A stepwise approach for determining the model applicability domain is proposed. Four stages are applied to account for the diversity and complexity of the current SAR/QSAR models, reflecting their mechanistic rationality (including metabolic activation of chemicals) and transparency. General parametric requirements are imposed in the first stage, specifying in the domain only those chemicals that fall in the range of variation of the physicochemical properties of the chemicals in the training set. The second stage defines the structural similarity between chemicals that are correctly predicted by the model. The structural neighborhood of atom-centered fragments is used to determine this similarity. The third stage in defining the domain is based on a mechanistic understanding of the modeled phenomenon. Here, the model domain combines the reliability of specific reactive groups hypothesized to cause the effect and the domain of explanatory variables determining the parametric requirements in order for functional groups to elicit their reactivity. Finally, the reliability of simulated metabolism (metabolites, pathways, and maps) is taken into account in assessing the reliability of predictions, if metabolic activation of chemicals is a part of the (Q)SAR model. Some of the stages of the proposed approach for defining the model domain can be eliminated depending on the availability and quality of the experimental data used to derive the model, the specificity of (Q)SARs, and the goals of their ultimate application. The performance of the proposed definition of the model domain is tested using several examples of (Q)SARs that have been externally validated, including models for predicting acute toxicity, skin sensitization, and biodegradation. The results clearly showed that credibility in predictions of QSAR models for chemicals belonging to their domain is much higher than for chemicals outside this domain.

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.

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The 500 MW cut-off for skin sensitization is a myth, probably derived from the widespread misconception that ability to efficiently penetrate the stratum corneum is a key determinant of sensitization potency. The scarcity of sensitizers with MW > 500 simply reflects the general scarcity of chemicals with MW > 500.

The TImes MEtabolism Simulator platform used for predicting skin sensitization (TIMES-SS) is a hybrid expert system that was developed at Bourgas University using funding and data from a consortium comprised 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 three-dimensional quantitative structure-activity relationships. Here, we describe an external validation exercise that was recently carried out. As part of this exercise, data were generated for 40 new chemicals in the murine local lymph node assay (LLNA) and then compared with predictions made by TIMES-SS. The results were promising with an overall good concordance (83%) between experimental and predicted values. The LLNA results were evaluated with respect to reaction chemistry principles for sensitization. Additional testing on a further four chemicals was carried out to explore some of the specific reaction chemistry findings in more detail. Improvements for TIMES-SS, where appropriate, were put forward together with proposals for further research work. TIMES-SS is a promising tool to aid in the evaluation of skin sensitization potential under legislative programs such as REACH.

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.

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.

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