Skin-sensitization structure-activity relationships for aldehydes

Common fragrance chemicals activate dendritic cells in coculture with keratinocytes

BACKGROUND

Fragrances are important contact allergens; however, investigation of their skin sensitization potency has been challenging in new approach methods (NAMs). Many fragrance chemicals are susceptible to autoxidation or can be metabolized by enzymes constitutively expressed in skin keratinocytes. Strong sensitizers can be formed in both of these processes. Further, keratinocytes can modulate the dendritic cell (DC) activation and maturation potential, a key event in the acquisition of contact allergy.

OBJECTIVES

To evaluate the 2D coculture model consisting of keratinocytes and DCs using different weak to moderate sensitizing fragrance chemicals. Further, to investigate fragrances and related oxidation products in the in vitro model and compare to in vivo data.

METHODS

Chemicals were tested in the coculture activation test (COCAT), consisting of HaCaT keratinocytes and THP-1 cells. THP-1 cell surface expression of costimulatory and adhesion molecules (CD86 and CD54) collected after 24 h incubation with the chemicals was analysed using flow cytometry.

RESULTS

Twenty-four molecules were tested positive, three were negative (n = 27). Four pairs were evaluated, with aldehydes showing a 6- to 13-fold stronger responses compared to their corresponding alcohols.

CONCLUSIONS

Results provide insight into the activation of DC in their natural environment of keratinocytes. α,β-Unsaturated alcohols were classified as weaker sensitizers compared to their corresponding aldehydes. In sum, testing of fragrances retrieved results in good agreement with in vivo data.

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).

Mapping Chemical Respiratory Sensitization: How Useful Are Our Current Computational Tools?

Chemical respiratory sensitization is an immunological process that manifests clinically mostly as occupational asthma and is responsible for 1 in 6 cases of adult asthma, although this may be an underestimate of the prevalence, as it is under-diagnosed. Occupational asthma results in unemployment for roughly one-third of those affected due to severe health issues. Despite its high prevalence, chemical respiratory sensitization is difficult to predict, as there are currently no validated models and the mechanisms are not entirely understood, creating a significant challenge for regulatory bodies and industry alike. The Adverse Outcome Pathway (AOP) for respiratory sensitization is currently incomplete. However, some key events have been identified, and there is overlap with the comparatively well-characterized AOP for dermal sensitization. Because of this, and the fact that dermal sensitization is often assessed by in vivo, in chemico, or in silico methods, regulatory bodies are defaulting to the dermal sensitization status of chemicals as a proxy for respiratory sensitization status when evaluating chemical safety. We identified a data set of known human respiratory sensitizers, which we used to investigate the accuracy of a structural alert model, Toxtree, designed for skin sensitization and the Centre for Occupational and Environmental Health (COEH)'s model, a model developed specifically for occupational asthma. While both models had a reasonable level of accuracy, the COEH model achieved the highest balanced accuracy at 76%; when the models agreed, the overall accuracy was 87%. There were important differences between the models: Toxtree had superior performance for some structural alerts and some categories of well-characterized skin sensitizers, while the COEH model had high accuracy in identifying sensitizers that lacked identified skin sensitization reactivity domains. Overall, both models achieved respectable accuracy. However, neither model addresses potency, which, along with data quality, remains a hurdle, and the field must prioritize these issues to move forward.

Sensitive Method for the Identification of Potential Sensitizing Impurities in Reaction Mixtures by Fluorescent Nitrobenzoxadiazole-Labeled Glutathione

Allergic contact dermatitis is a critical issue in the development of new chemicals. Minor impurities with strong skin-sensitizing properties can be generated as byproducts. However, it is very difficult to identify these skin sensitizers in product mixtures. In this study, fluorescent nitrobenzoxadiazole-labeled glutathione (NBD-GSH) was synthesized to identify small amounts of skin sensitizers in reaction mixtures. Twelve known skin sensitizers and three nonsensitizers were reacted with NBD-GSH. Adducts formed only with the skin sensitizers, which allowed for their detection by a fluorescence detector. Liquid chromatography-mass spectrometry (LC-MS) analyses showed that NBD-GSH reacted with the skin sensitizers via its thiol and amino groups. An adduct of NBD-GSH with the strong skin sensitizer 1-chloro-2,4-dinitrobenzene was detected with a limit of detection of 6 × 10^-8 mol/L by high-performance liquid chromatography with fluorescence detection. When a reaction mixture from primary alcohol oxidation was incubated with NBD-GSH, a NBD-GSH adduct formed with skin-sensitizing aldehyde impurities and could be specifically detected by LC-MS with fluorescence detection. This method will be useful for detection and identification of small amounts of skin sensitizers in raw materials, intermediates, reaction mixtures, and end products in the chemical industry.

Investigating DNA adduct formation by flavor chemicals and tobacco byproducts in electronic nicotine delivery system (ENDS) using in silico approaches

The presence of flavors is one of the commonly cited reasons for use of e-cigarettes by youth; however, the potential harms from inhaling these chemicals and byproducts have not been extensively studied. One mechanism of interest is DNA adduct formation, which may lead to carcinogenesis. We identified two chemical classes of flavors found in tobacco products and byproducts, alkenylbenzenes and aldehydes, documented to form DNA adducts. Using in silico toxicology approaches, we identified structural analogs to these chemicals without DNA adduct information. We conducted a structural similarity analysis and also generated in silico model predictions of these chemicals for genotoxicity, mutagenicity, carcinogenicity, and skin sensitization. The empirical and in silico data were compared, and we identified strengths and limitations of these models. Good concordance (80-100%) was observed between DNA adduct formation and models predicting mammalian mutagenicity (mouse lymphoma sassy L5178Y) and skin sensitization for both chemical classes. On the other hand, different prediction profiles were observed for the two chemical classes for the modeled endpoints, unscheduled DNA synthesis and bacterial mutagenicity. These results are likely due to the different mode of action between the two chemical classes, as aldehydes are direct acting agents, while alkenylbenzenes require bioactivation to form electrophilic intermediates, which form DNA adducts. The results of this study suggest that an in silico prediction for the mouse lymphoma assay L5178Y, may serve as a surrogate endpoint to help predict DNA adduct formation for chemicals found in tobacco products such as flavors and byproducts.

Application of Spectro-DPRA, KeratinoSens™ and h-CLAT to estimation of the skin sensitization potential of cosmetics ingredients

Ethical issues in animal toxicity testing have led to the search for alternative methods to determine the skin sensitization potential of cosmetic products. The emergence of ethical testing issues has led to the development of many alternative methods that can reliably estimate skin sensitization potentials. However, a single alternative method may not be able to achieve high predictivity due to the complexity of the skin sensitization mechanism. Therefore, several prediction assays, including both in chemico and in vitro test methods, were investigated and integrated based on the skin sensitization adverse outcome pathway. In this study, we evaluated three different integrated approaches to predict a human skin sensitization hazard using data from in vitro assays (KeratinoSens™ and human cell line activation test [h-CLAT]), and a newly developed in chemico assay (spectrophotometric direct peptide reactivity assay [Spectro-DPRA]). When the results of the in chemico and in vitro assays were combined, the predictivity of human data increased compared with that of a single assay. The highest predictivity was obtained for the approach in which sensitization potential was determined by Spectro-DPRA followed by final determination using the result of KeratinoSens™ and h-CLAT assays (96.3% sensitivity, 87.1% specificity, 86.7% positive predictive value, 96.4% negative predictive value and 91.4% accuracy compared with human data). While further optimization is needed, we believe this integrated approach may provide useful predictive data when determining the human skin sensitization potential of chemicals.

High-throughput screening (HTS)-based spectrophotometric direct peptide reactivity assay (Spectro-DPRA) to predict human skin sensitization potential

Some cosmetic ingredients can act as a chemical hapten to induce an immune response; therefore, evaluating the sensitizing potential of cosmetic ingredients is essential. We previously developed a novel in chemico direct peptide reactivity assay involving a spectrophotometric evaluation (Spectro-DPRA) for animal skin sensitization tests (local lymph node assay; LLNA). Based on previous research, we expanded the test materials to confirm the effectiveness of the Spectro-DPRA method for predicting the animal skin sensitization potential, and further determined the feasibility of the method for estimating the human skin sensitization potential. Spectro-DPRA showed 83.1% or 89.1% accuracy compared to a conventional LLNA or prediction based on human data, respectively, with a combination model using both a cysteine peptide and lysine peptide cut-off. To identify the effect of the lipophilicity of a chemical on predicting the skin sensitization potential, we applied our prediction model to chemicals with a Log P_ow range of -1 to 4. Overall predictability was increased, and the accuracy compared to the LLNA and human data was 91.5% and 94.9%, respectively, in the combination cut-off prediction model. In conclusion, Spectro-DPRA serves as an easy, rapid, and high-throughput in chemico screening method with high accuracy to predict the human skin sensitization potential of chemicals.

An Integrated Decision-tree Testing Strategy for Skin Sensitisation with Respect to the Requirements of the EU REACH Legislation

This report presents some of the results of a joint research project, sponsored by Defra and conducted by FRAME and Liverpool John Moores University, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with the REACH system. This report focuses on the use of alternative (non-animal) methods (both in vitro and in silico) for skin sensitisation testing. The manuscript reviews in vitro tests based on protein-ligand binding, dendritic/Langerhans cells and T-lymphocyte activation, and also the QSAR models and expert systems available for this endpoint. These tests are then incorporated into an integrated, decision-tree testing strategy, which also includes the Local Lymph Node Assay (in its original and new reduced protocols) and the traditional guinea-pig tests (which should only be used as a last resort). The aim of the strategy is to minimise the use of animals in testing for skin sensitisation, while satisfying the scientific and logistical demands of the EU REACH legislation.

Predicting skin sensitization potential of organic compounds based on toxicity enhancement to Tetrahymena pyriformis, fathead minnow, and Daphnia magna

Skin sensitization is an important occupational health problem and immunotoxicity endpoint. Considering animal welfare and time and cost savings, many alternative approaches, such as those conducted in vitro, in silico, and in chemo, have been proposed and applied to predict skin sensitization of compounds. Toxicologically, sensitizers can elicit excess toxicity at greater levels than non-sensitizers due to their capacity to react with proteins/peptides. Based on this understanding, calculated toxicity enhancements (T_e) of 65 organic compounds from three in vitro bioassays, i.e. 48-hr ciliate (Tetrahymena pyriformis) growth inhibition, and both 96-hr fathead minnow and 48-hr Daphnia magna acute lethal toxicities, were employed to qualitatively and quantitatively predict skin sensitization potencies of the test agents. The sensitivity, specificity, and accuracy reaching 80% strongly suggested toxicity enhancement was an excellent parameter for predicting skin sensitization. Linear regressions of skin sensitization against toxicity enhancement were fitted for each bioassay, and they were improved after the sensitizers were categorized into different reaction mechanistic domains, which, in decreasing order of contribution from T_e to sensitization, were S_NAr > S_N1 > MA. These results indicated that toxicity bioassays are useful tools and that T_e could be a useful parameter that might be applied to predict skin sensitization.

Skin Sensitization: Modeling Based on Skin Metabolism Simulation and Formation of Protein Conjugates

A quantitative structure-activity relationship (QSAR) system for estimating skin sensitization potency has been developed that incorporates skin metabolism and considers the potential of parent chemicals and/or their activated metabolites to react with skin proteins. A training set of diverse chemicals was compiled and their skin sensitization potency assigned to one of three classes. These three classes were, significant, weak, or nonsensitizing. Because skin sensitization potential depends upon the ability of chemicals to react with skin proteins either directly or after appropriate metabolism, a metabolic simulator was constructed to mimic the enzyme activation of chemicals in the skin. This simulator contains 203 hierarchically ordered spontaneous and enzyme controlled reactions. Phase I and phase II metabolism were simulated by using 102 and 9 principal transformations, respectively. The covalent interactions of chemicals and their metabolites with skin proteins were described by 83 reactions that fall within 39 alerting groups. The SAR/QSAR system developed was able to correctly classify about 80% of the chemicals with significant sensitizing effect and 72% of nonsensitizing chemicals. For some alerting groups, three-dimensional (3D)-QSARs were developed to describe the multiplicity of physicochemical, steric, and electronic parameters. These 3D-QSARs, so-called pattern recognition-type models, were applied each time a latent alerting group was identified in a parent chemical or its generated metabolite(s). The concept of the mutual influence amongst atoms in a molecule was used to define the structural domain of the skin sensitization model. The utility of the structural model domain and the predictability of the model were evaluated using sensitization potency data for 96 chemicals not used in the model building. The TIssue MEtabolism Simulator (TIMES) software was used to integrate a skin metabolism simulator and 3D-QSARs to evaluate the reactivity of chemicals thus predicting their likely skin sensitization potency.

Evaluation of in silico models for the identification of respiratory sensitizers

Low molecular weight (LMW) respiratory sensitizers can cause occupational asthma but due to a lack of adequate test methods, prospective identification of respiratory sensitizers is currently not possible. This article presents the evaluation of structure-activity relationship (SAR) models as potential methods to prospectively conclude on the sensitization potential of LMW chemicals. The predictive performance of the SARs calculated from their training sets was compared to their performance on a dataset of newly identified respiratory sensitizers and nonsensitizers, derived from literature. The predictivity of the available SARs for new substances was markedly lower than their published predictive performance. For that reason, no single SAR model can be considered sufficiently reliable to conclude on potential LMW respiratory sensitization properties of a substance. The individual applicability domains (ADs) of the models were analyzed for adequacies and deficiencies. Based on these findings, a tiered prediction approach is subsequently proposed. This approach combines the two SARs with the highest positive and negative predictivity taking into account model specific chemical AD issues. The tiered approach provided reliable predictions for one-third of the respiratory sensitizers and nonsensitizers of the external validation set compiled by us. For these chemicals, a positive predictive value of 96% and a negative predictive value of 89% were obtained. The tiered approach was not able to predict the other two-thirds of the chemicals, meaning that additional information is required and that there is an urgent need for other test methods, e.g., in chemico or in vitro, to reach a reliable conclusion.

Hexyl cinnamal: consideration of skin-sensitizing properties and suitability as a positive control

BACKGROUND

Hexyl cinnamal (HCA) is a widely used fragrance chemical, the low skin-sensitizing potency of which has made it a common choice for the use as a positive control for predictive toxicology assays. However, HCA is commonly negative in current candidate in vitro alternatives test methods.

OBJECTIVE

To review the evidence that HCA is a classifiable skin sensitizer against the standards set by the Globally Harmonized Scheme (GHS), and determine whether it represents an appropriate choice for a positive control substance for predictive testing.

METHODS

Using the GHS criteria, mechanistic data, and in vitro, in vivo and human evidence relating to HCA and skin sensitization have been reviewed.

RESULTS

The chemistry of HCA is consistent with potential for skin sensitization and predictive in vivo test data support this conclusion. However, the human data are relatively sparse, consistent with HCA possessing a low capacity to induce skin sensitization under conditions of consumer exposures.

CONCLUSIONS

Using GHS criteria (and applying a precautionary approach) HCA would classify as a weaker skin sensitizer than predicted by the local lymph node assay (LLNA). However, given the human experience, it is necessary to consider whether HCA is the most appropriate choice for use as a positive regulatory control.

A genomic biomarker signature can predict skin sensitizers using a cell-based in vitro alternative to animal tests

BACKGROUND

Allergic contact dermatitis is an inflammatory skin disease that affects a significant proportion of the population. This disease is caused by an adverse immune response towards chemical haptens, and leads to a substantial economic burden for society. Current test of sensitizing chemicals rely on animal experimentation. New legislations on the registration and use of chemicals within pharmaceutical and cosmetic industries have stimulated significant research efforts to develop alternative, human cell-based assays for the prediction of sensitization. The aim is to replace animal experiments with in vitro tests displaying a higher predictive power.

RESULTS

We have developed a novel cell-based assay for the prediction of sensitizing chemicals. By analyzing the transcriptome of the human cell line MUTZ-3 after 24 h stimulation, using 20 different sensitizing chemicals, 20 non-sensitizing chemicals and vehicle controls, we have identified a biomarker signature of 200 genes with potent discriminatory ability. Using a Support Vector Machine for supervised classification, the prediction performance of the assay revealed an area under the ROC curve of 0.98. In addition, categorizing the chemicals according to the LLNA assay, this gene signature could also predict sensitizing potency. The identified markers are involved in biological pathways with immunological relevant functions, which can shed light on the process of human sensitization.

CONCLUSIONS

A gene signature predicting sensitization, using a human cell line in vitro, has been identified. This simple and robust cell-based assay has the potential to completely replace or drastically reduce the utilization of test systems based on experimental animals. Being based on human biology, the assay is proposed to be more accurate for predicting sensitization in humans, than the traditional animal-based tests.

Can mutagenicity information be useful in an Integrated Testing Strategy (ITS) for skin sensitization?

Our previous work has investigated the utility of mutagenicity data in the development and application of Integrated Testing Strategies (ITS) for skin sensitization by focusing on the chemical mechanisms at play and substantiating these with experimental data where available. The hybrid expert system TIMES (Tissue Metabolism Simulator) was applied in the identification of the chemical mechanisms since it encodes a comprehensive set of established structure-activity relationships for both skin sensitization and mutagenicity. Based on the evaluation, the experimental determination of mutagenicity was thought to be potentially helpful in the evaluation of skin sensitization potential. This study has evaluated the dataset reported by Wolfreys and Basketter (Cutan. Ocul. Toxicol. 23 (2004), pp. 197-205). Upon an update of the experimental data, the original reported concordance of 68% was found to increase to 88%. There were several compounds that were 'outliers' in the two experimental evaluations which are discussed from a mechanistic basis. The discrepancies were found to be mainly associated with the differences between skin and liver metabolism. Mutagenicity information can play a significant role in evaluating sensitization potential as part of an ITS though careful attention needs to be made to ensure that any information is interpreted in the appropriate context.

Prediction of skin sensitization potential using D-optimal design and GA-kNN classification methods

Modelling of skin sensitization data of 255 diverse compounds and 450 calculated descriptors was performed to develop global predictive classification models that are applicable to whole chemical space. With this aim, we employed two automated procedures, (a) D-optimal design to select optimal members of the training and test sets and (b) k-Nearest Neighbour classification (kNN) method along with Genetic Algorithms (GA-kNN Classification) to select significant and independent descriptors in order to build the models. This methodology helped us to derive multiple models, M1-M5, that are stable and robust. The best among them, model M1 (CCR(train) = 84.3%, CCR(test) = 87.2% and CCR(ext) = 80.4%), is based on six neighbours and nine descriptors and further suggests that: (a) it is stable and robust and performs better than the reported models in literature, and (b) the combination of D-optimal design and GA-kNN classification approach is a very promising approach. Consensus prediction based on the models M1-M5 improved the CCR of training, test and external validation datasets by 3.8%, 4.45% and 3.85%, respectively, over M1. From the analysis of the physical meaning of the selected descriptors, it is inferred that the skin sensitization potential of small organic compounds can be accurately predicted using calculated descriptors that code for the following fundamental properties: (i) lipophilicity, (ii) atomic polarizability, (iii) shape, (iii) electrostatic interactions, and (iv) chemical reactivity.

The use of Mechanisms and Modes of Toxic Action in Integrated Testing Strategies: The Report and Recommendations of a Workshop held as part of the European Union OSIRIS Integrated Project

This report on The Potential of Mode of Action (MoA) Information Derived from Non-testing and Screening Methodologies to Support Informed Hazard Assessment, resulted from a workshop organised within OSIRIS (Optimised Strategies for Risk Assessment of Industrial Chemicals through Integration of Non-test and Test Information), a project partly funded by the EU Commission within the Sixth Framework Programme. The workshop was held in Liverpool, UK, on 30 October 2008, with 35 attendees. The goal of the OSIRIS project is to develop integrated testing strategies (ITS) fit for use in the REACH system, that would enable a significant increase in the use of non-testing information for regulatory decision making, and thus minimise the need for animal testing. One way to improve the evaluation of chemicals may be through categorisation by way of mechanisms or modes of toxic action. Defining such groups can enhance read-across possibilities and priority settings for certain toxic modes or chemical structures responsible for these toxic modes. Overall, this may result in a reduction of in vivo testing on organisms, through combining available data on mode of action and a focus on the potentially most-toxic groups. In this report, the possibilities of a mechanistic approach to assist in and guide ITS are explored, and the differences between human health and environmental areas are summarised.

Dendritic cells: biology of the skin

Allergic contact dermatitis results from a T-cell-mediated, delayed-type hypersensitivity immune response induced by allergens. Skin dendritic cells (DCs) play a central role in the initiation of allergic skin responses. Following encounter with an allergen, DCs become activated and undergo maturation and differentiate into immunostimulatory DCs and are able to present antigens effectively to T cells. The frequency of allergic skin disorders has increased in the past decades. Therefore, the identification of potential sensitizing chemicals is important for skin safety. Traditionally, predictive testing for allergenicity has been conducted in animal models. For regulatory reasons, animal use for sensitization testing of compounds for cosmetic purposes is shortly to be prohibited in Europe. Therefore, new non-animal-based test methods need to be developed. Several DC-based assays have been described to discriminate allergens from irritants. Unfortunately, current in vitro methods are not sufficiently resilient to identify allergens and therefore need refinement. Here, we review the immunobiology of skin DCs (Langerhans' cells and dermal dendritic cells) and their role in allergic and irritant contact dermatitis and then explore the possible use of DC-based models for discriminating between allergens and irritants.

An Integrated Decision-tree Testing Strategy for Skin Sensitisation with Respect to the Requirements of the EU REACH Legislation

This report presents some of the results of a joint research project, sponsored by Defra and conducted by FRAME and Liverpool John Moores University, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity end-points associated with the REACH system. This report focuses on the use of alternative (non-animal) methods (both in vitro and in silico) for skin sensitisation testing. The manuscript reviews in vitro tests based on protein-ligand binding, dendritic/Langerhans cells and T-lymphocyte activation, and also the QSAR models and expert systems available for this endpoint. These tests are then incorporated into an integrated, decision-tree testing strategy, which also includes the Local Lymph Node Assay (in its original and new reduced protocols) and the traditional guinea-pig tests (which should only be used as a last resort). The aim of the strategy is to minimise the use of animals in testing for skin sensitisation, while satisfying the scientific and logistical demands of the EU REACH legislation.

Identification of mechanisms of toxic action for skin sensitisation using a SMARTS pattern based approach

Skin sensitisation is a key endpoint under REACH as it is costly and its assessment currently has a high dependency on animal testing. In order to reduce both the cost and the numbers of animals tested, it is likely that (quantitative) structure-activity relationships ((Q)SAR) and read-across methods will be utilised as part of intelligent testing strategies. The majority of skin sensitisers elicit their effect via covalent bond formation with skin proteins. These reactions have been understood in terms of well defined nucleophilic-electrophilic reaction chemistry. Thus, a first step in (Q)SAR analysis is the assignment of a chemical's potential mechanism of action enabling it to be placed in an appropriate reactivity domain. The aim of this study was to design a series of SMARTS patterns capable of defining these reactivity domains. This was carried out using a large database of local lymph node assay (LLNA) results that had had potential mechanisms of action assigned to them using expert knowledge. A simple algorithm was written enabling the SMARTS patterns to be used to screen a database of SMILES strings. The SMARTS patterns were then evaluated using a second, smaller, test set of LLNA results which had also had potential mechanisms of action assigned by experts. The results showed that the SMARTS patterns provided an excellent method of identifying potential electrophilic mechanisms. The findings are supported, in part, by molecular orbital calculations which confirm assignment of reactive mechanism of action. The ability to define a chemical's potential reaction mechanism is likely to be of significant benefit to regulators and risk assessors as it enables category formation and subsequent read-across to be performed.

Categorical QSAR Models for Skin Sensitization based upon Local Lymph Node Assay Classification Measures Part 2: 4D-Fingerprint Three-State and Two-2-State Logistic Regression Models

Three and four state categorical quantitative structure-activity relationship (QSAR) models for skin sensitization have been constructed using data from the murine Local Lymph Node Assay studies. These are the same data we previously used to build two-state (sensitizer, nonsensitizer) QSAR models (Li et al., 2007, Chem. Res. Toxicol. 20, 114-128). 4D-fingerprint descriptors derived from the 4D-molecular similarity paradigm are used to generate these models. A training set of 196 and a test set of 22 structurally diverse compounds were used in this study. Logistic regression, and partial least square coupled logistic regression were used to build the models. The three-state QSAR model gives a classification accuracy of 73.4% for the training set and 63.6% for the test set, while the random average value of classification accuracy for any three-state data set is 33.3%. The two-2-state [four categories in total] QSAR model gives a classification accuracy of 83.2% for the training set and 54.6% for the test set, while the random average value of classification accuracy for any two-2-state data set is 25%. An analysis of the skin-sensitization models developed in this study, as well as the two-state QSAR models developed in our previous analysis, suggests that the "moderate" sensitizers may be the main source of limited model accuracy.

The local lymph node assay and the assessment of relative potency: status of validation

For the prediction of skin sensitization potential, the local lymph node assay (LLNA) is a fully validated alternative to guinea-pig tests. More recently, information from LLNA dose-response analyses has been used to assess the relative potency of skin sensitizing chemicals. These data are then deployed for risk assessment and risk management. In this commentary, the utility and validity of these relative potency measurements are reviewed. It is concluded that the LLNA does provide a valuable assessment of relative sensitizing potency in the form of the estimated concentration of a chemical required to produce a threefold stimulation of draining lymph node cell proliferation compared with concurrent controls (EC3 value) and that all reasonable validation requirements have been addressed successfully. EC3 measurements are reproducible in both intra- and interlaboratory evaluations and are stable over time. It has been shown also, by several independent groups, that EC3 values correlate closely with data on relative human skin sensitization potency. Consequently, the recommendation made here is that LLNA EC3 measurements should now be regarded as a validated method for the determination of the relative potency of skin sensitizing chemicals, a conclusion that has already been reached by a number of independent expert groups.

An evaluation of selected global (Q)SARs/expert systems for the prediction of skin sensitisation potential

Skin sensitisation potential is an endpoint that needs to be assessed within the framework of existing and forthcoming legislation. At present, skin sensitisation hazard is normally identified using in vivo test methods, the favoured approach being the local lymph node assay (LLNA). This method can also provide a measure of relative skin sensitising potency which is essential for assessing and managing human health risks. One potential alternative approach to skin sensitisation hazard identification is the use of (Quantitative) structure activity relationships ((Q)SARs) coupled with appropriate documentation and performance characteristics. This represents a major challenge. Current thinking is that (Q)SARs might best be employed as part of a battery of approaches that collectively provide information on skin sensitisation hazard. A number of (Q)SARs and expert systems have been developed and are described in the literature. Here we focus on three models (TOPKAT, Derek for Windows and TOPS-MODE), and evaluate their performance against a recently published dataset of 211 chemicals. The current strengths and limitations of one of these models is highlighted, together with modifications that could be made to improve its performance. Of the models/expert systems evaluated, none performed sufficiently well to act as a standalone tool for hazard identification.

Sensitization to 26 fragrances to be labelled according to current European regulation

To study the frequency of sensitization to 26 fragrances to be labelled according to current European regulation. During 4 periods of 6 months, from 1 January 2003 to 31 December 2004, 26 fragrances were patch tested additionally to the standard series in a total of 21 325 patients; the number of patients tested with each of the fragrances ranged from 1658 to 4238. Hydroxymethylpentylcyclohexene carboxaldehyde (HMPCC) was tested throughout all periods. The following frequencies of sensitization (rates in %, standardized for sex and age) were observed: tree moss (2.4%), HMPCC (2.3), oak moss (2.0), hydroxycitronellal (1.3), isoeugenol (1.1), cinnamic aldehyde (1.0), farnesol (0.9), cinnamic alcohol (0.6), citral (0.6), citronellol (0.5), geraniol (0.4), eugenol (0.4), coumarin (0.4), lilial (0.3), amyl-cinnamic alcohol (0.3), benzyl cinnamate (0.3), benzyl alcohol (0.3), linalool (0.2), methylheptin carbonate (0.2), amyl-cinnamic aldehyde (0.1), hexyl-cinnamic aldehyde (0.1), limonene (0.1), benzyl salicylate (0.1), gamma-methylionon (0.1), benzyl benzoate (0.0), anisyl alcohol (0.0). 1) Substances with higher sensitization frequencies were characterized by a considerable number of '++/+++' reactions. 2) Substances with low sensitization frequencies were characterized by a high number of doubtful/irritant and a low number of stronger (++/+++) reactions. 3) There are obviously fragrances among the 26 which are, with regard to contact allergy, of great, others of minor, and some of no importance at all.

Global (Q)SARs for skin sensitisation: assessment against OECD principles

As part of a European Chemicals Bureau contract relating to the evaluation of (Q)SARs for toxicological endpoints of regulatory importance, we have reviewed and analysed (Q)SARs for skin sensitisation. Here we consider some recently published global (Q)SAR approaches against the OECD principles and present re-analysis of the data. Our analyses indicate that "statistical" (Q)SARs which aim to be global in their applicability tend to be insufficiently robust mechanistically, leading to an unacceptably high failure rate. Our conclusions are that, for skin sensitisation, the mechanistic chemistry is very important and consequently the best non-animal approach currently applicable to predict skin sensitisation potential is with the help of an expert system. This would assign compounds into mechanistic applicability domains and apply mechanism-based (Q)SARs specific for those domains and, very importantly, recognise when a compound is outside its range of competence. In such situations, it would call for human expert input supported by experimental chemistry studies as necessary.

Quantitative structure activity relationship model for predicting the depletion percentage of skin allergic chemical substances of glutathione

A quantitative model was developed to predict the depletion percentage of glutathione (DPG) compounds by gene expression programming (GEP). Each kind of compound was represented by several calculated structural descriptors involving constitutional, topological, geometrical, electrostatic and quantum-chemical features of compounds. The GEP method produced a nonlinear and five-descriptor quantitative model with a mean error and a correlation coefficient of 10.52 and 0.94 for the training set, 22.80 and 0.85 for the test set, respectively. It is shown that the GEP predicted results are in good agreement with experimental ones, better than those of the heuristic method.

4D-fingerprint categorical QSAR models for skin sensitization based on the classification of local lymph node assay measures

Currently, the only validated methods to identify skin sensitization effects are in vivo models, such as the local lymph node assay (LLNA) and guinea pig studies. There is a tremendous need, in particular due to novel legislation, to develop animal alternatives, for eaxample, quantitative structure-activity relationship (QSAR) models. Here, QSAR models for skin sensitization using LLNA data have been constructed. The descriptors used to generate these models are derived from the 4D-molecular similarity paradigm and are referred to as universal 4D-fingerprints. A training set of 132 structurally diverse compounds and a test set of 15 structurally diverse compounds were used in this study. The statistical methodologies used to build the models are logistic regression (LR) and partial least-square coupled logistic regression (PLS-LR), which prove to be effective tools for studying skin sensitization measures expressed in the two categorical terms of sensitizer and non-sensitizer. QSAR models with low values of the Hosmer-Lemeshow goodness-of-fit statistic, X(2)HL, are significant and predictive. For the training set, the cross-validated prediction accuracy of the logistic regression models ranges from 77.3% to 78.0%, whereas that of the PLS-logistic regression models ranges from 87.1% to 89.4%. For the test set, the prediction accuracy of logistic regression models ranges from 80.0% to 86.7%, whereas that of the PLS-logistic regression models ranges from 73.3% to 80.0%. The QSAR models are made up of 4D-fingerprints related to aromatic atoms, hydrogen bond acceptors, and negatively partially charged atoms.

Investigating protein haptenation mechanisms of skin sensitisers using human serum albumin as a model protein

Covalent modification of skin proteins by electrophiles is a key event in the induction of skin sensitisation but not skin irritation although the exact nature of the binding mechanisms has not been determined empirically for the vast majority of sensitisers. It is also unknown whether immunologically relevant protein targets exist in the skin contributing to effecting skin sensitisation. To determine the haptenation mechanism(s) and spectra of amino acid reactivity in an intact protein for two sensitisers expected to react by different mechanisms, human serum albumin (HSA) was chosen as a model protein. The aim of this work was also to verify for selected non-sensitisers and irritants that no protein haptenation occurs even under forcing conditions. HSA was incubated with chemicals and the resulting complexes were digested with trypsin and analysed deploying matrix-assisted laser desorption/ionization mass spectrometry, reverse phase high performance liquid chromatography and nano-electrospray tandem mass spectrometry. The data confirmed that different residues (lysine, cysteine, histidine and tyrosine) are covalently modified in a highly selective and differential manner by the sensitisers 2,4-dinitro-1-chlorobenzene and phenyl salicylate. Additionally, non-sensitisers 2,4-dichloro-1-nitrobenzene, butyl paraben and benzaldehyde and irritants benzalkonium chloride and sodium dodecyl sulphate did not covalently modify HSA under any conditions. The data indicate that covalent haptenation is a prerequisite of skin sensitisation but not irritation. The data also suggest that protein modifications are targeted to certain amino acids residing in chemical microenvironments conducive to reactivity within an intact protein. Deriving such information is relevant to our understanding of antigen formation in the immunobiology of skin sensitisation and in the development of in vitro protein haptenation assays.

Structure?activity relationships for skin sensitization: recent improvements to Derek for Windows

Derek for Windows (DfW) is a knowledge-based expert system that predicts the toxicity of a chemical from its structure. Its predictions are based in part on alerts that describe structural features or toxicophores associated with toxicity. Recently, improvements have been made to skin sensitization alerts within the DfW knowledge base in collaboration with Unilever. These include modifications to the alerts describing the skin sensitization potential of aldehydes, 1,2-diketones, and isothiazolinones and consist of enhancements to the toxicophore definition, the mechanistic classification, and the extent of supporting evidence provided. The outcomes from this collaboration demonstrate the importance of updating and refining computer models for the prediction of skin sensitization as new information from experimental and theoretical studies becomes available.

Application of the random forest method in studies of local lymph node assay based skin sensitization data

The random forest and classification tree modeling methods are used to build predictive models of the skin sensitization activity of a chemical. A new two-stage backward elimination algorithm for descriptor selection in the random forest method is introduced. The predictive performance of the random forest model was maximized by tuning voting thresholds to reflect the unbalanced size of classification groups in available data. Our results show that random forest with a proposed backward elimination procedure outperforms a single classification tree and the standard random forest method in predicting Local Lymph Node Assay based skin sensitization activity. The proximity measure obtained from the random forest is a natural similarity measure that can be used for clustering of chemicals. Based on this measure, the clustering analysis partitioned the chemicals into several groups sharing similar molecular patterns. The improved random forest method demonstrates the potential for future QSAR studies based on a large number of descriptors or when the number of available data points is limited.

Aldehyde sources, metabolism, molecular toxicity mechanisms, and possible effects on human health

Aldehydes are organic compounds that are widespread in nature. They can be formed endogenously by lipid peroxidation (LPO), carbohydrate or metabolism ascorbate autoxidation, amine oxidases, cytochrome P-450s, or myeloperoxidase-catalyzed metabolic activation. This review compares the reactivity of many aldehydes towards biomolecules particularly macromolecules. Furthermore, it includes not only aldehydes of environmental or occupational concerns but also dietary aldehydes and aldehydes formed endogenously by intermediary metabolism. Drugs that are aldehydes or form reactive aldehyde metabolites that cause side-effect toxicity are also included. The effects of these aldehydes on biological function, their contribution to human diseases, and the role of nucleic acid and protein carbonylation/oxidation in mutagenicity and cytotoxicity mechanisms, respectively, as well as carbonyl signal transduction and gene expression, are reviewed. Aldehyde metabolic activation and detoxication by metabolizing enzymes are also reviewed, as well as the toxicological and anticancer therapeutic effects of metabolizing enzyme inhibitors. The human health risks from clinical and animal research studies are reviewed, including aldehydes as haptens in allergenic hypersensitivity diseases, respiratory allergies, and idiosyncratic drug toxicity; the potential carcinogenic risks of the carbonyl body burden; and the toxic effects of aldehydes in liver disease, embryo toxicity/teratogenicity, diabetes/hypertension, sclerosing peritonitis, cerebral ischemia/neurodegenerative diseases, and other aging-associated diseases.

A future approach to measuring relative skin sensitising potency: a proposal

Current approaches to skin sensitisation risk assessment are dependent upon the availability of information regarding two fundamental parameters. Firstly, data relating to the relative skin sensitising potency of the chemical, and secondly, information regarding likely conditions of human exposure. During the past two decades, much has been achieved in terms of refining methods capable of informing these parameters. For example, the development of the local lymph node assay (LLNA) has made it possible to predict skin sensitising hazard, and to determine relative skin sensitising potency, in a way that was not possible previously. Taken together with accurate information about predicted exposure, such potency data can be used to facilitate the derivation of effective risk assessments. However, although the LLNA provides an integrated assessment of skin sensitising activity, it does require the use of experimental animals and there is growing enthusiasm for designing robust alternative approaches that will reduce or obviate that need. Progress is being made in defining alternative experimental strategies that avoid animal use, but it is clear that accurate characterisation of skin sensitisation hazards will require the effective integration of various sources of information. For this reason, we exemplify here one possible approach that, in theory, provides a framework for not only the identification of skin sensitising chemicals, but also the estimation of relative sensitising potency. This paradigm depends upon development of an understanding of the various biological, biochemical and chemical factors that impact on the allergenic properties of chemicals and the acquisition of skin sensitisation, and an ability to measure these in vitro.

QSARs for the aquatic toxicity of aromatic aldehydes from Tetrahymena data

The aim of the study was to develop quantitative structure-activity relationships (QSARs) for a large group of 77 aromatic aldehydes tested for acute toxicity to the ciliate Tetrahymena pyriformis using mechanistically interpretable descriptors. The resulting QSARs revealed that the 1-octanol/water partition coefficient (log K(ow)), is the most important descriptor of aldehyde aquatic toxic potency. The model with log K(ow) was improved by adding electronic descriptor (the maximum acceptor superdelocalizability in a molecule--A(max)) based on calculations with the semi-empirical AM1 model. The two descriptors reflect the two main processes responsible for demonstration of acute aquatic toxicity, namely penetration through cell membranes (log K(ow)) and interaction with the biomacromolecules (A(max)) into the cells. Results showed that generally the studied group of aldehydes could be modeled by this simple two-descriptor approach. However, the group of 2- and/or 4-hydroxylated aldehydes demonstrates enhanced toxicity compared to the other aldehydes. Transformation to quinone-like structures is proposed as the explanation for this enhanced potency. The 2- and/or 4-hydroxylated aldehydes are modeled successfully by [log(1/IGC50) = 0.540(0.038) log K(ow) + 8.30(2.88)A(max) - 3.11(0.92), n = 25, R2 = 0.916, R(CV)2 = 0.896, s = 0.141, F = 120], while the other aldehydes are modeled by the relationship [log(1/IGC50) = 0.583 (0.034)log K(ow) + 9.80(2.62)A(max) - 4.04 (0.85), n = 52, R2 = 0.864, R(CV)2 = 0.844, s = 0.203, F = 156], which is similar to the general benzene model.

An α,β-unsaturated oxime identified as a strong contact allergen

The aim of this study was to examine the possible skin sensitizing effect of oximes, employing an alpha,beta-unsaturated oxime as the model compound. Oximes are not frequently used as biologically active compounds. However, they have been shown to possess both anti-inflammatory and anti-allergic activities. Furthermore, in a recent study, a number of oximes and oxime-ethers of hydroxylated benzaldehydes and acetophenones were found to be powerful antioxidants suggested to be used in consumer products such as cosmetics and food. Although there are only few reports on the sensitizing effect of oximes, their ability to be hydrolyzed to the corresponding ketones or aldehydes makes them potential contact allergens. The oxime investigated in this study was demonstrated to be a strong contact allergen in both mice and guinea pigs, capable of sensitize the control animals after only one dermal exposure. In order to elucidate the mechanisms for the formation of the complete antigen, a variety of analogues with different reactivity were tested. The results indicate that alpha,beta-unsaturated oximes can react with proteins via several different pathways. Most likely, a metabolic transformation is involved. Due to the strong allergenic effect of the oxime investigated, we strongly advise against the use of such oximes in consumer products until a better understanding of their interactions with biological macromolecules has been obtained.