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Development of interspecies correlation estimation (ICE) models to predict the reproduction toxicity of EDCs to aquatic species
Chemosphere 224 (2019) 833e839
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Development of interspecies correlation estimation (ICE) models to predict the reproduction toxicity of EDCs to aquatic species Juntao Fan a, Zhenguang Yan a, *, Xin Zheng a, Jin Wu b, Shuping Wang a, Pengyuan Wang a, Qiuying Zhang a a b
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China College of Architecture and Civil Engineering, Beijing University of Technology, Beijing, 100124, China
h i g h l i g h t s The ICE methodology was applied to predict the reproduction toxicity of EDCs to aquatic species. Six statistically significant ICE models with relatively high cross-validation success rate indicated that the model fit was robust. The action of EDCs for each species pair might involve the same mechanisms, and taxonomic relationships did not influence the prediction precision. A proven ICE model can greatly increase the amount of EDCs chronic toxicity data for predicted species.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 January 2019 Received in revised form 1 March 2019 Accepted 2 March 2019 Available online 3 March 2019
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. © 2019 Elsevier Ltd. All rights reserved.
Handling Editor: Jim Lazorchak Keywords: Endocrine disrupting chemicals Interspecies correlation estimation Reproduction toxicity Aquatic species Ecological risks
1. Introduction More and more evidence shows that increasing numbers of chemicals that constitute endocrine disrupting chemicals (EDCs) enter aquatic ecosystems and threaten the wellbeing of aquatic organisms (McLachlan, 2001; Hutchinson et al., 2006; Norris and Carr, 2006). EDCs are defined as exogenous substances or mixtures that can cause adverse health effects on individuals or
* Corresponding author. E-mail address: [email protected] (Z. Yan). https://doi.org/10.1016/j.chemosphere.2019.03.007 0045-6535/© 2019 Elsevier Ltd. All rights reserved.
populations of organisms, or their offspring, and the endpoints mainly involve specie's primary sexual characteristics related to reproduction (Futran Fuhrman et al., 2015). EDCs are mostly easily enriched in the ecosystem through the food chain, so even at very low concentrations can influence the synthesis, secretion, transport, binding, action or decomposition of hormones, and thereby affecting the reproduction of organisms, which has caused longterm concern for the ecological risks of EDCs (Cline et al., 2002; Devillers, 2009; Dang, 2016). Hazard assessment is usually based on the traditional endpoints of toxicity such as survival, development and growth, which do not provide adequate protection, because EDCs influence reproductive
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ability at concentrations lower than those associated with longevity and development (Caldwell et al., 2008; Jin et al., 2014; Futran Fuhrman et al., 2015). However, the reproductive toxicity data of EDCs were mostly collected from experiments related to the life history or part of the life history of organisms, which have a long cycle and high cost, making it difficult to accumulate enough reproductive toxicity data of EDCs in a short time. This has restricted our comprehension of the sensitivity of species towards most of the EDCs infiltrating ecosystems, making sensible decisions more difficult (Wright-Walters et al., 2011; Bejarano and Barron, 2016). One method for addressing the uncertainty in species sensitivity is the development and implementation of the Interspecies Correlation Estimation (ICE) model. This model predicts species sensitivity to listed taxa by means of least squares regression of the sensitivity of a substitute species and a projected taxon (species, genus or family) (Bejarano and Barron, 2014; Willming et al., 2016). The ICE model was originally developed by the US Environmental Protection Agency (USEPA). A network online prediction tool was available after continuous development and upgrade, with built-in prediction modules for aquatic organisms and wildlife (Asfaw et al., 2003). The ICE model allows for accurate predictions of toxicity and produces protective toxicity estimates, such as species sensitivity distribution (SSD) and HC5 (hazardous concentration, 5th percentile), for evaluating contaminant threat to species of interest (Golsteijn et al., 2012; Wu et al., 2015; Brill et al., 2016). While ICE models exist for a variety of chemicals, they were not established for assessing the reproductive toxicity of EDCs. Fish reproduction fitness was determined to be the most sensitive endpoint in aquatic organisms (Caldwell et al., 2008). Vitellogenin (VTG), gonadso-matic index (GSI), secondary sexual characteristics, steroid concentrations in blood plasma and gonadal histology are believed to be the reproduction biomarkers for measuring the endpoints for assessing the threats of EDCs, but changes of these endpoints require long-term observations (Hutchinson et al., 2006). Therefore, semi-lethal concentration (LC50) and semi-effect concentration (EC50) cannot be used to measure the chronic reproductive toxicity of EDCs. The no observable effects concentration (NOEC) is considered appropriate to measure this effect instead, while the collection of the chronic toxicity data requires longer time (Caldwell et al., 2008; Jin et al., 2011). This has led to a lack of EDCs reproductive toxicity data, making it difficult to assess the ecological risk of EDCs. The aims of the present analysis were to evaluate if an EDC ICE model could be established using the available data collected from existing toxicity databases with acceptable uncertainty. The models and methods here can be used to predict the unknown reproductive toxicity of EDCs to a wide range of aquatic species, which facilitates the development of EDCs benchmarks that effectively protect aquatic ecosystems, thereby reducing their ecological risks.
species existed, the geometric mean value was selected. To establish the ICE models, seven most commonly used model organisms were selected for their relatively more reproductive toxicity data, including Brachydanio rerio (B. rerio), Cyprinus carpio (C. carpio), Pimephales promelas (P. promelas), Oncorhynchus mykiss (O. mykiss), Oryzias latipes (O. latipes), Daphnia magna (D. magna) and Ceriodaphnia dubia (C. dubia). All species are paired with each other by common chemical. 2.2. Model development A diagram is provided to illustrate the main steps associated with the development, selection and confirmation of ICE models (Fig. 1) in order to facilitate the understanding of the methodology applied here. T tests, correlation analysis and other statistical methods were applied in describing the establishment of ICE models (Dyer et al., 2006; Raimondo et al., 2007). Each pair of species, containing three or more common chemicals in both as required, were used to develop the linear regression models. The relationship between sensitivity of the surrogate and predicted species of each ICE model are described as: Log10 (Predicted Toxicity) ¼ a Log10 (Surrogate Toxicity) þ b
(1)
Here, Predicted Toxicity represents the NOEC value of the projected taxa, Surrogate Toxicity is the NOEC value of the substitute species, a is the slope of the regression line, and b represents the intercept. 2.3. Model verification In the current analysis, the NOEC value derived from endpoints of the reproduction data were used to construct the ICE models. Only statistically significant ICE models (p-value 0.05) were included in further analysis. The models were subsequently evaluated by the regression diagnostics analysis for potential influential data before the model validation. Based on the above analysis, the consistency and extrapolative ability of ICE models with statistical
2. Material and methods 2.1. EDCs reproduction toxicity data collection Reproduction toxicity data (endpoint of NOEC) for EDCs were obtained from available toxicity databases (ECOTOX Database, http://cfpub.epa.gov/ecotox/). Due to the limited number of toxicity data, data representing all the different biomarkers of reproductive toxicity were collected, in order to meet the requirement of ICE model development (Raimondo et al., 2016). When no NOEC could be obtained, the maximum acceptable toxic concentration (MATC) or minimum observable effects concentration (LOEC) was applied instead (Hutchinson et al., 2006; Caldwell et al., 2008; Jin et al., 2014). Where various data points on the same endpoint and
Fig. 1. Model building diagram describing the process associated with the development, selection and verification of ICE models.
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significance were evaluated using leave-one-out cross-validation (Fig. 1). In this approach, each pair of NOEC values for substitute and projected species are methodically eliminated from the initial model (Raimondo et al., 2007, 2010; Awkerman et al., 2009). The model was rebuilt based on the remaining data, and the toxicity value of the eliminated projected species was estimated using the corresponding substitute toxicity value. The “N-fold” difference of each predicted and real value is applied to evaluate the precision of the projected toxicity value in order to assess the consistency among models. A previous study showed that inter-laboratory variance in acute toxicity experimental data for a certain species and chemical can reach as much as a 5-fold difference for aquatic organisms (Raimondo et al., 2007). In the current analysis, the NOEC, chronic reproductive toxicity, was derived and because some aquatic organisms can be adaptive to chronic toxic stress, the interlaboratory variation of chronic toxicity test data should be even greater (Lotufo et al., 2018). A 10-fold difference is thus considered a suitable fit of predicted ICE values. Finally, the ICE models with pvalue 0.05 and cross-validation successful rate 60% were selected. 3. Results 3.1. EDCs data compilation and pairing There are different numbers of chemicals with reproductive toxicity among the 7 species belonging to 4 families (Table 1). The largest number of data were collected from the Daphniidae, with 453 data in D. magna and 132 data in C. dubia, followed by 132 data in O. latipes, 83 data in P. promelas, 71 data in B. rerio in Cyprinidae, and 42 data in O. mykiss belonging to the Salmonidae. C. carpio has the least data, only 4 data. The results showed that Daphniidae, especially D. magna was the most commonly used specie for testing reproduction toxicity. The number of common chemicals of pairing species showed that the D. magna - C. dubia pair has the largest number, with 67 common chemicals, while the B. rerio - C. carpio pair, and the B. rerio - P. promelas pair have the least number, with 2 and 2 common chemicals, respectively. 3.2. Development and verification of the model Out of 26 models with sufficient data, 16 models in total for 6 surrogate species exhibited statistical significance (P-value < 0.05) (Table 2). The square correlation coefficient (R2) of statistically significant models ranges from 0.311 to 0.985, and the positive
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slope of statistically significant models ranges from 0.287 to 1.1 (Table 2). The distribution of model parameters shows that 87.5% of all intercepts ranged from 0.8 and 0.8, and that 93.8% of all slopes were within 0.5e1.5. This comparatively restricted variance indicates the likeness across the majority of models. An exception was the pair of D. magna- O. latipes, where the slope (0.287) and intercept (1.411) of the same model were both beyond these ranges, with the smallest R2 ¼ 0.311. The standard regression diagnostics method was applied to validate the statistically significant ICE models. The distribution of the standardized residuals of each significant model (Fig. 2) indicated that the residuals are all within the horizontal band, with 0 as the center and ±4 as the width, and mostly lie between 2 and 2, only few residuals outside this range, suggesting that the variance of the error term was consistent. The leave-one-out cross-validation results showed that 37.5% (6 models) of the 16 models had a cross-validation success rate > 60% (Fig. 3). The 6 validated models showed an even narrower variability of parameters compared with the result derived from the 16 statistically significant models, with a square correlation coefficient (R2) ranging from 0.617 to 0.829, and a positive slope ranging from 0.74 to 1.1. This result suggests more similarities across the validated models. To further evaluate the consistency and predictive ability of the statistically significant models, the MSE cutoffs linked to crossvalidation success rates of 70% and 60% were identified and were assumed to have high reliability and moderate reliability, respectively (Fig. 4). Accordingly, the MSE cutoff was 0.6 and 0.9, respectively. Only 6.3% (one model) of ICE models had MSE0.6 with the high reliability, while over 30% (5 models) had MSEs between 0.6 and 0.9 with the moderate reliability. All of the models were selected to discuss the impact of taxonomic distance on the model reliability (Fig. 5). The model of B. rerio - O. latipes pair, with the high reliability (R2 ¼ 0.829 and cross-validation success rate ¼ 78%) has a taxonomic distance ¼ 4 between predicted and surrogate species. The moderate reliable model of P. promelas - D. magna pair (R2 ¼ 0.617 and crossvalidation success rate ¼ 71%) has a taxonomic distance ¼ 6 between predicted and surrogate species. The moderate reliable model of D. magna - C. dubia pair (R2 ¼ 0.732 and cross-validation success rate ¼ 69%) has a taxonomic distance ¼ 2. The moderate reliable model of O. mykiss - P. promelas pair (R2 ¼ 0.985 and crossvalidation success rate ¼ 60%) has a taxonomic distance ¼ 4. The regression analysis showed that the taxonomic distance had no significant impacts on R2 and cross-validation success rate (p
Table 1 List of selected specie pairs and the information of common chemicals (number of common chemicals 5). Specie pairs
Number of common chemicals
Chemical classes*
Number of NOEC
Number of LOEC or MATC
B. rerio-P. promelas B. rerio-O. latipes B. rerio-D.magna B. rerio-C. dubia P. promela-O. mykiss P. promela-O. latipes P. promela-D.magna P. promela-C. dubia O. mykiss-D.magna O. mykiss-C. dubia O. latipes-D.magna O. latipes-C. dubia D. magna-C. dubia
10 9 34 16 5 12 38 19 18 7 17 11 67
A; A; A; A; A; A; A; A; A; A; A; A; A;
10 9 34 14 4 12 38 18 18 7 17 10 67
0 0 0 2 1 0 0 1 0 0 0 1 0
B; C; D; E; F B; D; G; H B;C; D;E; F;H; G; I; J; K; L B;C; E;F; G; H; I;K F; J; M B; D; F; N; O; P B;C; D;E; F;G; H; J; K; L; M; O; P B;C; E;G; H; I;K; M; N B;C; E;F; G; H; I;K; M D; F; B; D; F; G; M; Q; O; R; T B; D; G; F; N; O B; C; D; E; F; G; H; I; J; K; L; M; N; O; P; Q; R; S; T; U
Chemical classes*: A: Steroids; B:Phenols; C: Nitramine; D: Pesticide; E: Surfactants; F: Inorganic compounds; G: Nonsteroidal antiinflammatory drugs; H: Fluoxetine; I: Aniline Compounds; J: Monocyclic aromatic compounds; K: Antibiotics; L: Oxidant; M: Plasticizer; N: Polycyclic aromatic hydrocarbons; O: Propanalol; P: Ketoconazole; Q: Choline; R: Phenyl ether; T: Antineoplastic agents; U: Carbon nanotubes.
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Table 2 Results of Interspecies Correlation Estimation (p. value 0.05). Species.X
Species.Y
R2
p.value
intercept
slope
Cross-validation success rate
B. rerio P. promelas B. rerio O. latipes P. promelas O. mykiss P. promelas D. magna P. promelas C. dubia O. mykiss D. magna O. latipes D. magna D. magna C. dubia
P. promelas B. rerio O. latipes B.rerio O. mykiss P. promelas D. magna P. promelas C. dubia P. promelas D. magna O. mykiss D. magna O. latipes C. dubia D. magna
0.66 0.66 0.83 0.83 0.98 0.98 0.62 0.62 0.59 0.59 0.50 0.50 0.31 0.31 0.73 0.73
0.0080 0.0080 0.0020 0.0020 0.0080 0.0080 8.4E-9 8.4E-9 0.00020 0.00020 0.0010 0.0010 0.025 0.025 6.0E-20 6.0E-20
0.23 0.75 0.80 0.74 0.43 0.38 0.75 0.10 1.3 0.54 0.16 0.79 0.34 1.4 0.19 0.39
0.63 1.0 0.75 1.1 1.1 0.92 0.83 0.74 0.69 0.85 0.72 0.70 1.1 0.29 0.91 0.81
40% 10% 78% 67% 40% 60% 71% 66% 53% 53% 39% 56% 53% 47% 69% 69%
Fig. 2. Diagnostic analysis of homogeneity of variances.
value > 0.05). The results indicate that the reliability of these ICE models maybe independent of the taxonomic distance. The consistency between predicted and observed reproduction toxicity derived from the reliable models proved the accuracy of cross-validation success rate (Fig. 6). Sixty-seven percent of the projected toxicity values fell within a 10-fold difference of the experimental data. Thirty-three percent of predicted toxicity values exhibited >10-fold difference from the experimental data. The mean fold-difference values were 4.1 and 19.1 for taxonomic distances 4 and 2, respectively, which also indicates that taxonomic relationships did not influence the prediction precision. 3.3. Potential practical application of ICE models One of the biggest benefits of applying a proven ICE model is that it greatly increases the amount of EDCs data for predicted species, without the need for extensive animal experiments, thus providing data for rapid assessment of EDCs ecological risks. For example, there are largest numbers of EDCs data (453 data) in the D. magna. The proven ICE models derived from D. magna - C. dubia (132 data) and D. magna - P. promelas (83 data) surrogate-predicted pairs, can increase the EDCs data 88% and 400% for C. dubia and P. promelas, respectively. These increased data facilitate the generation of protective toxicity estimates such as ICE-based SSD for ecological risk assessment. This is essential for the aquatic
Fig. 3. The ICE models with p-value < 0.05 and cross-validation success rates >60%.
ecosystem management, because after the effective control of traditional pollutants (such as ammonia nitrogen), attention should be paid to the control of some chemicals may impair reproductive health of aquatic species at much lower concentrations, and the ICE model will be an important supporting tool. 4. Discussion The availability of reliable toxicity information is a significant challenge in assessing the possible threats of EDC exposure and its influence on the reproduction health of aquatic species. Due to limited data, the water environmental benchmark of EDCs cannot be developed from a reproductive health perspective. These
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Fig. 6. Comparison of observed and ICE-predicted toxicity values for EDCs. The solid line represents the 1:1 line (equal toxicity), while the dotted lines represent a 10-fold difference between these values. Fig. 4. Assessment of model reliability based on the relationship between model square error (MSE) and cross-validation success rate for ICE models.
benchmarks are much lower than the acute concentrations affecting the survival of aquatic organisms and are easily overlooked, but the aquatic ecosystems may be impaired by a long-term cumulative effects (Coelho et al., 2013; Feng et al., 2013; Bejarano et al., 2017). The current study developed ICE-based estimation models for EDC reproduction toxicity that are applicable to a wide range of aquatic organisms to overcome this limitation. The statistically significant ICE models with 60%e78% cross-validation success rates had a relatively low MSE (range 0.25e0.9), which suggests that the model fit is robust. These model relationships imply that similar modes of action of EDCs for each species pair might exist. A previous study showed that inter-laboratory changes in acute toxicity test data for a provided species and chemical can reach as
high as a 5-fold difference for aquatic organisms (Raimondo et al., 2007; Bejarano and Barron, 2014). The predicted-observed differences data should be greater in the reproduction toxicity tests, because some aquatic organisms can be adaptive to chronic toxic stress (Lotufo et al., 2018). Thus, a difference of 10-fold is considered a suitable fit of predicted ICE values in the current study. The result showed that 67% of the projected values from the ICE models fell within 10-fold difference of the experimental data, which indicates that it is feasible and promising to predict the reproductive toxicity of EDCs using the ICE models. Taxonomic distance was earlier demonstrated to impact model performance and the consistency of ICE models based on chemicals with diverse action mechanisms (Raimondo et al., 2007, 2010). In contrast, EDC-specific ICE models developed in this study indicated that taxonomic relatedness did not influence model performance. These findings imply that the ICE models established with EDCs with a mutual generic mechanism of chemical action, such as the
Fig. 5. The relationship between taxonomic distance and model reliability.
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same endpoint, are suitable for use in predicting toxicity over a wide variety of aquatic species, and can facilitate the enhancement of estimations over ICE models with diverse mechanisms of action (Bejarano and Barron, 2014). Much more ICE models have been previously developed to predict acute toxicity to generate SSD, and the robust models among them were selected to predict the toxicity for kinds of chemicals, such as petroleum and dispersant (Bejarano and Barron, 2014), zinc (Feng et al., 2013), ibuprofen and sulfamethoxazole (Futran Fuhrman et al., 2015), Benzo[a]pyrene (Wu et al., 2016). Most of these models showed better performance than the models did in the current study. The main reason is that the number of acute toxicity data is much larger than the chronic toxicity data of EDCs used to construct the ICE model. Previous studies have shown that regardless of the algorithm and type of feature, both the precision of the prediction and its stability increased exponentially with increases in sample size (Cui and Gong, 2018; Varoquaux, 2018). This relatively small data size also caused another uncertainty, that is the different biomarkers or exposure levels were used together to develop the models rather than considering them separately. Previous studies showed that different biomarkers or exposure levels may make difference for deriving a toxicity data (Futran Fuhrman et al., 2015; Dang, 2016). However, few established standards are available for the long-term chronic exposure to EDCs. In future, the improvement in unified standard, as well as the increasing number of data are all likely to improve the precision and replicability of the EDCs ICE models, and make it possible to develop a model targeting a particular biomarker. Insufficient chronic toxicity data of EDCs to construct the ICE model, however, may be rapidly replaced by model predictions without the need of animal experiments; for instance, by using quantitative structure activity relationships (QSARs) derived from the chemical structure. QSAR predictions affords proxy values for the establishment of EDC ICE models, or alternatively could be applied to the development of QSAR substitute predicted species ICE models (Dyer et al., 2006; Chicu and Putz, 2009). One of the improvements in ICE models in the future might include the increased accessibility of comprehensive structural chemistry findings, which are presently lacking in the majority of available EDC toxicity data (Bejarano and Barron, 2014). 5. Conclusion We applied the ICE methodology to predict the reproduction toxicity of EDCs to aquatic species. In total, 16 ICE models of EDCs for 6 surrogate species were statistically significant, and 37.5% (6 models) of the 16 models had a cross-validation success rates >60%, indicating that the model fit is robust. These robust ICE model relationships indicate that similar modes of action for each species pair might exist, and the taxonomic relationships did not influence the prediction precision. The high cross-validation success rate corroborated the similarity between the projected and experimental values for the EDC ICE models. The result 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 data for rapid assessment of EDCs ecological risks. Acknowledgment This study was financially supported by the Major Science and Technology Program for Water Pollution Control and Treatment (grant no. 2017ZX07301002-01). The authors thank 3 anonymous referees for their valuable comments on the manuscript.
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Chemosphere journal homepage: www.elsevier.com/locate/chemosphere
Development of interspecies correlation estimation (ICE) models to predict the reproduction toxicity of EDCs to aquatic species Juntao Fan a, Zhenguang Yan a, *, Xin Zheng a, Jin Wu b, Shuping Wang a, Pengyuan Wang a, Qiuying Zhang a a b
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China College of Architecture and Civil Engineering, Beijing University of Technology, Beijing, 100124, China
h i g h l i g h t s The ICE methodology was applied to predict the reproduction toxicity of EDCs to aquatic species. Six statistically significant ICE models with relatively high cross-validation success rate indicated that the model fit was robust. The action of EDCs for each species pair might involve the same mechanisms, and taxonomic relationships did not influence the prediction precision. A proven ICE model can greatly increase the amount of EDCs chronic toxicity data for predicted species.
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a b s t r a c t
Article history: Received 9 January 2019 Received in revised form 1 March 2019 Accepted 2 March 2019 Available online 3 March 2019
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. © 2019 Elsevier Ltd. All rights reserved.
Handling Editor: Jim Lazorchak Keywords: Endocrine disrupting chemicals Interspecies correlation estimation Reproduction toxicity Aquatic species Ecological risks
1. Introduction More and more evidence shows that increasing numbers of chemicals that constitute endocrine disrupting chemicals (EDCs) enter aquatic ecosystems and threaten the wellbeing of aquatic organisms (McLachlan, 2001; Hutchinson et al., 2006; Norris and Carr, 2006). EDCs are defined as exogenous substances or mixtures that can cause adverse health effects on individuals or
* Corresponding author. E-mail address: [email protected] (Z. Yan). https://doi.org/10.1016/j.chemosphere.2019.03.007 0045-6535/© 2019 Elsevier Ltd. All rights reserved.
populations of organisms, or their offspring, and the endpoints mainly involve specie's primary sexual characteristics related to reproduction (Futran Fuhrman et al., 2015). EDCs are mostly easily enriched in the ecosystem through the food chain, so even at very low concentrations can influence the synthesis, secretion, transport, binding, action or decomposition of hormones, and thereby affecting the reproduction of organisms, which has caused longterm concern for the ecological risks of EDCs (Cline et al., 2002; Devillers, 2009; Dang, 2016). Hazard assessment is usually based on the traditional endpoints of toxicity such as survival, development and growth, which do not provide adequate protection, because EDCs influence reproductive
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ability at concentrations lower than those associated with longevity and development (Caldwell et al., 2008; Jin et al., 2014; Futran Fuhrman et al., 2015). However, the reproductive toxicity data of EDCs were mostly collected from experiments related to the life history or part of the life history of organisms, which have a long cycle and high cost, making it difficult to accumulate enough reproductive toxicity data of EDCs in a short time. This has restricted our comprehension of the sensitivity of species towards most of the EDCs infiltrating ecosystems, making sensible decisions more difficult (Wright-Walters et al., 2011; Bejarano and Barron, 2016). One method for addressing the uncertainty in species sensitivity is the development and implementation of the Interspecies Correlation Estimation (ICE) model. This model predicts species sensitivity to listed taxa by means of least squares regression of the sensitivity of a substitute species and a projected taxon (species, genus or family) (Bejarano and Barron, 2014; Willming et al., 2016). The ICE model was originally developed by the US Environmental Protection Agency (USEPA). A network online prediction tool was available after continuous development and upgrade, with built-in prediction modules for aquatic organisms and wildlife (Asfaw et al., 2003). The ICE model allows for accurate predictions of toxicity and produces protective toxicity estimates, such as species sensitivity distribution (SSD) and HC5 (hazardous concentration, 5th percentile), for evaluating contaminant threat to species of interest (Golsteijn et al., 2012; Wu et al., 2015; Brill et al., 2016). While ICE models exist for a variety of chemicals, they were not established for assessing the reproductive toxicity of EDCs. Fish reproduction fitness was determined to be the most sensitive endpoint in aquatic organisms (Caldwell et al., 2008). Vitellogenin (VTG), gonadso-matic index (GSI), secondary sexual characteristics, steroid concentrations in blood plasma and gonadal histology are believed to be the reproduction biomarkers for measuring the endpoints for assessing the threats of EDCs, but changes of these endpoints require long-term observations (Hutchinson et al., 2006). Therefore, semi-lethal concentration (LC50) and semi-effect concentration (EC50) cannot be used to measure the chronic reproductive toxicity of EDCs. The no observable effects concentration (NOEC) is considered appropriate to measure this effect instead, while the collection of the chronic toxicity data requires longer time (Caldwell et al., 2008; Jin et al., 2011). This has led to a lack of EDCs reproductive toxicity data, making it difficult to assess the ecological risk of EDCs. The aims of the present analysis were to evaluate if an EDC ICE model could be established using the available data collected from existing toxicity databases with acceptable uncertainty. The models and methods here can be used to predict the unknown reproductive toxicity of EDCs to a wide range of aquatic species, which facilitates the development of EDCs benchmarks that effectively protect aquatic ecosystems, thereby reducing their ecological risks.
species existed, the geometric mean value was selected. To establish the ICE models, seven most commonly used model organisms were selected for their relatively more reproductive toxicity data, including Brachydanio rerio (B. rerio), Cyprinus carpio (C. carpio), Pimephales promelas (P. promelas), Oncorhynchus mykiss (O. mykiss), Oryzias latipes (O. latipes), Daphnia magna (D. magna) and Ceriodaphnia dubia (C. dubia). All species are paired with each other by common chemical. 2.2. Model development A diagram is provided to illustrate the main steps associated with the development, selection and confirmation of ICE models (Fig. 1) in order to facilitate the understanding of the methodology applied here. T tests, correlation analysis and other statistical methods were applied in describing the establishment of ICE models (Dyer et al., 2006; Raimondo et al., 2007). Each pair of species, containing three or more common chemicals in both as required, were used to develop the linear regression models. The relationship between sensitivity of the surrogate and predicted species of each ICE model are described as: Log10 (Predicted Toxicity) ¼ a Log10 (Surrogate Toxicity) þ b
(1)
Here, Predicted Toxicity represents the NOEC value of the projected taxa, Surrogate Toxicity is the NOEC value of the substitute species, a is the slope of the regression line, and b represents the intercept. 2.3. Model verification In the current analysis, the NOEC value derived from endpoints of the reproduction data were used to construct the ICE models. Only statistically significant ICE models (p-value 0.05) were included in further analysis. The models were subsequently evaluated by the regression diagnostics analysis for potential influential data before the model validation. Based on the above analysis, the consistency and extrapolative ability of ICE models with statistical
2. Material and methods 2.1. EDCs reproduction toxicity data collection Reproduction toxicity data (endpoint of NOEC) for EDCs were obtained from available toxicity databases (ECOTOX Database, http://cfpub.epa.gov/ecotox/). Due to the limited number of toxicity data, data representing all the different biomarkers of reproductive toxicity were collected, in order to meet the requirement of ICE model development (Raimondo et al., 2016). When no NOEC could be obtained, the maximum acceptable toxic concentration (MATC) or minimum observable effects concentration (LOEC) was applied instead (Hutchinson et al., 2006; Caldwell et al., 2008; Jin et al., 2014). Where various data points on the same endpoint and
Fig. 1. Model building diagram describing the process associated with the development, selection and verification of ICE models.
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significance were evaluated using leave-one-out cross-validation (Fig. 1). In this approach, each pair of NOEC values for substitute and projected species are methodically eliminated from the initial model (Raimondo et al., 2007, 2010; Awkerman et al., 2009). The model was rebuilt based on the remaining data, and the toxicity value of the eliminated projected species was estimated using the corresponding substitute toxicity value. The “N-fold” difference of each predicted and real value is applied to evaluate the precision of the projected toxicity value in order to assess the consistency among models. A previous study showed that inter-laboratory variance in acute toxicity experimental data for a certain species and chemical can reach as much as a 5-fold difference for aquatic organisms (Raimondo et al., 2007). In the current analysis, the NOEC, chronic reproductive toxicity, was derived and because some aquatic organisms can be adaptive to chronic toxic stress, the interlaboratory variation of chronic toxicity test data should be even greater (Lotufo et al., 2018). A 10-fold difference is thus considered a suitable fit of predicted ICE values. Finally, the ICE models with pvalue 0.05 and cross-validation successful rate 60% were selected. 3. Results 3.1. EDCs data compilation and pairing There are different numbers of chemicals with reproductive toxicity among the 7 species belonging to 4 families (Table 1). The largest number of data were collected from the Daphniidae, with 453 data in D. magna and 132 data in C. dubia, followed by 132 data in O. latipes, 83 data in P. promelas, 71 data in B. rerio in Cyprinidae, and 42 data in O. mykiss belonging to the Salmonidae. C. carpio has the least data, only 4 data. The results showed that Daphniidae, especially D. magna was the most commonly used specie for testing reproduction toxicity. The number of common chemicals of pairing species showed that the D. magna - C. dubia pair has the largest number, with 67 common chemicals, while the B. rerio - C. carpio pair, and the B. rerio - P. promelas pair have the least number, with 2 and 2 common chemicals, respectively. 3.2. Development and verification of the model Out of 26 models with sufficient data, 16 models in total for 6 surrogate species exhibited statistical significance (P-value < 0.05) (Table 2). The square correlation coefficient (R2) of statistically significant models ranges from 0.311 to 0.985, and the positive
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slope of statistically significant models ranges from 0.287 to 1.1 (Table 2). The distribution of model parameters shows that 87.5% of all intercepts ranged from 0.8 and 0.8, and that 93.8% of all slopes were within 0.5e1.5. This comparatively restricted variance indicates the likeness across the majority of models. An exception was the pair of D. magna- O. latipes, where the slope (0.287) and intercept (1.411) of the same model were both beyond these ranges, with the smallest R2 ¼ 0.311. The standard regression diagnostics method was applied to validate the statistically significant ICE models. The distribution of the standardized residuals of each significant model (Fig. 2) indicated that the residuals are all within the horizontal band, with 0 as the center and ±4 as the width, and mostly lie between 2 and 2, only few residuals outside this range, suggesting that the variance of the error term was consistent. The leave-one-out cross-validation results showed that 37.5% (6 models) of the 16 models had a cross-validation success rate > 60% (Fig. 3). The 6 validated models showed an even narrower variability of parameters compared with the result derived from the 16 statistically significant models, with a square correlation coefficient (R2) ranging from 0.617 to 0.829, and a positive slope ranging from 0.74 to 1.1. This result suggests more similarities across the validated models. To further evaluate the consistency and predictive ability of the statistically significant models, the MSE cutoffs linked to crossvalidation success rates of 70% and 60% were identified and were assumed to have high reliability and moderate reliability, respectively (Fig. 4). Accordingly, the MSE cutoff was 0.6 and 0.9, respectively. Only 6.3% (one model) of ICE models had MSE0.6 with the high reliability, while over 30% (5 models) had MSEs between 0.6 and 0.9 with the moderate reliability. All of the models were selected to discuss the impact of taxonomic distance on the model reliability (Fig. 5). The model of B. rerio - O. latipes pair, with the high reliability (R2 ¼ 0.829 and cross-validation success rate ¼ 78%) has a taxonomic distance ¼ 4 between predicted and surrogate species. The moderate reliable model of P. promelas - D. magna pair (R2 ¼ 0.617 and crossvalidation success rate ¼ 71%) has a taxonomic distance ¼ 6 between predicted and surrogate species. The moderate reliable model of D. magna - C. dubia pair (R2 ¼ 0.732 and cross-validation success rate ¼ 69%) has a taxonomic distance ¼ 2. The moderate reliable model of O. mykiss - P. promelas pair (R2 ¼ 0.985 and crossvalidation success rate ¼ 60%) has a taxonomic distance ¼ 4. The regression analysis showed that the taxonomic distance had no significant impacts on R2 and cross-validation success rate (p
Table 1 List of selected specie pairs and the information of common chemicals (number of common chemicals 5). Specie pairs
Number of common chemicals
Chemical classes*
Number of NOEC
Number of LOEC or MATC
B. rerio-P. promelas B. rerio-O. latipes B. rerio-D.magna B. rerio-C. dubia P. promela-O. mykiss P. promela-O. latipes P. promela-D.magna P. promela-C. dubia O. mykiss-D.magna O. mykiss-C. dubia O. latipes-D.magna O. latipes-C. dubia D. magna-C. dubia
10 9 34 16 5 12 38 19 18 7 17 11 67
A; A; A; A; A; A; A; A; A; A; A; A; A;
10 9 34 14 4 12 38 18 18 7 17 10 67
0 0 0 2 1 0 0 1 0 0 0 1 0
B; C; D; E; F B; D; G; H B;C; D;E; F;H; G; I; J; K; L B;C; E;F; G; H; I;K F; J; M B; D; F; N; O; P B;C; D;E; F;G; H; J; K; L; M; O; P B;C; E;G; H; I;K; M; N B;C; E;F; G; H; I;K; M D; F; B; D; F; G; M; Q; O; R; T B; D; G; F; N; O B; C; D; E; F; G; H; I; J; K; L; M; N; O; P; Q; R; S; T; U
Chemical classes*: A: Steroids; B:Phenols; C: Nitramine; D: Pesticide; E: Surfactants; F: Inorganic compounds; G: Nonsteroidal antiinflammatory drugs; H: Fluoxetine; I: Aniline Compounds; J: Monocyclic aromatic compounds; K: Antibiotics; L: Oxidant; M: Plasticizer; N: Polycyclic aromatic hydrocarbons; O: Propanalol; P: Ketoconazole; Q: Choline; R: Phenyl ether; T: Antineoplastic agents; U: Carbon nanotubes.
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Table 2 Results of Interspecies Correlation Estimation (p. value 0.05). Species.X
Species.Y
R2
p.value
intercept
slope
Cross-validation success rate
B. rerio P. promelas B. rerio O. latipes P. promelas O. mykiss P. promelas D. magna P. promelas C. dubia O. mykiss D. magna O. latipes D. magna D. magna C. dubia
P. promelas B. rerio O. latipes B.rerio O. mykiss P. promelas D. magna P. promelas C. dubia P. promelas D. magna O. mykiss D. magna O. latipes C. dubia D. magna
0.66 0.66 0.83 0.83 0.98 0.98 0.62 0.62 0.59 0.59 0.50 0.50 0.31 0.31 0.73 0.73
0.0080 0.0080 0.0020 0.0020 0.0080 0.0080 8.4E-9 8.4E-9 0.00020 0.00020 0.0010 0.0010 0.025 0.025 6.0E-20 6.0E-20
0.23 0.75 0.80 0.74 0.43 0.38 0.75 0.10 1.3 0.54 0.16 0.79 0.34 1.4 0.19 0.39
0.63 1.0 0.75 1.1 1.1 0.92 0.83 0.74 0.69 0.85 0.72 0.70 1.1 0.29 0.91 0.81
40% 10% 78% 67% 40% 60% 71% 66% 53% 53% 39% 56% 53% 47% 69% 69%
Fig. 2. Diagnostic analysis of homogeneity of variances.
value > 0.05). The results indicate that the reliability of these ICE models maybe independent of the taxonomic distance. The consistency between predicted and observed reproduction toxicity derived from the reliable models proved the accuracy of cross-validation success rate (Fig. 6). Sixty-seven percent of the projected toxicity values fell within a 10-fold difference of the experimental data. Thirty-three percent of predicted toxicity values exhibited >10-fold difference from the experimental data. The mean fold-difference values were 4.1 and 19.1 for taxonomic distances 4 and 2, respectively, which also indicates that taxonomic relationships did not influence the prediction precision. 3.3. Potential practical application of ICE models One of the biggest benefits of applying a proven ICE model is that it greatly increases the amount of EDCs data for predicted species, without the need for extensive animal experiments, thus providing data for rapid assessment of EDCs ecological risks. For example, there are largest numbers of EDCs data (453 data) in the D. magna. The proven ICE models derived from D. magna - C. dubia (132 data) and D. magna - P. promelas (83 data) surrogate-predicted pairs, can increase the EDCs data 88% and 400% for C. dubia and P. promelas, respectively. These increased data facilitate the generation of protective toxicity estimates such as ICE-based SSD for ecological risk assessment. This is essential for the aquatic
Fig. 3. The ICE models with p-value < 0.05 and cross-validation success rates >60%.
ecosystem management, because after the effective control of traditional pollutants (such as ammonia nitrogen), attention should be paid to the control of some chemicals may impair reproductive health of aquatic species at much lower concentrations, and the ICE model will be an important supporting tool. 4. Discussion The availability of reliable toxicity information is a significant challenge in assessing the possible threats of EDC exposure and its influence on the reproduction health of aquatic species. Due to limited data, the water environmental benchmark of EDCs cannot be developed from a reproductive health perspective. These
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Fig. 6. Comparison of observed and ICE-predicted toxicity values for EDCs. The solid line represents the 1:1 line (equal toxicity), while the dotted lines represent a 10-fold difference between these values. Fig. 4. Assessment of model reliability based on the relationship between model square error (MSE) and cross-validation success rate for ICE models.
benchmarks are much lower than the acute concentrations affecting the survival of aquatic organisms and are easily overlooked, but the aquatic ecosystems may be impaired by a long-term cumulative effects (Coelho et al., 2013; Feng et al., 2013; Bejarano et al., 2017). The current study developed ICE-based estimation models for EDC reproduction toxicity that are applicable to a wide range of aquatic organisms to overcome this limitation. The statistically significant ICE models with 60%e78% cross-validation success rates had a relatively low MSE (range 0.25e0.9), which suggests that the model fit is robust. These model relationships imply that similar modes of action of EDCs for each species pair might exist. A previous study showed that inter-laboratory changes in acute toxicity test data for a provided species and chemical can reach as
high as a 5-fold difference for aquatic organisms (Raimondo et al., 2007; Bejarano and Barron, 2014). The predicted-observed differences data should be greater in the reproduction toxicity tests, because some aquatic organisms can be adaptive to chronic toxic stress (Lotufo et al., 2018). Thus, a difference of 10-fold is considered a suitable fit of predicted ICE values in the current study. The result showed that 67% of the projected values from the ICE models fell within 10-fold difference of the experimental data, which indicates that it is feasible and promising to predict the reproductive toxicity of EDCs using the ICE models. Taxonomic distance was earlier demonstrated to impact model performance and the consistency of ICE models based on chemicals with diverse action mechanisms (Raimondo et al., 2007, 2010). In contrast, EDC-specific ICE models developed in this study indicated that taxonomic relatedness did not influence model performance. These findings imply that the ICE models established with EDCs with a mutual generic mechanism of chemical action, such as the
Fig. 5. The relationship between taxonomic distance and model reliability.
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same endpoint, are suitable for use in predicting toxicity over a wide variety of aquatic species, and can facilitate the enhancement of estimations over ICE models with diverse mechanisms of action (Bejarano and Barron, 2014). Much more ICE models have been previously developed to predict acute toxicity to generate SSD, and the robust models among them were selected to predict the toxicity for kinds of chemicals, such as petroleum and dispersant (Bejarano and Barron, 2014), zinc (Feng et al., 2013), ibuprofen and sulfamethoxazole (Futran Fuhrman et al., 2015), Benzo[a]pyrene (Wu et al., 2016). Most of these models showed better performance than the models did in the current study. The main reason is that the number of acute toxicity data is much larger than the chronic toxicity data of EDCs used to construct the ICE model. Previous studies have shown that regardless of the algorithm and type of feature, both the precision of the prediction and its stability increased exponentially with increases in sample size (Cui and Gong, 2018; Varoquaux, 2018). This relatively small data size also caused another uncertainty, that is the different biomarkers or exposure levels were used together to develop the models rather than considering them separately. Previous studies showed that different biomarkers or exposure levels may make difference for deriving a toxicity data (Futran Fuhrman et al., 2015; Dang, 2016). However, few established standards are available for the long-term chronic exposure to EDCs. In future, the improvement in unified standard, as well as the increasing number of data are all likely to improve the precision and replicability of the EDCs ICE models, and make it possible to develop a model targeting a particular biomarker. Insufficient chronic toxicity data of EDCs to construct the ICE model, however, may be rapidly replaced by model predictions without the need of animal experiments; for instance, by using quantitative structure activity relationships (QSARs) derived from the chemical structure. QSAR predictions affords proxy values for the establishment of EDC ICE models, or alternatively could be applied to the development of QSAR substitute predicted species ICE models (Dyer et al., 2006; Chicu and Putz, 2009). One of the improvements in ICE models in the future might include the increased accessibility of comprehensive structural chemistry findings, which are presently lacking in the majority of available EDC toxicity data (Bejarano and Barron, 2014). 5. Conclusion We applied the ICE methodology to predict the reproduction toxicity of EDCs to aquatic species. In total, 16 ICE models of EDCs for 6 surrogate species were statistically significant, and 37.5% (6 models) of the 16 models had a cross-validation success rates >60%, indicating that the model fit is robust. These robust ICE model relationships indicate that similar modes of action for each species pair might exist, and the taxonomic relationships did not influence the prediction precision. The high cross-validation success rate corroborated the similarity between the projected and experimental values for the EDC ICE models. The result 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 data for rapid assessment of EDCs ecological risks. Acknowledgment This study was financially supported by the Major Science and Technology Program for Water Pollution Control and Treatment (grant no. 2017ZX07301002-01). The authors thank 3 anonymous referees for their valuable comments on the manuscript.
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