Interspecies Correlation Estimation Research Articles

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.

Deriving ecological risk thresholds for soil molybdenum in China based on interspecies correlation estimation and quantitative ion character–activity relationship models

Soil molybdenum (Mo) levels can reach ecologically hazardous levels. China has not yet established the relevant thresholds, posing challenges for environmental management. Therefore, we present our data relevant to Mo toxicity for several important species. By normalizing soil properties, we obtained a correlation model of Mo toxicity to Hordeum vulgare, as well as 31 models for the toxicity of other elements including Cu and Ni to invertebrates and microbial processes. Using interspecies correlation estimation (ICE) extrapolation, the sensitivity coefficient (0.12–0.71) for five plants were found. For invertebrates and microbial processes lacking Mo data, we used regression analysis to establish Mo toxicity models based on the soil quantitative ion character–activity relationships (s–QICAR; R2 =0.70–0.95) and known toxicities of other metal elements to invertebrate and microbial processes. Furthermore, combining species sensitivity distribution calculations, the HC5 values for protecting 95% of soil species from Mo in three typical soil scenarios in China were calculated. After correction, the predicted no-effect concentrations were 6.8, 4.8, and 3.4 mg/kg, respectively. This study innovatively combined ICE and s − QICAR to derive soil Mo thresholds. Our results can provide a basis for decision-making in the assessment and management of soil Mo pollution.

Aquatic life criteria of hydrothermal liquefaction wastewater via ecotoxicity test and modeling

Wastewater resulting from hydrothermal liquefaction (HTL-AP) of biowaste is gaining attention as an emerging hazardous material. However, there is a lack of specific and systematic ecotoxicity studies on HTL-AP. This study addresses this gap by conducting acute toxicity tests on HTL-AP using typical aquatic species and integrating these results with predicted toxicity values from interspecies correlation estimation models to establish aquatic life criteria. HTL-AP exhibited significant toxicity with LC50 of 956.12–3645.4 mg/L, but demonstrated moderate toxicity compared to common freshwater pollutants like commercial microbicides, personal care products, and insect repellents. The resulting hazardous concentration for 5 % of species (HC5), the criterion maximum concentration, and the short-term water quality criteria for aquatic were 506.0, 253.0, and 168.7 mg/L, respectively. Notably, certain organisms like Misgurnus anguillicaudatus and Cipangopaludina chinensis showed high tolerance to HTL-AP, likely due to their metabolic capabilities on HTL-AP components. The significant decrease in HC5 values for some HTL-AP substances compared to pure compounds could indicate the synergistic inhibition effects among HTL-AP compositions. Furthermore, according to the established criteria, HTL-AP required significantly less diluted water (13 t) than carbendazim (1009 t) to achieve biosafety, indicating a safer release. This research establishes a preliminary water quality criterion for HTL-AP, offering a valuable reference for risk assessment and prediction in the utilization of HTL-AP within environmental contexts.

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.

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.

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.

Original and improved interspecies correlation estimation models in China for potential application in water quality criteria.

Fluoranthene (FLU) has gained much attention in recent years because of its continuous discharge in natural waters and toxicity to aquatic ecosystems. However, it is difficult to control and manage FLU pollution because of the lack of a rational and scientific water quality criteria (WQC) of FLU. To solve these data gaps, the US EPA established an interspecies correlation estimation (ICE) model, which can be utilized to develop the SSD and HC5 (hazardous concentration, 5th percentile). Moreover, an improved model was developed using a combination of North American ICE models supplemented with China-specific species. In this study, to verify the applicability of the two ICE models, measured acute toxicity data for FLU were obtained from 9 acute toxicity tests using indigenous Chinese aquatic species from different taxonomic levels. Original and improved ICE-based SSD curves, which were generated using 3 surrogate species (Daphnia magna, Oncorhynchus mykiss, and Lepomis macrochirus), were compared with SSD curves based on measured data. The results showed that HC5 was 1.838, 1.062, and 0.570mg/L for the original ICE, improved ICE, and measured data, respectively. The improved ICE-based HC5 value for FLU was within twofold of the HC5 value based on measure data, while the original ICE-based HC5 value was threefold higher than the HC5 value based on measure data. This indicated that the improved ICE had better predictability in extrapolating data with acceptable deviation than the original ICE. Furthermore, their differences between HC5 derived from two SSD curves were not significant. Generally, the improved ICE model was verified as a valid approach for generating SSDs with limited toxicity data and for deriving WQC for FLU.

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.

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.

Use of Interspecies Correlation Estimation (ICE) Models to Derive Water Quality Criteria of Microplastics for Protecting Aquatic Organisms.

Microplastics (MPs) in the water environment pose a potential threat to aquatic organisms. The Species Sensitivity Distribution (SSD) method was used to assess the ecological risks of microplastics on aquatic organisms in this study. However, the limited toxicity data of aquatic organisms made it impossible to derive water quality criteria (WQC) for MPs and difficult to implement an accurately ecological risk assessment. To solve the data gaps, the USEPA established the interspecies correlation estimation (ICE) model, which could predict toxicity data to a wider range of aquatic organisms and could also be utilized to develop SSD and HC5 (hazardous concentration, 5th percentile). Herein, we collected the acute toxicity data of 11 aquatic species from 10 families in 5 phyla to fit the metrical-based SSDs, meanwhile generating the ICE-based-SSDs using three surrogate species (Oncorhynchus mykiss, Hyalella Azteca, and Daphnia magna), and finally compared the above SSDs, as well as the corresponding HC5. The results showed that the measured HC5 for acute MPs toxicity data was 112.3 μg/L, and ICE-based HC5 was 167.2 μg/L, which indicated there were no significant differences between HC5 derived from measured acute and ICE-based predicted values thus the ICE model was verified as a valid approach for generating SSDs with limited toxicity data and deriving WQC for MPs.

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.

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 QSAR-ICE-SSD Model Prediction of the PNECs for Per- and Polyfluoroalkyl Substances and Their Ecological Risks in an Area of Electroplating Factories.

Per- and polyfluoroalkyl substances (PFASs) are a class of highly fluorinated aliphatic compounds that are persistent and bioaccumulate, posing a potential threat to the aquatic environment. The electroplating industry is considered to be an important source of PFASs. Due to emerging PFASs and many alternatives, the acute toxicity data for PFASs and their alternatives are relatively limited. In this study, a QSAR–ICE–SSD composite model was constructed by combining quantitative structure-activity relationship (QSAR), interspecies correlation estimation (ICE), and species sensitivity distribution (SSD) models in order to obtain the predicted no-effect concentrations (PNECs) of selected PFASs. The PNECs for the selected PFASs ranged from 0.254 to 6.27 mg/L. The ΣPFAS concentrations ranged from 177 to 983 ng/L in a river close to an electroplating industry in Shenzhen. The ecological risks associated with PFASs in the river were below 2.97 × 10−4.

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.

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.

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.

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

ABSTRACTEcological 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.

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.