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In the regulatory assessment of chemicals, the use of non-testing methods such as (quantitative) structure–activity relationship models ([Q]SARs), is increasingly required or encouraged, in order to increase the efficiency and effectiveness of the risk assessment process, and to minimise the reliance on animal testing. The main question for the assessor concerns the usefulness of the non-testing approach, which can be broken down into the practical applicability of the method and the adequacy of the predictions. A framework for assessing and documenting (Q)SAR models and their predictions has been established at the European and international levels. Exactly how the framework is applied in practice will depend on the provisions of the specific legislation and the context in which the non-testing data are being used. The framework leaves largely open the question of how to determine the adequacy of predicted data. In fact, there is a considerable need to develop clear guidance on how the predictions generated by non-testing methods can be translated into regulatory conclusions and decisions. This chapter describes the current framework for documenting (Q)SAR models and their predictions, and discuses how it might be built upon to provide more detailed guidance on how to use (Q)SAR predictions in regulatory decision making. Some of the scientific issues that need to be considered, as well the difficulties encountered, are illustrated with respect to some widely used software tools (Toxtree, Caesar, ToxBoxes, and DEREK for Windows) and their predictions of genotoxicity for two case study compounds, sodium nitroguaiacolate and methylparathion.

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