Abstract
Because it is only possible to test chemicals for effects on a restricted range of species and exposure scenarios, ecotoxicologists are faced with a significant challenge of how to translate the measurements in model species into predictions of impacts for the wider range of species in ecosystems. Because of this challenge, within ecotoxicology there is no more fundamental aspect than to understand the nature of the traits that determine sensitivity. To account for the uncertainties of species extrapolations in risk assessment, “safety factors” or species sensitivity distributions are commonly used. While valuable as pragmatic tools, these approaches have no mechanistic grounding. Here we highlight how mechanistic information that is increasingly available for a range of traits can be used to understand and potentially predict species sensitivity to chemicals. We review current knowledge on how toxicokinetic, toxicodynamic, physiological, and ecological traits contribute to differences in sensitivity. We go on to discuss how this information is being used to make predictions of sensitivity using correlative and trait-based approaches, including comparisons of target receptor orthologs. Finally, we discuss how the emerging knowledge and associated tools can be used to enhance theoretical and applied ecotoxicological research through improvements in mechanistic modeling, predictive ecotoxicology, species sensitivity distribution development, mixture toxicity assessment, chemical design, biotechnology application and mechanistically informed monitoring.
Highlights
Chemicals are the business of some of the largest industrial sectors
The specific nature of the TD interactions cover mechanisms through which chemicals interact at target sites to result, triggering a MIE that leads to the biochemical, cellular, tissue organism that together comprise the toxicant adverse outcome pathways (AOPs) (Figure 2)
Examples of traits that may affect TK rates include lipid content variations affecting the nature of narcotic interactions, presence/absence of target receptors to determine whether species experience toxicity through specific trigger AOPs, gene duplication and loss varying receptor ortholog compliment and target receptor sequence leading to binding site differences that affect ligand-receptor interaction strengths
Summary
Chemicals are the business of some of the largest industrial sectors. Whether as raw materials from mining, oil and gas, as agrochemicals, textile, plastics, cosmetics, personal care, cleaning or through use in the pharmaceutical industry, chemicals pervade every aspect of modern life. Mebane (2010) identified a data-set of 27 species within 21 genera for cadmium; while Zhao and Chen (2016) extracted sensitivity data for 207 freshwater and saltwater species for the organophosphate insecticide chlorpyrifos in what is probably the largest ecotoxicological effect database currently available for any substance In those relatively few cases where the ecotoxicological effects literature is large and taxonomically broad, the distribution of toxicity metrics (e.g., LCxs, ECxs, NOECs etc.) can be modeled as a statistical distribution to assess the pattern and order of species sensitivities. The need to include an additional assessment factor as a blunt tool to support precautionary analysis indicates that, while undoubtedly useful, descriptive SSD models provide only a partial solution to the pan-species problem in chemical risk assessment
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