Consensus of classification trees for skin sensitisation hazard prediction

Application of Virtual Drug Study to New Drug Research and Development: Challenges and Opportunity

In recent years, virtual drug study, as an emerging research strategy, has become increasingly important in guiding and promoting new drug research and development. Researchers can integrate a variety of technical methods to improve the efficiency of all phases of new drug research and development, including the use of artificial intelligence, modeling and simulation for target identification, compound screening and pharmacokinetic characteristics evaluation, and the application of clinical trial simulation to carry out clinical research. This paper aims to elaborate on the application of virtual drug study in the key stages of new drug research and development and discuss the opportunities and challenges it faces in supporting new drug research and development.

In Silico Prediction of Skin Sensitization for Compounds via Flexible Evidence Combination Based on Machine Learning and Dempster-Shafer Theory

Skin sensitization is increasingly becoming a significant concern in the development of drugs and cosmetics due to consumer safety and occupational health problems. In silico methods have emerged as alternatives to traditional in vivo animal testing due to ethical and economic considerations. In this study, machine learning methods were used to build quantitative structure-activity relationship (QSAR) models on five skin sensitization data sets (GPMT, LLNA, DPRA, KeratinoSens, and h-CLAT), achieving effective predictive accuracies (correct classification rates of 0.688-0.764 on test sets). To address the complex mechanisms of human skin sensitization, the Dempster-Shafer theory was applied to merge multiple QSAR models, resulting in an evidence-based integrated decision model. Various evidence combinations and combination rules were explored, with the self-defined Q3 rule showing superior balance. The combination of evidence such as GPMT and KeratinoSens and h-CLAT achieved a correct classification rate (CCR) of 0.880 and coverage of 0.893 while maintaining the competitiveness of other combinations. Additionally, the Shapley additive explanations (SHAP) method was used to interpret important features and substructures related to skin sensitization. A comparative analysis of an external human test set demonstrated the superior performance of the proposed method. Finally, to enhance accessibility, the workflow was implemented into a user-friendly software named HSkinSensDS.

Propylene glycol, skin sensitisation and allergic contact dermatitis: A scientific and regulatory conundrum

Propylene glycol (PG) has widespread use in pharmaceuticals, cosmetics, fragrances and personal care products. PG is not classified as hazardous under the Globally Harmonised System of Classification and Labelling of Chemicals (GHS) but poses an intriguing scientific and regulatory conundrum with respect to allergic contact dermatitis (ACD), the uncertainty being whether and to what extent PG has the potential to induce skin sensitisation. In this article we review the results of predictive tests for skin sensitisation with PG, and clinical evidence for ACD. Patch testing in humans points to PG having the potential to be a weak allergen under certain conditions, and an uncommon cause of ACD in subjects without underlying/pre-disposing skin conditions. In clear contrast PG is negative in predictive toxicology tests for skin sensitisation, including guinea pig and mouse models (e.g. local lymph node assay), validated in vitro test methods that measure various key events in the pathway leading to skin sensitisation, and predictive methods in humans (Human Repeat Insult Patch and Human Maximisation Tests). We here explore the possible scientific basis for this intriguing inconsistency, recognising there are arguably no known contact allergens that are universally negative in, in vitro, animal and human predictive tests methods.

Application of ARE-reporter systems in drug discovery and safety assessment

The human body is continuously exposed to xenobiotics and internal or external oxidants. The health risk assessment of exogenous chemicals remains a complex and challenging issue. Alternative toxicological test methods have become an essential strategy for health risk assessment. As a core regulator of constitutive and inducible expression of antioxidant response element (ARE)-dependent genes, nuclear factor erythroid 2-related factor 2 (Nrf2) plays a critical role in maintaining cellular redox homeostasis. Consistent with the properties of Nrf2-mediated antioxidant response, Nrf2-ARE activity is a direct indicator of oxidative stress and thus has been used to identify and characterize oxidative stressors and redox modulators. To screen and distinguish chemicals or environmental insults that affect the cellular antioxidant activity and/or induce oxidative stress, various in vitro cell models expressing distinct ARE reporters with high-throughput and high-content properties have been developed. These ARE-reporter systems are currently widely applied in drug discovery and safety assessment. In the present review, we provide an overview of the basic structures and applications of various ARE-reporter systems employed for discovering Nrf2-ARE modulators and characterizing oxidative stressors.

Exploring Low-Toxicity Chemical Space with Deep Learning for Molecular Generation

Creating a wide range of new compounds that not only have ideal pharmacological properties but also easily pass long-term toxicity evaluation is still a challenging task in current drug discovery. In this study, we developed a conditional generative model by combining a semisupervised variational autoencoder (SSVAE) with an MGA toxicity predictor. Our aim is to generate molecules with low toxicity, good drug-like properties, and structural diversity. For multiobjective optimization, we have developed a method with hierarchical constraints on the toxicity space of small molecules to generate drug-like small molecules, which can also minimize the effect on the diversity of generated results. The evaluation results of the metrics indicate that the developed model has good effectiveness, novelty, and diversity. The generated molecules by this model are mainly distributed in low-toxicity regions, which suggests that our model can efficiently constrain the generation of toxic structures. In contrast to simply filtering toxic ones after generation, the low-toxicity molecular generative model can generate molecules with structural diversity. Our strategy can be used in target-based drug discovery to improve the quality of generated molecules with low-toxicity, drug-like, and highly active properties.

Mixtures-Inclusive In Silico Models of Ocular Toxicity Based on United States and International Hazard Categories

Computational modeling grounded in reliable experimental data can help design effective non-animal approaches to predict the eye irritation and corrosion potential of chemicals. The National Toxicology Program (NTP) Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) has compiled and curated a database of in vivo eye irritation studies from the scientific literature and from stakeholder-provided data. The database contains 810 annotated records of 593 unique substances, including mixtures, categorized according to UN GHS and US EPA hazard classifications. This study reports a set of in silico models to predict EPA and GHS hazard classifications for chemicals and mixtures, accounting for purity by setting thresholds of 100% and 10% concentration. We used two approaches to predict classification of mixtures: conventional and mixture-based. Conventional models evaluated substances based on the chemical structure of its major component. These models achieved balanced accuracy in the range of 68-80% and 87-96% for the 100% and 10% test concentration thresholds, respectively. Mixture-based models, which accounted for all known components in the substance by weighted feature averaging, showed similar or slightly higher accuracy of 72-79% and 89-94% for the respective thresholds. We also noted a strong trend between the pH feature metric calculated for each substance and its activity. Across all the models, the calculated pH of inactive substances was within one log10 unit of neutral pH, on average, while for active substances, pH varied from neutral by at least 2 log10 units. This pH dependency is especially important for complex mixtures. Additional evaluation on an external test set of 673 substances obtained from ECHA dossiers achieved balanced accuracies of 64-71%, which suggests that these models can be useful in screening compounds for ocular irritation potential. Negative predictive value was particularly high and indicates the potential application of these models in a bottom-up approach to identify nonirritant substances.

Evaluating Confidence in Toxicity Assessments Based on Experimental Data and In Silico Predictions

Understanding the reliability and relevance of a toxicological assessment is important for gauging the overall confidence and communicating the degree of uncertainty related to it. The process involved in assessing reliability and relevance is well defined for experimental data. Similar criteria need to be established for in silico predictions, as they become increasingly more important to fill data gaps and need to be reasonably integrated as additional lines of evidence. Thus, in silico assessments could be communicated with greater confidence and in a more harmonized manner. The current work expands on previous definitions of reliability, relevance, and confidence and establishes a conceptional framework to apply those to in silico data. The approach is used in two case studies: 1) phthalic anhydride, where experimental data are readily available and 2) 4-hydroxy-3-propoxybenzaldehyde, a data poor case which relies predominantly on in silico methods, showing that reliability, relevance, and confidence of in silico assessments can be effectively communicated within Integrated approaches to testing and assessment (IATA).

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.

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.

Beyond dermal exposure: The respiratory tract as a target organ in hazard assessments of cosmetic ingredients

Dermal contact is the main route of exposure for most cosmetics; however, inhalation exposure could be significant for some formulations (e.g., aerosols, powders). Current cosmetic regulations do not require specific tests addressing respiratory irritation and sensitisation, and despite the prohibition of animal testing for cosmetics, no alternative methods have been validated to assess these endpoints to date. Inhalation hazard is mainly determined based on existing human and animal evidence, read-across, and extrapolation of data from different target organs or tissues, such as the skin. However, because of mechanistic differences, effects on the skin cannot predict effects on the respiratory tract, which indicates a substantial need for the development of new approach methodologies addressing respiratory endpoints for inhalable chemicals in general. Cosmetics might present a particularly significant need for risk assessments of inhalation exposure to provide a more accurate toxicological evaluation and ensure consumer safety. This review describes the differences in the mechanisms of irritation and sensitisation between the skin and the respiratory tract, the progress that has already been made, and what still needs to be done to fill the gap in the inhalation risk assessment of cosmetic ingredients.

In silico Prediction of Skin Sensitization: Quo vadis?

Skin direct contact with chemical or physical substances is predisposed to allergic contact dermatitis (ACD), producing various allergic reactions, namely rash, blister, or itchy, in the contacted skin area. ACD can be triggered by various extremely complicated adverse outcome pathways (AOPs) remains to be causal for biosafety warrant. As such, commercial products such as ointments or cosmetics can fulfill the topically safe requirements in animal and non-animal models including allergy. Europe, nevertheless, has banned animal tests for the safety evaluations of cosmetic ingredients since 2013, followed by other countries. A variety of non-animal in vitro tests addressing different key events of the AOP, the direct peptide reactivity assay (DPRA), KeratinoSens™, LuSens and human cell line activation test h-CLAT and U-SENS™ have been developed and were adopted in OECD test guideline to identify the skin sensitizers. Other methods, such as the SENS-IS are not yet fully validated and regulatorily accepted. A broad spectrum of in silico models, alternatively, to predict skin sensitization have emerged based on various animal and non-animal data using assorted modeling schemes. In this article, we extensively summarize a number of skin sensitization predictive models that can be used in the biopharmaceutics and cosmeceuticals industries as well as their future perspectives, and the underlined challenges are also discussed.

Amino acid derivative reactivity assay-organic solvent reaction system: A novel alternative test for skin sensitization capable of assessing highly hydrophobic substances

The amino acid derivative reactivity assay (ADRA) is an in chemico alternative to animal testing that focuses on protein binding. The ADRA is a skin sensitization test that solves problems associated with the direct peptide reactivity assay. However, when utilizing the ADRA to evaluate highly hydrophobic substances with octanol/water partition coefficients (logKow) of >6, the test substances may not dissolve in the reaction solution, which can prevent the accurate assessment of skin sensitization. Therefore, we developed the ADRA-organic solvent (ADRA-OS) reaction system, which is a novel skin sensitization test that enables the assessment of highly hydrophobic substances with a logKow of >6. We discovered that the organic solvent ratio, the triethylamine concentration, and the ethylenediaminetetraacetic acid disodium salt dihydrate concentration participate in reactions with the nucleophile N-(2-(1-naphthyl)acetyl)-l-cysteine (NAC) and sensitizers that are used in ADRA and in stabilizing NAC. Thus, we determined the optimal reaction composition of the ADRA-OS according to L₉ (3³ ) orthogonal array experiments. Using this test, we assessed 14 types of highly hydrophobic substances. When we compared the results with ADRA, we found that ADRA-OS reaction system has high solubility for highly hydrophobic substances and that it has a high predictive capacity (sensitivity: 63%, specificity: 100%, accuracy: 79%). The implication of the results is that the novel ADRA-OS reaction system should provide a useful method for assessing the skin sensitization of highly hydrophobic substances with a logKow of >6.

The unreliability of the reliability criteria in the estimation of QSAR for skin sensitivity: A pun or a reliable law?

Some new products, which include common personal-care products, drugs, household items, can be hazardous in aspect personal care products/cosmetics and their ingredients (i.e. the above can effect human skin). International organizations (e.g. the Organisation for Economic Co-operation and Development-OECD) recommend evaluating individual ingredients when assessing the safety of personal care or cosmetic products. Thus, checking up that "popular at the market" substances are non-toxic, do not penetrate into or through normal or compromised human skin, and therefore, pose no risk to human health is an essential element of modern toxicology. The development of reliable models of toxicological endpoints is a tool to carry out the above checking up via quantitative structure-activity relationships (QSARs). The reliability of the QSAR is the current task of mathematical statistics. Recently, the index of ideality of correlation (IIC) and correlation intensity index (CII) were suggested as criteria of predictive potential (i.e. reliability) of QSAR-models. Here, the abilities of these criteria were studied for the case of building up models for skin sensitivity (LLNA, local lymph node assay). Computational experiments have confirmed that the IIC demonstrates an obvious ability to improve the predictive potential of models of skin sensitization. The applying of the CII for the case of skin sensitization also improves the quality of the model. However, the best models for skin sensitization were observed if the above-mentioned criteria are applied jointly (n = 268; R² = 0.60; RMSE = 0.63).

The activity of methacrylate esters in skin sensitisation test methods II. A review of complementary and additional analyses

Allergic contact dermatitis is an important occupational health issue, and there is a need to identify accurately those chemicals that have the potential to induce skin sensitisation. Hazard identification was performed initially using animal (guinea pig and mouse) models. More recently, as a result of the drive towards non-animal methods, alternative in vitro and in silico approaches have been developed. Some of these new in vitro methods have been formally validated and have been assigned OECD Test Guideline status. The performance of some of these recently developed in vitro methods, and of 2 quantitative structure-activity relationships (QSAR) approaches, with a series of methacrylate esters has been reviewed and reported previously. In this article that first review has been extended further with additional data and complementary analyses. Results obtained using in vitro methods (Direct Peptide Reactivity Assay, DPRA; ARE-Nrf2 luciferase test methods, KeratinoSens and LuSens; Epidermal Sensitisation Assay, EpiSensA; human Cell Line Activation Test, h-CLAT, and the myeloid U937 Skin Sensitisation test, U-SENS), and 2 QSAR approaches (DEREK™-nexus and TIMES-SS), with 11 methacrylate esters and methacrylic acid are reported here, and compared with existing data from the guinea pig maximisation test and the local lymph node assay. With this series of chemicals it was found that some in vitro tests (DPRA and ARE-Nrf2 luciferase) performed well in comparison with animal test results and available human skin sensitisation data. Other in vitro tests (EpiSensA and h-CLAT) proved rather more problematic. Results with DEREK™-nexus and TIMES-SS failed to reflect accurately the skin sensitisation potential of the methacrylate esters. The implications for assessment of skin sensitising activity are discussed.

Evaluating Skin Sensitization Via Soft and Hard Multivariate Modeling

Allergic contact dermatitis is the most frequent manifestation of immunotoxicity in humans with a prevalence rate of 15% to 20% over general population. Skin sensitization is a complex end point that was for a long time being evaluated using animal testing. Great efforts have been made to completely substitute the use of animals and replace them by integrating data from in vitro and in chemico assays with in silico calculated parameters. However, it remains undefined how to make the best use of the cumulative data in such a way that information gain is maximized and accomplished with the fewest number of tests possible. In this work, 3 skin sensitization prediction models were considered: one to discriminate sensitizers from non-sensitizers, considering a 2-level scale; one according to the GHS, considering a 3-level scale; and the other to categorize potency in a 6-level scale, according to available human data. We used a data set of known human skin allergens for which in vitro, in chemico, and in silico descriptors where available to build classifiers based on soft and hard multivariate modeling. Model building, optimization, and refinement resulted in 100% accuracy in distinguishing between sensitizers and non-sensitizers. The same model was able to perform the characterization, in 3 and 6 levels, respectively, with 98.8 and 97.5% accuracy. Combining data from in vitro and in chemico tests with in silico descriptors is relatively simple to implement and some predictors are fitting the adverse outcome pathway for skin sensitization.

Consensus versus Individual QSARs in Classification: Comparison on a Large-Scale Case Study

Consensus strategies have been widely applied in many different scientific fields, based on the assumption that the fusion of several sources of information increases the outcome reliability. Despite the widespread application of consensus approaches, their advantages in quantitative structure–activity relationship (QSAR) modeling have not been thoroughly evaluated, mainly due to the lack of appropriate large-scale data sets. In this study, we evaluated the advantages and drawbacks of consensus approaches compared to single classification QSAR models. To this end, we used a data set of three properties (androgen receptor binding, agonism, and antagonism) for approximately 4000 molecules with predictions performed by more than 20 QSAR models, made available in a large-scale collaborative project. The individual QSAR models were compared with two consensus approaches, majority voting and the Bayes consensus with discrete probability distributions, in both protective and nonprotective forms. Consensus strategies proved to be more accurate and to better cover the analyzed chemical space than individual QSARs on average, thus motivating their widespread application for property prediction. Scripts and data to reproduce the results of this study are available for download.

Comparing and contrasting the coverage of publicly available structural alerts for protein binding

The molecular initiating event for many mechanisms of toxicological action comprise the reactive, covalent binding between an exogenous electrophile and an endogenous nucleophile. The target sites for electrophiles are typically peptides, proteins, enzymes or DNA. Of these, the formation of covalent adducts with proteins and DNA are perhaps the most established as they are most closely associated with skin sensitisation and genotoxicity endpoints. As such, being able to identify electrophilic features within a chemical structure provides a starting point to characterise its reactivity profile. There are a number of software tools that have been developed to help identify structural features indicative of electrophilic reactive potential to address various purposes, including: 1) to facilitate category formation for read-across of toxicity effects such as skin sensitisation potential, as well as 2) to profile substances to identify potential confounding factors to rationalise their activity in high-throughput screening (HTS) assays. Here, three such schemes that have been published in the literature as collections of SMARTS patterns and their associated chemical-biological reaction domains have been compared. The goals are 1) to better understand their scope and coverage, and 2) to assess their performance relative to a published skin sensitisation dataset where manual annotations to assign likely mechanistic domains based on expert judgement were already available. The 3 schemes were then applied to the Tox21 library and the consensus outcome was reported to highlight the proportion of chemicals likely to exhibit a reactivity response, specific to a mechanistic reaction domain, but non-specific with respect to target-tissue based activity. ToxPrint fingerprints were computed and activity enrichments computed to compare the structural features identified for the skin sensitisation dataset and Tox21 chemicals for each 'consensus' reaction domain. Enriched ToxPrints were also used to identify ToxCast assays potentially informative for reactivity.

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.

Skin toxicology and 3Rs-Current challenges for public health protection

Driven by the fast paced development of complex test systems in vitro, mass spectrometry and omics, we finally have the tools to unravel the molecular events that underlie toxicological adversity. Yet, timely regulatory adaptation of these new tools continues to pose major challenges even for organs readily accessible such as skin. The reasons for this encompass a need for conservatism as well as the need of tests to serve an existing regulatory framework rather than to produce scientific knowledge. It is important to be aware of this in order to align regulatory skin toxicity with the 3R principles more readily. While most chemical safety testing is still based on animal data, regulatory frameworks have seen a strong push towards non-animal approaches. The endpoints corrosion, irritation, sensitisation, absorption and phototoxicity, for example, can now be covered in vitro with the corresponding test guidelines (TGs) being made available by the OECD. However, in vitro approaches tend to be more reductionist. Hence, a combination of several tests is usually preferable to achieve satisfying predictivity. Moreover, the test systems and their combined use need to be standardised and are therefore subject not only to validation but also to the ongoing development of so-called integrated approaches to testing and assessment (IATAs). Concomitantly, skin models are being refined to deliver the complexity required for increased applicability and predictivity. Given the importance of regulatory applicability for 3R-derived approaches to have a long-lasting impact, this review examines the state of regulatory implementation and perspectives, respectively.

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.

Skin Sensitization Testing-What's Next?

There is an increasing demand for alternative in vitro methods to replace animal testing, and, to succeed, new methods are required to be at least as accurate as existing in vivo tests. However, skin sensitization is a complex process requiring coordinated and tightly regulated interactions between a variety of cells and molecules. Consequently, there is considerable difficulty in reproducing this level of biological complexity in vitro, and as a result the development of non-animal methods has posed a major challenge. However, with the use of a relevant biological system, the high information content of whole genome expression, and comprehensive bioinformatics, assays for most complex biological processes can be achieved. We propose that the Genomic Allergen Rapid Detection (GARD™) assay, developed to create a holistic data-driven in vitro model with high informational content, could be such an example. Based on the genomic expression of a mature human dendritic cell line and state-of-the-art machine learning techniques, GARD™ can today accurately predict skin sensitizers and correctly categorize skin sensitizing potency. Consequently, by utilizing advanced processing tools in combination with high information genomic or proteomic data, we can take the next step toward alternative methods with the same predictive accuracy as today’s in vivo methods—and beyond.

Tri-culture system for pro-hapten sensitizer identification and potency classification

Allergic contact dermatitis (ACD) is an inflammatory disease that impacts 15-20% of the general population and accurate screening methods for chemical risk assessment are needed. However, most approaches poorly predict pre- and pro-hapten sensitizers, which require abiotic or metabolic conversion prior to inducing sensitization. We developed a tri-culture system comprised of MUTZ-3-derived Langerhans cells, HaCaT keratinocytes, and primary dermal fibroblasts to mimic the cellular and metabolic environments of skin sensitization. A panel of non-sensitizers and sensitizers was tested and the secretome was evaluated. A support vector machine (SVM) was used to identify the most predictive sensitization signature and classification trees identified statistical thresholds to predict sensitizer potency. The SVM computed 91% tri-culture prediction accuracy using the top 3 ranking biomarkers (IL-8, MIP-1β, and GM-CSF) and improved the detection of pre- and pro-haptens. This in vitro assay combined with in silico data analysis presents a promising approach and offers the possibility of multi-metric analysis for enhanced ACD sensitizer screening.

Integrated Approaches to Testing and Assessment

The concept of Integrated Approaches to Testing and Assessment (IATA) has been advanced by the Organisation for Economic Cooperation and Development (OECD) member countries to enable a progressive shift from traditional chemical assessments largely based on the observation of the adverse effect in animal models, using individual methods or predefined batteries of standard toxicity tests, to assessment strategies integrating diverse lines of evidence. The flexible nature of IATA allows the inclusion of mechanistic data generated with non-animal methods and with new technologies (e.g. high-throughput and high content methods). The assessment process within IATA is typically conducted through weight-of-evidence which inevitably includes the elements of subjective expert judgement. For these reasons, IATA cannot be fully harmonized across sectors and countries. Nevertheless, some of the IATA components, such as defined approaches, which consist of a fixed data interpretation procedure (DIP) applied to data generated with a defined set of information sources, can be harmonized. The focus of this MiniReview is to provide an illustration of the differences between the IATA developed so far in the areas of regulatory toxicology, and ongoing activities related to the international harmonization of defined approaches that rely on multiple non-animal information sources.

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.

Non-animal assessment of skin sensitization hazard: Is an integrated testing strategy needed, and if so what should be integrated?

There is an expectation that to meet regulatory requirements, and avoid or minimize animal testing, integrated approaches to testing and assessment will be needed that rely on assays representing key events (KEs) in the skin sensitization adverse outcome pathway. Three non-animal assays have been formally validated and regulatory adopted: the direct peptide reactivity assay (DPRA), the KeratinoSens™ assay and the human cell line activation test (h-CLAT). There have been many efforts to develop integrated approaches to testing and assessment with the "two out of three" approach attracting much attention. Here a set of 271 chemicals with mouse, human and non-animal sensitization test data was evaluated to compare the predictive performances of the three individual non-animal assays, their binary combinations and the "two out of three" approach in predicting skin sensitization potential. The most predictive approach was to use both the DPRA and h-CLAT as follows: (1) perform DPRA - if positive, classify as sensitizing, and (2) if negative, perform h-CLAT - a positive outcome denotes a sensitizer, a negative, a non-sensitizer. With this approach, 85% (local lymph node assay) and 93% (human) of non-sensitizer predictions were correct, whereas the "two out of three" approach had 69% (local lymph node assay) and 79% (human) of non-sensitizer predictions correct. The findings are consistent with the argument, supported by published quantitative mechanistic models that only the first KE needs to be modeled. All three assays model this KE to an extent. The value of using more than one assay depends on how the different assays compensate for each other's technical limitations. Copyright © 2017 John Wiley & Sons, Ltd.

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.

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.

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.