Identification of the Structural Requirements for Mutagenicity by Incorporating Molecular Flexibility and Metabolic Activation of Chemicals I: TA100 Model

Traditional attempts to model genotoxicity data have been limited to congeneric data sets,
primarily because the mechanism of action was ignored, and frequently, the chemicals required
metabolism to the active species. In this exercise, the COmmon REactivity PAtterns (COREPA)
approach was used to delineate the structural requirements for eliciting mutagenicity in terms
of ranges of descriptors associated with three-dimensional molecular structures. The database
used to build the mutagenicity model includes 1196 structurally diverse chemicals tested in
the Ames assay by the National Toxicology Program. This manuscript describes the development of the TA100 model that predicts the results of mutagenicity testing using only the Ames
TA100 strain. The TA100 model was developed using 148 chemicals that tested positive in
TA100 strain without rat liver enzymes (S-9) and 188 chemicals that tested positive in TA100
strain with rat liver enzymes. A decision tree was developed by first comparing the reactivity
profile of chemicals that were positive in TA100 without rat liver enzymes to the reactivity
profile of the remaining 1048 chemicals. This approach correctly identified 82% of the primary
acting mutagens and 94% of the nonmutagens in the training set. The 188 chemicals in the
training set that are positive only in the presence of metabolic activation would pass through
the decision tree as negative. The next step was to identify the chemicals that are positive
only in the presence of metabolic activation. To accomplish this, a series of hierarchically ordered
metabolic transformations were used to develop an S-9 metabolism simulator that was applied
to each of the 1048 chemicals. The potential metabolites were then screened through the
decision tree to identify reactive mutagens. This model correctly identified 77% of the
metabolically activated chemicals in a training set. A computer system that applies the
COREPA models and predicts mutagenicity of chemicals, including their metabolic activation,
was developed. Each prediction is accompanied by a probabilistic estimate of the chemical
being in the structural domain covered by the training set.