Quantification of an Adverse Outcome Pathway Network by Bayesian Regression and Bayesian Network Modeling

Moe, S. Jannicke; Wolf, Raoul; Xie, Li; Landis, Wayne G.; Kotamäki, Niina; Tollefsen, Knut Erik

doi:10.1002/ieam.4348

Cited by 30 publications

(20 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They can offer a transparent way of evaluating the required characterization of uncertainty for pesticide risk assessment as well as for ecological risk assessment in general (Carriger & Newman, 2012). Moreover, their application is not only carried out for risk estimation (e.g., risk quotient) but also used to predict ecological effect from stressors more directly (e.g., decline in species abundance [Mitchell et al, 2021]) and to develop quantitative Adverse Outcome Pathways (Moe, Wolf, et al, 2021). Dreier et al (2020) pointed out that the use of effect and exposure distribution allows for a competent risk assessment and communication approach.…”

Section: Resultsmentioning

confidence: 99%

“…Bayesian networks are increasingly being used in environmental risk assessment (Moe, Wolf, et al, 2021). They can offer a transparent way of evaluating the required characterization of uncertainty for pesticide risk assessment as well as for ecological risk assessment in general (Carriger & Newman, 2012).…”

Section: Evaluation Of the Bayesian Network Approach For Risk Charact...mentioning

confidence: 99%

See 1 more Smart Citation

Development of a Bayesian network for probabilistic risk assessment of pesticides

Mentzel

Grung

Tollefsen

et al. 2021

Integr Envir Assess & Manag

Self Cite

View full text Add to dashboard Cite

Conventional environmental risk assessment of chemicals is based on a calculated risk quotient, representing the ratio of exposure to effects of the chemical, in combination with assessment factors to account for uncertainty. Probabilistic risk assessment approaches can offer more transparency by using probability distributions for exposure and/or effects to account for variability and uncertainty. In this study, a probabilistic approach using Bayesian network modeling is explored as an alternative to traditional risk calculation. Bayesian networks can serve as meta‐models that link information from several sources and offer a transparent way of incorporating the required characterization of uncertainty for environmental risk assessment. To this end, a Bayesian network has been developed and parameterized for the pesticides azoxystrobin, metribuzin, and imidacloprid. We illustrate the development from deterministic (traditional) risk calculation, via intermediate versions, to fully probabilistic risk characterization using azoxystrobin as an example. We also demonstrate the seasonal risk calculation for the three pesticides. Integr Environ Assess Manag 2022;18:1072–1087. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Evaluation Of the Bayesian Network Approach For Risk Charact...mentioning

confidence: 99%

Development of a Bayesian network for probabilistic risk assessment of pesticides

Mentzel

Grung

Tollefsen

et al. 2021

Integr Envir Assess & Manag

Self Cite

View full text Add to dashboard Cite

show abstract

“…Bayesian methods have been recommended for uncertainty analysis in the process of identifying limitations in scientific knowledge and evaluating their implications for scientific conclusion by the European Food Safety Authority (EFSA et al, 2018). Bayesian networks (BNs) are a branch of Bayesian approaches that have been increasingly used in environmental risk assessment and management (Moe et al, 2021b, Aguilera et al, 2011, Kaikkonen et al, 2021). BNs are probabilistic and graphical models, more specifically directed acyclic graphs (DAG) (Kanes et al, 2017) that have no feedback loops (Troldborg et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

Probabilistic risk assessment of pesticides under future agricultural and climate scenarios using a Bayesian network

Mentzel

Grung

Holten

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The use of Bayesian networks (BN) for environmental risk assessment has increased in recent years. One reason is that they offer a more transparent way to characterize risk and evaluate uncertainty than the traditional risk assessment paradigms. In this study, we explore a new approach to probabilistic risk assessment by developing and applying a BN as a meta-model for a Norwegian agricultural site. The model uses predictions from a process-based pesticide exposure model (World Integrated System for Pesticide Exposure - WISPE) in the exposure characterization and species sensitivity data from toxicity tests in the effect characterization. The probability distributions for exposure and effect are then combined into a risk characterization (i.e. the probability distribution of a risk quotient), which is a common measure of the exceedance of an environmentally safe exposure threshold. In this way, we aim to use the BN model to better account for variabilities of both pesticide exposure and effects to the aquatic environment than traditional risk assessment. Furthermore, the BN is able to link different types of future scenarios to the exposure assessment, taking into account both effects of climate change on pesticides fate and transport, and changes in pesticide application. We used climate projections from IPCC scenario A1B and two global circulation models (ECHAM5-r3 and HADCM3-Q0), which projected daily values of temperature and precipitation for Northern Europe until 2100. In Northern Europe, increased temperature and precipitation is expected to cause an increase in weed infestations, plant disease and insect pests, which in turn can result in altered agricultural practices, such as the use of new crop types and changes in pesticide application patterns. We used the WISPE model to link climate and pesticide application scenarios, environmental factors such as soil properties and field slope together with chemical properties (e.g. half-life in soil, water solubility, soil adsorption), to predict the pesticide exposure in streams adjacent to the agricultural fields. The model was parameterized and evaluated for five selected pesticides: the herbicides clopyralid, fluroxypyr-meptyl, and 2-(4-chloro-2-methylphenoxy) acetic acid (MCPA), and the fungicides prothiocanzole and trifloxystrobin. This approach enabled the estimation and visualization of probability distribution of the risk quotients representing the alternative climate models and application scenarios for the future time horizons 2050 and 2075. The currently used climate projections resulted in only minor changes in future risk directly through the meteorological variables. A stronger increase in risk was predicted for the scenarios with increased pesticide application, which in turn can represent an adaptation to a future climate with higher pest pressures. Further advancement of BN modelling as demonstrated herein is anticipated to aid targeted management of ecological risks in support of future research, industry and regulatory needs.

show abstract

“…The framework proposes a tiered exposure-driven and evidence-based approach to RAX, in which NAMs are used to strengthen a mode/mechanism of action hypothesis and support data on kinetics and exposure (Alexander-White, Bury et al 2022). Another promising tool to quantify the uncertainty of AOPs is a Bayesian network (BN), which has recently been applied successfully in the context of AOP based assessments (Muller, Lin et al 2015;Jeong, Song et al 2018;Moe, Wolf et al 2021). A BN can be used within the assessment of the AOPs to integrate data from different types of NAMs, such as in vitro high-throughput assays and in vivo toxicological data.…”

Section: Cumulative Distribution Function (Cdf) or Complementary Cumu...mentioning

confidence: 99%

Development of a Roadmap for Action on New Approach Methodologies in Risk Assessment

Escher

Partosch

Konzok

et al. 2022

EFS3

View full text Add to dashboard Cite

While whole animal studies have their place in risk assessment of food and feed components, it is thought that more modern approaches such as human focused new approached methodologies (NAMs) would bring advantages including a greater focus to the human species, a focus on molecular mechanism and kinetics and the possibility of addressing susceptible populations. This report outlines the thinking from the authors and culminates in activity proposals in seven distinct but interacting scientific areas i.e. development of additional AOPs/AOP networks (AOPs), advanced cell culture models including Organ on a chip (OoC), toxicokinetic assessment with a focus on physiological based kinetic modelling (PBK), exposome, human susceptibility, data integration and new concepts in human risk assessment. Furthermore, the development of a Forum is proposed to facilitate the implementation of new approaches and concepts in risk assessment. The report was compiled by the project team, renowned experts in the various areas, and recommendations were discussed with EFSA and further refined following consultation with external experts via a dedicated workshop. The authors are convinced that if the recommendations are taken up, there will be a significant impact in the field, resulting in increasing the uptake and utilisation of these emerging technologies by all stakeholders involved.

show abstract

Quantification of an Adverse Outcome Pathway Network by Bayesian Regression and Bayesian Network Modeling

Cited by 30 publications

References 68 publications

Development of a Bayesian network for probabilistic risk assessment of pesticides

Development of a Bayesian network for probabilistic risk assessment of pesticides

Probabilistic risk assessment of pesticides under future agricultural and climate scenarios using a Bayesian network

Development of a Roadmap for Action on New Approach Methodologies in Risk Assessment

Contact Info

Product

Resources

About