A multi-tiered workflow for constructing a quantitative adverse outcome pathway network for gamma radiation-induced reproductive inhibition in Lemna minor

While numerous qualitative adverse outcome pathways (AOPs) have been developed to describe the progressive toxicity of ionizing radiation, quantitative AOPs (qAOPs) to detail how effects quantitatively propagate from the initial molecular interaction to an adverse outcome remains limited. This study addresses this gap by proposing a multi-tiered workflow for developing a qAOP network (qAOPN) to characterise the detailed relationship between deposition of ionizing energy and decreased population growth rate in aquatic macrophytes, based on published quantitative dose-response (DR) data for gamma (Co-60) radiation-induced reproductive inhibition in the aquatic plant Lemna minor. First, a preliminary AOPN was assembled for the effects of ionizing radiation on plants captured in established AOPs #386, #387, and #388 (www.aopwiki.org). Weight of evidence (WoE) assessment from the AI-informed tool AOP-helpFinder and a narrative review were then applied to validate the confidence of individual key events relationships (KERs). Further quantification of the KERs was performed using a combination of point of departure (PoD) analysis via benchmark dose (BMD) modelling, structure equation modelling (SEM) and multiple nonlinear regression modelling (MNLRM). This approach effectively identified the most sensitive events within the AOPN and determined which linear AOPs were most relevant for low-dose radiation exposure. The resulting multi-tiered analysis enhanced the understanding of the underlying toxicity pathways, identified data gaps, and demonstrate how quantitative qAOPNs can be used to predict adverse (apical) impacts. The study presents a generic workflow for constructing qAOPNs that can expand the utility of mechanistically-informative AOPs towards quantitative models applicable both to chemical and non-chemical stressors.