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A Systems Biology Approach to Advancing Adverse Outcome Pathways for Risk AssessmentNon-model Species in Ecological Risk Assessment

A Systems Biology Approach to Advancing Adverse Outcome Pathways for Risk Assessment: Non-model... [Ecological risk assessors are increasingly recognizing the need for objectively characterizing the sensitivity of specific ecological receptors of interest to environmental contaminants. Current testing strategies in support of ecological risk assessments primarily rely on extensive animal testing, and on extrapolation from standard laboratory model species to native species of relevance in local ecosystems. In addition to the huge costs and large numbers of animals needed, it has been shown that these approaches are often not adequately predictive, and thus, protective of organisms of interest. This chapter reviews the current challenges and developments in ecological and chemical risk assessment of non-model ecological species with specific reference to the current paradigm shift in toxicity testing from classic empirical live animal testing approaches to alternative concepts. The status and applicability of (high-throughput) in vitro systems, predictive toxicity-pathway models such as adverse outcome pathways (AOPs), quantitative structure-activity relationship (QSARs), and computational approaches are discussed in context with their potential to address current uncertainties in cross-species extrapolation of chemical hazards and associated regulatory needs. Specifically, comparative ‘omics and systems biology approaches are increasingly seen as powerful tools for cross-species extrapolation based on the assumption that structural and functional similarities or differences of specific molecular targets or pathways are likely to be one of the main drivers of the intrinsic sensitivity of organisms to contaminants. However, there are a number of uncertainties that remain to be addressed before these approaches and associated computational tools such as USEPA’s SeqAPASS tool become a viable option in non-model species risk assessment. Main concerns include the limited number of mature toxicity pathways currently available, their limited taxonomic application and their mostly qualitative nature. Furthermore, large data gaps exist with regard to toxicodynamic and toxicokinetic properties of chemicals in ecological species that determine target site concentrations, and that are critical factors influencing intrinsic sensitivity. The chapter concludes by providing a theoretical road map for future research building on the current promising advances in the field of ecotoxicogenomics and computational biology combined with alternative testing approaches using in vitro systems and early life stage animal tests to anchor pathways to species-specific biological outcomes.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Systems Biology Approach to Advancing Adverse Outcome Pathways for Risk AssessmentNon-model Species in Ecological Risk Assessment

Editors: Garcia-Reyero, Natàlia; Murphy, Cheryl A.
Hecker, Markus
Springer Journals — Feb 25, 2018
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25 pages

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Publisher
Springer International Publishing
Copyright
© Springer International Publishing AG 2018
ISBN
978-3-319-66082-0
Pages
107 –132
DOI
10.1007/978-3-319-66084-4_6
Publisher site
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Abstract

[Ecological risk assessors are increasingly recognizing the need for objectively characterizing the sensitivity of specific ecological receptors of interest to environmental contaminants. Current testing strategies in support of ecological risk assessments primarily rely on extensive animal testing, and on extrapolation from standard laboratory model species to native species of relevance in local ecosystems. In addition to the huge costs and large numbers of animals needed, it has been shown that these approaches are often not adequately predictive, and thus, protective of organisms of interest. This chapter reviews the current challenges and developments in ecological and chemical risk assessment of non-model ecological species with specific reference to the current paradigm shift in toxicity testing from classic empirical live animal testing approaches to alternative concepts. The status and applicability of (high-throughput) in vitro systems, predictive toxicity-pathway models such as adverse outcome pathways (AOPs), quantitative structure-activity relationship (QSARs), and computational approaches are discussed in context with their potential to address current uncertainties in cross-species extrapolation of chemical hazards and associated regulatory needs. Specifically, comparative ‘omics and systems biology approaches are increasingly seen as powerful tools for cross-species extrapolation based on the assumption that structural and functional similarities or differences of specific molecular targets or pathways are likely to be one of the main drivers of the intrinsic sensitivity of organisms to contaminants. However, there are a number of uncertainties that remain to be addressed before these approaches and associated computational tools such as USEPA’s SeqAPASS tool become a viable option in non-model species risk assessment. Main concerns include the limited number of mature toxicity pathways currently available, their limited taxonomic application and their mostly qualitative nature. Furthermore, large data gaps exist with regard to toxicodynamic and toxicokinetic properties of chemicals in ecological species that determine target site concentrations, and that are critical factors influencing intrinsic sensitivity. The chapter concludes by providing a theoretical road map for future research building on the current promising advances in the field of ecotoxicogenomics and computational biology combined with alternative testing approaches using in vitro systems and early life stage animal tests to anchor pathways to species-specific biological outcomes.]

Published: Feb 25, 2018

Keywords: Ecological Risk Assessment; Adverse Outcome Pathway (AOPs); Cross-species Extrapolation; Quantitative Structure–activity Relationship (QSARs); Live Animal Testing

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