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Cross species extrapolation: Opportunities in a 21st century regulatory non-animal testing world
Citation:
LaLone, C., G. Hodges, M. Krishan, AND B. McAtee. Cross species extrapolation: Opportunities in a 21st century regulatory non-animal testing world. SETAC Europe Meeting, Duluth, MN, May 02 - 06, 2021.
Impact/Purpose:
With efforts to reduce the use of animals in chemical safety assessments and the need to continue to protect human health and species in the environment, new non-animal testing methods are being developed and vetted. A number of these new approaches utilize bioinformatic techniques, which are computer-driven methods that allow for rapid, in-depth, exploration of biological data to understand species similarities and differences that can aid in understanding chemical susceptibility across species, and thus assist with efforts in extrapolating available toxicity knowledge from tested species to untested species. As such methods are evolving, it is important to have interactions among researchers and decision-makers so as to ensure the tools and methods developed meet the needs of the regulatory community and are interoperable with one another. This session proposal aims to enhance communication among researchers and decision-makers regarding interoperability and cooperation to advance the use of such computationally derived results to inform regulatory decisions.
Description:
Regulatory decisions surrounding chemical safety are based primarily, although not exclusively, on human and environmental protection. Historically, there has been a reliance on animal toxicity testing to inform hazard and risk assessment. Typically, mammalian data drive human health considerations and studies from select non-mammalian species representing different taxa drive environmental considerations, with limited cross-talk between the knowledge-streams. The global regulatory landscape is experiencing an evolution in thinking surrounding animals in toxicity testing; in the US, for example, there is now a major drive to greatly reduce the use in in vivo testing. Therefore, the data landscape is also changing, requiring greater use of mechanistic, cell-based, and computationally-derived information for consideration as alternatives to animal testing. To establish confidence in mechanistic data and provide evidence as to how it relates to apical level changes at an individual or population level, the adverse outcome pathway (AOP) conceptual framework is being considered or actively adopted in many regulatory, industry and academic settings. The AOP framework has been described as an approach for developing causal linkages between levels of biological organization allowing for prediction of adverse effects. Such pathway-based approaches provide opportunities to incorporate data generated from all species and biological levels to understand the biology to mutually inform both human and environmental safety. This approach can be applied for considering both mammalian data to provide insights to non-mammalian species and vice versa. In fact, a critical component of the AOP framework is determining the taxonomic domain of applicability with an emphasis on conservation (or lack thereof) of the biology and, in particular pathways across species for the purpose of understanding how broadly available knowledge can be extrapolated. Fortunately, this shifting paradigm in toxicity testing has inspired the use of bioinformatics and specifically the development of tools and workflows for computationally exploring and predicting the taxonomic relevance of existing and newly generated toxicity data and knowledge across species. To date, a number of these methods/tools (e.g., SeqAPASS, EcoDrug, phylogenetic workflows) have been released to the public, peer-reviewed, and published. However, there is a growing recognition that although each tool brings important information to the challenge of cross-species extrapolation, maximum value in supporting decision making can be achieved only when each individual tool is developed whilst considering its application in the context of, or alignment with other approaches. . Therefore, we propose a session to enhance communication among researchers and decision-makers regarding interoperability and cooperation to advance the use of such computationally derived results to inform regulatory decisions.