk assessments. Information on skin sensitization potential is required in various regulatory frameworks, such as the Directive of the European Parliament and the Council on Registration, Evaluation and Authorization of Chemicals (REACH). The identification of skin-sensitizing chemicals previously required the use of animal testing, which is now being replaced by alternative methods. Alternative methods in the field of skin sensitization are based on the measurement or prediction of key events (KE), i.e., (i) the molecular triggering event, i.e., the covalent binding of electrophilic substances to nucleophilic centers in skin proteins; (ii) the activation of keratinocytes; (iii) the activation of dendritic cells; (iv) the proliferation of T cells. This review article focuses on the current state of knowledge regarding the methods corresponding to each of the key events in skin sensitization and considers the latest trends in the development and modification of these methods.

Amino Reactivity of Glutardialdehyde and Monoaldehydes─Chemoassay Profile vs Skin Sensitization Potency

Chemoassay profiling of organic electrophiles through the direct peptide reactivity assay has become an OECD-accepted nonanimal component in the REACH evaluation of potential skin sensitizers. For aldehydes forming imines (Schiff bases), however, existing chemoassays yielded inconclusive results, indicating issues with their NH₂ sensitivity and the reversibility of the reaction. In the present study, a new kinetic chemoassay employing the N terminus of glycine-para-nitroanilide, Gly-pNA, as a model nucleophile for protein NH₂ groups is introduced and applied to nine aliphatic monoaldehydes and glutardialdehyde (1,5-pentanedial) that have log K_ow (octanol/water partition coefficient) values from 0.63 to 3.99. The Gly-pNA second-order rate constants k₁ range from 8.56 to 150 L·mol^-1·min^-1 for the monoaldehydes. Interestingly, glutardialdehyde with a k₁ of 17 731 L·mol^-1·min^-1 is 170-fold more reactive than its monoaldehyde counterpart pentanal. This can be rationalized by hydration or tautomerization of the dialdehyde to monoaldehydic forms, now facilitating Schiff base formation through an intramolecular H bond. Comparison with murine local lymph node assay data from the literature reveals that adduct stability in terms of reaction thermodynamics (K = k₁/k_-1^pseudo) rather than formation kinetics (k₁) governs the skin sensitization potency of Schiff-base-forming aldehydes. The discussion includes analytically determined adduct patterns, and the impact of α- and β-carbon substitution as well as hydrophobicity on aldehyde reactivity.

Semi-correlations as a tool to model for skin sensitization

Semi-correlation specifically assesses the correlation between a binary variable and a continuous variable. Semi-correlations were applied to develop binary models for various endpoints. We applied the semi-correlation to develop models of two kinds of skin sensitization one related to animals (local lymph node assay LLNA) and one to human beings (direct peptide reactivity assay DPRA and/or human cell line activation test h-CLAT). The models refer to binary classification for a two-level strategy: the first level (analysis of all compounds) is used in the format "sensitizer or non-sensitizer", and the second level (only sensitizers) is a further classification in the format "strong or weak sensitizer". The ranges of statistical characteristics of the models depend on the endpoint, LLNA or DPRA/h-CLAT: for the first level, sensitivity: 0.69-0.88, specificity: 0.75-0.89, accuracy: 0.77-0.87, Matthew's correlation coefficient (MCC): 0.54-0.57 and for the second level, sensitivity: 0.70-1.0, specificity: 0.78-0.83, accuracy: 0.77-0.87, MCC: 0.54-0.76. Thus, the described approach can be applied to building up models of the skin sensitization potency.

Towards a tiered test strategy for plant protection products to address mixture toxicity by alternative approaches in human health assessment

Plant protection products (PPPs) consist of pesticide active substances and co-formulants. Generally, active substance effects are assumed to dominate in PPP toxicity. Nevertheless, co-formulants may well affect the toxicity of PPPs via toxicodynamic and toxicokinetic interaction. To account for potential mixture effects and improve PPP data requirements for application in risk assessment, a tiered test strategy is proposed. The strategy is based on a comparison of PPP and active substance toxicity, which enables the prioritisation of PPPs for further testing, adaptation of the toxicological threshold value or removal of toxic co-formulants from the PPP. Moreover, it focuses on the integrative assessment of existing information and newly generated data using alternative test methods. The proposed strategy will improve PPP toxicological assessment by accounting for mixture toxicity, providing a set of regulatory options for risk assessment and the necessary data for hazard assessment. The predictivity of alternative methods for PPPs will improve by evaluation of their reliability and uncertainty. © 2020 The Authors. Pest Management Science published by JohnWiley & Sons Ltd on behalf of Society of Chemical Industry.

Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy

In silico methods and models are increasingly used for predicting properties of chemicals for hazard identification and hazard characterisation in the absence of experimental toxicity data. Many in silico models are available and can be used individually or in an integrated fashion. Whilst such models offer major benefits to toxicologists, risk assessors and the global scientific community, the lack of a consistent framework for the integration of in silico results can lead to uncertainty and even contradictions across models and users, even for the same chemicals. In this context, a range of methods for integrating in silico results have been proposed on a statistical or case-specific basis. Read-across constitutes another strategy for deriving reference points or points of departure for hazard characterisation of untested chemicals, from the available experimental data for structurally-similar compounds, mostly using expert judgment. Recently a number of software systems have been developed to support experts in this task providing a formalised and structured procedure. Such a procedure could also facilitate further integration of the results generated from in silico models and read-across. This article discusses a framework on weight of evidence published by EFSA to identify the stepwise approach for systematic integration of results or values obtained from these "non-testing methods". Key criteria and best practices for selecting and evaluating individual in silico models are also described, together with the means to combining the results, taking into account any limitations, and identifying strategies that are likely to provide consistent results.

The 21st Century movement within the area of skin sensitization assessment: From the animal context towards current human-relevant in vitro solutions

In a regulatory context, skin sensitization hazard and risk evaluations of manufactured products and their ingredients (e.g. cosmetics) are mandatory in several regions. Great efforts have been made within the field of 21st Century Toxicology to provide non-animal testing approaches to assess the skin allergy potential of materials (e.g. chemicals, mixtures, nanomaterials, particles). Mechanistic understanding of skin sensitization process through the adverse outcome pathway (AOP) has promoted the development of in vitro methods, demonstrating accuracies superior to the traditional animal testing. These in vitro testing approaches are based on one of the four AOP key events (KE) of skin sensitization: formation of immunogenic hapten-protein complexes (KE-1 or the molecular initiating event, MIE), inflammatory keratinocyte responses (KE-2), dendritic cell activation (KE-3), and T-lymphocyte activation and proliferation (KE-4). This update provides an overview of the historically used in vivo methods as well as the current in chemico and in cell methods with and without OECD guideline designations to analyze the progress towards human-relevant in vitro test methods for safety assessment of the skin allergenicity potential of materials. Here our focus is to review 96 in vitro testing approaches directed to the KEs of the skin sensitization AOP.

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.

The activity of methacrylate esters in skin sensitisation test methods: A review

Skin sensitisation associated with allergic contact dermatitis is an important occupational and environmental disease. The identification of skin sensitisation hazards was traditionally performed using animal tests; originally guinea pig assays and subsequently the murine local lymph node assay (LLNA). More recently there has, for a variety of reasons, been an increased interest in, and requirement for, non-animal assays. There are now available both validated in vitro assays and a variety of approaches based on consideration of quantitative structure-activity relationships (QSAR). With the increased availability and use of non-animal alternatives for skin sensitisation testing there is a continuing need to monitor the performance of these approaches using series of chemicals that do not normally form part of validation exercises. Here we report studies conducted with 11 methacrylate esters and methacrylic acid in which results obtained with 3 validated in vitro tests for which there are OECD guidelines (the Direct Peptide Reactivity Assay, DPRA; ARE-Nrf2 luciferase test methods, and - with some chemicals - a dendritic cell activation test, the myeloid U937 Skin Sensitisation test [U-SENS] assay) have been compared with QSAR approaches (DEREK and TIMES-SS), and with LLNA and guinea pig maximisation test (GPMT) data. The conclusions drawn from these data are that - with this series of chemicals at least - there is a strong correlation between the results of animal tests and the in vitro assays considered, but not with either DEREK or TIMES-SS.

Contact Allergy to Fragrances: In Vitro Opportunities for Safety Assessment

The majority of cosmetic products contain fragrances to make products more pleasant to the consumer, as we all like goods that smell nice. Unfortunately, contact allergy to fragrance compounds is among the most frequent findings in patients with suspected allergic contact dermatitis. In order to revert this and to reduce contact allergy to cosmetics, it is imperative to improve safety assessment of cosmetic products for skin sensitization. In the era of animal ban for cosmetic ingredients, this represents a challenge. Luckily, in the last decades, substantial progress has been made in the understanding of the mechanism of chemical-induced contact allergy and several in vitro methods are available for hazard identification. The purpose of this manuscript is to explore the possibility of non-animal testing for quantitative risk assessment of fragrance-induced contact allergy, essential for cosmetic products, which cannot be tested on animals.

Skin and respiratory chemical allergy: confluence and divergence in a hybrid adverse outcome pathway

Sensitisation of the respiratory tract to chemicals resulting in respiratory allergy and allergic asthma is an important occupational health problem, and presents toxicologists with no shortage of challenges. A major issue is that there are no validated or, even widely recognised, methods available for the identification and characterisation of chemical respiratory allergens, or for distinguishing respiratory allergens from contact allergens. The first objective here has been review what is known (and what is not known) of the mechanisms through which chemicals induce sensitisation of the respiratory tract, and to use this information to construct a hybrid Adverse Outcome Pathway (AOP) that combines consideration of both skin and respiratory sensitisation. The intention then has been to use the construction of this hybrid AOP to identify areas of commonality/confluence, and areas of departure/divergence, between skin sensitisation and sensitisation of the respiratory tract. The hybrid AOP not only provides a mechanistic understanding of how the processes of skin and respiratory sensitisation differ, buy also a means of identifying areas of uncertainty about chemical respiratory allergy that benefit from a further investment in research.

Fragrances Categorized According to Relative Human Skin Sensitization Potency

Background

The development of non-animal alternatives for skin sensitization potency prediction is dependent upon the availability of a sufficient dataset whose human potency is well characterized. Previously, establishment of basic categorization criteria for 6 defined potency categories, allowed 131 substances to be allocated into them entirely on the basis of human information.

Objectives

To supplement the original dataset with an extended range of fragrance substances.

Methods

A more fully described version of the original criteria was used to assess 89 fragrance chemicals, allowing their allocation into one of the 6 potency categories.

Results

None of the fragrance substances were assigned to the most potent group, category 1, whereas 11 were category 2, 22 were category 3, 37 were category 4, and 19 were category 5. Although none were identified as non-sensitizing, note that substances in category 5 also do not pass the threshold for regulatory classification.

Conclusions

The combined datasets of >200 substances placed into potency categories solely on the basis of human data provides an essential resource for the elaboration and evaluation of predictive non-animal methods.

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.

Integrated testing strategies can be optimal for chemical risk classification

There is an urgent need to refine strategies for testing the safety of chemical compounds. This need arises both from the financial and ethical costs of animal tests, but also from the opportunities presented by new in-vitro and in-silico alternatives. Here we explore the mathematical theory underpinning the formulation of optimal testing strategies in toxicology. We show how the costs and imprecisions of the various tests, and the variability in exposures and responses of individuals, can be assembled rationally to form a Markov Decision Problem. We compute the corresponding optimal policies using well developed theory based on Dynamic Programming, thereby identifying and overcoming some methodological and logical inconsistencies which may exist in the current toxicological testing. By illustrating our methods for two simple but readily generalisable examples we show how so-called integrated testing strategies, where information of different precisions from different sources is combined and where different initial test outcomes lead to different sets of future tests, can arise naturally as optimal policies.

Comparison and validation of an in vitro skin sensitization strategy using a data set of 33 chemical references

Allergic contact dermatitis (ACD) is an adverse health effect that develops following repeated exposure to skin sensitizing chemicals. An animal testing ban has been applied in EU, leading to development of reliably predictive non-animal methods. Several in vitro methods have been developed as alternatives but one single non-animal test method is not been sufficient to fully address since the LLNA test ban. Here, we have selected an ITS (Integrated Testing Strategy) for skin sensitization which focuses on three in vitro methods that covered the first three steps of the AOP (DPRA, SENS-IS or h-CLAT). The aim of this study was to compare these three methods due to the WoE approach based on a 2-out-of-3-assessment. The results of 33 references were compared to in vivo data (especially human). We have shown that tested firstly DPRA and SENS-IS have permitted to conclude on 29 of 33 chemicals, whereas DPRA and h-CLAT on 25, and SENS-IS and h-CLAT on 23. With this sequence, DPRA and SENS-IS and then h-CLAT in case of equivocal results, we conclude more quickly by performing fewer tests. Thereby, we have shown that it is better to follow a preferential sequence than testing chemicals simultaneously with these three methods.

Application of IATA - A case study in evaluating the global and local performance of a Bayesian network model for skin sensitization

The information characterizing key events in an Adverse Outcome Pathway (AOP) can be generated from in silico, in chemico, in vitro and in vivo approaches. Integration of this information and interpretation for decision making are known as integrated approaches to testing and assessment (IATA). One such IATA was published by Jaworska et al., which describes a Bayesian network model known as ITS-2. The current work evaluated the performance of ITS-2 using a stratified cross-validation approach. We also characterized the impact of replacing the most significant component of the network, output from the expert system TIMES-SS, with structural alert information from the OECD Toolbox and Toxtree. Lack of structural alerts or TIMES-SS predictions yielded a sensitization potential prediction of 79%. If the TIMES-SS prediction was replaced by a structural alert indicator, the network predictivity increased up to 87%. The original network's predictivity was 89%. The local applicability domain of the original ITS-2 network was also evaluated using reaction mechanistic domains to understand what types of chemicals ITS-2 was able to make the best predictions for. We found that the original network was successful at predicting which chemicals would be sensitizers, but not at predicting their potency.

Behaviour of chemical respiratory allergens in novel predictive methods for skin sensitisation

Asthma resulting from sensitisation of the respiratory tract to chemicals is an important occupational health issue, presenting many toxicological challenges. Most importantly there are no recognised predictive methods for respiratory allergens. Nevertheless, it has been found that all known chemical respiratory allergens elicit positive responses in assays for skin sensitising chemicals. Thus, chemicals failing to induce a positive response in skin sensitisation assays such as the local lymph node assay (LLNA) lack not only skin sensitising activity, but also the potential to cause respiratory sensitisation. However, it is unclear whether it will be possible to regard chemicals that are negative in in vitro skin sensitisation tests also as lacking respiratory sensitising activity. To address this, the behaviour of chemical respiratory allergens in the LLNA and in recently validated non-animal tests for skin sensitisation have been examined. Most chemical respiratory allergens are positive in one or more newly validated non-animal test methods, although the situation varies between individual assays. The use of an integrated testing strategy could provide a basis for recognition of most respiratory sensitising chemicals. However, a more complete picture of the performance characteristics of such tests is required before specific recommendations can be made.

How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24-25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.

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.

The adverse outcome pathway for skin sensitisation: Moving closer to replacing animal testing

This article outlines the work of the Organisation for Economic Co-operation and Development (OECD) that led to being jointly awarded the 2015 Lush Black Box Prize. The award-winning work centred on the development of 'The Adverse Outcome Pathway for Skin Sensitisation Initiated by Covalent Binding to Proteins'. This Adverse Outcome Pathway (AOP) has provided the mechanistic basis for the integration of skin sensitisation-related information. Recent developments in integrated approaches to testing and assessment, based on the AOP, are summarised. The impact of the AOP on regulatory policy and on the Three Rs are discussed. An overview of the next generation of the skin sensitisation AOP module in the OECD QSAR Toolbox, based on more-recent work at the Laboratory of Mathematical Chemistry, is also presented.

The use of peptide reactivity assays for skin sensitisation hazard identification and risk assessment

Over the past 20 years or more, investigators have been developing non-animal test methods for use in assessing the skin sensitisation potential of chemicals. In parallel with this effort, the key biological events of skin sensitisation have been well-characterised in an Adverse Outcome Pathway (AOP) proposed by the Organisation for Economic Co-operation and Development (OECD). The key molecular initiating event of this AOP is haptenation or covalent modification of epidermal proteins. In this review, the strengths and limitations of the Direct Peptide Reactivity Assay (DPRA) are described, and the more recently developed Peroxidase Peptide Reactivity Assay (PPRA). The DPRA has been formally validated and incorporated into an OECD Test Guideline (TG442C). The DPRA shows promise for assisting in hazard identification as well as for assessing skin sensitisation potency when used in an integrated testing strategy.

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.

Fragility of epidermis in newborns, children and adolescents

Within their first days of life, newborns' skin undergoes various adaptation processes needed to accommodate the transition from the wet uterine environment to the dry atmosphere. The skin of newborns and infants is considered as a physiological fragile skin, a skin with lower resistance to aggressions. Fragile skin is divided into four categories up to its origin: physiological fragile skin (age, location), pathological fragile skin (acute and chronic), circumstantial fragile skin (due to environmental extrinsic factors or intrinsic factors such as stress) and iatrogenic fragile skin. Extensive research of the past 10 years have proven evidence that at birth albeit showing a nearly perfect appearance, newborn skin is structurally and functionally immature compared to adult skin undergoing a physiological maturation process after birth at least throughout the first year of life. This article is an overview of all known data about fragility of epidermis in 'fragile populations': newborns, children and adolescents. It includes the recent pathological, pathophysiological and clinical data about fragility of epidermis in various dermatological diseases, such as atopic dermatitis, acne, rosacea, contact dermatitis, irritative dermatitis and focus on UV protection.

QSAR models of human data can enrich or replace LLNA testing for human skin sensitization

Skin sensitization is a major environmental and occupational health hazard. Although many chemicals have been evaluated in humans, there have been no efforts to model these data to date. We have compiled, curated, analyzed, and compared the available human and LLNA data. Using these data, we have developed reliable computational models and applied them for virtual screening of chemical libraries to identify putative skin sensitizers. The overall concordance between murine LLNA and human skin sensitization responses for a set of 135 unique chemicals was low (R = 28-43%), although several chemical classes had high concordance. We have succeeded to develop predictive QSAR models of all available human data with the external correct classification rate of 71%. A consensus model integrating concordant QSAR predictions and LLNA results afforded a higher CCR of 82% but at the expense of the reduced external dataset coverage (52%). We used the developed QSAR models for virtual screening of CosIng database and identified 1061 putative skin sensitizers; for seventeen of these compounds, we found published evidence of their skin sensitization effects. Models reported herein provide more accurate alternative to LLNA testing for human skin sensitization assessment across diverse chemical data. In addition, they can also be used to guide the structural optimization of toxic compounds to reduce their skin sensitization potential.

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.

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.

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.

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).

Biology-inspired microphysiological system approaches to solve the prediction dilemma of substance testing

The recent advent of microphysiological systems - microfluidic biomimetic devices that aspire to emulate the biology of human tissues, organs and circulation in vitro - is envisaged to enable a global paradigm shift in drug development. An extraordinary US governmental initiative and various dedicated research programs in Europe and Asia have led recently to the first cutting-edge achievements of human single-organ and multi-organ engineering based on microphysiological systems. The expectation is that test systems established on this basis would model various disease stages, and predict toxicity, immunogenicity, ADME profiles and treatment efficacy prior to clinical testing. Consequently, this technology could significantly affect the way drug substances are developed in the future. Furthermore, microphysiological system-based assays may revolutionize our current global programs of prioritization of hazard characterization for any new substances to be used, for example, in agriculture, food, ecosystems or cosmetics, thus, replacing laboratory animal models used currently. Thirty-six experts from academia, industry and regulatory bodies present here the results of an intensive workshop (held in June 2015, Berlin, Germany). They review the status quo of microphysiological systems available today against industry needs, and assess the broad variety of approaches with fit-for-purpose potential in the drug development cycle. Feasible technical solutions to reach the next levels of human biology in vitro are proposed. Furthermore, key organ-on-a-chip case studies, as well as various national and international programs are highlighted. Finally, a roadmap into the future is outlined, to allow for more predictive and regulatory-accepted substance testing on a global scale.

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.