Validation of Toxtree and SciQSAR in silico predictive software using a publicly available benchmark mutagenicity database and their applicability for the qualification of impurities in pharmaceuticals

Abstract

The draft ICH M7 guidance (US FDA, 2013) recommends that the computational assessment of bacterial mutagenicity for the qualification of impurities in pharmaceuticals be performed using an expert rule-based method and a second statistically-based (Q)SAR method. The public nonproprietary 6489 compound Hansen benchmark mutagenicity data set was used as an external validation data set for Toxtree, a free expert rule-based SAR software. This is the largest known external validation of Toxtree. The Toxtree external validation specificity, sensitivity, concordance and false negative rate for this mutagenicity data set was 66%, 80%, 74% and 20%, respectively. This mutagenicity data set was also used to create a statistically-based SciQSAR-Hansen mutagenicity model. In a 10% leave-group-out internal cross validation study the specificity, sensitivity, concordance and false negative rate for the SciQSAR mutagenicity model was 71%, 83%, 77% and 17%, respectively. Combining Toxtree and SciQSAR predictions and scoring a positive finding in either software as a positive mutagenicity finding reduced the false negative rate to 7% and increased sensitivity to 93% at the expense of specificity which decreased to 53%. The results of this study support the applicability of Toxtree, and the SciQSAR-Hansen mutagenicity model for the qualification of impurities in pharmaceuticals.

Keywords: False negatives; ICH M7; Mutagenicity; Pharmaceuticals; QSAR; Qualification of impurities; SAR; SciQSAR; Toxtree.