The OECD QSAR Toolbox is a software application intended to be used by governments, the chemical industry and other stakeholders in filling gaps in (eco)toxicity data needed for assessing the hazards of chemicals. The development and release of the Toolbox is a cornerstone in the computerization of hazard assessment, providing an 'all inclusive' tool for the application of category approaches, such as read-across and trend analysis, in a single software application, free of charge. The Toolbox incorporates theoretical knowledge, experimental data and computational tools from various sources into a logical workflow. The main steps of this workflow are substance identification, identification of relevant structural characteristics and potential toxic mechanisms of interaction (i.e. profiling), identification of other chemicals that have the same structural characteristics and/or mechanism (i.e. building a category), data collection for the chemicals in the category and use of the existing experimental data to fill the data gap(s). The description of the Toolbox workflow and its main functionalities is the scope of the present article.

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.

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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