Several pieces of legislation have led to an increased interest in the use of in silico methods, specifically the formation of chemical categories for the assessment of toxicological endpoints. For a number of endpoints, this requires a detailed knowledge of the electrophilic reaction chemistry that governs the ability of an exogenous chemical to form a covalent adduct. Historically, this chemistry has been defined as compilations of structural alerts without documenting the associated electrophilic chemistry mechanisms. To address this, this article has reviewed the literature defining the structural alerts associated with covalent protein binding and detailed the associated electrophilic reaction chemistry. This information is useful to both toxicologists and regulators when using the chemical category approach to fill data gaps for endpoints involving covalent protein binding. The structural alerts and associated electrophilic reaction chemistry outlined in this review have been incorporated into the OECD (Q)SAR Toolbox, a freely available software tool designed to fill data gaps in a regulatory environment without the need for further animal testing.

The aim of this work was to develop a high-quality 1-octanol/water partition coefficient-dependent (log P) baseline quantitative structure-activity relationship (QSAR) for the toxicity (log IGC(50)(-1)) of classic non-polar narcotics to Tetrahymena pyriformis, and subsequently use this model to define the domain of applicability for baseline narcosis. The toxicities to T. pyriformis of 514 possible non-polar narcotics were assessed. A QSAR to predict toxicity was created from a training set of 87 classic non-polar narcotics (the saturated alcohols and ketones): log IGC(50)(-1) = 0.78 log P-2.01 (n = 87, r(2) = 0.96). This model was then used to predict the toxicity of the remaining chemicals. The chemicals from the large dataset which were poorly predicted by the model (i.e. the prediction was > +/-0.5 log units from the experimental value) were used to aid the definition of structural categories of chemicals which are not non-polar narcotics. Doing so has enabled the domain for non-polar narcosis to be defined in terms of structural categories. Defining domains of applicability for QSAR models is important if they are to be considered for making predictions of toxicity for regulatory purposes.

It is important that in silico models for use in chemical safety legislation, such as REACH, are compliant with the OECD Principles for the Validation of (Q)SARs. Structural alert models can be useful under these circumstances but lack an adequately defined applicability domain. This paper examines several methods of domain definition for structural alert models with the aim of assessing which were the most useful. Specifically, these methods were the use of fragments, chemical descriptor ranges, structural similarity, and specific applicability domain definition software. Structural alerts for mutagenicity in Derek for Windows (DfW) were used as examples, and Ames test data were used to define and test the domain of chemical space where the alerts produce reliable results. The usefulness of each domain was assessed on the criterion that confidence in the correctness of predictions should be greater inside the domain than outside it. By using a combination of structural similarity and chemical fragments a domain was produced where the majority of correct positive predictions for mutagenicity were within the domain and a large proportion of the incorrect positive predictions outside it. However this was not found for the negative predictions; there was little difference between the percentage of true and false predictions for inactivity which were found as either within or outside the applicability domain. A hypothesis for the occurrence of this difference between positive and negative predictions is that differences in structure between training and test compounds are more likely to remove the toxic potential of a compound containing a structural alert than to add an unknown mechanism of action (structural alert) to a molecule which does not already contain an alert. This could be especially true for well studied end points such as the Ames assay where the majority of mechanisms of action are likely to be known.

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An important molecular initiating event for genotoxicity is the ability of a compound to bind covalently with DNA. However, not all compounds that can undergo covalent binding mechanisms will result in genotoxicity. One approach to solving this problem, when in silico prediction techniques are being used, is to develop tools that allow chemicals to be grouped into categories based on their ability to bind covalently to DNA. For this analysis to take place, compounds need to be placed within categories where the trend in toxicity can be explained by simple descriptors, such as hydrophobicity. However, this can occur only when the compounds within a category are structurally and mechanistically similar. Chemistry-based profilers have the ability to screen compounds and highlight those with similar structures to a target compound, and are thus likely to act via a similar mechanism of action. Here, examples are reported to highlight how structure-based profilers can be used to form categories and hence fill data gaps. The importance of developing a well-defined and robust category is discussed in terms of both mechanisms of action and structural similarity.