International Consortium to Advance Cross-Species Extrapolation of the Effects of Chemicals in Regulatory Toxicology

Abstract

Regulatory decisions surrounding chemical safety are based on human and environmental (ecological) protection goals. Historically, such decisions have relied on data from animal toxicity testing to inform hazard and risk assessment and determine whether chemicals pose a threat to human or environmental health. Traditionally, mammalian toxicity test data have driven human health considerations, and studies from select species representing different taxa have driven ecological considerations. Crosstalk and collaboration between human and ecological health knowledge-streams have been limited. This represents a barrier for realizing the ultimate protection goal, which is the health of the planet and all its inhabitants, as exemplified by the One Health approach (https://www.cdc.gov/onehealth/). However, there are global efforts within governments, nongovernmental organizations, academic research organizations, and industry sectors to bridge this gap and focus on achieving optimal health outcomes without the need for animal testing through recognition of the interconnectedness between people and all species that share the environment. With this in mind, it is recognized that focused and concerted efforts to advance methods for cross-species extrapolation that leverage existing toxicity data from both mammals and other model organisms can be used to protect all species. To expedite the development and regulatory acceptance of computational methods, particularly bioinformatics, for informing cross-species extrapolation for the evaluation of chemical safety, there is a need to bring together tool/database/method developers and regulators in a global cross-sector collaborative consortium. These collaborations will help define regulatory needs, spark the creation of a bioinformatics toolbox, demonstrate the utility of various tools through coordinated application, and enhance communications with various stakeholders. The International Consortium to Advance Cross-Species Extrapolation in Regulation (ICACSER; https://www.setac.org/page/scixspecies) is being developed to align with both the One Health approach and the shifting paradigm in regulatory toxicology articulated by the National Research Council in 2007. Specifically, a strategy was described to include more efficient and cost-effective toxicity testing that takes advantage of cell-based and computational approaches for evaluating chemical safety in the 21st century (National Research Council, 2007). Such methods move away from the whole-animal testing that historically focused on apical endpoints, such as reproduction, growth, development, and mortality, toward testing molecular-, cellular-, and organ-level changes that can be predictive of upstream apical changes in biology and used for regulatory decision-making (National Research Council, 2007). It was envisioned that such a shift in toxicology would simultaneously reduce animal use. The objective of the present Focus article was to describe the challenges surrounding cross-species extrapolation in regulation and introduce new approach methods (NAMs) in bioinformatics that can enhance and broaden the ability to extrapolate toxicity knowledge beyond model organisms to the diversity of species through efforts lead by the developing ICACSER (Textbox 1). The global regulatory landscape is currently experiencing an evolution in thinking surrounding animals in toxicity testing, with the aim of eliminating or greatly reducing their use in toxicology. It has been 60 years since Russel and Burch developed the 3Rs (Replacement, Reduction, and Refinement) principles, providing a framework for performing more humane animal research (Burden et al., 2015). These principles have since been incorporated into test guidelines and legislation around the world, primarily to ensure that when conducted, experiments use the fewest animals necessary to answer the question at hand and maintain animal welfare standards. In recent years, attention has focused on replacement opportunities, because there is recognition that traditional animal models (e.g., rodents) are not always the best predictive system for humans, or as surrogates for other species of concern. For example, in 2013 a change to legislation in Europe banned the marketing of personal care products containing ingredients that have been tested on animals (https://ec.europa.eu/health/sites/default/files/endocrine_disruptors/docs/cosmetic_1223_2009_regulation_en.pdf), and legislation to ban animal testing for cosmetic safety has subsequently been enacted in many other countries worldwide (Burden et al., 2015). Recent legislation has underscored the willingness of authorities to use and consider in vitro and other NAMs for regulatory safety evaluation. For example, in 2017, the European Union Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulatory information requirements were amended to make animal testing the last resort for filling data gaps. In 2019, the US Environmental Protection Agency (USEPA) Administrator issued a directive to eliminate mammalian regulatory and research studies completely by 2035, with associated funds to develop alternative methods (https://www.epa.gov/research/administrator-memo-prioritizing-efforts-reduce-animal-testing-september-10-2019). Even more recently, the Scientific Committee on Consumer Safety (2021) produced its 11th revision of guidance for the testing of cosmetic ingredients to provide greater focus on the application of NAMs. Government pledges and changes to legislation such as these, along with a growing appreciation of the need to further develop relevant and predictive methods for safety assessment that do not necessarily rely on animals, show that the data landscape is also changing. There is now greater focus on the generation of mechanistic, cell-based, and computationally derived information for consideration as alternatives to animal testing. This shift is exemplified by data collected from available ecotoxicology literature curated in the ECOTOXicology Knowledgebase (ECOTOX; https://cfpub.epa.gov/ecotox/) from the 1980s to the present. The number of growth and mortality studies added to ECOTOX have remained relatively consistent throughout the last four decades, whereas increases have been observed in the reporting of molecular and cellular effects data since 2000. To facilitate the use of the increasing influx of mechanistic data to establish causal links to apical changes in individuals or populations, the adverse outcome pathway (AOP) conceptual framework is being considered or actively adopted in many regulatory, industry, and academic settings (Ankley et al., 2010). The AOP framework is an approach for gathering existing pathway-based knowledge and developing causal linkages between levels of biological organization allowing for prediction of adverse effects of regulatory significance (Ankley et al., 2010). The AOP framework was designed to help remove the silos between human health and ecological assessments by allowing mammalian data to provide insights into potential effects on nonmammalian species and vice versa. This is accomplished by defining the taxonomic domain of applicability with an emphasis on the structural and functional conservation (or lack thereof) of biological mechanisms across species for the purpose of understanding how broadly available knowledge can be extrapolated from one species to others. Adverse outcome pathways provide the opportunity to extrapolate effects across species, qualitatively and quantitatively, through the identification of conserved early events in the AOP, termed molecular initiating events (MIEs), in which the chemical interacts with a biomolecule, as well as at subsequent key events and key event relationships (KERs) as the pathways move from the molecular level to cells, organs, and tissues out to individuals and populations (Villeneuve et al., 2014). The demonstration of conservation across species at these various levels of biological organization may decrease the numbers and diversity of species needed for toxicity testing, including nonhuman primates. For example, if evidence exists that early pathway events are structurally and functionally conserved across vertebrates, then additional testing in more vertebrate species may not be necessary to gain further information to make the causal linkages across early key events in the AOP framework. Similarly, if there is strong evidence of, for example, chemical–protein interactions in vertebrate species combined with evidence of a lack of conservation in invertebrate species, this knowledge could reduce the need for additional toxicity testing in invertebrate species. From this perspective, it is not only the target or surrogate species that is considered, but also the conservation of the biological pathway in the context of species for which that pathway is relevant. This shift in perspective allows more effective use of existing toxicological data and improved efficiency in chemical safety assessments by reducing the number of, and reliance on, toxicity tests in animals. However, there are still key challenges that need to be resolved to fully capitalize on the application of AOPs for extrapolating across species for risk assessment purposes. Perhaps the most notable of these challenges are the need to increase knowledge on the extent of functional conservation of downstream effects across species and the need to recognize that some adverse biological responses will be caused by multiple MIEs and/or multiple pathways comprising biological networks and/or by toxicokinetic considerations (Rivetti et al., 2020). Extrapolating toxicity from one species, typically a model organism, to others, considering both toxicokinetics and toxicodynamics, is extremely challenging (Figure 1). From an environmental risk assessment (ERA) standpoint, this complexity is exemplified by the overarching aim to protect ecosystems that are comprised of a diverse range of species, each with potentially different sensitivities to the array of chemicals and other stressors to which they may be exposed. Simplifying this complexity has led to the use of extrapolation factors and species sensitivity distributions (SSDs) in ERAs (Spurgeon et al., 2020). Factor-based extrapolation approaches are generalized and do not consider physiological, spatial, or temporal species differences, whereas SSDs are determined using cumulative distributions of measured species sensitivity (often expressed as toxicity values). Although SSDs have a long history of use in helping to determine safe limits of chemical exposure, they suffer from a necessary trade-off between the use of high-quality data and the need for toxicity information from a large number of species. Although both approaches are practical tools supporting chemical safety decision-making, they overlook the details of pathway-based similarities and differences that dictate taxon-specific sensitivity to stressors. Taxa-specific differences become especially important when a protection goal is necessary for a threatened or endangered species. An understanding of chemical exposure, including absorption, distribution, metabolism, and elimination (ADME), and the organism's life stage, life history, and traits is a major consideration relative to cross-species extrapolation of effects. A number of studies link differences in biological traits to the differences in species' responses to chemical exposure (Spurgeon et al., 2020). Traits related to differences observed in ADME, including species behavior, surface area-to-body mass scaling relationships, and metabolic capacities, are particularly critical in this respect. Understanding how differences in ADME among species impact internal chemical concentrations at target sites is critical to fully implement mechanistically based cross-species extrapolation for risk assessment. If internal concentrations of a chemical fail to reach a threshold activation concentration at the target, a MIE will not be triggered and the AOP will not proceed to the adverse effect. Conversely, chemical concentrations substantially above a threshold may lead to general disruption of membranes and molecular processes and cell stress in what has been termed the cytotoxic burst (CTB) phenomenon with respect to in vitro assays, which can mask or overwhelm more specific pathway perturbations observed at lower concentrations (Judson et al., 2016). Similar observations have been made when considering narcotic effects pertaining to in vivo studies. Additional complexity comes from differences in toxicokinetics/metabolic rates across different cell types, tissues, individuals, and species. Other factors determining species sensitivities to chemicals include the life stages of organisms and whether there was an acute or chronic exposure. These become critical considerations to enable the replacement of traditional in vivo test systems with in vitro (e.g., cell-based) assays or other NAMs, such as omics, on both an intra- and interspecies level. Thus, from an applied risk assessment perspective, cross-species extrapolation cannot be divorced from an understanding of the specific exposure scenario, although screening level assessments may be more flexible. Although the magnitude of the challenge to connect potential for exposure to potential for effect in risk assessment should not be underestimated, the number of available models to better characterize and understand chemical concentrations and distribution within organisms is growing. Tools such as the MERLIN-Expo software (https://merlin-expo.eu/) and GastroPlus (https://www.simulations-plus.com/software/gastroplus/) provide increasingly good mechanistic modeling approaches for simulating chemical distribution in humans. More recent physiologically based pharmacokinetic models in fish provide options for increased cross-species extrapolation (Rivetti et al., 2020). Nonetheless, there remains a significant research challenge to address toxicokinetic modeling for less well-characterized species. In addition, cross-species extrapolation of chemical effects depends heavily on knowledge of taxonomic conservation of key biological pathways (Table 1). For the most part, these considerations are either qualitative or neglected in decision-making, primarily because empirical data are lacking for the majority of species and cannot readily be generated. However, more detailed considerations of exposure and effects across the diversity of life are becoming more attainable for cross-species extrapolation due to advances in informatics, including bioinformatics, systematic methods, and toxicokinetic and toxicodynamic modeling. Model organisms have served as surrogates that provide empirical data for regulatory actions including risk assessment and have been the cornerstone of historical toxicity testing. These data are also used to demonstrate the predictivity of NAMs, such as high-throughput technologies, omics, organs on a chip, in vitro and subcellular fraction assays, and AOPs. Toxicity testing with mammalian species such as nonhuman primates, rat, mouse, rabbit, guinea pig, dog, sheep, and pig have been used for human health risk assessment. Nonmammalian species such as fathead minnow, rainbow trout, Japanese medaka, zebrafish, Japanese quail, African clawed frog, cladocerans, and green algae have been used historically to represent diverse taxonomic groups and trophic levels in the context of ERA. These species often were selected as model organisms due to characteristics that make them amenable to laboratory testing (e.g., developmental windows, ease of maintenance, and generation time) rather than their appropriateness as surrogates to represent toxicity in other species. The sensitivity of an organism to a chemical stressor has been assumed to be a function of their relatedness, but evolutionary relationships have yet to be consistently considered in extrapolating from surrogate species in the context of chemical safety evaluations. The NAMs provide innovative and enhanced opportunities to accelerate chemical safety assessment and ensure that a risk assessment is grounded in biology rather than a reliance on a small and exclusive set of regulatory animal tests. As the need for rapid chemical screening and testing with reduced reliance on animals increases, there are opportunities to exploit existing data at several levels of biological organization (e.g., from sequence data to transcriptomics analyses to historical whole-organism studies) to define the taxonomic domain of applicability for biological pathways. This information can be used in rapid, cost-effective computational approaches for regulatory decision-making. The AOP framework facilitates cross-species extrapolation based on pathway conservation through the definition of key events at each level of biological organization. Because the taxonomic applicability of each KER is defined by conservation of neighboring upstream and downstream key events, existing toxicological data can be incorporated into a chemical assessment to complement from NAMs that primarily focus on early key The development of AOPs that for a pathway to from one key event to the will help to increase understanding and will be critical for development and of data into regulatory decision-making. approaches to testing and assessments that existing toxicological data from structurally related chemicals for downstream key events with from NAMs can increase in toxicity for chemicals for which data are limited. In testing the or of existing data will help additional testing when with a focus on species most to be A with this approach is that from assays with new chemicals can be incorporated into the of toxicological knowledge to increase ability to make based on NAMs. The predictive computational tools that have been most used by safety and regulators for understanding the and of of chemicals are around of models based on chemical More recent have focused more on taxonomic and mechanistic of increasing opportunities for understanding mechanisms across species et al., However, these approaches are still in determining structural chemical The application of can be enhanced by with approaches that exploit the of biological and mechanistic knowledge now the shifting paradigm in toxicity testing has the use of bioinformatics and the development of tools and for computationally the taxonomic of existing and toxicity data across species. For example, such as the to and other methods and have been to the and (Spurgeon et al., 2020). However, there is recognition that although each of these important information to the challenge of cross-species in is of the and use of these of data in regulatory decision-making. The utility of the data from NAMs is enhanced through with other information from and computational approaches to a considering toxicokinetics and for understanding species There are opportunities to systematic methods for literature using and which for more rapid and and of the The information can be used to conservation of biological and function and pathway-based taxonomic of It is envisioned that a of and methods for cross-species by empirical will enhance the utility of approaches to understand cross-species extrapolation for regulatory and acceptance in this alternatives to animal testing. that use bioinformatics approaches from and development are to be applied to chemical safety For example, modeling that computationally models from existing has been used to from diverse taxa that can be used in molecular and molecular et al., an opportunity to the of which can be with derived These advances in computational to and interactions can be applied for species extrapolation in the context of biological pathways. This greater in understanding species similarities and differences in which could a to of chemical across the diversity of species. As predictive toxicology using bioinformatics for cross-species extrapolation the from sequence to to the utility of these methods can when with test methods historically used for regulatory decision-making (Textbox The AOPs are well to align predictive approaches for species extrapolation to decision-making, in the context of as an framework for these although they may not yet have been applied to the challenge of cross-species can be by the level of biological organization they (Table from events to The AOP framework can efforts for tools that address the of biological can help to define data when a level is and can facilitate the of information across the different levels of biological organization. The development of tools to increase in cross-species will the use of existing data collected from many species when one is performing a chemical risk simultaneously the risk assessments for a of species. an of tools for defining the taxonomic domain of applicability and to data that inform challenges in species the global regulatory landscape and a for cross-species extrapolation of toxicity knowledge to chemical safety decision-making across both human health and the environment. a bioinformatics that for consistent cross-species extrapolation of toxicity knowledge in a human and environmental health the of approaches to cross-species extrapolation by to inform human health and ecological chemical safety assessment. The overarching purpose of this will be to the of regulatory and facilitate methods for species extrapolation for the optimal use of existing toxicological data and increased in NAMs for toxicity testing (Textbox The ICACSER is currently through a a number of key The Committee is to have an all regulatory, and and including both human health and environmental the Committee is comprised of from the the the Health and Environmental and the National for the and of in Research and to are around the regulatory to and opportunities for application of cross-species In the Committee has to and available tools and the of a development using for of is a key of the ICACSER through both and applied regulatory mechanisms (e.g., of relevant use of existing animal model data to inform chemical safety assessments for regulatory decision-making is a as toxicology toward less animal testing. To advance beyond in cross-species extrapolation and advantage of existing knowledge that can be applied to a number of species, it is to bring together a global with in the including both and to focus the of toxicokinetics in understanding and cross-species extrapolation the focus of the ICACSER is on on the opportunities in bioinformatics methods for regulatory decision-making. If this will risk to make better use of existing toxicological information and more consider the impact of chemicals on a of species. and for providing on an This has been in with the requirements of the of Research and US Environmental Protection The expressed in this are of the and do not necessarily the or of the the of or products or for use. The in this are of the and do not represent of and and and and associated and tools are available from the

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