This review collects information on sources of skin sensitisation data and computational tools for the estimation of skin sensitisation potential, such as expert systems and (quantitative) structure-activity relationship (QSAR) models. The review also captures current thinking of what constitutes an integrated testing strategy (ITS) for this endpoint. The emphasis of the review is on the usefulness of the models for the regulatory assessment of chemicals, particularly for the purposes of the new European legislation for the Registration, Evaluation, Authorisation and Restriction of CHemicals (REACH), which entered into force on 1 June 2007. Since there are no specific databases for skin sensitisation currently available, a description of experimental data found in various literature sources is provided. General (global) models, models for specific chemical classes and mechanisms of action and expert systems are summarised. This review was prepared as a contribution to the EU funded Integrated Project, OSIRIS. LIST OF ABBREVIATIONS CEFIC European Chemical Industry Council DC Dendritic Cells EC3 Concentration (weight percent) giving a stimulation index value of 3 in the LLNA EC European Commission ECB European Chemicals Bureau ECETOC European Centre for Ecotoxicology and Toxicology of Chemicals ECVAM European Centre for Validation of Alternative Methods ELUMO Energy of the Lowest Unoccupied Molecular Orbital EPA Environmental Protection Agency ESIS European chemical Substances Information System (ECB) EU European Union F Fischer Statistic FCA Freunds Complete Adjuvant GPMT Guinea Pig Maximisation Test HMT Human Maximisation Test HOMO Highest occupied molecular orbital HPV High Production Volume ICCA International Council of Chemical Associations ICCVAM Interagency Coordinating Committee on the Validation of Alternative Methods ITS Integrated (Intelligent) Testing Strategy JRC Joint Research Centre k Rate constant Ka Acid Dissociation Constant LC Langerhans Cells LLNA Local lymph node assay LUMO Lowest unoccupied molecular orbital MLR Multiple Linear Regression MO Molecular Orbital MSIAT Modified Single Injection Adjuvant Test MW Molecular Weight OECD Organisation for Economic Co-operation and Development PCA Principal Components Analysis pEC3 Negative logarithm of EC3 on a molar basis. Calculated as log [(molecular weight)/EC3] Q 2 Predictive ability of a model QMM Quantitative Mechanistic Model QMRF (Q)SAR Model Reporting Format QSAR Quantitative Structure-Activity Relationship R 2 Goodness of fit RAI Relative Alkylation Index REACH Registration Evaluation and Authorisation of CHemicals RIP REACH Implementation Project SAR Structure-Activity Relationship SB Schiff Base SI Stimulation index σ*: sigma* Taft substituent constant SMILES Simplified Molecular Line Entry system SN2 Substitution reaction, nucleophilic, bimolecular SNAr Substitution reaction, nucleophilic, aromatic TG Test Guideline TIMES-SS TIssue MEtabolism Simulator for Skin Sensitisation

/pdf/review-of-data-sources-qsars-and-integrated-testing-1lgefj7lcu.pdf

Review of Data Sources, QSARs and Integrated Testing Strategies for Skin Sensitisation

Skin sensitization is a significant environmental and occupational health concern. The possibility of workers and consumers becoming sensitized is a major problem for individuals, employers and marketing certain products. Consequently, there exists an important need to accurately identify chemicals that have the potential to cause skin sensitization. Under Registration, Evaluation, Authorization and Restriction of Chemicals (REACH), the sensitizing potential needs to be assessed for substances manufactured or imported at levels of 1 ton or greater per annum. Assessment of skin sensitization has traditionally relied on animal testing, but there are now strong pressures to reduce and ultimately eliminate the use of animals for this purpose. Building on research dating back at least 7 decades, a quite detailed mechanistic understanding of skin sensitization, both in terms of the underlying biological processes and the underlying chemistry, has been developed, with significant advances being made in the present century. This chapter presents an overview of the current biological and chemical mechanistic understanding, and reviews recent progress in nonanimal predictive modeling for skin sensitization, with an emphasis on how the understanding of these mechanisms can be applied in combination with in chemico and in silico approaches to hazard and risk assessment.

Chemistry Based Nonanimal Predictive Modeling for Skin Sensitization

http://nopr.niscair.res.in/bitstream/123456789/47898/1/IJCA%2026A%287%29%20554-555.pdf

Quantification of Molecular Shape & Its Correlation with Physicochemical Properties

The "Workshop on Regulatory Use of (Q)SARs for Human Health and Environmental Endpoints," organized by the European Chemical Industry Council and the International Council of Chemical Associations, gathered more than 60 human health and environmental experts from industry, academia, and regulatory agencies from around the world. They agreed, especially industry and regulatory authorities, that the workshop initiated great potential for the further development and use of predictive models, that is, quantitative structure-activity relationships [(Q)SARs], for chemicals management in a much broader scope than is currently the case. To increase confidence in (Q)SAR predictions and minimization of their misuse, the workshop aimed to develop proposals for guidance and acceptability criteria. The workshop also described the broad outline of a system that would apply that guidance and acceptability criteria to a (Q)SAR when used for chemical management purposes, including priority setting, risk assessment, and classification and labeling.

Summary of a Workshop on Regulatory Acceptance of (Q)SARs for Human Health and Environmental Endpoints. Mini-Monograph: Regulatory Acceptance of (Q)SARs.

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. 

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

..................................................................................................................................................... 5 Introduction................................................................................................................................................ 6 Biological Mechanisms of Skin Sensitisation............................................................................................ 7 Chemical Mechanisms of Skin Sensitisation............................................................................................. 8 Skin sensitisation predictive testing: in vivo methods ............................................................................... 8 Regulatory classifications .......................................................................................................................... 9 Sources of Skin sensitisation data.............................................................................................................. 9 (Quantitative) Structure-Activity Relationship models and expert systems............................................ 10 Published SARs........................................................................................................................................ 11 Statistical QSAR Models ......................................................................................................................... 13 Expert systems ......................................................................................................................................... 24 Knowledge-based systems.................................................................................................................... 24 Statistical Expert Systems..................................................................................................................... 25 Hybrid Expert Systems ......................................................................................................................... 27 Local QSAR Models................................................................................................................................ 28 Quantitative Mechanistic Models (QMM)............................................................................................... 35 Conclusions.............................................................................................................................................. 38 References ................................................................................................................................................ 40

Skin Sensitisation (Q)SARs/Expert Systems: from Past, Present to Future

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Skin Sensitisation (Q)SARs/Expert Systems: from Past, Present to Future