Mode of Action Classifications in the EnviroTox Database: Development and Implementation of a Consensus MOA Classification

Can next generation ecological risk assessment decisions be made today?-A case study of regulatory risk assessment in the United States

We examined the need for new in vivo avian toxicity testing for three common industrial chemicals (1,2 dichloropropane, 1,1,2-trichloroethane and triphenyl phosphate) based on estimated avian exposures using fugacity and multimedia fate models for current conditions of use compared to hazard information including existing in vivo test data for the chemicals and analogs, interspecies correlation estimates and results from hundreds of acute avian dietary toxicity studies. The data indicated that acute avian toxicity is not likely to be observed below 10 ppm in the diet for any chemical with the exception of those with a specific mode of toxic action. Modeling indicated low exposure potential for terrestrial birds to any of the three chemicals, with estimated dietary concentration of less than 0.001 ppm. Despite uncertainty associated with the underlying data sources, the four order of magnitude gap between potential exposure and a minimum hazard threshold suggests that additional avian in vivo testing would not generate valuable data. However, a weight of evidence approach for integrating data is necessary to engender greater confidence among government decision-makers in cases where data from a particular in vivo study is not expected to improve risk decision-making and an existing data gap can remain unfilled.

Which distribution to choose for deriving a species sensitivity distribution? Implications from analysis of acute and chronic ecotoxicity data

Species sensitivity distributions (SSDs) estimated by fitting a statistical distribution to ecotoxicity data are indispensable tools used to derive the hazardous concentration for 5 % of species (HC5) and thereby a predicted no-effect concentration in environmental risk assessment. Whereas various statistical distributions are available for SSD estimation, the fundamental question of which statistical distribution should be used has received limited systematic analysis. We aimed to address this knowledge gap by applying four frequently used statistical distributions (log-normal, log-logistic, Burr type III, and Weibull distributions) to acute and chronic SSD estimation using aquatic toxicity data for 191 and 31 chemicals, respectively. Based on the differences in the corrected Akaike's information criterion (AICc) as well as visual inspection of the fitting of the lower tails of SSD curves, the log-normal SSD was generally better or equally good for the majority of chemicals examined. Together with the fact that the ratios of HC5 values of other alternative SSDs to those of log-normal SSDs generally fell within the range 0.1-10, our findings indicate that the log-normal distribution can be a reasonable first candidate for SSD derivation, which does not contest the existing widespread use of log-normal SSDs.

A Chronic Aquatic Hazard Assessment for the Perfume Raw Material Octahydro-tetramethyl-naphthalenyl-ethanone

Octahydro-tetramethyl-naphthalenyl-ethanone (OTNE) is a high-production volume fragrance material used in various down-the-drain consumer products. To assess aquatic risk, the Research Institute for Fragrance Materials (RIFM) uses a tiered data-driven framework to determine a risk characterization ratio, where the ratio of the predicted-environmental concentration to the predicted-no-effect concentration (PNEC) of <1 indicates an acceptable level of risk. Owing to its high production volume and the conservative nature of the RIFM framework, RIFM identified the need to utilize a species sensitivity distribution (SSD) approach to reduce the PNEC uncertainty for OTNE. Adding to the existing Daphnia magna, Danio rerio, and Desmodesmus subspicatus chronic studies, eight new chronic toxicity studies were conducted on the following species: Navicula pelliculosa, Chironomus riparius, Lemna gibba, Ceriodaphnia dubia, Hyalella azteca, Pimephales promelas, Anabaena flos-aquae, and Daphnia pulex. All toxicity data were summarized as chronic 10% effect concentration estimates using the most sensitive biological response. Daphnia magna was the most sensitive (0.032 mg/L), and D. subspicatus was the least sensitive (>2.6 mg/L, the OTNE solubility limit). The 5th percentile hazardous concentration (HC5) derived from the cumulative probability distribution of the chronic toxicity values for the 11 species was determined to be 0.0498 mg/L (95% confidence interval 0.0097-0.1159 mg/L). A series of "leave-one-out" and "add-one-in" simulations indicated the SSD was stable and robust. Add-one-in simulations determined that the probability of finding a species sensitive enough to lower the HC5 two- or threefold was 1/504 and 1/15,300, respectively. Given the high statistical confidence in this robust SSD, an additional application factor protection is likely not necessary. Nevertheless, to further ensure the protection of the environment, an application factor of 2 to the HC5, resulting in a PNEC of 0.0249 mg/L, is recommended. When combined with environmental exposure information, the overall hazard assessment is suitable for a probabilistic environmental risk assessment. Environ Toxicol Chem 2024;43:1378-1389. © 2024 SETAC.

Bio-QSARs 2.0: Unlocking a new level of predictive power for machine learning-based ecotoxicity predictions by exploiting chemical and biological information

Practical, legal, and ethical reasons necessitate the development of methods to replace animal experiments. Computational techniques to acquire information that traditionally relied on animal testing are considered a crucial pillar among these so-called new approach methodologies. In this light, we recently introduced the Bio-QSAR concept for multispecies aquatic toxicity regression tasks. These machine learning models, trained on both chemical and biological information, are capable of both cross-chemical and cross-species predictions. Here, we significantly extend these models' applicability. This was realized by increasing the quantity of training data by a factor of approximately 20, accomplished by considering both additional chemicals and aquatic organisms. Additionally, variable test durations and associated random effects were accommodated by employing a machine learning algorithm that combines tree-boosting with mixed-effects modeling (i.e., Gaussian Process Boosting). We also explored various biological descriptors including Dynamic Energy Budget model parameters, taxonomic distances, as well as genus-specific traits and investigated the inclusion of mode-of-action information. Through these efforts, we developed Bio-QSARs for fish and aquatic invertebrates with exceptional predictive power (R squared of up to 0.92 on independent test sets). Moreover, we made considerable strides to make models applicable for a range of use cases in environmental risk assessment as well as research and development of chemicals. Models were made fully explainable by implementing an algorithmic multicollinearity correction combined with SHapley Additive exPlanations. Furthermore, we devised novel approaches for applicability domain construction that take feature importance into account. We are hence confident these models, which are available via open access, will make a significant contribution towards the implementation of new approach methodologies and ultimately have the potential to support "Green Chemistry" and "Green Toxicology".

Curated mode-of-action data and effect concentrations for chemicals relevant for the aquatic environment

Chemicals in the aquatic environment can be harmful to organisms and ecosystems. Knowledge on effect concentrations as well as on mechanisms and modes of interaction with biological molecules and signaling pathways is necessary to perform chemical risk assessment and identify toxic compounds. To this end, we developed criteria and a pipeline for harvesting and summarizing effect concentrations from the US ECOTOX database for the three aquatic species groups algae, crustaceans, and fish and researched the modes of action of more than 3,300 environmentally relevant chemicals in literature and databases. We provide a curated dataset ready to be used for risk assessment based on monitoring data and the first comprehensive collection and categorization of modes of action of environmental chemicals. Authorities, regulators, and scientists can use this data for the grouping of chemicals, the establishment of meaningful assessment groups, and the development of in vitro and in silico approaches for chemical testing and assessment.

Spatial variation in the sensitivity of freshwater macroinvertebrate assemblages to chemical stressors

Assessing spatial variation in the chemical sensitivity of natural assemblages will enhance ecological relevance and reduce uncertainty in ecological risk assessments and the derivation of environmental quality standards (EQSs). However, the majority of species in natural communities have not undergone toxicity testing for any chemical, which poses a major challenge when assessing their sensitivity. We investigated spatial variation and patterns in the sensitivity of 4084 freshwater macroinvertebrate assemblages across England to 5 general-acting chemicals (heavy metals) and 13 specifically acting chemicals (insecticides) using a novel hierarchical species sensitivity distribution method based on taxonomic relatedness. Furthermore, we explored how river typology relates to spatial variation in assemblage sensitivity to chemicals and the potential impacts of such variation on current EQSs. Our findings revealed that, whereas assemblages with similar taxonomic compositions exhibit comparable sensitivity distributions, assemblages with different taxonomic compositions could have very similar or very different sensitivity distributions. The variation in assemblage sensitivity was greater for specifically acting chemicals than for general-acting chemicals and exhibited spatial clustering patterns. These spatial clustering patterns varied depending on the chemical, and the regions where assemblages were most sensitive to metals were generally not the same as the regions where assemblages were most sensitive to insecticides. Spatial variation in assemblage sensitivity was related to river typology with sensitive assemblages being more common than expected in lowland calcareous (or mixed geology) rivers within very small to small catchments. Comparing spatial variation in assemblage-specific chemical sensitivity to EQSs, we found that the operational EQSs in England would protect most study assemblages (i.e., > 99.5 %), although a small proportion of assemblages may face potential risks associated with azinphos-methyl, copper, and malathion. In many cases the EQSs were very precautionary, potentially requiring expensive control measures or restricting beneficial chemical use with no additional environmental benefit. The development of spatially defined EQSs, possibly based on river types, could be developed to target areas that require the highest level of protection and thus strike a balance between the benefits of chemical use and environmental protection.

Comparison of in silico, in vitro, and in vivo toxicity benchmarks suggests a role for ToxCast data in ecological hazard assessment

Large repositories of in vitro bioactivity data such as US EPA's Toxicity Forecaster (ToxCast) provide a wealth of publicly accessible toxicity information for thousands of chemicals. These data can be used to calculate point-of-departure (POD) estimates via concentration-response modeling that may serve as lower bound, protective estimates of in vivo effects. However, the data are predominantly based on mammalian models and discussions to date about their utility have largely focused on potential integration into human hazard assessment, rather than application to ecological risk assessment. The goal of the present study was to compare PODs based on (1) quantitative structure-activity relationships (QSARs), (2) the 5th centile of the activity concentration at cutoff (ACC), and (3) lower-bound cytotoxic burst (LCB) from ToxCast, with the distribution of in vivo PODs compiled in the Ecotoxicology Knowledgebase (ECOTOX). While overall correlation between ToxCast ACC5 and ECOTOX PODs for 649 chemicals was weak, there were significant associations among PODs based on LCB and ECOTOX, LCB and QSARs, and ECOTOX and QSARs. Certain classes of compounds showed moderate correlation across datasets (eg, antimicrobials/disinfectants), while others, such as organophosphate insecticides, did not. Unsurprisingly, more precise classifications of the data based on ECOTOX effect and endpoint type (eg, apical vs biochemical; acute vs chronic) had a significant effect on overall relationships. Results of this research help to define appropriate roles for data from new approach methodologies in chemical prioritization and screening of ecological hazards.

Chemical Mixtures and Multiple Stressors: Same but Different?

Ecosystems are strongly influenced by multiple anthropogenic stressors, including a wide range of chemicals and their mixtures. Studies on the effects of multiple stressors have largely focussed on nonchemical stressors, whereas studies on chemical mixtures have largely ignored other stressors. However, both research areas face similar challenges and require similar tools and methods to predict the joint effects of chemicals or nonchemical stressors, and frameworks to integrate multiple chemical and nonchemical stressors are missing. We provide an overview of the research paradigms, tools, and methods commonly used in multiple stressor and chemical mixture research and discuss potential domains of cross-fertilization and joint challenges. First, we compare the general paradigms of ecotoxicology and (applied) ecology to explain the historical divide. Subsequently, we compare methods and approaches for the identification of interactions, stressor characterization, and designing experiments. We suggest that both multiple stressor and chemical mixture research are too focused on interactions and would benefit from integration regarding null model selection. Stressor characterization is typically more costly for chemical mixtures. While for chemical mixtures comprehensive classification systems at suborganismal level have been developed, recent classification systems for multiple stressors account for environmental context. Both research areas suffer from rather simplified experimental designs that focus on only a limited number of stressors, chemicals, and treatments. We discuss concepts that can guide more realistic designs capturing spatiotemporal stressor dynamics. We suggest that process-based and data-driven models are particularly promising to tackle the challenge of prediction of effects of chemical mixtures and nonchemical stressors on (meta-)communities and (meta-)food webs. We propose a framework to integrate the assessment of effects for multiple stressors and chemical mixtures. Environ Toxicol Chem 2023;42:1915-1936. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Current Status of Omics in Biological Quality Elements for Freshwater Biomonitoring

Freshwater ecosystems have been experiencing various forms of threats, mainly since the last century. The severity of this adverse scenario presents unprecedented challenges to human health, water supply, agriculture, forestry, ecological systems, and biodiversity, among other areas. Despite the progress made in various biomonitoring techniques tailored to specific countries and biotic communities, significant constraints exist, particularly in assessing and quantifying biodiversity and its interplay with detrimental factors. Incorporating modern techniques into biomonitoring methodologies presents a challenging topic with multiple perspectives and assertions. This review aims to present a comprehensive overview of the contemporary advancements in freshwater biomonitoring, specifically by utilizing omics methodologies such as genomics, metagenomics, transcriptomics, proteomics, metabolomics, and multi-omics. The present study aims to elucidate the rationale behind the imperative need for modernization in this field. This will be achieved by presenting case studies, examining the diverse range of organisms that have been studied, and evaluating the potential benefits and drawbacks associated with the utilization of these methodologies. The utilization of advanced high-throughput bioinformatics techniques represents a sophisticated approach that necessitates a significant departure from the conventional practices of contemporary freshwater biomonitoring. The significant contributions of omics techniques in the context of biological quality elements (BQEs) and their interpretations in ecological problems are crucial for biomonitoring programs. Such contributions are primarily attributed to the previously overlooked identification of interactions between different levels of biological organization and their responses, isolated and combined, to specific critical conditions.

Using membrane-water partition coefficients in a critical membrane burden approach to aid the identification of neutral and ionizable chemicals that induce acute toxicity below narcosis levels

The risk assessment of thousands of chemicals used in our society benefits from adequate grouping of chemicals based on the mode and mechanism of toxic action (MoA). We measure the phospholipid membrane-water distribution ratio (D_MLW) using a chromatographic assay (IAM-HPLC) for 121 neutral and ionized organic chemicals and screen other methods to derive D_MLW. We use IAM-HPLC based D_MLW as a chemical property to distinguish between baseline narcosis and specific MoA, for reported acute toxicity endpoints on two separate sets of chemicals. The first set comprised 94 chemicals of US EPA's acute fish toxicity database: 47 categorized as narcosis MoA, 27 with specific MoA, and 20 predominantly ionic chemicals with mostly unknown MoA. The narcosis MoA chemicals clustered around the median narcosis critical membrane burden (CMB_narc) of 140 mmol kg^-1 lipid, with a lower limit of 14 mmol kg^-1 lipid, including all chemicals labelled Narcosis_I and Narcosis_II. This maximum 'toxic ratio' (TR) between CMB_narc and the lower limit narcosis endpoint is thus 10. For 23/28 specific MoA chemicals a TR >10 was derived, indicative of a specific adverse effect pathway related to acute toxicity. For 10/12 cations categorized as "unsure amines", the TR <10 suggests that these affect fish via narcosis MoA. The second set comprised 29 herbicides, including 17 dissociated acids, and evaluated the TR for acute toxic effect concentrations to likely sensitive aquatic plant species (green algae and macrophytes Lemna and Myriophyllum), and non-target animal species (invertebrates and fish). For 21/29 herbicides, a TR >10 indicated a specific toxic mode of action other than narcosis for at least one of these aquatic primary producers. Fish and invertebrate TRs were mostly <10, particularly for neutral herbicides, but for acidic herbicides a TR >10 indicated specific adverse effects in non-target animals. The established critical membrane approach to derive the TR provides for useful contribution to the weight of evidence to bin a chemical as having a narcosis MoA or less likely to have acute toxicity caused by a more specific adverse effect pathway. After proper calibration, the chromatographic assay provides consistent and efficient experimental input for both neutral and ionizable chemicals to this approach.

Construction of an In Silico Structural Profiling Tool Facilitating Mechanistically Grounded Classification of Aquatic Toxicants

The performance of chemical safety assessment within the domain of environmental toxicology is often impeded by a shortfall of appropriate experimental data describing potential hazards across the many compounds in regular industrial use. In silico schemes for assigning aquatic-relevant modes or mechanisms of toxic action to substances, based solely on consideration of chemical structure, have seen widespread employment─including those of Verhaar, Russom, and later Bauer (MechoA). Recently, development of a further system was reported by Sapounidou, which, in common with MechoA, seeks to ground its classifications in understanding and appreciation of molecular initiating events. Until now, this Sapounidou scheme has not seen implementation as a tool for practical screening use. Accordingly, the primary purpose of this study was to create such a resource─in the form of a computational workflow. This exercise was facilitated through the formulation of 183 structural alerts/rules describing molecular features associated with narcosis, chemical reactivity, and specific mechanisms of action. Output was subsequently compared relative to that of the three aforementioned alternative systems to identify strengths and shortcomings as regards coverage of chemical space.

Relationships Between Aquatic Toxicity, Chemical Hydrophobicity, and Mode of Action: Log Kow Revisited

Relationships between toxicity and chemical hydrophobicity have been known for nearly 100 years in mammals and fish, typically using the log of the octanol:water partition coefficient (Kow). The current study reassessed the influence of mode of action (MOA) on acute aquatic toxicity-log Kow relationships using a comprehensive database of 617 organic chemicals with curated and standardized acute toxicity data that did not exceed solubility limits, their consensus log Kow values, and weight of evidence-based MOA classifications (including 6 broad and 26 specific MOAs). A total of 166 significant (p < 0.05) log Kow-toxicity models were developed across six taxa groups that included QSARs for 5 of the broad and 13 of the specific MOAs. In this study, we demonstrate that QSARs based on MOAs can significantly increase LC50 prediction accuracy for specific acting chemicals. Prediction accuracy increases when QSARs are built based on highly specific MOAs, rather than broad MOA classifications. Additionally, we demonstrate that building QSAR models with chemicals in specific MOA groupings, rather than broader MOA groups leads to significantly better estimates. We also evaluated the differences between models developed from mass-based (µg/L) and mole-based (µmol/L) toxicity data and demonstrate that both are suitable for QSAR development with no clear trend in greater model accuracy. Overall, the results reveal that, despite high variance in all taxa and MOA groups, specific MOA-based models can improve the accuracy of aquatic toxicity predictions over more general groupings.Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.The affiliations are correct.

New evidence for deleterious effects of environmental contaminants on the male gamete

The decreasing trend in human and domestic animal fertility in recent decades has resulted in the question of whether reduced sperm quality is associated with changes in global climate and the environment. Proposed causes for reduced sperm quality include environmental contaminants, which enter into the body of animals through the food chain and are transported to the reproductive tract, where contaminating agents can have effects on fertilization capacities of gametes. In this review, there is a focus on various environmental contaminants and potential effects on male fertility. Human-derived contaminants, particularly endocrine-disrupting phthalates and the pesticide atrazine, are discussed. Naturally occurring toxins are also addressed, in particular mycotoxins such as aflatoxin which can be components in food consumed by humans and animals. Mechanisms by which environmental contaminants reduce male fertility are not clearly defined; however, are apparently multifactorial (i.e., direct and indirect effects) with there being diverse modes of action. Results from studies with humans, rodents and domestic animals indicate there are deleterious effects of contaminants on male gametes at various stages of spermatogenesis (i.e., in the testis) during passage through the epididymis, and in mature spermatozoa, after ejaculation and during capacitation. Considering there is never detection of a single contaminant, this review addresses synergistic or additive effects of combinations of contaminants. There is new evidence highlighted for the long-lasting effects of environmental contaminants on spermatozoa and developing embryos. Understanding the risk associated with environmental contaminants for animal reproduction may lead to new management strategies, thereby improving reproductive processes.

Can Chemical Toxicity in Saltwater Be Predicted from Toxicity in Freshwater? A Comprehensive Evaluation Using Species Sensitivity Distributions

Species sensitivity distributions (SSDs) play an important role in ecological risk assessment. Estimating SSDs requires toxicity data for many species, but reports on saltwater species are often limited compared to freshwater species. This limitation can constrain informed management of saltwater quality for the protection of marine ecosystems. We investigated the relationships between the parameters (i.e., mean and standard deviation [SD]) of freshwater and saltwater log-normal SSDs to determine how accurately saltwater toxicity could be estimated from freshwater toxicity test data. We estimated freshwater and saltwater SSDs for 104 chemicals with reported acute toxicity data for five or more species and compared their means, SDs, and hazardous concentrations for 5% of the species (HC5) derived from the acute SSDs. Standard major axis regression analyses generally showed that log-log relationships between freshwater and saltwater SSD means, SDs, and HC5 values were nearly 1:1. In addition, the ratios of freshwater-to-saltwater SSD means and HC5 values for most of the 104 chemicals fell within the range 0.1-10. Although such a strong correlation was not observed for SSD SDs (r²  < 0.5), differences between freshwater and saltwater SSD SDs were relatively small. These results indicate that saltwater acute SSDs can be reasonably estimated using freshwater acute SSDs. Because the differences of the means and SDs between freshwater and saltwater SSDs were larger when the number of test species used for SSD estimation was lower (i.e., five to seven species in the present study), obtaining toxicity data for an adequate number of species will be key to better approximation of a saltwater acute SSD from a freshwater acute SSD for a given chemical. Environ Toxicol Chem 2022;41:2021-2027. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Ecological Thresholds of Toxicological Concern: A Review

The ecological threshold of toxicological concern (ecoTTC) is analogous to traditional human health-based TTCs but with derivation and application to ecological species. An ecoTTC is computed from the probability distribution of predicted no effect concentrations (PNECs) derived from either chronic or extrapolated acute toxicity data for toxicologically or chemically similar groups of chemicals. There has been increasing interest in using ecoTTCs in screening level environmental risk assessments and a computational platform has been developed for derivation with aquatic species toxicity data (https://envirotoxdatabase.org/). Current research and development areas include assessing mode of action-based chemical groupings, conservatism in estimated PNECs and ecoTTCs compared to existing regulatory values, and the influence of taxa (e.g., algae, invertebrates, and fish) composition in the distribution of PNEC values. The ecoTTC continues to develop as a valuable alternative strategy within the toolbox of traditional and new approach methods for ecological chemical assessment. This brief review article describes the ecoTTC concept and potential applications in ecological risk assessment, provides an overview of the ecoTTC workflow and how the values can be derived, and highlights recent developments and ongoing research. Future applications of ecoTTC concept in different disciplines are discussed along with opportunities for its use.

Ecological Risk Assessment of Pharmaceuticals in the Transboundary Vecht River (Germany and The Netherlands)

Millions of people rely on active pharmaceutical ingredients (APIs) to prevent and cure a wide variety of illnesses in humans and animals, which has led to a steadily increasing consumption of APIs across the globe and concurrent releases of APIs into the environment. In the environment, APIs can have a detrimental impact on wildlife, particularly aquatic wildlife. Therefore, it is essential to assess their potential adverse effects to aquatic ecosystems. The European Water Framework Directive sets out that risk assessment should be performed at the catchment level, crossing borders where needed. The present study defines ecological risk profiles for surface water concentrations of 8 APIs (carbamazepine, ciprofloxacin, cyclophosphamide, diclofenac, erythromycin, 17α-ethinylestradiol, metformin, and metoprolol) in the Vecht River, a transboundary river that crosses several German and Dutch regions. Ultimately, 3 main goals were achieved: 1) the geo-referenced estimation of API concentrations in surface water using the geography-referenced regional exposure assessment tool for European rivers; 2) the derivation of new predicted-no-effect concentrations for 7 of the studied APIs, of which 3 were lower than previously derived values; and 3) the creation of detailed spatially explicit ecological risk profiles of APIs under 2 distinct water flow scenarios. Under average flow conditions, carbamazepine, diclofenac, and 17α-ethinylestradiol were systematically estimated to surpass safe ecological concentration thresholds in at least 68% of the catchment's water volume. This increases to 98% under dry summer conditions. Environ Toxicol Chem 2022;41:648-662. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Daphnia magna and Ceriodaphnia dubia Have Similar Sensitivity in Standard Acute and Chronic Toxicity Tests

The cladocerans Daphnia magna and Ceriodaphnia dubia have been used for decades to assess the hazards of chemicals and effluents, but toxicity data for these species have traditionally been treated separately. Numerous standard acute and chronic test guidelines have been developed for both species. In the present study, data were compiled and curated for acute survival (48 h) and growth and reproduction tests with D. magna (21 days chronic) and C. dubia (7 days chronic) toxicity assays. Orthogonal regressions were developed to statistically compare the acute and chronic sensitivity of D. magna and C. dubia across a diversity of chemicals and modes of action. Acute orthogonal regressions between D. magna and D. pulex, a widely accepted surrogate species, were used to set a data-driven benchmark for what would constitute a suitable D. magna surrogate. The results indicate that there is insufficient evidence to suggest a difference in acute or chronic sensitivity of D. magna and C. dubia in standard toxicity tests. Further, the variability in the acute D. magna and C. dubia regressions were of the same magnitude as that in D. magna and D. pulex regressions. Slope and y-intercept values were also comparable. The absence of significant differences in toxicity values suggests similar species sensitivity in standard tests across a range of chemical classes and modes of action. Environ Toxicol Chem 2022;41:134-147. © 2021 SETAC.

In Silico Methods for Environmental Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives

This chapter aims to introduce the reader to the basic principles of environmental risk assessment of chemicals and highlights the usefulness of tiered approaches within weight of evidence approaches in relation to problem formulation i.e., data availability, time and resource availability. In silico models are then introduced and include quantitative structure-activity relationship (QSAR) models, which support filling data gaps when no chemical property or ecotoxicological data are available. In addition, biologically-based models can be applied in more data rich situations and these include generic or species-specific models such as toxicokinetic-toxicodynamic models, dynamic energy budget models, physiologically based models, and models for ecosystem hazard assessment i.e. species sensitivity distributions and ultimately for landscape assessment i.e. landscape-based modeling approaches. Throughout this chapter, particular attention is given to provide practical examples supporting the application of such in silico models in real-world settings. Future perspectives are discussed to address environmental risk assessment in a more holistic manner particularly for relevant complex questions, such as the risk assessment of multiple stressors and the development of harmonized approaches to ultimately quantify the relative contribution and impact of single chemicals, multiple chemicals and multiple stressors on living organisms.

Use of the Species Sensitivity Distribution Approach to Derive Ecological Threshold of Toxicological Concern (eco-TTC) for Pesticides

The species sensitivity distribution (SSD) calculates the hazardous concentration at which 5% of species (HC5) will be potentially affected. For many compounds, HC5 values are unavailable impeding the derivation of SSD curves. Through a detailed bibliographic survey, we selected HC5 values (from acute toxicity tests) for freshwater aquatic species and 129 pesticides. The statistical distribution and variability of the HC5 values within the chemical classes were evaluated. Insecticides are the most toxic compounds in the aquatic communities (HC5 = 1.4 × 10-3 µmol L-1), followed by herbicides (HC5 = 3.3 × 10-2 µmol L-1) and fungicides (HC5 = 7.8 µmol L-1). Subsequently, the specificity of the mode of action (MoA) of pesticides on freshwater aquatic communities was investigated by calculating the ratio between the estimated baseline toxicity for aquatic communities and the HC5 experimental values gathered from the literature. Moreover, we proposed and validated a scheme to derive the ecological thresholds of toxicological concern (eco-TTC) of pesticides for which data on their effects on aquatic communities are not available. We proposed eco-TTCs for different classes of insecticides, herbicides, and fungicides with a specific MoA, and three eco-TTCs for those chemicals with unavailable MoA. We consider the proposed approach and eco-TTC values useful for risk management purposes.

A case study of PAH contamination using blue mussels as a bioindicator in a small Greenlandic fishing harbor

This study investigated the impact of local anthropogenic activity on the marine environment around the remote harbor of Qeqertarsuaq, West Greenland. Blue mussels (Mytilus sp.) were used as a bioindicator, and their physiological condition was found to decrease with increasing proximity to the harbor. Subsequently, the distribution of 19 polycyclic aromatic hydrocarbons (PAHs) and 9 groups of alkylated PAHs were measured in mussel and sediment samples. The highest values were found in a rocky collection area 15 m from a wooden pier frequented by small boats. A PAH source investigation, indicated a mixed source from light fuel oils and creosote used as boat coating. Finally, correlations between the mussels morphological condition and the PAH pollution were found to be significant for 4-, 5-, and 6-ring PAHs. In conclusion, the results indicate that pollution sources in harbors have significant effects on the local environment and should be considered in arctic conservation research.

A toxicity pathway-based approach for modeling the mode of action framework of lead-induced neurotoxicity

BACKGROUND

The underlying mechanisms of lead (Pb) toxicity are not fully understood, which makes challenges to the traditional risk assessment. There is growing use of the mode of action (MOA) for risk assessment by integration of experimental data and system biology. The current study aims to develop a new pathway-based MOA for assessing Pb-induced neurotoxicity.

METHODS

The available Comparative Toxicogenomic Database (CTD) was used to search genes associated with Pb-induced neurotoxicity followed by developing toxicity pathways using Ingenuity Pathway Analysis (IPA). The spatiotemporal sequence of disturbing toxicity pathways and key events (KEs) were identified by upstream regulator analysis. The MOA framework was constructed by KEs in biological and chronological order.

RESULTS

There were a total of 71 references showing the relationship between lead exposure and neurotoxicity, which contained 2331 genes. IPA analysis showed that the neuroinflammation signaling pathway was the core toxicity pathway in the enriched pathways relevant to Pb-induced neurotoxicity. The upstream regulator analysis demonstrated that the aryl hydrocarbon receptor (AHR) signaling pathway was the upstream regulator of the neuroinflammation signaling pathway (11.76% overlap with upstream regulators, |Z-score|=1.451). Therefore, AHR activation was recognized as the first key event (KE1) in the MOA framework. The following downstream molecular and cellular key events were also identified. The pathway-based MOA framework of Pb-induced neurotoxicity was built starting with AHR activation, followed by an inflammatory response and neuron apoptosis.

CONCLUSION

Our toxicity pathway-based approach not only advances the development of risk assessment for Pb-induced neurotoxicity but also brings new insights into constructing MOA frameworks of risk assessment for new chemicals.

Comparisons of PNEC derivation logic flows under example regulatory schemes and implications for ecoTTC

Derivation of Predicted No Effect Concentrations (PNECs) for aquatic systems is the primary deterministic form of hazard extrapolation used in environmental risk assessment. Depending on the data availability, different regulatory jurisdictions apply application factors (AFs) to the most sensitive measured endpoint to derive the PNEC for a chemical. To assess differences in estimated PNEC values, two PNEC determination methodologies were applied to a curated public database using the EnviroTox Platform (www.EnviroToxdatabase.org). PNECs were derived for 3647 compounds using derivation procedures based on example US EPA and a modified European Union chemical registration procedure to allow for comparisons. Ranked probability distributions of PNEC values were developed and 5th percentile values were calculated for the entire dataset and scenarios where full acute or full chronic data sets were available. The lowest PNEC values indicated categorization based on chemical attributes and modes of action would lead to improved extrapolations. Full acute or chronic datasets gave measurably higher 5th percentile PNEC values. Algae were under-represented in available ecotoxicity data but drove PNECs disproportionately. Including algal inhibition studies will be important in understanding chemical hazards. The PNEC derivation logic flows are embedded in the EnviroTox Platform providing transparent and consistent PNEC derivations and PNEC distribution calculations.

A chemical prioritization process: Applications to contaminants of emerging concern in freshwater ecosystems (Phase I)

Contaminants of emerging concern (CECs), such as pharmaceuticals, personal care products, and hormones, are frequently found in aquatic ecosystems around the world. Information on sublethal effects from exposure to commonly detected concentrations of CECs is lacking and the limited availability of toxicity data makes it difficult to interpret the biological significance of occurrence data. However, the ability to evaluate the effects of CECs on aquatic ecosystems is growing in importance, as detection frequency increases. The goal of this study was to prioritize the chemical hazards of 117 CECs detected in subsistence species and freshwater ecosystems on the Grand Portage Indian Reservation and adjacent 1854 Ceded Territory in Minnesota, USA. To prioritize CECs for management actions, we adapted Minnesota Pollution Control Agency's Aquatic Toxicity Profiles framework, a tool for the rapid assessment of contaminants to cause adverse effects on aquatic life by incorporating chemical-specific information. This study aimed to 1) perform a rapid-screening assessment and prioritization of detected CECs based on their potential environmental hazard; 2) identify waterbodies in the study region that contain high priority CECs; and 3) inform future monitoring, assessment, and potential remediation in the study region. In water samples alone, 50 CECs were deemed high priority. Twenty-one CECs were high priority among sediment samples and seven CECs were high priority in fish samples. Azithromycin, DEET, diphenhydramine, fluoxetine, miconazole, and verapamil were high priority in all three media. Due to the presence of high priority CECs throughout the study region, we recommend future monitoring of particular CECs based on the prioritization method used here. We present an application of a chemical hazard prioritization process and identify areas where the framework may be adapted to meet the objectives of other management-related assessments.

Development of an Enhanced Mechanistically Driven Mode of Action Classification Scheme for Adverse Effects on Environmental Species

This study developed a novel classification scheme to assign chemicals to a verifiable mechanism of (eco-)toxicological action to allow for grouping, read-across, and in silico model generation. The new classification scheme unifies and extends existing schemes and has, at its heart, direct reference to molecular initiating events (MIEs) promoting adverse outcomes. The scheme is based on three broad domains of toxic action representing nonspecific toxicity (e.g., narcosis), reactive mechanisms (e.g., electrophilicity and free radical action), and specific mechanisms (e.g., associated with enzyme inhibition). The scheme is organized at three further levels of detail beyond broad domains to separate out the mechanistic group, specific mechanism, and the MIEs responsible. The novelty of this approach comes from the reference to taxonomic diversity within the classification, transparency, quality of supporting evidence relating to MIEs, and that it can be updated readily.

Derivation of algal acute to chronic ratios for use in chemical toxicity extrapolations

Algal toxicity studies are required by regulatory agencies for a variety of purposes including classification and labeling and environmental risk assessment of chemicals. Algae are also frequently the most sensitive taxonomic group tested. Acute to chronic ratios (ACRs) have been challenging to derive for algal species because of the complexities of the underlying experimental data including: a lack of universally agreed upon algal inhibition endpoints; evolution of experimental designs over time and by different standardization authorities; and differing statistical approaches (e.g., regression versus hypothesis-based effect concentrations). Experimental data for developing globally accepted algal ACRs have been limited because of data availability, and in most regulatory frameworks an ACR of 10 is used regardless of species, chemical type or mode of action. Acute and chronic toxicity (inhibition) data on 17 algal species and 442 chemicals were compiled from the EnviroTox database (https://envirotoxdatabase.org/) and a proprietary database of algal toxicity records. Information was probed for growth rate, yield, and final cell density endpoints focusing primarily on studies of 72 and 96 h duration. Comparisons of acute and chronic data based on either single (e.g., growth rate) and multiple (e.g., growth rate, final cell density) endpoints were used to assess acute and chronic relationships. Linear regressions of various model permutations were used to compute ACRs for multiple combinations of taxa, chemicals, and endpoints, and showed that ACRs for algae were consistently around 4 (ranging from 2.43 to 5.62). An ACR of 4 for algal toxicity is proposed as an alternative to a default value of 10, and recommendations for consideration and additional research and development are provided.

Assessment of ecological hazards and environmental fate of disinfectant quaternary ammonium compounds

Disinfectant quaternary ammonium compounds (Quats) have diverse uses in a variety of consumer and commercial products, particularly cleaning products. With the emergence of the COVID-19 pandemic, they have become a primary tool to inactivate the SARS-CoV-2 virus on surfaces. Disinfectant Quats have very low vapor pressure, and following the use phase of the products in which they are found, disposal is typically "down-the-drain" to wastewater treatment systems. Consequently, the potential for the greatest environmental effect is to the aquatic environment, from treated effluent, and potentially to soils, which might be amended with wastewater biosolids. Among the earliest used and still common disinfectant Quats are the alkyl dimethyl benzyl ammonium chloride (ADBAC) compounds and the dialkyl dimethyl ammonium chloride (DDAC) compounds. They are cationic surfactants often found in consumer and commercial surface cleaners. Because of their biocidal properties, disinfectant Quats are heavily regulated for human and environmental safety around the world. Consequently, there is a robust database of information regarding the ecological hazards and environmental fate of ADBAC and DDAC; however, some of the data presented are from unpublished studies that have been submitted to and reviewed by regulatory agencies (i.e., EPA and European Chemicals Agency) to support antimicrobial product registration. We summarize the available environmental fate data and the acute and chronic aquatic ecotoxicity data for freshwater species, including algae, invertebrates, fish, and plants using peer-reviewed literature and unpublished data submitted to and summarized by regulatory agencies. The lower limit of the range of the ecotoxicity data for disinfectant Quats tends to be lower than that for other surface active agents, such as nonionic or anionic surfactants. However, ecotoxicity is mitigated by environmental fate characteristics, the data for which we also summarize, including high biodegradability and a strong tendency to sorb to wastewater biosolids, sediment, and soil. As a result, disinfectant Quats are largely removed during wastewater treatment, and those residues discharged in treated effluent are likely to rapidly bind to suspended solids or sediments, thus mitigating their toxicity.

Can We Reasonably Predict Chronic Species Sensitivity Distributions from Acute Species Sensitivity Distributions?

Estimation of species sensitivity distributions (SSDs) is an essential way to estimate the hazardous concentration for 5% of the species (HC5) and thus to derive a "safe" concentration. Here, we examined whether we can reasonably predict SSDs based on chronic no-observed-effect concentration or level (chronic SSDs) from SSDs based on acute median effective/lethal concentration (acute SSDs) by analyzing log-normal SSDs of 150 chemicals. Chronic SSD means were, on average, 10 times lower than acute SSD means. The standard deviations (SDs) of acute and chronic SSDs closely overlapped. Our detailed analysis suggests that the acute SSD SD can be used as an initial estimate of the chronic SSD SD if the number of tested species is ≥10. There were no significant differences in the ratios of chronic to acute SSD means or SDs among three different modes of action. The HC5 of chronic SSDs was, on average, 10 times lower than the acute SSD HC5. We suggest that multiplication of the acute HC5 by a factor of 0.1 is a defensible way to obtain a first approximation of the chronic HC5, particularly when relative ecological risks of chemicals are being evaluated. Further study is needed to develop methods for a more accurate estimation of chronic SSDs.

Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation

Honey bees (Apis mellifera) provide key ecosystem services as pollinators bridging agriculture, the food chain and ecological communities, thereby ensuring food production and security. Ecological risk assessment of single Plant Protection Products (PPPs) requires an understanding of the exposure and toxicity. In silico tools such as QSAR models can play a major role for the prediction of structural, physico-chemical and pharmacokinetic properties of chemicals as well as toxicity of single and multiple chemicals. Here, the first integrative honey bee QSAR model has been developed for PPPs using EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase i) to predict acute contact toxicity (LD₅₀) and ii) to profile the Mode of Action (MoA) of pesticides active substances. Three different classification-based and four regression-based models were developed and tested for their performance, thus identifying two models providing the most reliable predictions based on k-NN algorithm. The two-category QSAR model (toxic/non-toxic; n = 411) was validated using sensitivity (=0.93), specificity (=0.85), balanced accuracy (=0.90), and Matthews correlation coefficient (MCC = 0.78) as statistical parameters. The regression-based model (n = 113) was validated for its reliability and robustness (R² = 0.74; MAE = 0.52). Current study proposes the MoA profiling for 113 pesticides active substances and the first harmonised MoA classification scheme for acute contact toxicity in honey bees, including LD_50s data points from three different databases. The classification allows to further define MoAs and the target site of PPPs active substances, thus enabling regulators and scientists to refine chemical grouping and toxicity extrapolations for single chemicals and component-based mixture risk assessment of multiple chemicals. Relevant future perspectives are briefly addressed to integrate MoA, adverse outcome pathways (AOPs) and toxicokinetic information for the refinement of single-chemical/combined toxicity predictions and risk estimates at different levels of biological organization in the bee health context.

Effect of the structural factors of organic compounds on the acute toxicity toward Daphnia magna

The acute toxicity of organic compounds towards Daphina magna was subjected to QSAR analysis. The two-dimensional simplex representation of molecular structure (2D SiRMS) and the support vector machine (SVM), gradient boosting (GBM) methods were used to develop QSAR models. Adequate regression QSAR models were developed for incubation of 24 h. Their interpretation allowed us to quantitatively describe and rank the well-known toxicophores, to refine their molecular surroundings, and to distinguish the structural derivatives of the fragments that significantly contribute to the acute toxicity (LC₅₀) of organic compounds towards D. magna. Based on the results of the interpretation of the regression models, a molecular design (modification) of highly toxic compounds was performed in order to reduce their hazard. In addition, acceptable classification QSAR models were developed to reliably predict the following mode of action (MOA): specific and non-specific toxicity of organic compounds towards D. magna. When interpreting these models, we were able to determine the structural fragments and the physicochemical characteristics of molecules that are responsible for the manifestation of one of the modes of action. The on-line version of the OCHEM expert system (https://ochem.eu), HYBOT descriptors, and the random forest and SVM methods were used for a comparative QSAR investigation.

Predicting acute contact toxicity of organic binary mixtures in honey bees (A. mellifera) through innovative QSAR models

Pollinators such as honey bees are of considerable importance, because of the crucial pollination services they provide for food crops and wild plants. Since bees are exposed to a wide range of multiple chemicals "mixtures" both of anthropogenic (e.g. plant protection products) and natural origin (e.g. plant toxins), understanding their combined toxicity is critical. Although honey bees are employed worldwide as surrogate species for Apis and non-Apis bees in toxicity tests, it is practically unfeasible to perform in vivo tests for all mixtures of chemicals. Therefore, Quantitative Structure-Activity Relationships (QSAR) models can be developed using available data and can provide useful tools to predict such combined toxicity. Here, three different QSAR models within the CORAL software have been calibrated and validated for honey bees (A. mellifera) to predict the acute contact mixtures potency (LD_50-mix), in two regression based-models, and the nature of combined toxicity (synergism / non-synergism) in a classification-based model. Experimental data on binary mixtures (n = 123) (LD_50-mix) including dose response data (n = 97) and corresponding Toxic Unit values were retrieved from EFSA databases. The models were built using the principle of extraction of attributes from SMILES (or quasi-SMILES) while calculating so-called correlation weights for these attributes using Monte Carlo techniques. The two regression models were validated for their reliability and robustness (R² = 0.89, CCC = 0.92, Q² = 0.81; R² = 0.87, CCC = 0.89, Q² = 0.75). The classification model was validated using sensitivity (=0.86), specificity (=1), accuracy (=0.96), and Matthews correlation coefficient (MCC = 0.90) as qualitative statistical validation parameters. Results indicate that these QSAR models successfully predict acute contact toxicity of binary mixtures in honey bees and can support prioritisation of multiple chemicals of concerns. Data gaps and further development of QSAR models for honey bees are highlighted particularly for chronic and sub-lethal effects.

Application of new statistical distribution approaches for environmental mixture risk assessment: A case study

OBJECTIVES

There is growing evidence that single substances present below their individual thresholds of effect may still contribute to combined effects. In component-based mixture risk assessment (MRA), the risks can be addressed using information on the mixture components. This is, however, often hampered by limited availability of ecotoxicity data. Here, the possible use of ecotoxicological threshold concentrations of no concern (i.e. 5th percentile of statistical distribution of ecotoxicological values) is investigated to fill data gaps in MRA.

METHODS

For chemicals without available aquatic toxicity data, ecotoxicological threshold concentrations of no concern have been derived from Predicted No Effect Concentration (PNEC) distributions and from chemical toxicity distributions, using the EnviroTox tool, with and without considering the chemical mode of action. For exposure, chemical monitoring data from European rivers have been used to illustrate four realistic co-exposure scenarios. Based on those monitoring data and available ecotoxicity data or threshold concentrations when no data were available, Risk Quotients for individual chemicals were calculated, to then derive a mixture Risk Quotient (RQmix).

RESULTS

A risk was identified in two of the four scenarios. Threshold concentrations contribute from 24 to 95% of the whole RQmix; thus they have a large impact on the predicted mixture risk. Therefore they could only be used for data gap filling for a limited number of chemicals in the mixture. The use of mode of action information to derive more specific threshold values could be a helpful refinement in some cases.