Creation of a Curated Aquatic Toxicology Database: EnviroTox

Graph neural networks-enhanced relation prediction for ecotoxicology (GRAPE)

Exposure to toxic chemicals threatens species and ecosystems. This study introduces a novel approach using Graph Neural Networks (GNNs) to integrate aquatic toxicity data, providing an alternative to complement traditional in vivo ecotoxicity testing. This study pioneers the application of GNN in ecotoxicology by formulating the problem as a relation prediction task. GRAPE's key innovation lies in simultaneously modelling 444 aquatic species and 2826 chemicals within a graph, leveraging relations from existing datasets where informative species and chemical features are augmented to make informed predictions. Extensive evaluations demonstrate the superiority of GRAPE over Logistic Regression (LR) and Multi-Layer Perceptron (MLP) models, achieving remarkable improvements of up to a 30% increase in recall values. GRAPE consistently outperforms LR and MLP in predicting novel chemicals and new species. In particular, GRAPE showcases substantial enhancements in recall values, with improvements of ≥ 100% for novel chemicals and up to 13% for new species. Specifically, GRAPE correctly predicts the effects of novel chemicals (104 out of 126) and effects on new species (7 out of 8). Moreover, the study highlights the effectiveness of the proposed chemical features and induced network topology through GNN for accurately predicting metallic (74 out of 86) and organic (612 out of 674) chemicals, showcasing the broad applicability and robustness of the GRAPE model in ecotoxicological investigations. The code/data are provided at https://github.com/csiro-robotics/GRAPE.

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.

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".

Predictive value of the ToxCast/Tox21 high throughput toxicity screening data for approximating in vivo ecotoxicity endpoints and ecotoxicological risk in eco- surveillance applications

Ecotoxicology has long relied on assessing the hazard potential of chemicals through traditional in vivo testing methods to understand the possible risk exposure could pose to ecological taxa. In the past decade, the development of non-animal new approach methods (NAMs) for assessing chemical hazard and risk has quickly grown. These methods are often cheaper and faster than traditional toxicity testing, and thus are amenable to high-throughput toxicity testing (HTT), resulting in large datasets. The ToxCast/Tox21 HTT programs have produced in vitro data for thousands of chemicals covering a large space of biological activity. The relevance of these data to in vivo mammalian toxicity has been much explored. Interest has also grown in using these data to evaluate the risk of environmental exposures to taxa of ecological importance such as fish, aquatic invertebrates, etc.; particularly for the purpose of estimating the risk of exposure from real-world complex mixtures. Understanding the relationship and relative sensitivity of NAMs versus standardized ecotoxicological whole organism models is a key component of performing reliable read-across from mammalian in vitro data to ecotoxicological in vivo data. In this work, we explore the relationship between in vivo ecotoxicity data from several publicly available databases and the ToxCast/Tox21 data. We also performed several case studies in which we compare how using different ecotoxicity datasets, whether traditional or ToxCast-based, affects risk conclusions based on exposure to complex mixtures derived from existing large-scale chemical monitoring data. Generally, predictive value of ToxCast data for traditional in vivo endpoints (EPs) was poor (r ≤ 0.3). Risk conclusions, including identification of different chemical risk drivers and prioritized monitoring sites, were different when using HTT data vs. traditional in vivo data.

Towards Precision Ecotoxicology: Leveraging Evolutionary Conservation of Pharmaceutical and Personal Care Product Targets to Understand Adverse Outcomes Across Species and Life Stages

Translation of environmental science to the practice aims to protect biodiversity and ecosystem services, and our future ability to do so relies on the development of a precision ecotoxicology approach wherein we leverage the genetics and informatics of species to better understand and manage the risks of global pollution. A little over a decade ago, a workshop focusing on the risks of pharmaceuticals and personal care products (PPCPs) in the environment identified a priority research question, "What can be learned about the evolutionary conservation of PPCP targets across species and life stages in the context of potential adverse outcomes and effects?" We review the activities in this area over the past decade, consider prospects of more recent developments, and identify future research needs to develop next-generation approaches for PPCPs and other global chemicals and waste challenges. Environ Toxicol Chem 2024;43:526-536. © 2023 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Advancing chronic toxicity risk assessment in freshwater ecology by molecular characterization-based machine learning

The continuously increased production of various chemicals and their release into environments have raised potential negative effects on ecological health. However, traditional labor-intensive assessment methods cannot effectively and rapidly evaluate these hazards, especially for chronic risk. In this study, machine learning (ML) was employed to construct quantitative structure-activity relationship (QSAR) models, enabling the prediction of chronic toxicity to aquatic organisms by leveraging the molecular characteristics of pollutants, namely, the molecular descriptors, fingerprints, and graphs. The limited dataset size hindered the notable advantages of the graph attention network (GAT) model for the molecular graphs. Considering computational efficiency and performance (R2 = 0.78; RMSE = 0.77), XGBoost (XGB) was used for reliable QSAR-ML models predicting chronic toxicity using small- or medium-sized tabular data and the molecular descriptors. Further kernel density estimation analysis confirmed the high accuracy of the model for pollutant concentrations ranging from 10-3 to 102 mg/L, effectively aligning with most environmental scenarios. Model interpretation showed SlogP and exposure duration as the primary influential factors. SlogP, representing the distribution coefficient of a molecule between lipophilic and hydrophilic environments, had a negative effect on the toxicity outcomes. Additionally, the exposure duration played a crucial role in determining the chronic toxicity. Finally, the chronic toxicity data of bisphenol A validated the robustness and reliability of the model established in this research. Our study provided a robust and feasible methodology for chronic ecological risk evaluation of various types of pollutants and could facilitate and increase the use of ML applications in environmental fields.

Missing Genomic Resources for the Next Generation of Environmental Risk Assessment

Environmental risk assessment traditionally relies on a wide range of in vivo testing to assess the potential hazards of chemicals in the environment. These tests are often time-consuming and costly and can cause test organisms' suffering. Recent developments of reliable low-cost alternatives, both in vivo- and in silico-based, opened the door to reconsider current toxicity assessment. However, many of these new approach methodologies (NAMs) rely on high-quality annotated genomes for surrogate species of regulatory risk assessment. Currently, a lack of genomic information slows the process of NAM development. Here, we present a phylogenetically resolved overview of missing genomic resources for surrogate species within a regulatory ecotoxicological risk assessment. We call for an organized and systematic effort within the (regulatory) ecotoxicological community to provide these missing genomic resources. Further, we discuss the potential of a standardized genomic surrogate species landscape to enable a robust and nonanimal-reliant ecotoxicological risk assessment in the systems ecotoxicology era.

Handling concentration data below the analytical limit in environmental mixture risk assessment: A case-study on pesticide river monitoring

Aquatic organisms are exposed to ever-changing complex mixtures of chemicals throughout their lifetime. Component-Based Mixture Risk Assessment (CBMRA) is a well-established methodology for water contaminant-mixture management, the use of which is growing due to improved access to reference ecotoxicity data and extensive monitoring datasets. It enables the translation of measured exposure concentrations of chemicals into biological effect values, and thus to quantitatively estimate the risk of the whole water sample (i.e., as a mixture). However, many factors can bias the final risk decision by impacting the risk metric components; thus, a careful design of the CBMRA is needed, taking into primary consideration the specific features of the dataset and mixture risk assessment assignments. This study systematically addressed the effects of the most common approaches used for handling the concentrations of chemicals below the limit of detection/quantification (LOD/LOQ) in CBMRA. The main results included: i) an informed CBMRA procedure that enables the tracking of the risk decisions triggered by substances below LOD/LOQ, ii) a conceptual map and guidance criteria to support the selection of the most suitable approach for specific scenarios and related interpretation; iii) a guided implementation of the informed CBMRA on dataset of pesticide concentrations in Italian rivers in 2020 (702,097 records).

Occurrence and ecological risk assessment of organic UV filters in coastal waters of the Iberian Peninsula

This study aimed to assess the presence of 21 UVFs and metabolites in coastal regions of the Iberian Peninsula, to evaluate their environmental risk, and identify possible influential factors affecting their measured concentrations. Sampling was carried out in spring and summer to assess possible seasonal variations. UVFs were detected in 43 of the 46 sampling sites. Only 5 were found above LOD: BP4, OC, BP3 and metabolites BP1 and BP8. Samples collected in Mar Menor had the greatest variety of compounds per sample and the highest cumulative concentrations. The risk was characterized using Risk Quotients (RQ). BP1 showed a Low environmental Risk in 2 sites while for OC the RQ indicated a Moderate Risk in 22 points. The variables that contribute most to the variation are population density, sampling season, whether it was an open bay or not, and level of urbanization. The presence of WWTPs had a lower influence.

Analysis and derivation of the marine water quality criteria of phenol for Korean seas

Marine water quality criteria (WQC) have to be determined prior to the derivation of water quality based effluent limitations (WQBELs) for hazardous and noxious substances (HNS) discharged from marine industrial facilities. In this study, we carried out toxicity tests using ten native marine organisms and analyzed international toxicity data and data tested in this study to derive the WQC of phenol for Korean seas. By converting acute values to chronic ones with ACRs (acute-chronic ratios) of each trophic level according to well-verified method, we derived provisional WQC (0.96 mg/L) of phenol for Korean seas for the first time. The procedure to derive marine WQC and results of this study could provide the essential information for the establishment of national marine WQC and WQBELs for HNS discharged from marine industrial facilities.

A benchmark dataset for machine learning in ecotoxicology

The use of machine learning for predicting ecotoxicological outcomes is promising, but underutilized. The curation of data with informative features requires both expertise in machine learning as well as a strong biological and ecotoxicological background, which we consider a barrier of entry for this kind of research. Additionally, model performances can only be compared across studies when the same dataset, cleaning, and splittings were used. Therefore, we provide ADORE, an extensive and well-described dataset on acute aquatic toxicity in three relevant taxonomic groups (fish, crustaceans, and algae). The core dataset describes ecotoxicological experiments and is expanded with phylogenetic and species-specific data on the species as well as chemical properties and molecular representations. Apart from challenging other researchers to try and achieve the best model performances across the whole dataset, we propose specific relevant challenges on subsets of the data and include datasets and splittings corresponding to each of these challenge as well as in-depth characterization and discussion of train-test splitting approaches.

To Split or Not to Split: Characterizing Chemical Pollution Impacts in Aquatic Ecosystems with Species Sensitivity Distributions for Specific Taxonomic Groups

Bridging applied ecology and ecotoxicology is key to protect ecosystems. These disciplines show a mismatch, especially when evaluating pressures. Contrasting to applied ecology, ecotoxicological impacts are often characterized for whole species assemblages based on Species Sensitivity Distributions (SSDs). SSDs are statistical models describing per chemical across-species sensitivity variation based on laboratory toxicity tests. To assist in the aligning of the disciplines and improve decision-support uses of SSDs, we investigate taxonomic-group-specific SSDs for algae/cyanobacteria/aquatic plants, invertebrates, and vertebrates for 180 chemicals with sufficient test data. We show that splitting improves pollution impact assessments for chemicals with a specific mode of action and, surprisingly, for narcotic chemicals. We provide a framework for splitting SSDs that can be applied to serve in environmental protection, life cycle assessment, and management of freshwater ecosystems. We illustrate that using split SSDs has potentially large implications for the decision-support of SSD-based outputs around the globe.

Physiological variables in machine learning QSARs allow for both cross-chemical and cross-species predictions

A major challenge in ecological risk assessment is estimating chemical-induced effects across taxa without species-specific testing. Where ecotoxicological data may be more challenging to gather, information on species physiology is more available for a broad range of taxa. Physiology is known to drive species sensitivity but understanding about the relative contribution of specific underlying processes is still elusive. Consequently, there remains a need to understand which physiological processes lead to differences in species sensitivity. The objective of our study was to utilize existing knowledge about organismal physiology to both understand and predict differences in species sensitivity. Machine learning models were trained to predict chemical- and species-specific endpoints as a function of both chemical fingerprints/descriptors and physiological properties represented by dynamic energy budget (DEB) parameters. We found that random forest models were able to predict chemical- and species-specific endpoints, and that DEB parameters were relatively important in the models, particularly for invertebrates. Our approach illuminates how physiological properties may drive species sensitivity, which will allow more realistic predictions of effects across species without the need for additional animal testing.

Weight of evidence tools in the prediction of acute fish toxicity

Acute fish toxicity (AFT) is a key endpoint in nearly all regulatory implementations of environmental hazard assessments of chemicals globally. Although it is an early tier assay, the AFT assay is complex and uses many juvenile fish each year for the registration and assessment of chemicals. Thus, it is imperative to seek animal alternative approaches to replace or reduce animal use for environmental hazard assessments. A Bayesian Network (BN) model has been developed that brings together a suite of lines of evidence (LoEs) to produce a probabilistic estimate of AFT without the testing of additional juvenile fish. Lines of evidence include chemical descriptors, mode of action (MoA) assignment, knowledge of algal and daphnid acute toxicity, and animal alternative assays such as fish embryo tests and in vitro fish assays (e.g., gill cytotoxicity). The effort also includes retrieval, assessment, and curation of quality acute fish toxicity data because these act as the baseline of comparison with model outputs. An ideal outcome of this effort would be to have global applicability, acceptance and uptake, relevance to predominant fish species used in chemical assessments, be expandable to allow incorporation of future knowledge, and data to be publicly available. The BN model can be conceived as having incorporated principles of tiered assessment and whose outcomes will be directed by the available evidence in combination with prior information. We demonstrate that, as additional evidence is included in the prediction of a given chemical's ecotoxicity profile, both the accuracy and the precision of the predicted AFT can increase. Ultimately an improved environmental hazard assessment will be achieved. Integr Environ Assess Manag 2023;19:1220-1234. © 2022 SETAC.

Determination of aquatic hazard concentrations for the oil spill response product class of surface washing agents using species sensitivity distributions

Surface washing agents (SWAs) are a diverse class of oil spill response products intended to facilitate removal of stranded oil from shorelines. This class of agents has high application rates relative to other categories of spill response products, but global toxicity data is generally limited to two standard test species: inland silverside and mysid shrimp. Here, we provide a framework to maximize the utility of limited toxicity data across a product class. To characterize species sensitivity to SWAs, the toxicity of three agents spanning a range of chemical and physical properties were tested in eight species. The relative sensitivity of mysids shrimp and inland silversides as surrogate test organisms was determined. Toxicity normalized species sensitivity distributions (SSDn) were used to estimate fifth centile hazard concentration (HC5) values for SWAs with limited toxicity data. Chemical toxicity distributions (CTD) of SWA HC5 values were used to compute a fifth centile chemical hazard distribution (HD5) to provide a more comprehensive assessment of hazard across a spill response product class with limited toxicity data than traditional single species or single agent approaches can give.

Chemical prioritization of pharmaceuticals and personal care products in an urban tributary of the Potomac River

Pharmaceuticals and personal care products (PPCPs) are incredibly diverse in terms of chemical structures, physicochemical properties, and modes of action, making their environmental impacts challenging to assess. New chemical prioritization methodologies have emerged that compare contaminant monitoring concentrations to multiple toxicity data sources, including whole organism and high-throughput data, to develop a list of "high priority" chemicals requiring further study. We applied such an approach to assess PPCPs in Hunting Creek, an urban tributary of the Potomac River near Washington, DC, which has experienced extensive human population growth. We estimated potential risks of 99 PPCPs from surface water and sediment collected upstream and downstream of a major wastewater treatment plant (WWTP), nearby combined sewer overflows (CSO), and in the adjacent Potomac River. The greatest potential risks to the aquatic ecosystem occurred near WWTP and CSO outfalls, but risk levels rapidly dropped below thresholds of concern - established by previous chemical prioritization studies - in the Potomac mainstem. These results suggest that urban tributaries, rather than larger rivers, are important to monitor because their lower or intermittent flow may not adequately dilute contaminants of concern. Common psychotropics, such as fluoxetine and venlafaxine, presented the highest potential risks, with toxicity quotients often > 10 in surface water and > 1000 in sediment, indicating the need for further field studies. Several ubiquitous chemicals such as caffeine and carbamazepine also exceeded thresholds of concern throughout our study area and point to specific neurotoxic and endocrine modes of action that warrant further investigation. Since many "high priority" chemicals in our analysis have also triggered concerns in other areas around the world, better coordination is needed among environmental monitoring programs to improve global chemical prioritization efforts.

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.

The ATTAC guiding principles to openly and collaboratively share wildlife ecotoxicology data

The inability to quantitatively integrate scattered data regarding potential threats posed by the increasing total amount and diversity of chemical substances in our environment limits our ability to understand whether existing regulations and management actions sufficiently protect wildlife. Systematic literature reviews and meta-analyses are great scientific tools to build upon the current push for accessibility under the Open Science and FAIR movements. Despite the potential of such integrative analyses, the emergence of innovative findings in wildlife ecology and ecotoxicology is still too rare relative to the potential that is hidden within the entirety of the available scattered data. To promote the reuse of wildlife ecotoxicology data, we propose the ATTAC workflow which comprises five key steps (Access, Transparency, Transferability, Add-ons, and Conservation sensitivity) along the chain of collecting, homogenizing, and integrating data for subsequent meta-analyses. The ATTAC workflow brings together guidelines supporting both the data prime movers and re-users. As such, the ATTAC workflow could promote an open and collaborative wildlife ecotoxicology able to reach a major objective in this applied field, namely, providing strong scientific support for regulations and management actions to protect and preserve wildlife species.

Unveiling toxicity profile for food risk components: A manually curated toxicological databank of food-relevant chemicals

Rigorous risk assessment of chemicals in food and feed is essential to address the growing worldwide concerns about food safety. High-quality toxicological data on food-relevant chemicals are fundamental for risk modeling and assessment in the food safety area. The organization and analysis of substantial toxicity information can positively support decision-making by providing insight into toxicity trends. However, it remains challenging to systematically obtain fragmented toxicity data, and related toxicological resources are required to meet the current demands. In this study, we collected 221,439 experimental toxicity records for 5,657 food-relevant chemicals identified from extensive databases and literature, along with their information on chemical identification, physicochemical properties, environmental fates, and biological targets. Based on the aggregated data, a freely available web-based databank, Food-Relevant Available Chemicals Toxicology Databank (FRAC-TD) is presented, which supports multiple browsing ways and search criterions. Applying FRAC-TD for data-driven analysis, we revealed the underlying toxicity profiles of food-relevant chemicals in humans, mammals, and other species in the food chain. Expectantly, FRAC-TD could positively facilitate toxicological studies, toxicity prediction, and risk assessments in the food industry.

TOXRIC: a comprehensive database of toxicological data and benchmarks

The toxic effects of compounds on environment, humans, and other organisms have been a major focus of many research areas, including drug discovery and ecological research. Identifying the potential toxicity in the early stage of compound/drug discovery is critical. The rapid development of computational methods for evaluating various toxicity categories has increased the need for comprehensive and system-level collection of toxicological data, associated attributes, and benchmarks. To contribute toward this goal, we proposed TOXRIC (https://toxric.bioinforai.tech/), a database with comprehensive toxicological data, standardized attribute data, practical benchmarks, informative visualization of molecular representations, and an intuitive function interface. The data stored in TOXRIC contains 113 372 compounds, 13 toxicity categories, 1474 toxicity endpoints covering in vivo/in vitro endpoints and 39 feature types, covering structural, target, transcriptome, metabolic data, and other descriptors. All the curated datasets of endpoints and features can be retrieved, downloaded and directly used as output or input to Machine Learning (ML)-based prediction models. In addition to serving as a data repository, TOXRIC also provides visualization of benchmarks and molecular representations for all endpoint datasets. Based on these results, researchers can better understand and select optimal feature types, molecular representations, and baseline algorithms for each endpoint prediction task. We believe that the rich information on compound toxicology, ML-ready datasets, benchmarks and molecular representation distribution can greatly facilitate toxicological investigations, interpretation of toxicological mechanisms, compound/drug discovery and the development of computational methods.

Drivers of copper sensitivity in copepods: A meta-analysis of LC50s

Copper is both an essential trace element and a potent pesticide. The use of copper as an antifoulant has increased in the last decades in line with the expanding aquaculture and shipping industries. In aquatic environments, it also affects non-target taxa. One of which are copepods, which constitute the central link in the marine food web. Despite their ecological importance, there are no systematic reviews of the lethal concentration range and drivers of copper toxicity in this taxon. Here, we combined literature data from 31 peer-reviewed articles recording the Lethal Concentration 50 (LC50) for copper in copepods and the experiments' respective environmental, developmental, and taxonomic parameters. The LC50 is a traditional endpoint for toxicity testing used in standardized toxicity testing and many ecological studies. In total, we were able to extract 166 LC50 entries. The variability in the metadata allowed for a general analysis of the drivers of copper sensitivity in copepods. Using a generalized additive modeling approach, we find that temperature increases copper toxicity when above approximately 25℃. Counter to our expectations; salinity does not influence copper sensitivity across copepod species. Unsurprisingly, nauplii are more susceptible to copper exposure than adult copepods, and benthos-associated harpacticoids are less sensitive to copper than pelagic calanoids. Our final model can predict sensible specific-specific copper concentrations for future experiments, thus giving an informed analytical approach to range testing in future dose-response experiments. Our model can also potentially improve ecological risk assessment by accounting for environmental differences. The approach can be applied to other toxicants and taxa, which may reveal underlying patterns otherwise obscured by taxonomic and experimental variability.

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.

The ECOTOXicology Knowledgebase: A Curated Database of Ecologically Relevant Toxicity Tests to Support Environmental Research and Risk Assessment

The need for assembled existing and new toxicity data has accelerated as the amount of chemicals introduced into commerce continues to grow and regulatory mandates require safety assessments for a greater number of chemicals. To address this evolving need, the ECOTOXicology Knowledgebase (ECOTOX) was developed starting in the 1980s and is currently the world's largest compilation of curated ecotoxicity data, providing support for assessments of chemical safety and ecological research through systematic and transparent literature review procedures. The recently released version of ECOTOX (Ver 5, www.epa.gov/ecotox) provides single-chemical ecotoxicity data for over 12,000 chemicals and ecological species with over one million test results from over 50,000 references. Presented is an overview of ECOTOX, detailing the literature review and data curation processes within the context of current systematic review practices and discussing how recent updates improve the accessibility and reusability of data to support the assessment, management, and research of environmental chemicals. Relevant and acceptable toxicity results are identified from studies in the scientific literature, with pertinent methodological details and results extracted following well-established controlled vocabularies and newly extracted toxicity data added quarterly to the public website. Release of ECOTOX, Ver 5, included an entirely redesigned user interface with enhanced data queries and retrieval options, visualizations to aid in data exploration, customizable outputs for export and use in external applications, and interoperability with chemical and toxicity databases and tools. This is a reliable source of curated ecological toxicity data for chemical assessments and research and continues to evolve with accessible and transparent state-of-the-art practices in literature data curation and increased interoperability to other relevant resources. Environ Toxicol Chem 2022;41:1520-1539. © 2022 SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

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.

Comparison of Species Sensitivity Distributions for Sediment-Associated Nonionic Organic Chemicals Through Equilibrium Partitioning Theory and Spiked-Sediment Toxicity Tests with Invertebrates

Equilibrium partitioning (EqP) theory and spiked-sediment toxicity tests are useful methods to develop sediment quality benchmarks. However, neither approach has been directly compared based on species sensitivity distributions (SSDs) to date. In the present study, we compared SSDs for 10 nonionic hydrophobic chemicals (e.g., pyrethroid insecticides, other insecticides, and polycyclic aromatic hydrocarbons) based on 10-14-day spiked-sediment toxicity test data with those based on EqP theory using acute water-only tests. Because the exposure periods were different between the two tests, effective concentrations (i.e., median effective/lethal concentration) were corrected to compare SSDs. Accordingly, we found that hazardous concentrations for 50% and 5% of species (HC50 and HC5, respectively) differed by up to a factor of 100 and 129 between the two approaches, respectively. However, when five or more species were used for SSD estimation, their differences were reduced to a factor of 1.7 and 5.1 for HC50 and HC5, respectively, and the 95% confidence intervals of HC50 values overlapped considerably between the two approaches. These results suggest that when the number of test species is adequate, SSDs based on EqP theory and spiked-sediment tests are comparable in sediment risk assessments. Environ Toxicol Chem 2022;41:462-473. © 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.

Coral Ecotoxicological Data Evaluation for the Environmental Safety Assessment of Ultraviolet Filters

There is growing interest in the environmental safety of ultraviolet (UV) filters found in cosmetic and personal care products (CPCPs). The CPCP industry is assessing appropriate environmental risk assessment (ERA) methods to conduct robust environmental safety assessments for these ingredients. Relevant and reliable data are needed for ERA, particularly when the assessment is supporting regulatory decision-making. In the present study, we apply a data evaluation approach to incorporate nonstandard toxicity data into the ERA process through an expanded range of reliability scores over commonly used approaches (e.g., Klimisch scores). The method employs an upfront screening followed by a data quality assessment based largely on the Criteria for Reporting and Evaluating Ecotoxicity Data (CRED) approach. The method was applied in a coral case study in which UV filter toxicity data was evaluated to identify data points potentially suitable for higher tier and/or regulatory ERA. This is an optimal case study because there are no standard coral toxicity test methods, and UV filter bans are being enacted based on findings reported in the current peer-reviewed data set. Eight studies comprising nine assays were identified; four of the assays did not pass the initial screening assessment. None of the remaining five assays received a high enough reliability score (R_n ) to be considered of decision-making quality (i.e., R1 or R2). Four assays were suitable for a preliminary ERA (i.e., R3 or R4), and one assay was not reliable (i.e., R6). These results highlight a need for higher quality coral toxicity studies, potentially through the development of standard test protocols, to generate reliable toxicity endpoints. These data can then be used for ERA to inform environmental protection and sustainability decision-making. Environ Toxicol Chem 2021;40:3441-3464. © 2021 Personal Care Products Council. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

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.

Potential for Interspecies Toxicity Estimation in Soil Invertebrates

Interspecies correlation estimation (ICE) models are linear regressions that predict toxicity to a species with few data using a known toxicity value in a surrogate species. ICE models are well established for estimating toxicity to fish and aquatic invertebrates but have not been generally developed or applied to soil organisms. To facilitate the development of ICE models for soil invertebrates, a database of single chemical toxicity values was compiled from knowledgebases and reports that included 853 records encompassing 192 chemicals and 12 species. Most toxicity data for single chemicals tested in soil media were for species of earthworms, with only limited data for other species and taxa. ICE models were developed for eleven separate species pairs as least squares log-linear regressions of acute toxicity values of the same chemicals tested in both the surrogate and predicted species of soil organisms. Model uncertainty was assessed using leave one out cross-validation as the fold difference between a predicted and measured toxicity value. ICE models showed high accuracy within order (e.g., earthworm to earthworm), but less prediction accuracy in the two across-taxa models (Arthropoda to Annelida and the inverse). This study provides a proof-of-concept demonstration that ICE models can be developed for soil invertebrates.

Identification of concealed structural alerts using QSTR modeling for Pseudokirchneriella subcapitata

In the present work, QSTR modeling was conducted for microalga Pseudokirchneriella subcapitata using a data set of 271 molecules belonging to different types of chemical classes for the prediction of EC₅₀ for 72 hr based assays. The balanced QSTR model encompasses seven easily interpretable molecular descriptors and possesses statistical robustness with high predictive ability. This Genetic Algorithm Multi-linear regression (GA-MLR) model was subjected to internal validation, Y-randomization test, applicability domain analysis, and external validation as per the recommended OECD guidelines. The newly developed model fulfilled the threshold values for more than 20 recommended validation parameters including R² = 0.72, Q²_LOO = 0.70, etc. The developed QSTR model was successful in identifying the type of hybridization or specific type of atoms of previously reported and newer structural alerts. Thus, the model could be useful for data gap filling and expanding mechanistic interpretation of toxicity for different chemicals.

Ecotoxicological QSAR modeling of the acute toxicity of organic compounds to the freshwater crustacean Thamnocephalus platyurus

Growing interest in environmental toxicity assessment using Thamnocephalus platyurus as organism has led to an increased availability of acute toxicity data. Despite this growing interest in tests with this organism, however, to the best of our knowledge there are no computational models to predict the acute toxicity in T. platyurus. In view of the limited number of in silico models for this crustacean, we developed Quantitative Structure-Activity Relationship (QSAR) models for the prediction of acute toxicity towards T. platyurus, reflected by the 24h LC_50, using publicly available data according to the ISO 14380:2011 guideline. Two models were developed following the principles of QSAR modeling recommended by the Organization for Economic Cooperation and Development (OECD). We used partial least squares and gradient boosting machine techniques, which gave encouraging statistical quality in our data set.

In Silico Resources of Drug-Likeness as a Mirror: What Are We Lacking in Pesticide-Likeness?

Unfavorable bioavailability is an important aspect underlying the failure of drug candidates. Computational approaches for evaluating drug-likeness can minimize these risks. Over the past decades, computational approaches for evaluating drug-likeness have sped up the process of drug development and were also quickly derived to pesticide-likeness. As a result of many critical differences between drugs and pesticides, many kinds of methods for drug-likeness cannot be used for pesticide-likeness. Therefore, it is crucial to comprehensively compare and analyze the differences between drug-likeness and pesticide-likeness, which may provide a basis for solving the problems encountered during the evaluation of pesticide-likeness. Here, we systematically collected the recent advances of drug-likeness and pesticide-likeness and compared their characteristics. We also evaluated the current lack of studies on pesticide-likeness, the molecular descriptors and parameters adopted, the pesticide-likeness model on pesticide target organisms, and comprehensive analysis tools. This work may guide researchers to use appropriate methods for developing pesticide-likeness models. It may also aid non-specialists to understand some important concepts in drug-likeness and pesticide-likeness.

Water quality and ecological risks in European surface waters - Monitoring improves while water quality decreases

Aquatic ecosystems are at risk of being impaired by various organic chemicals, however comprehensive large-scale evaluations of waterbodies' status and trends are rare. Here, surface water monitoring data, gathered as part of the EU Water Framework Directive and comprising the occurrence of 352 organic contaminants (>8.3 mil. measurements; 2001-2015; 8213 sites) in 31 European countries, was used to evaluate past and current environmental risks for three aquatic species groups: fish, invertebrates, plants. Monitoring quality indices were defined per country and found to improve over time. Relationships became apparent between countries' monitoring quality index and their success in detecting contaminants. Across the EU, contaminants were more frequently found in recent years. Overall, 35.7% (n = 17,484) of sites exceeded at least one acute regulatory threshold level (RTL) each year, and average risks significantly increased over time for fish (τ = 0.498, p = 0.01) and aquatic invertebrates (τ = 0.429, p = 0.03). This indicates an increased chemical pressure to Europe's waterbodies and overall large-scale threshold exceedances. Pesticides were identified as the main risk drivers (>85% of RTL exceedances) with aquatic invertebrates being most acutely at risk in Europe. Agricultural land-use was clearly identified as the primary spatial driver of the observed aquatic risks throughout European surface waters. Issues in monitoring data heterogeneity were highlighted and also followed by subsequent improvement recommendations, strengthening future environmental quality assessments. Overall, aquatic ecosystem integrity remains acutely at risk across Europe, signaling the demand for continued improvements.

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.

Toward a Universal Acute Fish Threshold of Toxicological Concern

Threshold of toxicological concern (TTC) is a concept that has been around for decades in human health sciences. Ecotoxicology recently adopted a variant of this concept as eco-TTC. Adoption of the concept of TTC considerably reduces the amount of animal testing required for regulatory purposes. We provide an application of a universal TTC for the entirety of acute fish toxicity data (i.e., establishment of an exposure level below which there would be minimal probability of acute fish toxicity for any chemical, without consideration of mechanism of action). We calculated TTC values for a number of subgroups using various approaches. These approaches were evaluated using data from a cohort of 69 999 acute fish toxicological assays. This database was normalized/curated for units, exposure duration, quality assurance/control, and duplicates, which reduced it to 47 694 assays. Data were not normally but log-normally distributed, making geometric means the most appropriate statistical parameter. Thus, we developed descriptive statistics using geometric means with 95, 99, and 99.9% confidence intervals. Various assessment factors (akin to predicted-no-effect concentration derivation) were applied to the geometric means to derive TTCs. Other approaches employed were the calculation of y = 0 intercepts as well as development of 95 and 99.75% cutoffs of cumulative data as well as modular uncertainty scoring tool (MUST) analysis. All of the methodologies derived highly congruent TTCs ranging from to 2 to 8 μg/L except for the 99.75th percentile cutoff of 0.3 μg/L. The data would be most useful in making a binary testing/no testing required decision. For acute fish toxicity, a TTC value of 2 μg/L was most appropriate, based on the 95th percentile of data distribution without any assessment factor. Environ Toxicol Chem 2021;40:1740-1749. © 2021 SETAC.

Defining the Human-Biota Thresholds of Toxicological Concern for Organic Chemicals in Freshwater: The Proposed Strategy of the LIFE VERMEER Project Using VEGA Tools

Several tons of chemicals are released every year into the environment and it is essential to assess the risk of adverse effects on human health and ecosystems. Risk assessment is expensive and time-consuming and only partial information is available for many compounds. A consolidated approach to overcome this limitation is the Threshold of Toxicological Concern (TTC) for assessment of the potential health impact and, more recently, eco-TTCs for the ecological aspect. The aim is to allow a safe assessment of substances with poor toxicological characterization. Only limited attempts have been made to integrate the human and ecological risk assessment procedures in a "One Health" perspective. We are proposing a strategy to define the Human-Biota TTCs (HB-TTCs) as concentrations of organic chemicals in freshwater preserving both humans and ecological receptors at the same time. Two sets of thresholds were derived: general HB-TTCs as preliminary screening levels for compounds with no eco- and toxicological information, and compound-specific HB-TTCs for chemicals with known hazard assessment, in terms of Predicted No effect Concentration (PNEC) values for freshwater ecosystems and acceptable doses for human health. The proposed strategy is based on freely available public data and tools to characterize and group chemicals according to their toxicological profiles. Five generic HB-TTCs were defined, based on the ecotoxicological profiles reflected by the Verhaar classes, and compound-specific thresholds for more than 400 organic chemicals with complete eco- and toxicological profiles. To complete the strategy, the use of in silico models is proposed to predict the required toxicological properties and suitable models already available on the VEGAHUB platform are listed.

Toxicity of selected airborne nitrophenols on eukaryotic cell membrane models

Nitroaromatics belong to the group of toxic components of aerosol particles and atmospheric hydrometeors that enter the atmosphere through biomass burning and fuel combustion. In the present work, we report on the cytotoxic effects of a 2-, 3- and 4-nitrophenol mixture on a model eukaryotic-like cell membrane and compared it with in vitro cellular models BEAS-2B (immortalized bronchial epithelial cells) and A549 (cancerous alveolar epithelial cells). A selected model biomembrane comprised of DMPC (1,2-dimyristoyl-sn-glycero-3-phosphocholine), DPPC (1,2-dipalmitoyl-sn-glycero-3-phosphocholine) and POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) was studied. The electrochemical-based method, combined with atomic force microscopy (AFM) and phase-contrast microscopy imaging, allowed to get insights into the mechanism of cellular function disruption caused by airborne nitrophenols. The efficacy of the method is supported by the data obtained from in vitro experiments performed on cell models. The nitrophenol mixture exhibited cytotoxic effects at concentrations above 100 μg mL^-1, as demonstrated by phase-contrast microscopy in real lung cell lines. Electrochemical impedance spectroscopy (EIS) revealed the formation of membrane defects at a nitrophenol concentration of 200 μg mL^-1. AFM imaging confirmed the model membrane disintegration and phospholipids rearrangement in the presence of nitrophenols. These observations indicate that particle-bound nitrophenols induce substantial changes in cell membranes and make them more permeable to aerosol, resulting in major cellular damage in the lungs when inhaled. The study provides initial evidence of cellular membrane damage induced by three important nitrated phenols present in the environment.

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.

Ecotoxicology of Glyphosate, Its Formulants, and Environmental Degradation Products

The chemical and biological properties of glyphosate are key to understanding its fate in the environment and potential risks to non-target organisms. Glyphosate is polar and water soluble and therefore does not bioaccumulate, biomagnify, or accumulate to high levels in the environment. It sorbs strongly to particles in soil and sediments and this reduces bioavailability so that exposures to non-target organisms in the environment are acute and decrease with half-lives in the order of hours to a few days. The target site for glyphosate is not known to be expressed in animals, which reduces the probability of toxicity and small risks. Technical glyphosate (acid or salts) is of low to moderate toxicity; however, when mixed with some formulants such as polyoxyethylene amines (POEAs), toxicity to aquatic animals increases about 15-fold on average. However, glyphosate and the formulants have different fates in the environment and they do not necessarily co-occur. Therefore, toxicity tests on formulated products in scenarios where they would not be used are unrealistic and of limited use for assessment of risk. Concentrations of glyphosate in surface water are generally low with minimal risk to aquatic organisms, including plants. Toxicity and risks to non-target terrestrial organisms other than plants treated directly are low and risks to terrestrial invertebrates and microbial processes in soil are very small. Formulations containing POEAs are not labeled for use over water but, because POEA rapidly partitions into sediment, risks to aquatic organisms from accidental over-sprays are reduced in shallow water bodies. We conclude that use of formulations of glyphosate under good agricultural practices presents a de minimis risk of direct and indirect adverse effects in non-target organisms.

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.

A Compendium of Chemical Class and Use Type Open Access Databases

With an ever-increasing production and registration of chemical substances, obtaining reliable and up to date information on their use types (UT) and chemical class (CC) is of crucial importance. We evaluated the current status of open access chemical substance databases (DBs) regarding UT and CC information using the “Meta-analysis of the Global Impact of Chemicals” (MAGIC) graph as a benchmark. A decision tree-based selection process was used to choose the most suitable out of 96 databases. To compare the DB content for 100 weighted, randomly selected chemical substances, an extensive quantitative and qualitative analysis was performed. It was found that four DBs yielded more qualitative and quantitative UT and CC results than the current MAGIC graph: The European Bioinformatics Institute DB, ChemSpider, the English Wikipedia page, and the National Center for Biotechnology Information (NCBI). The NCBI, along with its subsidiary DBs PubChem and Medical Subject Headings (MeSH), showed the best performance according to the defined criteria. To analyse large datasets, harmonisation of the available information might be beneficial, as the available DBs mostly aggregate information without harmonising them.

Polycyclic aromatic compounds (PACs) in the Canadian environment: The challenges of ecological risk assessments

Ecological risk assessments (ERAs) of polycyclic aromatic compounds (PACs), as single congeners or in mixtures, present technical challenges that raise concerns about their accuracy and validity for Canadian environments. Of more than 100,000 possible PAC structures, the toxicity of fewer than 1% have been tested as individual compounds, limiting the assessment of complex mixtures. Because of the diversity in modes of PAC action, the additivity of mixtures cannot be assumed, and mixture compositions change rapidly with weathering. In vertebrates, PACs are rapidly oxygenated by cytochrome P450 enzymes, often to metabolites that are more toxic than the parent compound. The ability to predict the ecological fate, distribution and effects of PACs is limited by toxicity data derived from tests of a few responses with a limited array of test species, under optimal laboratory conditions. Although several models are available to predict PAC toxicity and rank species sensitivity, they were developed with data biased by test methods, and the reported toxicities of many PACs exceed their solubility limits. As a result, Canadian Environmental Quality Guidelines for a few individual PACs provide little support for ERAs of complex mixtures in emissions and at contaminated sites. These issues are illustrated by reviews of three case studies of PAC-contaminated sites relevant to Canadian ecosystems. Interactions among ecosystem characteristics, the behaviour, fate and distribution of PACs, and non-chemical stresses on PAC-exposed species prevented clear associations between cause and effect. The uncertainties of ERAs can only be reduced by estimating the toxicity of a wider array of PACs to species typical of Canada's diverse geography and environmental conditions. Improvements are needed to models that predict toxicity, and more field studies of contaminated sites in Canada are needed to understand the ecological effects of PAC mixtures.

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.

Quantifying Uncertainty in Ecotoxicological Risk Assessment: MUST, a Modular Uncertainty Scoring Tool

Whether conducting a risk, hazard, or alternatives assessment, one invariably struggles with the task of reconciling multiple available values of toxicological thresholds into a single outcome. When combining multiple pieces of evidence from many different sources, it is important to consider the role of data uncertainty. Uncertainty is inherent to all scientific data. However, in toxicological assessments, controversies and uncertainties are typically understated; they lack methodological transparency; or they poorly integrate qualitative and quantitative sources of information. Similarly, in model development, data curation is rarely performed with sufficient rigor, particularly when applying big data statistics. To overcome the hurdles of a decision process that must reconcile divergent data, we developed an uncertainty scoring tool that can be trained to reproduce specific decision-making paradigms and ensure consistency in the practitioner's judgment across complex scenarios. While designed to aid with ecotoxicological assessments and predictive model development, the tool's applicability extends to any decision-making process that calls for synthesis of incongruent data. Here, we highlight the development process, as well as demonstrate the method's utility in several prototypical ecotoxicological case studies.

Key Opportunities to Replace, Reduce, and Refine Regulatory Fish Acute Toxicity Tests

Fish acute toxicity tests are conducted as part of regulatory hazard identification and risk-assessment packages for industrial chemicals and plant protection products. The aim of these tests is to determine the concentration which would be lethal to 50% of the animals treated. These tests are therefore associated with suffering in the test animals, and Organisation for Economic Co-operation and Development test guideline 203 (fish, acute toxicity) studies are the most widely conducted regulatory vertebrate ecotoxicology tests for prospective chemical safety assessment. There is great scope to apply the 3Rs principles-the reduction, refinement, and replacement of animals-in this area of testing. An expert ecotoxicology working group, led by the UK National Centre for the Replacement, Refinement and Reduction of Animals in Research, including members from government, academia, and industry, reviewed global fish acute test data requirements for the major chemical sectors. The present study highlights ongoing initiatives and provides an overview of the key challenges and opportunities associated with replacing, reducing, and/or refining fish acute toxicity studies-without compromising environmental protection. Environ Toxicol Chem 2020;39:2076-2089. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

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.

Standartox: Standardizing Toxicity Data

An increasing number of chemicals such as pharmaceuticals, pesticides and synthetic hormones are in daily use all over the world. In the environment, chemicals can adversely affect populations and communities and in turn related ecosystem functions. To evaluate the risks from chemicals for ecosystems, data on their toxicity, which are typically produced in standardized ecotoxicological laboratory tests, is required. The results from ecotoxicological tests are compiled in (meta-)databases such as the United States Environmental Protection Agency (EPA) ECOTOXicology Knowledgebase (ECOTOX). However, for many chemicals, multiple ecotoxicity data are available for the same test organism. These can vary strongly, thereby causing uncertainty of related analyses. Given that most current databases lack aggregation steps or are confined to specific chemicals, we developed Standartox, a tool and database that continuously incorporates the ever-growing number of test results in an automated process workflow that ultimately leads to a single aggregated data point for a specific chemical-organism test combination, representing the toxicity of a chemical. Standartox can be accessed through a web application and an R package.