The metabolism of 14C-tazadolene succinate in the dog

Dring, L. G.; Bye, Alan; Constable, David; Lang, J. C. T.; Barsuhn, Craig L.; Johnston, A. M.

doi:10.3109/00498259209049902

Cited by 1 publication

(1 citation statement)

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“…Chemical Abstracts Service (CAS) number, name, Simplified Molecular Input Line Entry System (SMILES) notations, and mutagenic activity of all chemicals used in this study can be found in Table S1 of the Supporting Information. A specific training set with documented metabolic pathways by liver enzymes, predominantly of humans, rats, and dogs, for 179 chemicals was collected from literature ( − ) and the Internet (Tables S2−S5, Supporting Information). These metabolism data were used to train the simulator of metabolism to reproduce enzymatic reactions in liver.…”

Section: Methodsmentioning

confidence: 99%

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

Mekenyan

Dimitrov

Serafimova

et al. 2004

Chem. Res. Toxicol.

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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.

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