Estimation of wildlife hazard levels using interspecies correlation models and standard laboratory rodent toxicity data

Wildlife ecological risk assessment in the 21st century: Promising technologies to assess toxicological effects

Despite advances in toxicity testing and the development of new approach methodologies (NAMs) for hazard assessment, the ecological risk assessment (ERA) framework for terrestrial wildlife (i.e., air-breathing amphibians, reptiles, birds, and mammals) has remained unchanged for decades. While survival, growth, and reproductive endpoints derived from whole-animal toxicity tests are central to hazard assessment, nonstandard measures of biological effects at multiple levels of biological organization (e.g., molecular, cellular, tissue, organ, organism, population, community, ecosystem) have the potential to enhance the relevance of prospective and retrospective wildlife ERAs. Other factors (e.g., indirect effects of contaminants on food supplies and infectious disease processes) are influenced by toxicants at individual, population, and community levels, and need to be factored into chemically based risk assessments to enhance the "eco" component of ERAs. Regulatory and logistical challenges often relegate such nonstandard endpoints and indirect effects to postregistration evaluations of pesticides and industrial chemicals and contaminated site evaluations. While NAMs are being developed, to date, their applications in ERAs focused on wildlife have been limited. No single magic tool or model will address all uncertainties in hazard assessment. Modernizing wildlife ERAs will likely entail combinations of laboratory- and field-derived data at multiple levels of biological organization, knowledge collection solutions (e.g., systematic review, adverse outcome pathway frameworks), and inferential methods that facilitate integrations and risk estimations focused on species, populations, interspecific extrapolations, and ecosystem services modeling, with less dependence on whole-animal data and simple hazard ratios. Integr Environ Assess Manag 2024;20:725-748. © 2023 His Majesty the King in Right of Canada and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Reproduced with the permission of the Minister of Environment and Climate Change Canada. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Cross-species extrapolation of chemical sensitivity

Ecosystems are usually populated by many species. Each of these species carries the potential to show a different sensitivity towards all of the numerous chemical compounds that can be present in their environment. Since experimentally testing all possible species-chemical combinations is impossible, the ecological risk assessment of chemicals largely depends on cross-species extrapolation approaches. This review overviews currently existing cross-species extrapolation methodologies, and discusses i) how species sensitivity could be described, ii) which predictors might be useful for explaining differences in species sensitivity, and iii) which statistical considerations are important. We argue that risk assessment can benefit most from modelling approaches when sensitivity is described based on ecologically relevant and robust effects. Additionally, specific attention should be paid to heterogeneity of the training data (e.g. exposure duration, pH, temperature), since this strongly influences the reliability of the resulting models. Regarding which predictors are useful for explaining differences in species sensitivity, we review interspecies-correlation, relatedness-based, traits-based, and genomic-based extrapolation methods, describing the amount of mechanistic information the predictors contain, the amount of input data the models require, and the extent to which the different methods provide protection for ecological entities. We develop a conceptual framework, incorporating the strengths of each of the methods described. Finally, the discussion of statistical considerations reveals that regardless of the method used, statistically significant models can be found, although the usefulness, applicability, and understanding of these models varies considerably. We therefore recommend publication of scientific code along with scientific studies to simultaneously clarify modelling choices and enable elaboration on existing work. In general, this review specifies the data requirements of different cross-species extrapolation methods, aiming to make regulators and publishers more aware that access to raw- and meta-data needs to be improved to make future cross-species extrapolation efforts successful, enabling their integration into the regulatory environment.

Oryzias sinensis, a new model organism in the application of eco-toxicity and water quality criteria (WQC)

Fish play an important role as a primary eco-toxicity test organism in environmental hazard assessment. Toxicity data of native species are often sought for use in the derivation of water quality criteria (WQC). The Chinese medaka, Oryzias sinensis, is an endemic species of China. The acute toxicity of 6 chemicals on O. sinensis was tested in this work, and toxicity effect of 10 chemicals to O. sinensis was compared with 4 commonly used species globally. A total of 9 robust interspecies correlation estimation (ICE) models using O. sinensis as the surrogate species were constructed and used to derive predicted no effect concentration and hazardous concentrations of 5% species (HC₅) values based on species sensitivity distribution. Results showed that the 96 h median lethal concentration of Hg²⁺, Cr⁶⁺, linear alkylbenzene sulfonates, triclosan, 3,4-dchloroaniline, sodium chloride to O. sinensis were 0.29, 50, 6.0, 0.63, 9.2 and 14,400 mg/L, respectively. The sensitivity of O. sinensis and other 4 testing organisms were statistically indistinguishable (P > 0.05). No significant difference among HC₅-ICE, HC₅-measured and HC₅ from published literatures was identified. All results indicated the O. sinensis is a potential model organism in the application of eco-toxicity and WQC in China and other Asian countries.

Application of Interspecies Correlation Estimation (ICE) models and QSAR in estimating species sensitivity to pesticides

Ecological risk assessment is challenged by the need to assess hazard to the diverse communities of organisms inhabiting aquatic and terrestrial systems. Computational approaches, such as Quantitative Structure Activity Relationships (QSAR) and Interspecies Correlation Estimation (ICE) models, are useful tools that provide estimates of acute toxicity where data are lacking or limited for ecological risk assessments (ERA). This review describes the technical basis of ICE models for use in pesticide ERA that may be used in conjunction with QSAR model estimates or surrogate species toxicity data and demonstrates the potential for improving hazard assessment. Validation and uncertainty analysis of ICE model predictions are summarized and used as guidance for selecting ICE models and evaluating toxicity predictions. A user-friendly web-based ICE modelling platform (Web-ICE) is described and demonstrated through case studies. Case studies include the development of Species Sensitivity Distributions generated from QSAR and ICE estimates, comparative sensitivity for a pesticide and its degradate, and application of ICE-estimated toxicity values for listed species assessments.

Reliable and representative in silico predictions of freshwater ecotoxicological hazardous concentrations

A reliable quantification of the potential effects of chemicals on freshwater ecosystems requires ecotoxicological response data for a large set of species which is typically not available in practice. In this study, we propose a method to estimate hazardous concentrations (HCs) of chemicals on freshwater ecosystems by combining two in silico approaches: quantitative structure activity relationships (QSARs) and interspecies correlation estimation (ICE) models. We illustrate the principle of our QSAR-ICE method by quantifying the HCs of 51 chemicals at which 50% and 5% of all species are exposed above the concentration causing acute effects. We assessed the bias of the HCs, defined as the ratio of the HC based on measured ecotoxicity data and the HC based on in silico data, as well as the statistical uncertainty, defined as the ratio of the 95th and 5th percentile of the HC. Our QSAR-ICE method resulted in a bias that was comparable to the use of measured data for three species, as commonly used in effect assessments: the average bias of the QSAR-ICE HC50 was 1.2 and of the HC5 2.3 compared to 1.2 when measured data for three species were used for both HCs. We also found that extreme statistical uncertainties (>10⁵) are commonly avoided in the HCs derived with the QSAR-ICE method compared to the use of three measurements with statistical uncertainties up to 10¹². We demonstrated the applicability of our QSAR-ICE approach by deriving HC50s for 1,223 out of the 3,077 organic chemicals of the USEtox database. We conclude that our QSAR-ICE method can be used to determine HCs without the need for additional in vivo testing to help prioritise which chemicals with no or few ecotoxicity data require more thorough assessment.

Comparison of modes of action among different trophic levels of aquatic organisms for pesticides and medications based on interspecies correlations and excess toxicity: Theoretical consideration

Pesticides and medications have adverse effects in non-target organisms that can lead to different modes of action (MOAs). However, no study has been performed to compare the MOAs between different levels of aquatic species. In this study, theoretical equations of interspecies relationship and excess toxicity have been developed and used to investigate the MOAs among fish, Daphnia magna, Tetrahymena pyriformis and Vibrio fischeri for pesticides and medications. The analysis on the interspecies correlation and excess toxicity suggested that fungicides, herbicides and medications share the similar MOAs among the four species. On the other hand, insecticides share different MOAs among the four species. Exclusion of insecticides from the interspecies correlation can significantly improve regression coefficient. Interspecies relationship is dependent not only on the difference in interaction of chemicals with the target receptor(s), but also on the difference in bio-uptake between two species. The difference in physiological structures will result in the difference in bioconcentration potential between two different trophic levels of organisms. Increasing of molecular size or hydrophobicity will increase the toxicity to higher level of aquatic organisms; on the other hand, chemical ionization will decrease the toxicity to higher level organisms. Hydrophilic compounds can more easily pass through cell membrane than skin or gill, leading to greater excess toxicity to Vibrio fischeri, but not to fish and Daphnia magna.

Development of interspecies correlation estimation (ICE) models to predict the reproduction toxicity of EDCs to aquatic species

Endocrine disrupting chemicals (EDCs) threaten the reproductive fitness of aquatic organisms at concentrations lower than those associated with longevity and development. However, the small number of aquatic species assessed for reproductive toxicity has limited the ecological risk assessment of EDCs, making sensible decisions more difficult. In response to this, interspecies correlation estimation (ICE) models were established for EDCs to enable the estimation of reproduction toxicity values to a wider range of organisms. A total of 16 ICE models of EDCs for 6 surrogate species were statistically significant. Of the 16 models, 37.5% (6 models) had a cross-validation success rate > 60%, with a relatively small model squared error, indicating that the model fit is robust. These model results implied that the action of EDCs for each species pair might involve the same mechanisms, and taxonomic relationships did not influence the prediction precision. The cross-validation success rate corroborated the consistency between the projected and experimental values for the EDC ICE models. Sixty-seven percent of the projected values fell within a 10-fold difference of the experimental data. The results indicated that a proven ICE model can greatly increase the amount of EDCs chronic toxicity data for predicted species, without the need for extensive animal experiments, thus providing substitute chronic toxicity data for rapid assessment of EDCs ecological risks.

Development of algal interspecies correlation estimation models for chemical hazard assessment

Web-based Interspecies Correlation Estimation (ICE) is an application developed to predict the acute toxicity of a chemical from 1 species to another taxon. Web-ICE models use the acute toxicity value for a surrogate species to predict effect values for other species, thus potentially filling in data gaps for a variety of environmental assessment purposes. Web-ICE has historically been dominated by aquatic and terrestrial animal prediction models. Web-ICE models for algal species were essentially absent and are addressed in the present study. A compilation of public and private sector-held algal toxicity data were compiled and reviewed for quality based on relevant aspects of individual studies. Interspecies correlations were constructed from the most commonly tested algal genera for a broad spectrum of chemicals. The ICE regressions were developed based on acute 72-h and 96-h endpoint values involving 1647 unique studies on 476 unique chemicals encompassing 40 genera and 70 species of green, blue-green, and diatom algae. Acceptance criteria for algal ICE models were established prior to evaluation of individual models and included a minimum sample size of 3, a statistically significant regression slope, and a slope estimation parameter ≥0.65. A total of 186 ICE models were possible at the genus level, with 21 meeting quality criteria; and 264 ICE models were developed at the species level, with 32 meeting quality criteria. Algal ICE models will have broad utility in screening environmental hazard assessments, data gap filling in certain regulatory scenarios, and as supplemental information to derive species sensitivity distributions. Environ Toxicol Chem 2016;35:2368-2378. Published 2016 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

Sensitivity of ecological soil-screening levels for metals to exposure model parameterization and toxicity reference values

Ecological soil-screening levels (Eco-SSLs) were developed by the United States Environmental Protection Agency (USEPA) for the purposes of setting conservative soil screening values that can be used to eliminate the need for further ecological assessment for specific analytes at a given site. Ecological soil-screening levels for wildlife represent a simplified dietary exposure model solved in terms of soil concentrations to produce exposure equal to a no-observed-adverse-effect toxicity reference value (TRV). Sensitivity analyses were performed for 6 avian and mammalian model species, and 16 metals/metalloids for which Eco-SSLs have been developed. The relative influence of model parameters was expressed as the absolute value of the range of variation observed in the resulting soil concentration when exposure is equal to the TRV. Rank analysis of variance was used to identify parameters with greatest influence on model output. For both birds and mammals, soil ingestion displayed the broadest overall range (variability), although TRVs consistently had the greatest influence on calculated soil concentrations; bioavailability in food was consistently the least influential parameter, although an important site-specific variable. Relative importance of parameters differed by trophic group. Soil ingestion ranked 2nd for carnivores and herbivores, but was 4th for invertivores. Different patterns were exhibited, depending on which parameter, trophic group, and analyte combination was considered. The approach for TRV selection was also examined in detail, with Cu as the representative analyte. The underlying assumption that generic body-weight-normalized TRVs can be used to derive protective levels for any species is not supported by the data. Whereas the use of site-, species-, and analyte-specific exposure parameters is recommended to reduce variation in exposure estimates (soil protection level), improvement of TRVs is more problematic.

Furthering the derivation of predictive wildlife toxicity reference values for use in soil cleanup decisions

The development of media-specific ecological values for risk assessment includes the derivation of acceptable levels of exposure for terrestrial wildlife (e.g., birds, mammals, reptiles, and amphibians). Although the derivation and subsequent application of these values can be used for screening purposes, there is a need to identify toxicological effects thresholds specifically for making remedial decisions at individual contaminated sites. A workshop was held in the fall of 2012 to evaluate existing methods and recent scientific developments for refining ecological soil screening levels (Eco-SSLs) and improving the derivation of site-specific ecological soil clean-up values for metals (Eco-SCVs). This included a focused session on the development and derivation of toxicity reference values (TRVs) for terrestrial wildlife. Topics that were examined included: methods for toxicological endpoint selection, techniques for dose-response assessment, approaches for cross-species extrapolation, and tools to incorporate environmental factors (e.g., metal bioavailability and chemistry) into a reference value. The workgroup also made recommendations to risk assessors and regulators on how to incorporate site-specific wildlife life history and toxicity information into the derivation of TRVs to be used in the further development of soil cleanup levels.

Augmenting aquatic species sensitivity distributions with interspecies toxicity estimation models

Species sensitivity distributions (SSDs) are cumulative distribution functions of species toxicity values. The SSD approach is being used increasingly in ecological risk assessment but is often limited by available toxicity data needed for diverse species representation. In the present study, the authors evaluate augmenting aquatic species databases limited to standard test species using toxicity values extrapolated from interspecies correlation estimation (ICE) models for SSD development. The authors compared hazard concentrations at the 5th centile (HC5) of SSDs developed using limited measured data augmented with ICE toxicity values (augmented SSDs) with those estimated using larger measured toxicity datasets of diverse species (reference SSDs). When SSDs had similar species composition to the reference SSDs, 0.76 of the HC5 estimates were closer to the reference HC5; however, the proportion of augmented HC5s that were within 5-fold of the reference HC5s was 0.94, compared with 0.96 when predicted SSDs had random species assemblages. The range of toxicity values among represented species in all SSDs also depended on a chemical's mode of action. Predicted HC5 estimations for acetylcholinesterase inhibitors showed the greatest discrepancies from the reference HC5 when SSDs were limited to commonly tested species. The results of the present study indicate that ICE models used to augment datasets for SSDs do not greatly affect HC5 uncertainty. Uncertainty analysis of risk assessments using SSD hazard concentrations should address species composition, especially for chemicals with known taxa-specific differences in toxicological effects. This article is a US Government work and is in the public domain in the USA.

Evaluation of in silico development of aquatic toxicity species sensitivity distributions

Determining the sensitivity of a diversity of species to environmental contaminants continues to be a significant challenge in ecological risk assessment because toxicity data are generally limited to a few standard test species. This study assessed whether species sensitivity distributions (SSDs) could be generated with reasonable accuracy using only in silico modeling of toxicity to aquatic organisms. Ten chemicals were selected for evaluation that spanned several modes of actions and chemical classes. Median lethal concentrations (LC50s) were estimated using three internet-based quantitative structure activity relationship (QSAR) tools that employ different computational approaches: ECOSAR (Ecological Structure Activity Relationships), ASTER (Assessment Tools for the Evaluation of Risk), and TEST (Toxicity Estimation Software Tool). Each QSAR estimate was then used as input into the SSD module of the internet-based toxicity estimation program Web-ICE to generate an in silico estimated fifth percentile hazard concentration (HC5) for each of the ten chemicals. The accuracy of the estimated HC5s was determined by comparison to measured HC5s developed from an independent dataset of experimental acute toxicity values for a diversity of aquatic species. Estimated HC5s showed generally poor agreement with measured HC5s determined for all available aquatic species, but showed better agreement when species composition of the chemical specific SSDs were identical. These results indicated that LC50 variability and species composition were large sources of error in estimated HC5s. Additional research is needed to reduce uncertainty in HC5s using only in silico approaches and to develop computational approaches for predicting species sensitivity.

Do interspecies correlation estimations increase the reliability of toxicity estimates for wildlife?

For warm-blooded species, the hazardous dose of a chemical (HD50) is an upcoming and important characteristic in the assessment of toxic chemicals. Generally, experimental information is available for a limited number of warm-blooded species only, which causes statistical uncertainty. Furthermore, when small datasets contain an unrepresentative sample of species, they can cause systematic uncertainty in chemicals' hazardous doses. The number of species can be enlarged with interspecies correlation estimation (ICE) models, but these are uncertain themselves. The goal of this study is to quantify the possible gain in reliability of the HD50 values for warm-blooded wildlife species after enlargement of the sample size with ICE predictions. For 1137 chemicals, we compared systematic uncertainty and statistical uncertainty between HD50 values based on experimental data (HD50(Ex)) and on datasets combining experimental data and ICE predictions (HD50(Co)). HD50(Ex) values ranged between 1.0×10(-1) and 9.5×10(3)mgkg(wwt)(-1), and HD50(Co) values between 1.1×10(0) and 6.1×10(3)mgkg(wwt)(-1). For over 97 percent of the chemicals, HD50(Ex) values exceeded HD50(Co) values, with a systematic uncertainty (i.e. the ratio of HD50(Ex)/HD50(Co)) of typically 3.5. The limited availability of experimental toxicity data, predominantly for mammals, resulted in a systematic underestimation of the wildlife toxicity of a chemical. Statistical uncertainty factors (i.e. the ratio of the 95th/5th percentile) quantified the statistical uncertainty in the HD50 values. The statistical uncertainty factors ranged between 1.0×10(0) and 2.5×10(22) for the experimental dataset, and between 4.8×10(0) and 1.1×10(2) for the combined dataset. For all sample sizes, median statistical uncertainty factors were the largest for combined datasets. However, combining experimental toxicity data with ICE predictions makes it possible to reduce the upper limit of the range for statistical uncertainty factors. We conclude that, by combining experimental data with ICE model predictions, the validity of the HD50 value can be improved and high statistical uncertainty can be reduced, particularly in cases of limited toxicity data, i.e. data for mammals only or a sample size of n≤4.

Influence of taxonomic relatedness and chemical mode of action in acute interspecies estimation models for aquatic species

Ecological risks to aquatic organisms are typically assessed using acute toxicity data for relatively few species and with limited understanding of relative species sensitivity. We developed a comprehensive set of interspecies correlation estimation (ICE) models based on acute toxicity data for aquatic organisms and evaluated three key sources of model uncertainty: taxonomic relatedness, chemical mode of action (MOA), and model parameters. Models are least-squares regressions of acute toxicity of surrogate and predicted species. A total of 780 models were derived from acute values for 77 species of aquatic organisms and over 550 chemicals. Cross-validation of models showed that accurate model prediction was greatest for models with surrogate and predicted taxa within the same family (91% of predictions within 5-fold of measured values). Recursive partitioning provided user guidance for selection of robust models using model mean square error and taxonomic relatedness. Models built with a single MOA were more robust than models built using toxicity values with multiple MOAs, and improve predictions among species pairs with large taxonomic distance (e.g., within phylum). These results indicate that between-species toxicity extrapolation can be improved using MOA-based models for less related taxa pairs and for those specific MOAs.