Advancing AOPs for genotoxicity through quantifying KERs linking DNA alkylation to increase in mutations and chromosome aberrations

Quantitative Adverse Outcome Pathways (qAOPs) help translate mechanistic knowledge into quantitative toxicity predictions; however, their development is constrained by the limited availability and heterogeneity of quantitative data. Here, we investigated the feasibility of developing a qAOP for genotoxicity by modeling quantitative relationships linking DNA alkylation to increased mutations and structural chromosome aberrations through intermediate biological key events (KEs). We compiled and harmonized published concentration-response data from human lymphoblastoid TK6 cells exposed to ethyl methanesulfonate, methyl methanesulfonate, or N-ethyl-N-nitrosourea, covering eight genotoxicity assays representing five KEs with well-established dependencies. We evaluated sources of variability, compared points of departure (PoDs), and quantified relationships between successive events using response-response modeling. Substantial between-study variability could to a limited extent be explained through differences in experimental design while batch effects for individual studies and researchers were substantial. PoD estimates that were calculated from combined results of independent studies with similar experimental design, were often uncertain due to insufficient concentration-response characterization or conflicting results between studies. Nevertheless, response-response modeling suggested a threshold effect for certain key-event relationships (KERs). Specifically, DNA adducts seemed to be triggered at lower concentrations of alkylating agents compared to the subsequent KEs of DNA strand breaks and chromosome aberrations. Furthermore, these fitted response-response models can be used for prediction of downstream KEs from earlier ones. The general approach we developed for BMC analysis followed by response-response modeling is applicable to other AOPs as well. These findings highlight both the feasibility and limitations of using existing data for qAOP development.