Density Functional Theory Transition-State Modeling for the Prediction of Ames Mutagenicity in 1,4 Michael Acceptors.

Piers A Townsend,Matthew N Grayson

doi:10.1021/acs.jcim.9b00966

Piers A Townsend, Matthew N Grayson

Open Access

https://doi.org/10.1021/acs.jcim.9b00966

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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.

Highlights

Global frameworks such as green chemistry should sit at the forefront of chemical research in the modern era
Compound 1 was originally classified as Ames negative; it was corrected to Ames positive upon application of our model and assessment of the literature
We can predict with confidence that Michael acceptors with a lowest unoccupied molecular orbital (LUMO) energy < −1.85 eV and an activation energy 22.0 kcal/mol and LUMO energies > −1.83 eV will be Ames negative

Summary

Introduction

Global frameworks such as green chemistry should sit at the forefront of chemical research in the modern era To help achieve this ideology, Anastas and Warner proposed twelve principles of green chemistry that acts as fundamental guidelines for the development of greener chemical processes and products.[1] The field of computational toxicology is concerned with using qualitative and quantitative data to develop models that improve our understanding about the toxicological risk of chemicals.[2,3] This directly aligns with the fourth principle of green chemistry; to design safer chemicals and reduce toxicity wherever possible. Understanding the chemical mechanism of mutagenic activity is vital to producing less toxic chemicals.[5] The most widely used bioassay for testing mutagenic activity is the Ames test This test uses Salmonella typhimurium bacteria with pre-existing mutations that prevent the synthesis of histidine. These in silico prediction systems, such as Derek Nexus, group chemicals into categories (e.g., DNA reactive, see Figure 1) based around so-called “structural alerts”.8,9

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