Framework for Optimizing Selection of Interspecies Correlation Estimation Models to Address Species Diversity and Toxicity Gaps in an Aquatic Database

Weight of evidence assessment of effects of sulfoxaflor on aquatic invertebrates: sulfoxaflor environmental science review part II

Effects of sulfoxaflor (SFX) on aquatic invertebrates were assessed by comparing concentrations predicted to occur in or measured in surface waters to thresholds for adverse effects. Due to the specific mode of toxic action, fishes are relatively tolerant of the effects of SFX. Daphnia magna with an LC50 of 378 mg SFX L-1 (SD = 19.13) was similarly tolerant of the effects of SFX, while the LOEC was > 110 mg SFX L-1. A threshold for effects on aquatic insects, based on the chironomid midge, C. tentans, had LOAEL and NOAEL values of 0.0455 and 0.0618 mg L-1, respectively. The acute-to-chronic ratio was 18. Simulation models and parameters selected for a range of applications to crops predicted environmental concentrations (EECs) in surface waters to range from 2.2 to 7.7 µg L-1. Based on these EECs, the maximum hazard quotient (HQ) was 0.11, which is less than the US EPA level of concern (LOC) of 0.5, which would normally be the threshold to trigger regulatory action or higher-tier assessments. The risks posed by SFX to aquatic organisms are predicted to be de minimis. Hazard quotients based on EEC values predicted in the standard, USEPA farm pond estimated by use of the Pesticides in Water Calculator (PWC version 1.52) for scenarios of maximum application rates for cotton and LOAEL and NOAEL values for aquatic insects for SFX were less than or similar to those for other insecticides including neonicotinoids and organophosphorus compounds.

Addressing data gaps in deriving aquatic life ambient water quality criteria for contaminants of emerging concern: Challenges and the potential of in silico methods

The international community is becoming increasingly aware of the threats posed by contaminants of emerging concern (CECs) for ecological security. Aquatic life ambient water quality criteria (WQC) are essential for the formulation of risk prevention and control strategies for pollutants by regulatory agencies. Accordingly, we systematically evaluated the current status of WQC development for typical CECs through literature review. The results revealed substantial disparities in the WQC for the same chemical, with the coefficients of variation for all CECs exceeding 0.3. The reliance on low-quality data, high-uncertainty derivation methods, and limited species diversity highlights a substantial data gap. Newly developed in silico methods, with potential to predict the toxicity of untested chemicals, species, and conditions, were classified and integrated into a traditional WQC derivation framework to address the data gap for CECs. However, several challenges remain before such methods can achieve widespread acceptance. These include unstable model performance, the inability to predict chronic toxicity, undefined model applicability, difficulties in specifying toxicity effects and predicting toxicity for certain key species. Future research should prioritize: 1) improving model accuracy by developing specialized models trained with relevant, chemical-specific data or integrating chemical-related features into interspecies models; 2) enhancing species generalizability by developing multispecies models; 3) facilitating the derivation of environmentally relevant WQC by incorporating condition-related features into models; and 4) improving the regulatory acceptability of in silico methods by evaluating the reliability of "black-box" models.

Decoding Adverse Effects of Organic Contaminants in the Aquatic Environment: A Meta-analysis of Species Sensitivity, Hazard Prediction, and Ecological Risk Assessment

Aquatic organisms in the environment are frequently exposed to a variety of organic chemicals, while these biological species may show different sensitivities to different chemical groups present in the environment. This study evaluated species sensitivity, hazards, and risks of six classes of organic chemicals in the aquatic environment. None of the taxonomic groups were the most sensitive or tolerant to all chemicals, as one group sensitive to one class of chemicals might possess adaptations to other chemical groups. Polychlorinated biphenyls were generally the most toxic chemical group, followed by polybrominated diphenyl ethers, polycyclic aromatic hydrocarbons, and pharmaceuticals and personal care products, while per- and polyfluoroalkyl substances and phthalate esters were the less toxic chemical groups. The hazard of organic chemicals was closely related to their physicochemical properties, including hydrophobicity and molecular weight. It was shown that 20% of the evaluated chemicals exhibited medium or high ecological risks with the worst-case scenario in the Pearl River Estuary. This novel work represented a comprehensive comparison of chemical hazards and species sensitivity among different classes of organic chemicals, and the reported results herein have provided scientific evidence for ecological risk assessment and water quality management to protect aquatic ecosystems against organic chemicals.

Uncertainty analysis and updated user guidance for interspecies correlation estimation models and low toxicity compounds

Interspecies correlation estimation (ICE) models are log-linear relationships of acute sensitivity between two species that estimate the sensitivity of an untested species from the known sensitivity of a surrogate. As ICE model use increases globally, additional user guidance is required to ensure consistent use across chemicals and applications. The present study expands ICE uncertainty analyses and user guidance with a focus on low toxicity compounds whose acute values (i.e., reported as mg/L) can be greater than those used to develop a model. In these cases, surrogate values may be outside the ICE model domain and require additional extrapolations to predict acute toxicity. We use the extensive, standardized acute toxicity database underlying ICE models to broadly summarize inter-test variability of acute toxicity data as a measure by which model prediction accuracy can be evaluated. Using the data and models found on the USEPA Web-ICE (www3.epa.gov/webice), we created a set of "truncated" models from data corresponding to the lower 75th percentile of surrogate toxicity. We predicted toxicity for chemicals in the upper 25th percentile as both μg/L beyond the model domain and converted to mg/L (i.e., "scaled" value) and compared these predictions with those from cross-validation of whole ICE models and to the measured value. For ICE models with slopes in the range 0.66-1.33, prediction accuracy of scaled values did not differ from the accuracy of the models when data were entered as μg/L within or beyond the model domain. An uncertainty analysis of ICE confidence intervals was conducted and an interval range of two orders of magnitude was determined to minimize type I and II errors when accepting or rejecting ICE predictions. We updated the ICE user guidance based on these analyses to advance the state of the science for ICE model application and interpretation. Integr Environ Assess Manag 2024;20:1554-1565. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

Characterizing Freshwater Ecotoxicity of More Than 9000 Chemicals by Combining Different Levels of Available Measured Test Data with In Silico Predictions

Ecotoxicological impacts of chemicals released into the environment are characterized by combining fate, exposure, and effects. For characterizing effects, species sensitivity distributions (SSDs) estimate toxic pressures of chemicals as the potentially affected fraction of species. Life cycle assessment (LCA) uses SSDs to identify products with lowest ecotoxicological impacts. To reflect ambient concentrations, the Global Life Cycle Impact Assessment Method (GLAM) ecotoxicity task force recently recommended deriving SSDs for LCA based on chronic EC10s (10% effect concentration, for a life-history trait) and using the 20th percentile of an EC10-based SSD as a working point. However, because we lacked measured effect concentrations, impacts of only few chemicals were assessed, underlining data limitations for decision support. The aims of this paper were therefore to derive and validate freshwater SSDs by combining measured effect concentrations with in silico methods. Freshwater effect factors (EFs) and uncertainty estimates for use in GLAM-consistent life cycle impact assessment were then derived by combining three elements: (1) using intraspecies extrapolating effect data to estimate EC10s, (2) using interspecies quantitative structure-activity relationships, or (3) assuming a constant slope of 0.7 to derive SSDs. Species sensitivity distributions, associated EFs, and EF confidence intervals for 9862 chemicals, including data-poor ones, were estimated based on these elements. Intraspecies extrapolations and the fixed slope approach were most often applied. The resulting EFs were consistent with EFs derived from SSD-EC50 models, implying a similar chemical ecotoxicity rank order and method robustness. Our approach is an important step toward considering the potential ecotoxic impacts of chemicals currently neglected in assessment frameworks due to limited test data. Environ Toxicol Chem 2024;43:1914-1927. © 2024 The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Ecological risk assessment of commonly used antibiotics in aquatic ecosystems along the coast of China

The consistent input of antibiotics into aquatic environments may pose risks to various creatures and ecosystems. However, risk assessment of pharmaceuticals and personal care products (PPCPs) in aquatic environments is frequently limited by the lack of toxicity data. To investigate the risk of commonly used antibiotics to various aquatic creatures, we focused on the distribution patterns and temporal dynamics of antibiotics in the coastal estuary area of China and performed a comprehensive ecological risk assessment for four antibiotics: erythromycin (ERY), tetracycline (TCN), norfloxacin (NOR) and sulfamethoxazole (SMX). An interspecies correlation estimation (ICE)-species sensitivity distribution (SSD) combined model was applied to predict the toxicity data of untested aquatic species, and an accurate ecological risk assessment procedure was developed to evaluate the risk level of PPCPs. The results of risk quotient assessments and probabilistic risk assessments (PRAs) suggested that four objective antibiotics in the Chinese coastal estuary area were at a low risk level. These antibiotics posed a high risk in antibiotic-related global hot spots, with probabilistic risk values for ERY, NOR, SMX, and TCN of 81.33 %, 27.08 %, 21.13 %, and 15.44 %, respectively. We applied an extrapolation method to overcome the lack of toxicity data in ecological risk assessment, enhanced the ecological reality of water quality criteria derivation and reduced the uncertainty of risk assessment for antibiotics.

Prediction of HC5s for phthalate esters by use of the QSAR-ICE model and ecological risk assessment in Chinese surface waters

Due to their endocrine-disrupting effects and the risks posed in surface waters, in particular by chronic low-dose exposure to aquatic organisms, phthalate esters (PAEs) have received significant attention. However, most assessments of risks posed by PAEs were performed at a selection level, and thus limited by empirical data on toxic effects and potencies. A quantitative structure activity relationship (QSAR) and interspecies correlation estimation (ICE) model was constructed to estimate hazardous concentrations (HCs) of selected PAEs to aquatic organisms, then they were used to conduct a multiple-level environmental risk assessment for PAEs in surface waters of China. Values of hazardous concentration for 5% of species (HC5s), based on acute lethality, estimated by use of the QSAR-ICE model were within 1.25-fold of HC5 values derived from empirical data on toxic potency, indicating that the QSAR-ICE model predicts the toxicity of these three PAEs with sufficient accuracy. The five selected PAEs may be commonly measured in China surface waters at concentrations between ng/L and μg/L. Risk quotients according to median concentrations of the five PAEs ranged from 3.24 for di(2-ethylhexhyl) phthalate (DEHP) to 4.10 × 10-3 for dimethyl phthalate (DMP). DEHP and dibutyl phthalate (DBP) had risks to the most vulnerable aquatic biota, with the frequency of exceedances of the predicted no-effect concentration (PNECs) of 75.5% and 38.0%, respectively. DEHP and DBP were identified as having "high" or "moderate" risks. Results of the joint probability curves (JPC) method indicated DEHP posed "intermediate" risk to freshwater species with a maximum risk product of 5.98%. The multiple level system introduced in this study can be used to prioritize chemicals and other new pollutant in the aquatic ecological.

Predicted no-effect concentration for eight PAHs and their ecological risks in seven major river systems of China

The initial step in the assessment of the ecological risk of pollutants is to determine the predicted no-effect concentration (PNEC). However, ecological risk assessments of eight carcinogenic polycyclic aromatic hydrocarbons (PAHs), including dimethylbenz[a]anthracene (DMBA), methylcholanthrene (MCA), benzo(a)anthracene (BaA), chrysene (CHR), benzo(b)fluoranthene (BbF), benzo(k)fluoranthene (BkF), benzo(a)pyrene (BaP), and dibenzo(a,h)anthracene (DBA), are rarely conducted due to the lack of their PNECs based on test data. In this study, quantitative structure-activity relationship (QSAR) models and interspecies correlation estimation (ICE) models were combined to predict the acute toxicity of these eight target PHAs. A Kolmogorov-Smirnov analysis for species sensitivity distributions (SSDs) of native and all species was conducted. There was no significant difference between the predictions for native Chinese species and the predictions for all species by the QSAR-ICE models. In addition, the feasibility of the QSAR-ICE models was demonstrated by comparing the SSD curves constructed by measured toxicity data of BaP and those predicted by the QSAR-ICE models. The PNECs of the eight PAHs were estimated based on the SSDs and acute to chronic ratio (ACR) method; these data were 0.071 μg/L, 0.033 μg/L, 0.049 μg/L, 0.114 μg/L, 0.019 μg/L, 0.021 μg/L, 0.038 μg/L and 0.054 μg/L for DMBA, DBA, BaP, MCA, BaA, CHR, BbF, BkF, respectively. The higher PNECs of the alkylated PAHs suggested their lower ecological risks. Based on the mixed risk quotient (mRQ) of PAHs through the concentration addition (CA) model, high ecological risk watersheds, such as the Songhua River (mRQ = 1.95), the Liao River (mRQ = 4.59), and the Huai River (mRQ = 1.93), were identified.

Ranking the risks of eighty pharmaceuticals in surface water of a megacity: A multilevel optimization strategy

Pharmaceuticals in freshwater posed ecological risks to aquatic ecosystem, however, most risk assessments of pharmaceuticals were conducted at screening level, which were limited by the availability of the toxicity data. In this study, risks of 80 pharmaceuticals including 35 antibiotics, 13 antiviral drugs, 13 illicit drugs, and 19 antidepressants in surface water of Beijing were assessed with a proposed multilevel environmental risk optimization strategy. Target pharmaceuticals were detected in surface water samples with the detection frequency from 1.7 % to 100 % and the total concentrations from 31.1 ng/L to 2708 ng/L. Antiviral drugs were the dominant pharmaceuticals. Preliminary screening-level risk assessment indicated that 20 pharmaceuticals posed low to high risks with risk quotient from 0.14 (chloroquine diphosphate) to 27.8 (clarithromycin). Thirteen pharmaceuticals were recognized with low to high risks by an optimized risk assessment method. Of them, the refined probabilistic risk assessment of joint probability curves coupling with a quantitative structure activity relationship-interspecies correlation estimation (QSAR-ICE) model was applied. Clarithromycin, erythromycin and ofloxacin were identified to pose low risks with maximum risk products (RP) of 1.23 %, 0.41 % and 0.35 %, respectively, while 10 pharmaceuticals posed de minimis risks. Structural equation modeling disclosed that human land use and climate conditions influenced the risks of pharmaceuticals by indirectly influencing the concentrations of pharmaceuticals. The results indicated that the multilevel strategy coupling with QSAR-ICE model was appropriate and effective for screening priority pollutants, and the strategy can be used to prioritize pharmaceuticals and other emerging contaminants in the aquatic environment.

Quantitative multi-species toxicity modeling: Does a multi-species, machine learning model provide better performance than a single-species model for the evaluation of acute aquatic toxicity by organic pollutants?

The toxicological profile of any chemical is defined by multiple endpoints and testing procedures, including representative test species from different trophic levels. While computer-aided methods play an increasingly important role in supporting ecotoxicology research and chemical hazard assessment, most of the recently developed machine learning models are directed towards a single, specific endpoint. To overcome this limitation and accelerate the process of identifying potentially hazardous environmental pollutants, we are introducing an effective approach for quantitative, multi-species modeling. The proposed approach is based on canonical correlation analysis that finds a pair(s) of uncorrelated, linear combinations of the original variables that best defines the overall variability within and between multiple biological responses and predictor variables. Its effectiveness was confirmed by the machine learning model for estimating acute toxicity of diverse organic pollutants in aquatic species from three trophic levels: algae (Pseudokirchneriella subcapitata), daphnia (Daphnia magna), and fish (Oryzias latipes). The multi-species model achieved a favorable predictive performance that were in line with predictive models derived for the aquatic organisms individually. The chemical bioavailability and reactivity parameters (n-octanol/water partition coefficient, chemical potential, and molecular size and volume) were important to accurately predict acute ecotoxicity to the three aquatic organisms. To facilitate the use of this approach, an open-source, Python-based script, named qMTM (quantitative Multi-species Toxicity Modeling) has been provided.

Reliable and Representative Estimation of Extrapolation Model Application in Deriving Water Quality Criteria for Antibiotics

Deriving water quality benchmarks based on the species sensitivity distribution (SSD) is crucial for assessing the ecological risks of antibiotics. The application of extrapolation methods such as interspecies correlation estimation (ICE) and acute-to-chronic ratios (ACRs) can effectively supplement insufficient toxicity data for these emerging contaminants. Acute-to-chronic ratios can predict chronic toxicity from acute toxicity, and ICE can extrapolate an acute toxicity value from one species to another species. The present study explored the impact of two extrapolation methods on the reliability of SSDs by analyzing different scenarios. The results show that, compared with the normal and Weibull distributions, the logistic model was the best-fitting model. For most antibiotics, SSDs derived by extrapolation have high reliability, with 82.9% of R² values being higher than 0.9, and combining ICE and ACR methods can bring a maximum increase of 10% in R² . Based on the results of Monte Carlo simulation, the statistical uncertainty brought by ICE in SSD is 10-40 times larger than that brought by ACR, and combining the two methods could reduce uncertainty. In addition, the sensitivity test showed that whether the toxicity data came from extrapolation or actual measurement, the lower the value of toxicity endpoints was, the greater the bias caused by the corresponding species in every scenario. Combining the two aforementioned extrapolation methods could effectively increase the stability of SSD, with their bias nearly equal to 1. Environ Toxicol Chem 2023;42:191-204. © 2022 SETAC.

Predicting and assessing the toxicity and ecological risk of seven widely used neonicotinoid insecticides and their aerobic transformation products to aquatic organisms

Neonicotinoid insecticides (NIs) are widely used worldwide, accounting for 25 % of the global insecticide market, and are easily transported into surrounding aquatic ecological environments after application. At present, >80 % of surface water is contaminated by NIs globally. Some transformation products (TPs) of NIs can exhibit greater toxicity to aquatic organism than their parent products. However, few studies have evaluated the toxicity and ecological risk of the TPs of NIs. In this study, we aimed to assess the toxicity and ecological risk of seven widely used NIs and their aerobic TPs to aquatic organisms using a prediction method. We found that partial aerobic TPs of NIs have greater toxicity to aquatic organisms than their parent products, and some of them could severely damage aquatic ecosystems. Meanwhile, acetamiprid, thiacloprid, and several other TPs of NIs with a chlorinated ring structure showed strong bioconcentration abilities, which could potentially harm aquatic organisms through the food chain. Moreover, the widespread use of NIs has certain aquatic ecological risks, which should be controlled and limited. This study comprehensively evaluated the ecological risk of seven widely used NIs and their aerobic TPs to aquatic organisms for the first time. Our results could provide an important reference for assessment of the aquatic environmental risk posed by NIs and pollution control.

Chemical characteristics analysis of automobile exhaust particles and the method for evaluating its ecological effect

Automobile exhaust has become the main source of atmospheric particulate matter with the increase in the number of automobiles. Automobile exhaust particles (AEPs) discharged into the atmosphere can enter the aquatic environment through atmospheric deposition, rain runoff, leaching, drainage water and urban sewage and further affect aquatic organisms. However, there is no comprehensive theory and method to evaluate the ecological effects of AEPs on aquatic environment. The new European driving cycle (NEDC) and the world harmonized light-duty test cycle (WLTC) were used to analyze the ecotoxicity of AEPs. The SUV gasoline, SUV hybrid and sedan gasoline under WLTC were used for further analysis. The chemical characteristics of AEPs were analyzed, and the ecotoxicity of AEPs on aquatic organisms was studied with Vibrio fischeri and Danio rerio as test organisms. The ecological effect of AEPs was studied through species sensitivity distribution based on interspecies correlation estimation (ICE) models. The results showed that (ⅰ) polycyclic aromatic hydrocarbons (PAHs) were the main organic substances in AEPs. The total concentrations of PAHs in AEPs measured under the NEDC and WLTC were 237.4 and 159.8 mg kg^-1, respectively, and the EC₅₀ values for V. fischeri measured under the NEDC and WLTC were 42.02 and 47.05 mg L^-1, respectively. (ⅱ) Total heavy metal concentrations in AEPs from SUV gasoline, SUV hybrid, and sedan gasoline were 197.52, 104.86, and 89.68 mg kg^-1, respectively, and the LC₅₀ values for D. rerio were 3.22, 4.46 and 5.62 mg L^-1. Cu and Mn were the main toxic heavy metals in AEPs. (ⅲ) The PNEC values of AEPs from SUV gasoline, SUV hybrid and sedan gasoline were 0.57, 0.83 and 1.02 mg L^-1, respectively. This exploratory study can provide technical information on water ecological safety assessment for determining the impact of AEPs on the surface water environment and for further improving automobile exhaust emission standards.

Hazard assessment of chemical constituents in biocide formulations used in offshore oil and gas operations

Biocides used in offshore oil and gas operations could be present in water discharges, and thus identifying such chemicals and their hazard could help address concerns regarding non-target organisms. Aquatic toxicity data, queried from different sources and augmented with predictive models, were used to develop species sensitivity distributions and their corresponding 5th percentile hazard concentrations (HC5s). Curated data, including over 1000 empirical records for 137 species, indicated no evidence of bias when comparing sensitivity between marine and freshwater species, even when predicted data were used. HC5s facilitated estimation of an acute-to-chronic ratio (ACR = 10), appropriate for most chemicals and useful in filling data gaps. Comparison of chronic-HC5s with the default approach for deriving predicted no effect concentrations showed that the latter systematically overstates aquatic hazard. The present approach shows promise of using acute-to-chronic HC5 ratios for defining assessment factors for different chemical classes, instead of the use of generic assessment factors.

A QSAR-ICE-SSD model prediction of the PNECs for alkylphenol substances and application in ecological risk assessment for rivers of a megacity

Alkylphenols (APs) are ubiquitous and generally present in higher residue levels in the environment. The present work focuses on the development of a set of in silico models to predict the aquatic toxicity of APs with incomplete/unknown toxicity data in aquatic environments. To achieve this, a QSAR-ICE-SSD model was constructed for aquatic organisms by combining quantitative structure-activity relationship (QSAR), interspecies correlation estimation (ICE), and species sensitivity distribution (SSD) models in order to obtain the hazardous concentrations (HCs) of selected APs. The research indicated that the keywords "alkylphenol" and "nonylphenol" were most commonly studied. The selected ICE models were robust (R2: 0.70-0.99; p-value < 0.01). All models had a high reliability cross- validation success rates (>75%), and the HC5 predicted with the QSAR-ICE-SSD model was 2-fold than that derived with measured experimental data. The HC5 values demonstrated nearly linear decreasing trend from 2-MP to 4-HTP, while the decreasing trend from 4-HTP to 4-DP became shallower, indicates that the toxicity of APs to aquatic organisms increases with the addition of alkyl carbon chain lengths. The ecological risks assessment (ERA) of APs revealed that aquatic organisms were at risk from exposure to 4-NP at most river stations (the highest risk quotient (RQ) = 1.51), with the highest relative risk associated with 2.9% of 4-NP detected in 82.9% of the sampling sites. The targeted APs posed potential ecological risks in the Yongding and Beiyun River according to the mixture ERA. The potential application of QSAR-ICE-SSD models could satisfy the immediate needs for HC5 derivations without the need for additional in vivo testing.

Application of combined QSAR-ICE models in calculation of hazardous concentrations for linear alkylbenzene sulfonate

Linear alkylbenzene sulfonate (LAS) is a widely used anionic surfactant that exists as a mixture of various homologous structures in water environment. In the calculation of hazardous concentrations of LAS, cross-taxonomies toxicity estimation was often used instead of species-level-specific estimation for the normalization of toxicity data, which led to substantial uncertainties. In this study, combined quantitative structure-activity relationship (QSAR) and interspecific relationship estimation (ICE) models were developed to normalize the alkyl chain length of toxicity data and calculate the 5th percentile hazard concentrations (HC₅s) of LAS. Using seven acute QSAR models based on measured data and 29 acute QSAR-ICE models derived from them, the acute HC₅s of LAS were calculated as 2.09-3.67 mg/L. Furthermore, species- and family-level-specific QSAR and QSAR-ICE models were used to calculate chronic HC₅s (0.19-0.38 mg/L). Additionally, the sensitivity of biological toxicity to the hydrophobicity of LAS, represented by the slope of the QSAR models, had a significant correlation with the taxa of the species. Further risk assessment based on chronic HC₅s showed potential ecological risks in the Dianchi Lake basin and Haihe River basin in China, which should cause concern.

Water quality criteria for lanthanum for freshwater aquatic organisms derived via species sensitivity distributions and interspecies correlation estimation models

The increasing exploitation and application of rare earth elements (REEs) may induce hazardous risks to freshwater aquatic organisms. Due to the lack of water quality criteria (WQC) and sufficient reliable toxicity data, little information is available on the ecological risk of REEs in surface water. In this study, lanthanum (La) toxicity data were collected from published toxicological studies, and the data quality was assessed using a toxicological data reliability assessment tool. To obtain more toxicity data, Daphnia magna, Cyprinus carpio, and Dania rerio embryos were selected as surrogate species, and an interspecies correlation estimation (ICE) model was used to predict the toxicity of La for untested species. The species sensitivity distributions (SSDs) of La toxicity and WQC were investigated. Differences were observed in the hazardous concentrations for 5% of species (HC₅), but no statistically significant differences were noted in the SSD curves between the measured acute toxicity data and the predicted data. For the SSDs constructed from the measured toxicity data, the ICE-predicted toxicity data and all acute data supplemented with the ICE-predicted data, the acute WQC values of La were 88, 1022 and 256 μg/L, respectively. According to the SSD and corresponding HC₅ of chronic toxicity data, the chronic WQC was 14 μg/L. The results provide a scientific reference for establishing WQC for freshwater aquatic organisms and ecological risk assessments of REEs.

Comparison of modes of toxic action between Rana chensinensis tadpoles and Limnodrilus hoffmeisteri worms based on interspecies correlation, excess toxicity and QSAR for class-based compounds

Insecticides, fungicides, dinitrobenzenes, resorcinols, phenols and anilines are widely used in agricultural and industrial productions. However, their modes of toxic action are unclear in some nontarget organisms, such as worms and tadpoles. In this study, acute toxicity data was experimentally collected for Limnodrilus hoffmeisteri worms and Rana chensinensis tadpoles, respectively. Interspecies correlation and excess toxicity were calculated to determine modes of action (MOAs) between the two species for class-based compounds. The result showed that, although the interspecies correlation of toxicity between the tadpoles and worms is significant with a coefficient of determination (R²) of 0.83, tadpoles are more sensitive than the worms and toxicity values between these two species are not identical with an overall 0.43 log unit difference. Regression analysis revealed that the toxicity of nonpolar narcotics or baseline compounds is linearly related to hydrophobicity for both the tadpoles and worms and the two baseline models are parallel, suggesting that these nonpolar narcotics share the same MOA between the two species. The difference of baseline toxicities between the two species is attributed to differences in bioconcentration factors. Analysis of the excess toxicity calculated from the toxicity ratio (TR) suggested that phenols and anilines can be classified as polar narcotics, not only to fish, but also to the tadpoles and worms. These compounds are more toxic than the baseline compounds and quantitative structure-activity relationship (QSAR) models show that their toxicity is linearly related to chemical hydrophobicity and polarity. Analysis of the excess toxicity reveals that aminophenols and resorcinols can be classified as reactive compounds, and insecticides and fungicides can be classified as specifically-acting compounds for both species. These compounds exhibited significantly greater toxic effect to both the tadpoles and worms. QSAR models have been developed to describe the toxic mechanisms for nonpolar narcotics, polar narcotics, reactive chemicals and specifically-acting compounds, and a theoretical equation has been derived to explain the effect of bio-uptake and interaction of the chemical with target receptors for both tadpole and worm toxicity. Our study reveals that tadpole toxicity can be estimated from worm toxicity data and the two species can serve as surrogates for each other in the safety evaluation of organic pollutants.

Ecological risk assessment for difenoconazole in aquatic ecosystems using a web-based interspecies correlation estimation (ICE)-species sensitivity distribution (SSD) model

Difenoconazole is a typical triazole fungicide that can inhibit demethylation during ergosterol synthesis. Due to its wide use, difenoconazole is frequently detected in surface water, paddy water, agricultural water, and other aquatic environments. Presently, an assessment of the ecological risk posed by difenoconazole in aquatic ecosystems is lacking. Here, a web-based interspecies correlation estimation (ICE)-species sensitivity distribution (SSD) model was first applied to assess the ecological risk of difenoconazole in aquatic environments. Meanwhile, maximum acceptable concentration (MAC), maximum risk-free concentration (MRFC), and risk quotient (RQ) values were used to evaluate the potential risk of difenoconazole to aquatic organisms. Our results showed that an aquatic MAC value of 0.31 μg/L was acceptable for difenoconazole in aquatic environments. Further, the detected concentration of difenoconazole was lower than the MRFC value of 0.09 μg/L indicating no risk to aquatic organisms. Assessment data suggested that difenoconazole exhibited potential risks to eight studied aquatic ecosystems (including surface water, paddy water, and agricultural water) in different countries (RQ > 1), indicating that difenoconazole overuse could cause adverse effects to aquatic organisms in these aquatic ecosystems. Thus, restricted use and rational use of difenoconazole are recommended.

A Critical Review of the Availability, Reliability, and Ecological Relevance of Arctic Species Toxicity Tests for Use in Environmental Risk Assessment

There is a pressing need to understand the impact of contaminants on Arctic ecosystems; however, most toxicity tests are based on temperate species, and there are issues with reliability and relevance of bioassays in general. Together this may result in an underestimation of harm to Arctic organisms and contribute to significant uncertainty in risk assessments. To help address these concerns, a critical review to assess reported effects for these species, quantify methodological and endpoint relevance gaps, and identify future research needs for testing was performed. We developed uniform criteria to score each study, allowing an objective comparison across experiments to quantify their reliability and relevance. We scored a total of 48 individual studies, capturing 39 tested compounds, 73 unique Arctic test species, and 95 distinct endpoints published from 1975 to 2021. Our analysis shows that of 253 test substance and species combinations scored (i.e., a unique toxicity test), 207 (82%) failed to meet at least one critical study criterion that contributes to data reliability for use in risk assessment. Arctic-focused toxicity testing needs to ensure that exposures can be analytically confirmed, include environmentally realistic exposure scenarios, and report test methods more thoroughly. Significant data gaps were identified as related to standardized toxicity testing with Arctic species, diversity of compounds tested with these organisms, and the inclusion of ecologically relevant sublethal and chronic endpoints assessed in Arctic toxicity testing. Overall, there needs to be ongoing improvement in test conduction and reporting in the scientific literature to support effective risk assessments in an Arctic context. Environ Toxicol Chem 2022;41:46-72. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Prediction of chemical reproductive toxicity to aquatic species using a machine learning model: An application in an ecological risk assessment of the Yangtze River, China

The endocrine disrupting chemicals (EDCs) have been at the forefront of environmental issues for over 20 years and are a principle factor considered in every ecological risk assessment, but this kind of risk assessment faces difficulties. The expense, time cost of in vivo tests, and lack of toxicity data are key limiting factors for the ability to conduct ecological risk assessments of EDCs to aquatic species. In this study, a machine learning model named the support vector machine (SVM) was used to predict the reproductive toxicity of EDCs, and the performance of the models was evaluated. The results showed that the SVM model provided more accurate toxicity prediction data compared with the interspecies correlation estimation (ICE) model developed by previous study to predict the reproductive toxicity. The application of the predicted toxicity data was an important supplement to the observed data for the ecological risk assessment of EDCs in the Yangtze River, where estrogens and phenolic compounds have been found at some sampling sites in the middle and lower reaches. The results showed that the ecological risk of estrone, 17β-estradiol, and ethinyl estradiol were significant. This study revealed the application potential of machine learning models for the prediction of reproductive toxicity effects of EDCs. This can provide reliable alternative toxicity data for the ecological risk assessments of EDCs.

Predictive Toxicity Models for Chemically Related Substances: A Case Study with Nonionic Alcohol Ethoxylate Surfactant

Predictive toxicity models, including interspecies correlation estimation (ICE) models, are reliable alternatives to animal toxicity testing. The ICE models describe mathematical relationships facilitating toxicity prediction from one surrogate test species to a species of unknown sensitivity. The ICE models were developed from curated aquatic toxicity data for 19 nonionic alcohol ethoxylate (AE) surfactants manufactured through the same process. Comparison of AE-ICE predictions with observed values from external validation datasets indicates a reasonable predictive accuracy. Model predictions were also closer to observed values than predictions from previously published ICE models for other substance groups. Comparison of acute fifth percentile hazard concentrations (HC5s) from species sensitivity distributions enhanced with AE-ICE predictions with chronic HC5s published elsewhere produced an acute-to-chronic ratio of 5, which was used to estimate chronic HC5s. With both acute and chronic HC5s for 14 AE substances, regressions were made relative to their respective liposome-water partitioning coefficients (log K _lipw ), resulting in HC5-log K _lipw relationships useful in estimating HC5s for AE substances with known composition but with limited or unavailable toxicity data. The findings from this case study further demonstrate that ICE models are viable alternatives to toxicity testing and could be used as weight of evidence in hazard assessment evaluations. Environ Toxicol Chem 2021;40:2073-2082. © 2021 SETAC.

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

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

Development and use of interspecies correlation estimation models in China for potential application in water quality criteria

Establishment of numerical water quality criteria (WQC) has brought increasing interest in China. However, toxicity data to develop robust WQC values (number of toxicity data ≥8) of contaminants based solely on endemic and indigenous species are insufficient. In this study, interspecies correlation estimation (ICE) models were developed using a combination of North American ICE models supplemented with China-specific species to resolve this problem. A total of 207 significant surrogate-predicted models (p < 0.05, F-test) were derived: 119, 66 and 22 models for vertebrates, invertebrates and plant surrogate species, respectively. Model cross-validation success rate (≥80%), mean square error (MSE, ≤ 0.54), R² (≥0.78) and taxonomic distance (≤4, within the same class) were selected as guiding criteria to screen the resulted ICE models. The differences of 5th percentile hazard concentrations (HC₅s) for 6 chemicals (2,4-dichlorophenol, triclosan, tetrabromobisphenol A, nitrobenzene, perfluorooctane sulfonate and octabromodiphenyl ether) calculated from ICE-based and measured toxicity-based SSDs were within 3-fold among models. Although the number of derived ICE models was not comprehensive and continues to be improved, they can already be used in the development of WQC targeting protection of aquatic life and environmental risk assessments for chemicals lacking toxicity data.

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

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

Reliable and representative in silico predictions of freshwater ecotoxicological hazardous concentrations

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

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

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

Further Development and Refinement of Interspecies Correlation Estimation Models for Current-Use Dispersants

Limited species diversity in aquatic toxicity data for most current-use dispersants leads to uncertainties in hazard assessments, which impacts the broader discussion on dispersant use. Sufficient toxicity data are available for a re-evaluation of previously developed dispersant-interspecies correlation estimation (ICE) models. These models increase species diversity because toxicity predictions for untested species are made from the known toxicity for surrogates. Data were used to refine 4 and develop 25 new dispersant-ICE models. Most of the new models are for species not included in the >2000 existing ICE models, and contain a higher species diversity than the original dispersant-ICE models (19 vs 7 species). Dispersant-ICE models predict toxicity with reasonably accuracy: predictions were within 3-fold of observed values (new models: 70% of 132 predictions; refined models: 88% of 83 predictions), and species sensitivity distributions developed with ICE-predicted data only were in most cases not statistically significantly different from those developed with empirical data (83% of 23 paired comparisons). Examples of the practical application of dispersant-ICE models, including laboratory-to-field comparisons within the context of operational dispersant application rates, are also presented. The significance of these results is that dispersant-ICE models could fill gaps in species diversity, and thus help to address concerns about species sensitivities related to the use of dispersants. Environ Toxicol Chem 2019;38:1682-1691. © 2019 SETAC.

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

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

Critical review and analysis of aquatic toxicity data on oil spill dispersants

Oil spill response requires consideration of several countermeasures including chemical dispersants, but their potential toxicity to aquatic species poses a concern. Considerable in vivo aquatic toxicity data from laboratory exposures have been generated since 2010 for current-use dispersants. The objective of the present review is to provide a synthesis of these data to improve dispersant hazard assessments. Data from multiple studies were evaluated based on reliability criteria. Although procedures, standards, endpoints, and statistical approaches were usually described, nearly a quarter of sources did not provide sufficient information to judge study quality but were considered on a case-by-case basis. Data were used to develop dispersant-specific species sensitivity distributions and hazard concentrations protective of 95% of the species (HC5). Given data limitations, post-2010 toxicity data were augmented with pre-2010 data and model predictions. The HC5s calculated for 54 dispersants fell mostly within the moderate to slightly toxic range and were compared to field dispersant-only concentrations estimated from operational application rates under conservative assumptions. Based on available evidence, dispersants may not pose a significant risk under field conditions to most aquatic species, if proper application and dilution are taken into account. Recommendations on improved toxicity testing and reporting as well as research needs are also provided. Environ Toxicol Chem 2018;37:2989-3001. © 2018 SETAC.