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

Development of a hierarchical support vector regression-based in silico model for the prediction of the cysteine depletion in DPRA

Topical and transdermal treatments have been dramatically growing recently and it is crucial to consider skin sensitization during the drug discovery and development process for these administration routes. Various tests, including animal and non-animal approaches, have been devised to assess the potential for skin sensitization. Furthermore, numerous in silico models have been created, providing swift and cost-effective alternatives to traditional methods such as in vivo, in vitro, and in chemico methods for categorizing compounds. In this study, a quantitative structure-activity relationship (QSAR) model was developed using the innovative hierarchical support vector regression (HSVR) scheme. The aim was to quantitatively predict the potential for skin sensitization by analyzing the percent of cysteine depletion in Direct Peptide Reactivity Assay (DPRA). The results demonstrated accurate, consistent, and robust predictions in the training set, test set, and outlier set. Consequently, this model can be employed to estimate skin sensitization potential of novel or virtual compounds.

Prediction of skin sensitization using machine learning

As global awareness of animal welfare spreads, the development of alternative animal test models is increasingly necessary. The purpose of this study was to develop a practical machine-learning model for skin sensitization using three physicochemical properties of the chemicals: surface tension, melting point, and molecular weight. In this study, a total of 482 chemicals with local lymph node assay results were collected, and 297 datasets with 6 physico-chemical properties were used to develop Random Forest (RF) model for skin sensitization. The developed model was validated with 45 fragrance allergens announced by European Commission. The validation results showed that RF achieved better or similar classification performance with f1-scores of 54% for penal, 82% for ternary, and 96% for binary compared with Support Vector Machine (SVM) (penal, 41%; ternary, 81%; binary, 93%), QSARs (ChemTunes, 72% for ternary; OECD Toolbox, 89% for binary), and a linear model (Kim et al., 2020) (41% for penal), and we recommend the ternary classification based on Global Harmonized System providing more detailed and precise information. In the further study, the proposed model results were experimentally validated with the Direct Peptide Reactivity Assay (DPRA, OECD TG 442C approved model), and the results showed a similar tendency. We anticipate that this study will help to easily and quickly screen chemical sensitization hazards.

An integrated in silico-in vitro investigation to assess the skin sensitization potential of 4-Octylphenol

One of the major challenges in chemical toxicity testing is the possibility to protect human health against adverse effects with non-animal methods. In this paper, 4-Octylphenol (OP) was tested for skin sensitization and immunomodulatory effects using an integrated in silico-in vitro test approach. In silico tools (QSAR TOOLBOX 4.5, ToxTree and VEGA) were used together with several in vitro tests including HaCaT cells (quantification of IL-6; IL-8; IL-1α and IL-18 by ELISA and expression of genes TNF, IL1A, IL6 and IL8 by RT- qPCR), RHE model (quantification of IL-6; IL-8; IL-1α and IL-18 by ELISA) and THP-1 activation assay (CD86/CD54 expression and IL-8 release). Additionally, the immunomodulatory effect of OP was investigated using lncRNAs MALAT1 and NEAT1 expression and LPS-induced THP-1 activation (CD86/CD54 expression and IL-8 release). The in silico tools predicted OP as a sensitizer. In vitro tests are also concordant with the in silico prediction. OP increased IL-6 expression (HaCaT cells); IL-18 and IL-8 expressions (RHE model). An irritant potential was also shown by a great expression of IL-1α (RHE model); and increased expression of CD54 marker and IL-8 in THP-1 cells. Immunomodulatory effects of OP were demonstrated by the downregulation of NEAT1, MALAT1 (epigenetic markers), IL6 and IL8; and an increase in LPS-induced CD54 and IL-8 expressions. Overall, results indicate that OP is a skin sensitizer, being positive in three key events of the AOP for skin sensitization, also showing immunomodulatory effects.

Development of a peptide reactivity assay for screening botanicals and natural substances: Proof of concept studies

Consumer products containing botanicals or natural substances (BNS) are often preferred because there is a perception that 'natural' is safe. As with any product ingredient, a thorough safety assessment must be conducted, including a determination of skin sensitization potential. A modification of the Peroxidase Peptide Reactivity Assay (PPRA) was explored for screening BNS (B-PPRA) for their reactivity to a model cysteine peptide. The PPRA incorporates a horseradish peroxidase‑hydrogen peroxide (+HRP/P) oxidation system for the activation of potential pre- and pro-haptens. BNS test materials contained <2% botanical constituent in either glycerin/water or propylene glycol/water. Stock solutions prepared in acetonitrile were diluted to 8 working concentrations. Direct reactivity was determined in reaction mixtures containing peptide and deferoxamine in potassium phosphate buffer. Enzyme-mediated reactivity determinations were performed with addition of +HRP/P. Initial studies demonstrated that results were reproducible and impact of carrier low. To determine the sensitivity of the assay, experiments were conducted with chamomile extract spiked with three sensitizers. Peptide depletion was observed in the +HRP/P reaction mixtures with isoeugenol spikes as low as 0.05%. The B-PPRA shows promise as a screening method for skin sensitization potential and could become part of a framework for the skin sensitization safety assessment of BNS.

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.

Interpretation of murine local lymph node assay (LLNA) data for skin sensitization: Overload effects, danger signals and chemistry-based read-across

There is a large body of information on testing of chemicals for skin sensitization in the murine local lymph node assay (LLNA), in which potency is quantified by the EC3 value, derived from dose-response data. This information finds use in risk assessment and regulatory classification, and also in assessing the performance of non-animal methods. However, some LLNA results are not straightforward to interpret, and in some cases published EC3 values are questionable. These cases usually arise where the dose-response does not show a monotonic increasing pattern but is bell-shaped, or shows a decrease in response with increasing dose over the whole dose range tested. By analogy with a long-recognised phenomenon in guinea pig sensitization, this is referred to as the overload effect. Here a mechanistic rationale is presented to explain the overload effect, and at the same time to explain the production of danger signals even when the sensitizer is non-irritant. Some illustrative examples are presented where the overload effect can lead to misinterpretation of LLNA results, and chemistry-based read-across is applied to reinterpret the data.

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.

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

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

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

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

An in vitro human skin test for assessing sensitization potential

Sensitization to chemicals resulting in an allergy is an important health issue. The current gold-standard method for identification and characterization of skin-sensitizing chemicals was the mouse local lymph node assay (LLNA). However, for a number of reasons there has been an increasing imperative to develop alternative approaches to hazard identification that do not require the use of animals. Here we describe a human in-vitro skin explant test for identification of sensitization hazards and the assessment of relative skin sensitizing potency. This method measures histological damage in human skin as a readout of the immune response induced by the test material. Using this approach we have measured responses to 44 chemicals including skin sensitizers, pre/pro-haptens, respiratory sensitizers, non-sensitizing chemicals (including skin-irritants) and previously misclassified compounds. Based on comparisons with the LLNA, the skin explant test gave 95% specificity, 95% sensitivity, 95% concordance with a correlation coefficient of 0.9. The same specificity and sensitivity were achieved for comparison of results with published human sensitization data with a correlation coefficient of 0.91. The test also successfully identified nickel sulphate as a human skin sensitizer, which was misclassified as negative in the LLNA. In addition, sensitizers and non-sensitizers identified as positive or negative by the skin explant test have induced high/low T cell proliferation and IFNγ production, respectively. Collectively, the data suggests the human in-vitro skin explant test could provide the basis for a novel approach for characterization of the sensitizing activity as a first step in the risk assessment process.

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

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

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

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

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

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

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

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

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

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

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

In vitro-to-in vivo correlation of the skin penetration, liver clearance and hepatotoxicity of caffeine

This work illustrates the use of Physiologically-Based Toxicokinetic (PBTK) modelling for the healthy Caucasian population in in vitro-to-in vivo correlation of kinetic measures of caffeine skin penetration and liver clearance (based on literature experiments), as well as dose metrics of caffeine-induced measured HepaRG toxicity. We applied a simple correlation factor to quantify the in vitro and in vivo differences in the amount of caffeine permeated through the skin and concentration-time profiles of caffeine in the liver. We developed a multi-scale computational approach by linking the PBTK model with a Virtual Cell-Based Assay to relate an external oral and dermal dose with the measured in vitro HepaRG cell viability. The results revealed higher in vivo skin permeation profiles than those determined in vitro using identical exposure conditions. Liver clearance of caffeine derived from in vitro metabolism rates was found to be much slower than the optimised in vivo clearance with respect to caffeine plasma concentrations. Finally, HepaRG cell viability was shown to remain almost unchanged for external caffeine doses of 5-400 mg for both oral and dermal absorption routes. We modelled single exposure to caffeine only.

Reactivity of cosmetic UV filters towards skin proteins: model studies with Boc-lysine, Boc-Gly-Phe-Gly-Lys-OH, BSA and gelatin

OBJECTIVE

Organic UV filters are used as active ingredients in most sunscreens and also in a variety of daily care products. Their good (photo) stability is of special interest to guarantee protective function and to prevent interactions with the human skin. Due to the mostly electrophilic character of the UV filters, reactions with nucleophilic protein moieties like lysine side chains are conceivable. Prior studies showed that the UV filters octocrylene (OCR), butyl methoxydibenzoylmethane (BM-DBM), ethylhexyl salicylate (EHS), ethylhexyl methoxycinnamate (EHMC), benzophenone-3 (BP-3), ethylhexyl triazone (EHT) and dibenzoylmethane (DBM) were able to covalently bind to an HPTLC amino phase and the amino acid models ethanolamine and butylamine after slightly heating and/or radiation.

METHODS

Boc-protected lysine, the tetrapeptide Boc-Gly-Phe-Gly-Lys-OH, bovine serum albumin (BSA) and porcine gelatin were used as more complex models to determine the reactivity of the mentioned UV filters towards skin proteins under thermal or UV irradiation conditions.

RESULTS

After gentle heating at 37°C, benzophenone imines were identified as reaction products of BP-3 and OCR with Boc-lysine and the tetrapeptide, whereas DBM and BM-DBM yielded enamines. For EHMC, a Michael-type reaction occurred, which resulted in addition of Boc-lysine or the tetrapeptide to the conjugated double bond. Ester aminolysis of EHS and EHT mainly afforded the corresponding amides. Reactions of the UV filters with BSA changed the UV spectrum of BSA, generally associated with an increase of the absorption strength in the UVA or UVB range. For all protein models, the UV filters showed an increasing reactivity in the order EHT < EHMC < EHS < BP-3 < OCR < DBM < BM-DBM.

CONCLUSION

Especially the UV absorbers BM-DBM, OCR and BP-3, which are seen as common allergens or photoallergens, showed a high reactivity towards the different skin protein models. As the formation of protein adducts is recognized as important key element in the induction of skin sensitization, the results of this study can contribute to a better understanding of the underlying chemical mechanisms of such reactions.

Towards AOP application--implementation of an integrated approach to testing and assessment (IATA) into a pipeline tool for skin sensitization

Since the OECD published the Adverse Outcome Pathway (AOP) for skin sensitization, many efforts have focused on how to integrate and interpret nonstandard information generated for key events in a manner that can be practically useful for decision making. These types of frameworks are known as Integrated Approaches to Testing and Assessment (IATA). Here we have outlined an IATA for skin sensitization which focuses on existing information including non testing approaches such as QSAR and read-across. The IATA was implemented into a pipeline tool using OASIS technology to provide a means of systematically collating and compiling relevant information which could be used in an assessment of skin sensitization potential. A test set of 100 substances with available skin sensitization information was profiled using the pipeline IATA. In silico and in chemico profiling information alone was able to correctly predict skin sensitization potential, with a preliminary accuracy of 73.85%. Information from other relevant endpoints (e.g., Ames mutagenicity) was found to improve the accuracy (to 87.6%) when coupled with a reaction chemistry mechanistic understanding. This pipeline platform could be useful in the assessment of skin sensitization potential and marks a step change in how non testing approaches can be practically applied.

TIMES-SS--recent refinements resulting from an industrial skin sensitisation consortium

The TImes MEtabolism Simulator platform for predicting Skin Sensitisation (TIMES-SS) is a hybrid expert system, first developed at Bourgas University using funding and data from a consortium of industry and regulators. TIMES-SS encodes structure-toxicity and structure-skin metabolism relationships through a number of transformations, some of which are underpinned by mechanistic 3D QSARs. The model estimates semi-quantitative skin sensitisation potency classes and has been developed with the aim of minimising animal testing, and also to be scientifically valid in accordance with the OECD principles for (Q)SAR validation. In 2007 an external validation exercise was undertaken to fully address these principles. In 2010, a new industry consortium was established to coordinate research efforts in three specific areas: refinement of abiotic reactions in the skin (namely autoxidation) in the skin, refinement of the manner in which chemical reactivity was captured in terms of structure-toxicity rules (inclusion of alert reliability parameters) and defining the domain based on the underlying experimental data (study of discrepancies between local lymph node assay Local Lymph Node Assay (LLNA) and Guinea Pig Maximisation Test (GPMT)). The present paper summarises the progress of these activities and explains how the insights derived have been translated into refinements, resulting in increased confidence and transparency in the robustness of the TIMES-SS predictions.

A mechanistic approach to modeling respiratory sensitization

Chemical respiratory sensitization is an important occupational health problem which may lead to severely incapacitated human health, yet there are currently no validated or widely accepted models for identifying and characterizing the potential of a chemical to induce respiratory sensitization. This is in part due to the ongoing uncertainty about the immunological mechanisms through which respiratory sensitization may be acquired. Despite the lack of test method, regulations such as REACH still require an assessment of respiratory sensitization for risk assessment and/or for the purposes of classification and labeling. The REACH guidance describes an integrated evaluation strategy to characterize what information sources could be available to facilitate such an assessment. The components of this include a consideration of well-established structural alerts and existing data (whether it be derived from read-across, (quantitative) structure-activity relationships ((Q)SAR), in vivo studies etc.). There has been some progress in developing SARs as well as a handful of empirical QSARs. More recently, efforts have been focused on exploring whether the reaction chemistry mechanistic domains first characterized for skin sensitization are relevant for respiratory sensitization and to what extent modifications or refinements are needed to rationalize the differences between the two end points as far as their chemistry is concerned. This study has built upon the adverse outcome pathway (AOP) for skin sensitization that was developed and published by the OECD in 2012. We have structured a workflow to characterize the initiating events that are relevant in driving respiratory sensitization. OASIS pipeline technology was used to encode these events as components in a software platform to enable a prediction of respiratory sensitization potential to be made for new untested chemicals. This prediction platform could be useful in the assessment of respiratory sensitization potential or for grouping chemicals for subsequent read-across.

Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software

Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely.

Simulation of chemical metabolism for fate and hazard assessment. V. Mammalian hazard assessment

Animals and humans are exposed to a wide array of xenobiotics and have developed complex enzymatic mechanisms to detoxify these chemicals. Detoxification pathways involve a number of biotransformations, such as oxidation, reduction, hydrolysis and conjugation reactions. The intermediate substances created during the detoxification process can be extremely toxic compared with the original toxins, hence metabolism should be accounted for when hazard effects of chemicals are assessed. Alternatively, metabolic transformations could detoxify chemicals that are toxic as parents. The aim of the present paper is to describe specificity of eukaryotic metabolism and its simulation and incorporation in models for predicting skin sensitization, mutagenicity, chromosomal aberration, micronuclei formation and estrogen receptor binding affinity implemented in the TIMES software platform. The current progress in model refinement, data used to parameterize models, logic of simulating metabolism, applicability domain and interpretation of predictions are discussed. Examples illustrating the model predictions are also provided.

Assessing toxicological data quality: basic principles, existing schemes and current limitations

Existing toxicological data may be used for a variety of purposes such as hazard and risk assessment or toxicity prediction. The potential use of such data is, in part, dependent upon their quality. Consideration of data quality is of key importance with respect to the application of chemicals legislation such as REACH. Whether data are being used to make regulatory decisions or build computational models, the quality of the output is reflected by the quality of the data employed. Therefore, the need to assess data quality is an important requirement for making a decision or prediction with an appropriate level of confidence. This study considers the biological and chemical factors that may impact upon toxicological data quality and discusses the assessment of data quality. Four general quality criteria are introduced and existing data quality assessment schemes are discussed. Two case study datasets of skin sensitization data are assessed for quality providing a comparison of existing assessment methods. This study also discusses the limitations and difficulties encountered during quality assessment, including the use of differing quality schemes and the global versus chemical-specific assessments of quality. Finally, a number of recommendations are made to aid future data quality assessments.

Non-animal test methods for predicting skin sensitization potentials

Contact allergies are complex diseases, and it is estimated that 15-20 % of the general population suffers from contact allergy, with increasing prevalence. Evaluation of the sensitization potential of a substance is usually carried out in animal models. Nowadays, there is much interest in reducing and ultimately replacing current animal tests. Furthermore, as of 2013, the EU has posed a ban on animal testing of cosmetic ingredients that includes skin sensitization. Therefore, predictive and robust in vitro tests are urgently needed. In order to establish alternatives to animal testing, the in vitro tests must mimic the very complex interactions between the sensitizing chemical and the different parts of the immune system. This review article summarizes recent efforts to develop in vitro tests for predicting skin sensitizers. Cell-based assays, in chemico methods and, to a lesser extent, in silico methods are presented together with a discussion of their current status. With considerable progress having been achieved during the last years, the rationale today is that data from different non-animal test methods will have to be combined in order to obtain reliable hazard and potency information on potential skin sensitizers.

Potential of coculture in vitro models to study inflammatory and sensitizing effects of particles on the lung

Exposure to particulate matter (PM) like nanoparticles (NPs) has increased in the last century due to increased combustion processes, road traffic, etc. In addition, the progress in chemical and cosmetic industry led to many new compounds, e.g. fragrances, which humans are exposed to every day. Many chemicals are known to act as contact and some as respiratory sensitizers, causing allergic reactions. Exposure to small particles of less than 100 nm in diameter is linked with an increased risk of respiratory diseases, such as asthma or rhinitis. To date already more than 1000 customer products contain eNPs without knowing much about the health effects. In comparison to chemicals, the mechanisms by which PM and eNPs can cause sensitization are still not fully understood. Validated and regulatory accepted in vitro models to assess this hazard in its full range are still missing. While a huge number of animal studies contributed to our knowledge about sensitization processes, knowledge on involved cellular mechanisms is still limited. In this review relevant in vitro models to study and elucidate these mechanisms in more detail are presented and their potential to serve as part of a tiered testing strategy is discussed.

Application of chemical reaction mechanistic domains to an ecotoxicity QSAR model, the KAshinhou Tool for Ecotoxicity (KATE)

The validity of chemical reaction mechanistic domains defined by skin sensitisation in the Quantitative Structure-Activity Relationship (QSAR) ecotoxicity system, KAshinhou Tools for Ecotoxicity (KATE), March 2009 version, has been assessed and an external validation of the current KATE system carried out. In the case of the fish end-point, the group of chemicals with substructures reactive to skin sensitisation always exhibited higher root mean square errors (RMSEs) than chemicals without reactive substructures under identical C- or log P-judgements in KATE. However, in the case of the Daphnia end-point this was not so, and the group of chemicals with reactive substructures did not always have higher RMSEs: the Schiff base mechanism did not function as a high error detector. In addition to the RMSE findings, the presence of outliers suggested that the KATE classification rules needs to be reconsidered, particularly for the amine group. Examination of the dependency of the organism on the toxic action of chemicals in fish and Daphnia revealed that some of the reactive substructures could be applied to the improvement of the KATE system. It was concluded that the reaction mechanistic domains of toxic action for skin sensitisation could provide useful complementary information in predicting acute aquatic ecotoxicity, especially at the fish end-point.

Developing the Applicability Domain of In Silico Models: Relevance, Importance and Methods

The past two decades has seen the rapid growth in the development and utilisation of computational technologies to predict the toxicity of chemicals. Most notably, widespread pressure to both reduce and replace current animal testing regimes has led to in silico modelling becoming a widely utilised tool in toxicological screening. Unfortunately, given that computational models are open to misuse, there has been, and still is, significant reluctance to accept them for regulatory use. In an effort to combat this, the validation of both model and predictions is now at the forefront of research, with the concept of applicability domain being central to the validation process.

In this chapter the applicability domain concept is defined and numerous methods for its characterisation are detailed and explored with the aid of a case study example. These approaches are shown to span from relatively simple descriptor-based methods to more complex approaches based upon structural similarity or mechanism of action. Given the wealth of differing approaches available and the different information each method yields about the model, a stepwise scheme which considers numerous methods is recommended. With appreciation of model architecture and subsequent utilisation, this chapter shows that a robust and multifaceted applicability domain can be generated. Once defined, the applicability domain serves as a critical screening stage ensuring that a model is fit-for-purpose and predictions are made with maximal confidence.

Expert Systems for Toxicity Prediction

Expert systems offer the facility to predict a toxicity endpoint, as well sometimes as additional relevant information, simply by inputting the chemical structure of a compound. There is now a number of expert systems available, mostly on a commercial basis although a few are free to use or download. This chapter discusses nineteen currently available expert systems, and their performances (if known). Published studies of consensus predictions with these expert systems indicate that these give better results than do individual expert systems.

A test set of compounds with Tetrahymena pyriformis toxicities has been run through the two expert systems known to predict these toxicities; the predictions were quite good, with standard errors of prediction of 0.395 and 0.433 log unit. A further test set of compounds with local lymph node assay skin sensitisation data has been run through seven expert systems, and it was found that consensus predictions were better than were those from any individual expert system.

Tools for Category Formation and Read-Across: Overview of the OECD (Q)SAR Application Toolbox

The Organisation for Economic Co-operation and Development (OECD) has an active role in promoting the use of (quantitiative) structure-activity relationships ((Q)SARs) for regulatory purposes, launching a new (Q)SAR project in 2003 with the aim of improving regulatory acceptance of QSARs. Part of this project consisted of the development of the (Q)SAR Application Toolbox, a first verison of whichhas been released for free download. The main feature of the Toolbox is to allow the user to group chemicals into toxicologically meaningful categories and to fill data gaps by read-across and trend analysis. The seminal features of the Toolbox are: (1) identification of relevant structural characteristics and potential mechanism or mode of action of a target chemical; (2) identification of other chemicals that have the same structural characteristics and/or mechanism or mode of action; (3) use of existing experimental data to fill the data gap(s). This chapter will discuss the use and application of the Toolbox.

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.