Density functional theory transition-state modeling for the prediction of Ames mutagenicity in 1,4 Michael acceptors

Townsend, Piers A.; Grayson, Matthew N.

Abstract

Assessing the safety of new chemicals, without introducing the need for animal testing, is a task of great importance. The Ames test, a widely used bioassay to assess mutagenicity, can be an expensive, wasteful process with animal-derived reagents. Existing in silico methods for the prediction of Ames test results are traditionally based on chemical category formation and can lead to false positive predictions. Category formation also neglects the intrinsic chemistry associated with DNA reactivity. Activation energies and HOMO/LUMO energies for thirty 1,4 Michael acceptors were calculated using a model nucleobase and were further used to predict the Ames test result of these compounds. The proposed model builds upon existing work and examines the fundamental toxicant-target interactions using density functional theory transition-state modeling. The results show that Michael acceptors with activation energies <20.7 kcal/mol and LUMO energies < -1.85 eV are likely to act as direct mutagens upon exposure to DNA.

• Journal Article Type: Article
• Acceptance Date: Nov 1, 2020
• Online Publication Date: Nov 27, 2019
• Publication Date: Dec 23, 2019
• Deposit Date: Sep 5, 2022
• Journal: Journal of Chemical Information and Modeling
• Print ISSN: 1549-9596
• Electronic ISSN: 1549-960X
• Publisher: American Chemical Society
• Peer Reviewed: Peer Reviewed
• Volume: 59
• Issue: 12
• Pages: 5099-5103
• DOI: https://doi.org/10.1021/acs.jcim.9b00966
• Keywords: Computational Chemistry; Toxicology; Reaction Modelling; DFT
• Public URL: https://uwe-repository.worktribe.com/output/9948882
• Publisher URL: https://pubs.acs.org/doi/10.1021/acs.jcim.9b00966