Identifying the Structural Requirements for Chromosomal Aberration by Incorporating Molecular Flexibility and Metabolic Activation of Chemicals

Abstract

Modeling the potential of chemicals to induce chromosomal damage has been hampered by the diversity of mechanisms which condition this biological effect. The direct binding of a chemical to DNA is one of the underlying mechanisms that is also responsible for bacterial mutagenicity. Disturbance of DNA synthesis due to inhibition of topoisomerases and interaction of chemicals with nuclear proteins associated with DNA (e.g., histone proteins) were identified as additional mechanisms leading to chromosomal aberrations (CA). A comparative analysis of in vitro genotoxic data for a large number of chemicals revealed that more than 80% of chemicals that elicit bacterial mutagenicity (as indicated by the Ames test) also induce CA; alternatively, only 60% of chemicals that induce CA have been found to be active in the Ames test. In agreement with this relationship, a battery of models is developed for modeling CA. It combines the Ames model for bacterial mutagenicity, which has already been derived and integrated into the Optimized Approach Based on Structural Indices Set (OASIS) tissue metabolic simulator (TIMES) platform, and a newly derived model accounting for additional mechanisms leading to CA. Both models are based on the classical concept of reactive alerts. Some of the specified alerts interact directly with DNA or nuclear proteins, whereas others are applied in a combination of two- or three-dimensional quantitative structure-activity relationship models assessing the degree of activation of the alerts from the rest of the molecules. The use of each of the alerts has been justified by a mechanistic interpretation of the interaction. In combination with a rat liver S9 metabolism simulator, the model explained the CA induced by metabolically activated chemicals that do not elicit activity in the parent form. The model can be applied in two ways: with and without metabolic activation of chemicals.

Applicability of QSAR analysis to the evaluation of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment

“…Examples of the hydrid approach include models implemented in the OASIS TIMES (Mekenyan et al,. 2004;Mekenyan et al, 2007; as well as some literature-based models not implemented in software.…”

Integrated approach to testing and assessment for predicting rodent genotoxic carcinogenicity

“…The in vitro mutagenicity models combine a simulator of metabolism and a model assessing the reactivity of chemicals and their metabolites (Mekenyan et al ., ). The tissue metabolism simulator (TIMES) software allows prediction of mutagenicity with and without application of the simulator of S9 metabolism.…”

“…Tissue metabolism simulator genotoxicity and non-genotoxicity quantitative structure-activity relationship models Tissue metabolism simulator in vitro genotoxicity models. The in vitro mutagenicity models combine a simulator of metabolism and a model assessing the reactivity of chemicals and their metabolites (Mekenyan et al, 2007(Mekenyan et al, , 2012a(Mekenyan et al, , 2012b. The tissue metabolism simulator (TIMES) software allows prediction of mutagenicity with and without application of the simulator of S9 metabolism.…”

Androgen receptor binding affinity: a QSAR evaluation

“…A new upgrade of the mathematical formalism of the COREPA method [22] is now used to account for non-orthogonality of the conformer distributions. Currently developed models for estrogen binding affinity [22] and genotoxic effects (AMES mutagenicity and chromosomal aberrations) [23,24] demonstrate significantly improved performance of the model. A significant step in assessing the model performance is its external validation.…”

“…Conformers of an individual chemical that have free energy within a range of approximately 20 kcal mol À1 from the lowest energy structure (usually accepted as a threshold) often exhibited significant variation in potentially relevant electronic descriptors. The observation that relatively small energy differences between conformers can result in significant variations in electronic structure highlighted the necessity of including all energetically reasonable conformers when defining common reactivity patterns [24]. The recently developed non-deterministic method based on a genetic algorithm (GA) for coverage of the conformational space by a limited number of conformers was applied to generate conformers for the QSAR analysis [20].…”