Quantitative assessment of KEs in AOP464 Parkinson's disease

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that has been associated with exposure to pesticides. Nevertheless, neurodegeneration is not currently an endpoint that is covered in pesticide safety assessment. Adverse Outcome Pathways (AOPs) are increasingly being developed for risk assessment purposes. To make practical use of these AOPs, quantifying Key Event Relationships (KERs) is a critical next step. Here, we demonstrate one approach of quantifying an AOP (AOP464) for pesticide-induced PD. Dopaminergic cell lines were used to generate data for KEs ‘oxidative stress’, ‘mitochondrial damage’, and ‘cell death’. Effects on oxidative stress were determined with the H2DCFDA-AM probe after 1, 24, and 72 hour(s). Mitochondrial damage and cell death were determined after 24, and 72 hour(s) by Alamar blue and CFDA-AM assay, respectively. Six pesticides (dinoseb, endosulfan, folpet, mancozeb, pyraclostrobin, and rotenone) were used for all experiments. Preliminary data shows that the pesticides tested do not necessarily trigger the KEs in the same linear manner. For instance, mancozeb and folpet induce ROS after 1 hour of exposure, but do not significantly affect mitochondrial activity at 24 hours. In contrast, dinoseb and pyraclostrobin do not induce ROS at 1 hour, but do cause mitochondrial dysfunction at 24 hours. As such, a common linear response-response model is not applicable in this case study. Instead, a probabilistic model that combines effects of early KEs to predict cell death provides a more fitting approach. Here we show that although qualitative AOPs are linear by design, this linearity is not mirrored in vitro. For practical application of qAOPs, it is necessary that this is taken into consideration, for instance by applying a probabilistic modelling approach. Moreover, to support mechanistic understanding and increase confidence in the model, measuring KEs at multiple time points can be useful. Finally, this case study highlights that developing AOPs for use in safety assessment should be an iterative process in which KEs and KERs are drawn and redrawn based on new data.