Exploration of oxidative stress-mediated genetic toxicology modes of action using a pathway analysis, Connectivity Mapping, and transcriptional benchmark dosing-based framework

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Although current genetic toxicology practices can detect downstream genotoxicity effects, such as gene mutation and double-strand breaks, they are unable to detect the underlying mode of action (MoA) of a chemical or differentiate between direct- and indirect-acting genotoxicants without additional modification. The Adverse Outcome Pathway (AOP) framework is a useful tool to critically identify and evaluate MoAs and can enable subsequent quantitative dose-response assessments of genotoxicity endpoints. The recently developed AOP, "Oxidative DNA damage leading to chromosomal aberrations and mutations" (https://aopwiki.org/aops/296), pertains to 1 common genetic toxicology-relevant MoA: Oxidative stress. Reactive oxygen species (ROS) play a key role in regulating many biological processes; however, when disrupted, an excess of ROS can eventually lead to DNA damage and double-strand breaks. Here, we look at 18 compounds reported to have complete or mixed oxidative stress MoAs and use a combination of genomic tools such as Pathway analysis, Connectivity Mapping (CMap), and Transcriptional benchmark dose modeling to define a framework that can separate substances that test negative in vivo from true in vivo genotoxicants. TK6 cells were treated with the 18 compounds for 4 h, parallel micronucleus and genomics experiments were performed, and in vitro micronucleus data were used to infer dose for genomics analysis. The resulting genomic data were analyzed using pathway analysis for hypothesis generation; these hypotheses were tested using CMap and Transcriptional benchmark dose modeling. We demonstrate that a genomics-based workflow based on in vitro methods can be used to successfully separate in vivo genotoxicants from non-genotoxicants. These methods have the potential to evolve into Next Generation Risk Assessment tools that can be used for determining the contribution of the oxidative stress MoA in a predictive toxicology setting.

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