| A. Inspection of model output |
A review of the applicability domain information provided by the model’s software might increase or decrease reliability in the prediction. The results of the QSAR model might include a score (e.g., a probability of a positive outcome). The prediction reliability may be increased where a score indicating a high likelihood can be justified through an expert review of the available information.
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| B. Analysis of structural descriptors and corresponding training set data (see Note A) |
As part of the process of building a QSAR model, structural descriptors are selected (often automatically) when there is a statistical association to the (toxicological) data to be predicted; however, the selected descriptors might not be biologically meaningful for the predicted toxicological effect/mechanism, as discussed in Powley (2015). This assessment may be supported by inspecting the training set examples that match the descriptors wherever possible. An expert review may determine the result is incorrect if other structural moieties in the training set examples are more likely responsible for the biological activity, (i.e., the descriptors identified were coincidental and in fact irrelevant) (Amberg et al., 2016). Another scenario is when the structural descriptors map to experimental data that is incorrect and attributable to known problems with an assay. Again, these features may be discounted if they are not relevant to the toxicological effect or mechanism and this may lead to a reversal of the overall assessment. For example, chemicals containing acid halides may give false positive results due to possible interaction with the solvent DMSO in the Ames assay (Amberg et al., 2015). Descriptors identified as significant by the model that are also present in the query compound may be associated with a biological mechanism. An expert review may evaluate whether the mechanism is plausible for the query compound, including potential metabolism consideration. For example, does the highlighted feature represent a known reactive group or a known toxicophore? This analysis may lead to an increase in prediction reliability. In some systems, it is possible to inspect the training set’s experimental data and references for those examples that are primarily used in the prediction. An assessment of these full studies for these examples (as discussed in Section 2.5) could be used to justify an increase in the reliability of the prediction result. The structural diversity of the underlying chemicals for each significant descriptor may be reviewed as part of an expert review. Structural features that map to a large number of structurally diverse compounds would provide additional evidence that the toxicological effects or mechanisms associated with the descriptor could be extrapolated across different chemical classes (increasing reliability in the prediction), whereas a structural feature whose underlying data constitutes a congeneric series might not, especially if the query compound is structurally distant (decreasing reliability in the prediction).
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| C. Analysis of physicochemical descriptors used by model (see Note B) |
Is there any supporting information from the literature or elsewhere to support any correlation between the physicochemical properties identified as significant by the model and the toxicological effect/mechanism? An evaluation of the quality of the experimental data of the training set chemicals used for building of the model (e.g., if a guideline study was used to generate these data) may increase the reliability of the prediction result.
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| D. Assessment of other information |
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