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Updated: Jan 16, 2026
In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
A multi-tiered workflow for constructing a quantitative adverse outcome pathway network for gamma radiation-induced
1Norwegian Institute for Water Research (NIVA), Økernveien 94, OSLO N-0579, Norway; Norwegian University of Life Sciences (NMBU), Centre for Environmental Radioactivity, Post box 5003, Ås N-1432, Norway.
Abstract:
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.
Insights
This study develops a quantitative adverse outcome pathway network (qAOPN) to link ionizing radiation exposure to reduced plant growth. The workflow uses existing data and modeling to predict adverse effects, aiding risk assessment for radiation stressors.
Area of Science:
- Environmental Toxicology
- Radiation Biology
- Ecotoxicology
Background:
- Qualitative adverse outcome pathways (AOPs) describe radiation toxicity, but quantitative AOPs (qAOPs) detailing effect propagation are limited.
- Understanding quantitative dose-response (DR) relationships is crucial for predicting radiation's impact on ecosystems.
Purpose of the Study:
- To develop a multi-tiered workflow for constructing a quantitative AOP network (qAOPN).
- To characterize the relationship between ionizing energy deposition and decreased population growth rate in aquatic macrophytes.
- To establish a generic workflow for qAOPN construction applicable to various stressors.
Main Methods:
- Assembled a preliminary AOP network for ionizing radiation effects on plants.
- Applied weight of evidence (WoE) assessment and narrative review to validate key event relationships (KERs).
- Quantified KERs using benchmark dose (BMD) modeling, structure equation modeling (SEM), and multiple nonlinear regression modeling (MNLRM).
Main Results:
- Identified sensitive events within the qAOPN and determined relevant linear AOPs for low-dose radiation.
- Enhanced understanding of toxicity pathways and identified data gaps.
- Demonstrated the utility of qAOPNs for predicting adverse impacts from quantitative dose-response data.
Conclusions:
- The proposed workflow enables the development of qAOPNs for ionizing radiation.
- Quantitative AOPs can bridge mechanistic understanding with predictive modeling for environmental risk assessment.
- This generic workflow expands AOP utility for both chemical and non-chemical stressors.
