Species extrapolation for the 21st century

Abstract

Safety factors are used in ecological risk assessments to extrapolate from the toxic responses of laboratory test species to all species representing that group in the environment. More accurate extrapolation of species responses is important. Advances in understanding the mechanistic basis for toxicological responses and identifying molecular response pathways can provide a basis for extrapolation across species and, in part, an explanation for the variability in whole organism responses to toxicants. We highlight potential short- and medium-term development goals to meet our long-term aspiration of truly predictive in silico extrapolation across wildlife species' response to toxicants. A conceptual approach for considering cross-species extrapolation is presented. Critical information is required to establish evidence-based species extrapolation, including identification of critical molecular pathways and regulatory networks that are linked to the biological mode of action and species' homologies. A case study is presented that examines steroidogenesis inhibition in fish after exposure to fadrozole or prochloraz. Similar effects for each compound among fathead minnow, medaka, and zebrafish were attributed to similar inhibitor pharmacokinetic/pharmacodynamic distributions and sequences of cytochrome P45019A1/2 (CYP19A1/2). Rapid advances in homology modeling allow the prediction of interactions of chemicals with enzymes, for example, CYP19 aromatase, which would eventually allow a prediction of potential aromatase toxicity of new compounds across a range of species. Eventually, predictive models will be developed to extrapolate across species, although substantial research is still required. Knowledge gaps requiring research include defining differences in life histories (e.g., reproductive strategies), understanding tissue-specific gene expression, and defining the role of metabolism on toxic responses and how these collectively affect the power of interspecies extrapolation methods.

Comparative metabolism as a key driver of wildlife species sensitivity to human and veterinary pharmaceuticals

“…For fish, the same approach is feasible using in vitro hepatocyte assays for mainly carnivorous salmonid species such as rainbow trout (Oncorhynchus mykiss) versus the mainly herbivorous cyprinid species such as zebrafish (Danio rerio) or carp (Cyprinus carpio) [21,27]. For invertebrates, an in vivo approach would seem the best option and should be extended to both freshwater and marine species as part of an Adverse Outcome Pathways approach [41,[80][81][82]. Subsequently, the in vitro avian and fish metabolic data and the in vivo invertebrate data for reference APIs could be used to develop and validate in silico tools to better predict which enzymes are responsible for API metabolism.…”

Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: I. Challenges and research needs in ecotoxicology

“…The usefulness of the AOP framework for cross-species extrapolations has been highlighted previously (Celander et al, 2011;Perkins et al, 2013) and several examples of how toxicokinetic and toxicodynamic processes contribute to differences in species sensitivity have been given above. Below, we will briefly discuss how AOPs can be used to support common strategies applied for species extrapolation (Section 4.1) and present a venue for a broader integration of evolutionary life history theory with AOP concept, which could be developed into another useful approach to cross-species extrapolation (Section 4.2).…”

Pathway-based toxicity: history, current approaches and liver fibrosis and steatosis as prototypes

“…An Adverse Outcome Pathway Knowledge Base (AOP KB) is currently being developed by the OeCD extended Advisory Group on Molecular Screening and toxicogenomics (eAG MSt) and is being implemented by the JRC and the US ePA. 3,4,5,6,7 the AOP KB is an It system to capture, manage and share AOP information and will consist of three modules: 1) AOP-WIKI, a textbased tool allowing the management of AOP-related knowledge (AOPs, key events, relationships between them) in a Wikipedialike environment, 2) a graphical tool implementing quantitative models depicting the relationship between two events in an AOP (effectopedia) and 3) an Intermediate effects Database (AOP including homeostasis (Nichols et al, 2011), species differences (Celander et al, 2011) and extrapolation to population-level effects (Kramer et al, 2011).…”