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A quantitative structure-activity relationships approach to predict the toxicity of narcotic compounds to aquatic communities
Ecotoxicology and Environmental Safety 190 (2020) 110068
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A quantitative structure-activity relationships approach to predict the toxicity of narcotic compounds to aquatic communities
T
Antonio Finizio∗, Valeria Di Nica, Cristiana Rizzi, Sara Villa Department of Earth and Environmental Sciences, University of Milano Bicocca, P.zza della Scienza 1, Milano, 20126, Italy
A R T I C LE I N FO
A B S T R A C T
Keywords: QSAR Species sensitivity distribution Hazardous concentration (HC5) Narcotics Baseline toxicity for aquatic communities
Species may vary markedly in terms of their sensitivity to toxicants, and such variation can be described through the species sensitivity distribution (SSD) approach. Using SSD cumulative functions, it is possible to calculate the hazardous concentration for 5% of the species (HC5), namely the contaminant concentration at which 5% of species will be affected. HC5 is often utilised to derive the predicted no-effect concentration, or the concentration at which a chemical will likely have no toxic effects on the different species present in an ecosystem. However, the lack of sufficient ecotoxicological data frequently obstructs the derivation of SSD curves and consequently the HC5. In the last 30 years, quantitative structure-activity relationship (QSAR) models have been widely used to predict the toxicity of chemicals to single species. The aim of this study was to evaluate the possibility of extending the applicability domain of these models from single species to the community level by predicting the HC5 values for aquatic communities and bypassing the need to derive SSD curves. This approach's practical advantage is that it would allow information on the toxicity of contaminants to be obtained on a hierarchical scale (aquatic community), which is ecologically more relevant than on the scale of single species, without the need for a robust toxicity data set. In the first part of the study, two simple QSAR models were developed for narcotic and polar narcotic compounds. Then, the QSAR model developed for narcotic compounds was utilised to define the baseline toxicity for aquatic communities and to calculate the toxicity ratios for various specifically acting compounds (insecticides and herbicides).
1. Introduction The basic aim of an environmental risk assessment (ERA) for chemicals (EC European Commission, 1994, 2001, 2006a, 2006b, 2006c; USEPA, 1998; Hommen et al., 2010) is to derive threshold concentrations for environmental contaminants to protect the structure and functional attributes of natural ecosystems. Briefly, the assessment follows a tiered approach based on the characterisation of two aspects related to environmental risk, namely exposure and effects (Finizio and Villa, 2002). In the first tier (generally based on worst-case assumptions), the effects are measured using acute and chronic standardised laboratory toxicity tests performed on non-target species chosen as representatives of trophic chains (e.g. algae, Daphnia, and fish). The outcomes of these tests are utilised to calculate the predicted no-effect concentrations using appropriate safety factors (Finizio and Vighi, 2014). If the first step of the assessment indicates concerns, then highertier approaches may be considered to refine the ERA results. Suitable approaches for the refinement of effect results include microcosm and mesocosm studies (Campbell et al., 1999; Hommen et al., 2010; EFSA,
∗
2014; Van den Brink, 2013) or additional toxicity tests on a larger number of organisms to estimate the species sensitivity distribution (SSD) curves. From SSD curves, it is possible to derive the concentration at which a specified proportion (p) of species will be affected (HCp: hazardous concentration for p% of species) given a certain level of exposure; the most frequently estimated HCp is the HC5 (Posthuma et al., 2002). SSD curves are derived by collecting single-species toxicity data and assuming that the test species are random selections of a specified distribution (Kooijman, 1987). The basic assumption of the SSD concept is that the sensitivities of a set of species can be described by some type of statistical distribution (Stephan et al., 1985; Wagner and Løkke, 1991; Van Straalen and Denneman, 1989). Since its first applications (Klaine et al., 1996; Solomon et al., 1996; Hall et al., 1998; Suter, 2002; Van Straalen and van Leeuwen, 2002), the SSD approach has been used to assess the ecological risks posed by inorganic and organic contaminants as well as nanomaterials (Crommentuijn et al., 2000; Brix et al., 2001; Solomon et al., 2001; Wheeler et al., 2002; Brock et al., 2004; Gottschalk et al., 2013; Jonsson et al., 2015; Garner et al., 2015; Coll et al., 2016; Liu et al., 2016; Boeckman and Layton,
Corresponding author. E-mail address: antonio.fi[email protected] (A. Finizio).
https://doi.org/10.1016/j.ecoenv.2019.110068 Received 17 September 2019; Received in revised form 6 December 2019; Accepted 7 December 2019 0147-6513/ © 2019 Elsevier Inc. All rights reserved.
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μmol/L. Finally, for all the investigated chemicals, relevant physicochemical properties (log Kow and pKa) were retrieved through a literature search. The selected values are reported in Tables S1–S3.
2017; Chen et al., 2018). However, despite its widespread use, this approach has been the focus of criticism (Posthuma et al., 2002) related to several areas, such as the extrapolation techniques, statistics assumptions, and data limitations (Forbes and Forbes, 1993; Hopkin, 1993; Smith and Cairns, 1993; Chapman et al., 1998; Posthuma et al., 2002; Forbes and Calow, 2002). In addition, for a large number of chemical contaminants, ecotoxicological data is lacking even in the most comprehensive available databases, such as the US EPA AQUIRE database (USEPA, 2010) and the EU IUCLID and FOOTPRINT databases (EC, 2007a, 2007b; PPDB, 2009); the shortage of ecotoxicological data frequently impedes the derivation of SSD curves. Moreover, owing to experimental, ethical, and economic reasons, it is desirable to reduce the number of ecotoxicological tests as much as possible, particularly for vertebrates (EC, 2006b; EC, 2006c), by promoting the use of alternative non-testing methods to assess the ecotoxicological effects of contaminants. In this framework, the use of QSAR models are very useful as an alternative to laboratory tests. These models allow the prediction of the environmental toxicity of contaminants to single species, starting from molecular structure or physicochemical properties (Netzeva et al., 2008; Roy et al., 2015; Klüver et al., 2016, 2019) Several authors developed QSAR models based on a series of congeneric compounds (i.e. chemical classes) or by considering a chemical's mode (and mechanism) of action (Netzeva et al., 2008; Lozano et al., 2010). For instance, several QSAR models predict the toxicity of non-polar narcotics simply from the hydrophobicity of the compound, and often use the 1-octanol-water partition coefficient (log Kow) as a predictive descriptor. The narcotic mode of action is associated with the reversibly altered structure and function of cell membranes. In principle, each organic compound can act as a non-polar narcotic. Therefore, this mode of action is considered a baseline or minimal effect, and QSAR equations for this type of chemical can be used to predict a compound's minimum toxicity to single species (Veith et al., 1983). Polar narcotic compounds (or less inert chemicals) are relatively more toxic than predicted by baseline toxicity QSAR models (based on log Kow). In general, these chemicals are characterized by hydrogen bond donor acidity (e.g. phenols and anilines). QSAR models for polar narcotics confirms that their toxicity is highly dependent hydrophobicity (log Kow), however, in many cases they requires the introduction of other descriptors to account for the ionization of the compounds (i.e. pKa) (Könemann and Musch, 1981). Within this framework, this study first aimed to analyse the possibility of extending the QSAR approach from the single-species to the community level by developing simple predictive models of the HC5 for narcotic and polar narcotic compounds. Then, the QSAR model developed for narcotic compounds was utilised to define the baseline toxicity for aquatic communities and to calculate the toxicity ratios (TRs = HC5narc/HC5 for aquatic plants, invertebrates, or vertebrates) for several specifically acting compounds (insecticides and herbicides).
2.2. Species sensitivity distribution generation The SSD curves and the estimated HC5 values (μmol/L) were generated according to the method proposed by Aldenberg and Jaworska (2000) and using ETX software (Van Vlaardingen et al., 2003). A lognormal distribution model was fitted to a minimum of seven toxicity values. The HC5 values were calculated by separately considering aquatic plants, invertebrates, and vertebrates or considering all the organisms together. All toxicity data utilised for generating the SSD curves are reported in the supporting information materials (file: data.xls). 2.3. Development of QSAR models and statistical procedure QSAR models are computational tools that relate biological activities to chemical structures. They are used to predict the effects of untested chemicals based on their physical chemical properties or molecular descriptors (ECHA, 2008). In our study, we used both single and multiple linear regressions to link physical-chemical properties of nonpolar and polar narcotic compounds to their effects to aquatic communities (HC5). The dataset of the physical chemical properties are reported in Tables S1 and S2 in SI section. Each of the two developed predictive models was subsequently subject to cross-validation procedures to estimate their robustness and predictive performance (further details in S1and S2 paragraphs of SI section). All statistical analyses were performed using the XLStat® software package (www.xlstat.com). 3. Results and discussion QSAR models are based on mathematical correlations between a chemical response (e.g. toxicity) and chemical attributes defining the features of the analysed compounds (e.g. lipophilic, electronic, and steric features). QSAR models are widely utilised to predict the negative effects of contaminants on single species (ECHA, 2008; Netzeva et al., 2008; Focks et al., 2018). In this study, we suggested that the field of application of QSAR models can be widened from single species to the community level by directly predicting the HC5 values for narcotic and polar narcotic compounds. To test this hypothesis, we selected 28 narcotic and 32 polar narcotic compounds based on the classification of the mode of toxic action defined by Verhaar et al. (1992). In the following sections (3.1 and 3.2), the results related to the obtained QSAR models are reported and discussed. In addition, consideration of specifically acting compounds is reported in Section 3.3.
2. Materials and methods 3.1. Narcotic compounds 2.1. Selection of data In QSAR models, it is widely recognised that log Kow is the best reference descriptor of the hydrophobicity of a compound; in addition, there is a general consensus that narcosis-type toxicity (also called baseline toxicity or minimum toxicity) can be modelled using log Kow as the sole descriptor (Könemann, 1981; Veith et al., 1983; Ellison et al., 2015). A previous work highlighted that species differences in baseline toxicity are explained by differences in the membrane lipid content, and the lethal membrane concentrations for algae, Daphnia, and fish are nearly identical (Escher and Schwarzenbach, 2002). This finding helps to explain the interspecies toxicity correlations reported in Tremolada et al. (2004). According to these authors, the lower the specificity of the mode of action of the compounds (narcotics or less inert chemicals), the stronger the relationships. In the absence of a specific mode of action, these authors found excellent interspecific correlations between the
The present study focused on 71 chemicals belonging to different categories of organic compounds. For these compounds, the HC5 values (μmol/L) were calculated starting with single-species acute toxicity data, which were obtained from the ECOTOX database (USEPA, 2010). The selected endpoints were the median lethal concentration (LC50) for fish, the median effect concentration (EC50) for Daphnia, and the EC50 (biomass or growth rates) for algae and plants. The selection criteria were based on the test duration (96 h for fish and algae and 48 h for Daphnia). When more than one toxicity value was available, the geometric mean was calculated. Toxicity data reported as higher-than values were not considered. Furthermore, the HC5 (μg/L) values for 16 insecticides were already available in the study by Maltby et al. (2005). In the present work, these values were log transformed and expressed in 2
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Table 1 Calculated log HC5 (hazardous concentration for 5% of species) values for non-polar narcotic compounds. The HC5 (μmol/L) values are reported for each trophic level (HC5plants, HC5inv, and HC5vert) or by considering all the organisms together (HC5aqcom = HC5 of aquatic communities). The number of data points utilised for deriving the HC5 are also reported. Chemicals
log HC5plants
log HC5inv
log HC5vert
log HC5aqcom
Data (plants)
Data (inv.)
Data (vert.)
Data (aqcom)
Methanol Ethanol Propanol Isopropanol 1-butanol Isobutanol 1-pentanol 1-hexanol 1-heptanol 1-octanol Benzene Cl-benzene 1,2Cl-benzene 1,2,3Cl-benzene 1,2,4Cl-benzene 1,2,3,4Cl-benzene HCB Acetone Toluene Dibutylester Dichloromethane Diethyl ester Ethylene glycol Tetrachloroethylene 1,1,2-trichloroethane 1,2-dichloroethane 1,2-dichloropropane 1,3-dichloropropane
– – – – – – – – – – – – – – – – – – – – – – – – – – – –
5.40 5.15 4.32 4.64 – 4.09 – – 2.24 – 2.04 – 0.53 – 0.07 – – 4.81 2.04 – – – 5.20 8.18 2.55 – – –
– 5.10 – – – 4.18 – – 2.45 – 1.63 1.17 1.08 – 0.58 – – 4.89 1.73 – – – – 1.07 – 2.89 – –
5.35 5.12 4.39 4.66 4.21 4.15 3.05 2.82 2.24 1.55 1.83 1.21 0.87 0.25 0.24 −0.25 −1.41 4.84 1.89 −0.16 2.94 1.39 5.14 0.83 2.44 2.51 2.12 2.41
2 1 1 – – 1 – 1 1 3 3 2 4 2 4 1 1 3 3 2 – 1 1 1 5 1 2 2
13 15 21 10 5 7 5 2 20 6 9 3 8 4 22 3 2 25 18 4 3 3 10 13 11 5 3 2
5 7 6 3 4 8 6 4 9 6 13 10 10 4 11 3 5 10 16 6 11 5 3 9 6 7 5 4
20 23 28 13 9 16 11 7 30 15 25 15 22 10 37 7 8 38 37 12 14 9 14 23 22 13 10 8
pesticide toxicity data of Daphnia and fish (even for fungicides with algae/fish). As the HC5 values are extrapolated from SSD curves, which are derived from acute toxicity data (LC50 and/or EC50), we hypothesised possible relationships between the HC5 values of narcotic compounds and log Kow. The HC5 values for 28 non-polar narcotic compounds are reported in Table 1. Due to the scarcity of data, it was not possible to derive HC5 values for the aquatic plants (HC5plants); however, the calculation of HC5 values for invertebrates (HC5inv) and vertebrates (HC5vert) was possible for 14 and 11 substances, respectively. The differences between HC5inv and HC5vert were very small, thereby indicating a similar degree of toxicity of these substances to the two trophic levels of the aquatic community. Although no HC5plants values were calculated, the few toxicity data points on aquatic plant species available in the literature were of the same order of magnitude as those found for invertebrates and vertebrates. The HC5 values for aquatic communities (HC5aqcom) were derived by combining all the available data (plants, invertebrates, and vertebrates). All calculated HC5aqcom values passed the Anderson-Darling test, thereby indicating a normal distribution of the toxicity data set. The HC5aqcom values reported in Table 1 were utilised to develop QSAR models. In the first step, QSAR models were derived by considering single classes of congeneric compounds; subsequently, a more general QSAR model was obtained. Fig. S1-A and S1-B reports the relationships between log 1/HC5aqcom and log Kow (Table S1) for single congeneric classes of narcotic compounds, whereas the more general QSAR model, which included all the analysed narcotic compounds, is depicted in Fig. 1. In all cases, significant correlations were found, thereby indicating that hydrophobicity was the key descriptor explaining the toxicity of non-polar narcotic compounds not only for single species, but also for aquatic communities. The following equations were obtained:
log 1/HC5aqcom (alcohols) = −4.78 − log K ow (R² adj = 0.98; n= 10; p< 0.001)
(1)
log 1/HC5aqcom (benz. der.) = −4.06 + 0.97 × log K ow (R² adj = 0.99; n= 7; p< 0.0001)
(2)
log 1/HC5aqcom (non − polar narc.) = −4.52 + 1.05 × log K ow (R² adj = 0.97; p< 0.0001)
(3)
To evaluate the predictive capability and robustness of the general QSAR model (Eq. (3)), a cross-validation method was utilised using the leave-many-out (LMO) approach (groups of seven compounds were excluded from the original data set and used for cross-validation). This process was repeated four times by randomly choosing compounds to be excluded. The results obtained confirmed the robustness and predictive capability of the relationship between log 1/HC5aqcom and log Kow. More details are reported in Section S1 of the supporting materials. 3.2. Polar narcotic compounds Polar narcotic compounds are slightly more toxic than non-polar narcotic compounds and are generally 5 to 10 times more toxic than the baseline toxicity (Verhaar et al., 1992). Several QSAR models for single species are present in the literature for these compounds (Veith and Broderius, 1987; Urrestarazu Ramos et al., 1997). In general, they are bi-parametric equations in which one of the parameters is log Kow and the other is a descriptor of the electrophilicity of the compounds (i.e. eLumo; pKa) (Chen et al., 2007; Selassie and Verma, 2015). Table 2 presents the calculated HC5 values for 32 polar narcotics compounds. For this category of chemicals, toxicity data for primary producers were scarce, whereas data for primary and secondary consumers were sufficiently available to derive the HC5 values. All HC5aqcom values, which were calculated using all available ecotoxicity data, passed the Anderson-Darling test, which indicated a normal 3
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Fig. 1. Relationship between log 1/HC5aqcom (hazardous concentration for 5% of species for aquatic communities) and log Kow for narcotic compounds.
distribution of the toxicity data set. The data reported in Tables 2 and S2 were utilised to develop QSAR models by considering both single classes of congeners (anilines and phenols) and all the substances together. Equations (4)–(6) report the relationships between log 1/HC5aqcom and lipophilicity (log Kow). The obtained equations were as follows:
log 1/HC5aqcom (phenols) = −2.5 + 0.57 × log K ow (R²adj = 0.82; p < 0.0001)
log 1/HC5aqcom (all pol. narc.) = −1.8 + 0.42 × log K ow (R²adj = 0.40; p< 0.0001)acs
(6)
Even if they were significant, the obtained results indicated that the log Kow values did not completely explain the toxicity of these compounds to aquatic communities, particularly when all the compounds were considered together. Therefore, a further descriptor (pKa) was
log 1/HC5aqcom (anilines) = − 1.6 + 0.38 × log K ow (R² adj = 0.88; p< 0.001)
(5)
(4)
Table 2 Calculated log HC5 (hazardous concentration for 5% of species) values for polar narcotic compounds. The HC5 values (μmol/L) are reported for each trophic level (HC5plants, HC5inv, and HC5vert) or for all the organisms together (HC5aqcom = HC5 aquatic communities). The number of data points utilised for deriving the HC5 values are also reported. Chemicals
log HC5plants
log HC5inv
log HC5vert
log HC5aqcom
Data (plants)
Data (inv.)
Data (vert.)
Data (aqcom)
2,3,4,6Cl-phenol 2,3,4Cl-phenol 2,3,5Cl-phenol 2,4,5-chlorophenol 2,4,6-chlorophenol 2,4Cl-phenol 2,4-dichloraniline 2,4-dimethylphenol 2,4-dinitrophenol 2,6Cl-phenol 2Cl-aniline 2Cl-phenol 2-nitrotoluene 2-phenylphenol 3,4-chloromethylaniline 3,4-dichloraniline 3Cl-aniline 4Cl-aniline 4Cl-nitrobenzene 4Cl-phenol 4-nitrophenol 4-nitrotoluene 4-nonylphenol Aniline Benzaldehyde Formalin m-cresol o-cresol p-cresol Pentachlorophenol Phenol Quinoline
– – – – – – – – – – – – – – – – – – – – – – – – – – – – – −1.15 – –
0.11 – – 0.25 – 0.94 – – – – – – – – 0.62 0.55 – – – 1.37 1.35 – −0.40 1.06 – 3.40 – 1.85 – −0.59 1.43 –
−0.12 – – 0.31 0.35 0.93 – – 0.41 – – 1.60 – – – 0.99 – – – 1.21 1.64 – −0.62 2.02 – 2.85 1.62 1.91 1.51 −0.79 1.84 –
−0.14 0.67 0.37 0.32 0.37 0.74 0.55 0.93 0.59 1.21 0.88 1.20 1.61 0.83 0.71 0.56 0.68 0.81 0.79 1.24 1.48 1.62 −0.62 1.36 1.30 2.84 1.59 1.84 1.50 −0.83 1.70 2.09
2 1 – 2 4 4 7 1 3 2 4 3 3 2 – 3 4 2 2 6 4 3 1 2 – – 1 1 1 8 4 2
9 3 2 6 3 7 3 5 4 5 2 6 3 2 7 14 3 5 3 11 11 2 17 26 5 10 2 18 2 89 8 6
7 4 6 9 11 11 5 6 14 4 3 7 4 4 1 7 3 6 3 7 12 3 21 13 6 19 7 9 12 55 11 4
18 8 8 17 18 22 15 12 21 11 9 16 10 8 8 24 10 13 8 24 27 8 39 41 11 29 10 28 15 152 23 12
4
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Fig. 2. Two parametric quantitative structure activity relationships model for predicting the HC5 (hazardous concentration for 5% of species) of polar narcotic compounds for aquatic communities.
sensitive than the others (Sala et al., 2012; Giddings et al., 2019). For this reason, the possibility of developing QSAR models for specifically acting compounds, which comprised all the aquatic organisms, was not taken into consideration in this study. According to the classification scheme proposed by Verhaar et al. (1992), four modes of action can be described, namely class I: inert chemicals; class II: relatively inert chemicals; class III: reactive chemicals; and class IV: specifically acting chemicals. The inert chemicals are chemicals that do not interact with specific receptors in the organism, and are typically associated with non-polar narcosis. This classification was based on the TR, which is the ratio between the estimated baseline toxicity and the corresponding experimental LC50 for single species (TR = LC50baseline/LC50experimental). The LC50baseline was calculated using QSAR models for single species, which were developed for narcotic compounds and based on log Kow (EC, 1996). More recently, Tremolada et al. (2004) proposed a change in the classification scheme of Verhaar et al. (1992) (narcotics: 0.5 < log TR < 0.5; less inert chemicals: 0.5 < log TR < 1.5; reactive chemicals: 1.5 < log TR < 2.5; specifically acting chemicals: 2.5 < log TR < 4; highly specifically acting chemicals: log TR > 4). Similar to these previous studies, we proposed the use of the TR for groups of species (TRgs), as follows:
introduced in the model. Equation (7) presents the two-parameter QSAR model obtained when all the polar narcotic compounds were considered, as follows:
log 1/HC5aqcom (pol. narc.) = −1.86 + 0.51 × log K ow − 0.06 × pK a (R2adj = 0.83; p< 0.0001) (7) In Fig. 2, a graphical representation of the experimental vs. predicted log 1/HC5aqcom values is depicted (QSAR models for anilines and phenols are reported in Fig. S1-C and S1-D). The robustness and predictive capability of the obtained QSAR model for polar narcotic compounds were tested using the LMO technique. The overall data set was randomly split into four training sets (Table S5) and four validation sets (Table S6); the latter represented 30% of the entire data set. Further details are reported in Section S2. 3.3. Specifically acting compounds Table 3 shows the calculated log HC5 values for the three different taxonomic groups representative of the aquatic communities for 27 pesticides (insecticides and herbicides). For specifically acting chemicals such as pesticides, combining all the organisms would result in a bimodal SSD because at least one taxonomic group would be more
TR gs = HC5aqcom /HC5gs
(8)
Table 3 Calculated log HC5 (hazardous concentration for 5% of species) values (μmol/L) for the different trophic levels of aquatic communities for specifically acting compounds (16 insecticides and 11 herbicides). Chemicals Insecticides Azinphos-CH3 Carbaryl Carbofuran Chlorpyrifos Cypermethrin Deltamethrin Diazinon Diflubenzuron Herbicides Atrazine Ametryn Alachlor Butachlor Chlorsulfuron Cyanazine a
log HC5aqplants (μmol/L)
log HC5inv (μmol/L)
log HC5vert (μmol/L)
Chemicals
log HC5aqplants (μmol/L)
log HC5inv (μmol/L)
log HC5vert (μmol/L)
– 0.52 – – – – – –
−3.90a −1.88a −2.98a −3.70a −5.14a −4.75a −2.93a −3.79a
−2.95a 0.35a −0.51a −2.78a −3.39a −3.38a −0.77a −1.13a
Fenitrothion Fenvalerate λ-cyhalothrin Lindane Methoxychlor Parathion-C2H5 Parathion-CH3 Permethrin
0.45a – – – – – – –
−2.94a −4.51a −5.18a −2.57a −2.87a −3.10a −2.93a −3.61a
−0.72a −3.34a −3.75a −1.78a −1.88a −0.50a −2.25a −3.00a
−1.24 – −1.27 – −1.74 –
1.32 – 1.32 – – –
1.45 0.94 0.97 −0.14 – 1.23
Diuron Metolachlor Simazine Terbutryn Trifluralin
– −0.76 −0.64 – –
– – −1.00 – −1.39
0.49 1.20 1.48 0.85 −0.96
Data from Maltby et al. (2005). 5
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Table 4 Calculated log TRgs (toxicity ratio for groups of species) values of pesticides. Chemicals
log TRaqplants
log TRinv
log TRvert
Insecticides Azinphos-CH3 Carbaryl Carbofuran Chlorpyrifos Cypermethrin Deltamethrin Diazinon Diflubenzuron Fenitrothion Fenvalerate λ-cyhalothrin Lindane Methoxychlor Parathion-C2H5 Parathion-CH3 Permethrin
– 1.5 – – – – – – 0.5 – – – – – – –
5.3 3.9 5.3 3.3 3.0 2.4 4.0 4.2 3.9 3.8 2.3 3.2 2.0 3.6 4.3 1.7
4.4 1.7 2.8 2.4 1.2 1.1 1.8 −1.4 1.7 2.6 0.9 2.4 1.0 1.0 0.6 1.1
Chemicals
log TRaqplants
log TRinv
log TRvert
Herbicides Alachlor Ametryn Atrazine Butachlor Chlorsulfuron Cyanazine Diuron Metolachlor Simazine Terbutryn Trifluralin
2.5 – 2.9 – 7.3 – – 2.0 2.8 – –
−0.05 – 0.40 – – – – 0.04 1.20 – 0.30
0.3 2.7 0.2 0.4 – 3.4 2.2 – 0.7 1.5 −2.0
environmental quality criteria aiming to preserve the structure and functionality of aquatic ecosystems. Although there is still a scientific debate and criticism regarding the usefulness of the HC5, this parameter is frequently used as the basis for setting environmental quality standards and for higher-tier risk assessment. In this study, two simple QSAR models were proposed to predict the HC5 of non-polar and polar narcotic compounds. The practical advantage of this approach was that one could directly estimate the hazard posed by contaminants to aquatic communities instead of single species. In addition, the QSAR model for non-polar narcotics has been used to define the baseline toxicity for aquatic communities and to derive the TRgs, which can provide information about the toxicity of specifically acting compounds to a specific group in the aquatic community.
where: HC5aqcom = calculated baseline toxicity for the aquatic community using Eq. (6). HC5gs = hazardous concentration for 5% of species belonging to a specific group of aquatic species (plants, invertebrates, and vertebrates). The TRgs could be helpful to derive toxicity range factors to be applied to estimate the first approximation of the toxicity of specifically acting compounds to a specific group in the aquatic community. Table 4 shows the calculated log TRgs values for the investigated pesticides. Based on the classification scheme of Tremolada et al. (2004), all insecticides fell into the categories of specifically or highly specifically acting chemicals for both aquatic invertebrates and vertebrates (except for methoxychlor and permethrin for both taxonomic groups and diflubenzuron for vertebrates). On the contrary, they were classified as less inert chemicals for aquatic plants. Invertebrates were the most sensitive taxonomic group to insecticides. Particularly, many phosphorodithioates (azinphos-CH3, diazinon, and parathion-CH3) and the carbamate carbofuran were highly specific to invertebrates (log TRinv range = 4.2–5.3). These findings were mostly in agreement with the considerations given in Tremolada et al. (2004) for single species. These authors highlighted that the modes of action of organophosphorus and carbamate insecticides are more specific for Daphnia than for trout. Furthermore, these authors also indicated that phyrethroids are less specific than organophosphorus and carbamates for these two species, and are slightly more specific for trout than Daphnia. Eventually, these considerations can be upscaled from single species to taxonomic groups. However, at this hierarchical level, pyrethroids seemed to be more specific to invertebrates. The majority of herbicides were classified as specifically acting chemicals for aquatic plants, whereas they were classified as narcotic or less inert narcotic compounds for invertebrates and vertebrates (with the exception of ametryn cyanazine and terbutryn, which seemed to behave as specifically acting chemicals for fish). Chlorsulfuron seemed to be the herbicide with the most specific mechanism of action for aquatic plants. This was not surprising considering that for weed control, the effective dose of sulfonylureas is at the level of g/ha (1000 times lower than the dose of other herbicides).
CRediT authorship contribution statement Antonio Finizio: Project administration; Funding acquisition; Conceptualization; Methodology; Resources; Writing – Original draft; Valeria Di Nica: Application of model software; Formal analysis; Data curation. Cristiana Rizzi: Formal analysis; Application of model software; Data curation. Sara Villa: Conceptualization; Methodology; Supervision; Writing - review & editing. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecoenv.2019.110068. References Aldenberg, T., Jaworska, J.S., 2000. Uncertainty of the hazardous concentration and fraction affected for normal species sensitivity distributions. Ecotoxicol. Environ. Saf. 46, 1–18. https://doi.org/10.1006/eesa.1999.1869. Boeckman, C.J., Layton, R., 2017. Use of species sensitivity distributions to characterize hazard for insecticidal traits. J. Invertebr. Pathol. 142, 68–70. https://doi.org/10. 1016/j.jip.2016.08.006. Brix, K.V., De Forest, D.K., Adams, W.J., 2001. Assessing acute and chronic copper risks to freshwater aquatic life using species sensitivity distributions for different taxonomic groups. Environ. Toxicol. Chem. 20, 1846–1856. https://doi.org/10.1002/etc. 5620200831. Brock, T.C.M., Crum, S.J.H., Deneer, J.W., Heimbach, F., Roijackers, R.M.M., Sinkeldam, J.A., 2004. Comparing aquatic risk assessment methods for the photosynthesis-inhibiting herbicides metribuzin and metamitron. Environ. Pollut. 130, 403–426. https://doi.org/10.1016/j.envpol.2003.12.022. Campbell, P.J., Arnold, D.J.S., Brock, T.C.M., Grandy, N.J., Heger, W., Heimbach, F., Maund, S.J., Streloke, M., 1999. Guidance Document on Higher-Tier Aquatic Risk Assessment for Pesticides (HARAP). SETAC-Europe, Brussels (BE), pp. 179. Chapman, P.M., Fairbrother, A., Brown, D., 1998. A critical evaluation of safety (uncertainty) factors for ecological risk assessment. Environ. Toxicol. Chem. 17 (1), 99–108. Chen, C.Y., Ko, C.W., Lee, P.I., 2007. Toxicity of substituted anilines to
4. Conclusions Information on aquatic toxicity is generally required in order to assess the hazards and risks of contaminants and to define 6
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Giddings, J.M., Wirtz, J., Campana, D., Dobbs, M., 2019. Derivation of combined species sensitivity distributions for acute toxicity of pyrethroids to aquatic animals. Ecotoxicology 28, 242–250. https://doi.org/10.1007/s10646-019-02018-0. Gottschalk, F., Kost, E., Nowack, B., 2013. Engineered nanomaterials (ENM) in waters and soils: a risk quantification based on probabilistic exposure and effect modeling. Environ. Toxicol. Chem. 32, 1278–1287. https://doi.org/10.1002/etc.2177. Hall Jr., L.W., Scott, M.C., Killen, W.D., 1998. Ecological risk assessment of copper and cadmium in surface waters of Chesapeake Bay watershed. Environ. Toxicol. Chem. 17, 1172–1189. https://doi.org/10.1002/etc.5620170626. Hommen, U., Baveco, J.M., Galic, N., van den Brink, P.J., 2010. Potential application of ecological models in the European environmental risk assessment of chemicals. I. Review of protection goals in EU directives and regulations. Integr. Environ. Assess. Manag. 6 (3), 325–337. https://doi.org/10.1002/ieam.69. Hopkin, S.P., 1993. Ecological implications of “95% protection levels” for metals in soil. Oikos 66, 137–141. https://doi.org/10.2307/3545206. Jonsson, C.M., Silva, M.S.G.M., Macedo, V.S., Dantzger, D.D., Vallim, J.H., Marigo, A.L.S., Aoyama, H., 2015. Prediction of a low‐risk concentration of diflubenzuron to aquatic organisms and evaluation of clay and gravel in reducing the toxicity. Pan Am. J. Aquat. Sci. 10, 259–272. Klaine, S.J., Cobb, G.P., Dickerson, R.L., Dixon, K.R., Kendall, R.J., Smith, E.E., Solomon, K.R., 1996. An ecological risk assessment for the use of the biocide, dibromonitrilopropionamide (DBNPA), in industrial cooling systems. Environ. Toxicol. Chem. 15, 20–21. Klüver, N., Vogs, C., Altenburger, R., Escher, B.I., Scholz, S., 2016. Development of a general baseline toxicity QSAR model for the fish embryo acute toxicity test. Chemosphere 164, 164–173. https://doi.org/10.1016/j.chemosphere.2016.08.079. Klüver, N., Bittermann, K., Escher, B.I., 2019. QSAR for baseline toxicity and classification of specific modes of action of ionizable organic chemicals in the zebrafish embryo toxicity test. Aquat. Toxicol. 207, 110–119. https://doi.org/10.1016/j.aquatox.2018. 12.003. Könemann, H., 1981. Quantitative structure-activity relationships in fish toxicity studies Part 1: relationship for 50 industrial pollutants. Toxicology 19, 209–221. https://doi. org/10.1016/0300-483X(81)90130-X. Könemann, H., Musch, A., 1981. Quantitative structure-toxicity relationships in fish toxicity study Part 2: influence of pH on the QSAR of chlorophenols. Toxicology 19, 223–228. Kooijman, S.A.L.M., 1987. A safety factor for LC50 values allowing for differences in sensitivity among species. Water Res. 21, 269–276. https://doi.org/10.1016/00431354(87)90205-3. Liu, N., Wang, Y., Yang, Q., Lv, Y., Jin, X., Giesy, J.P., Johnson, A.C., 2016. Probabilistic assessment of risks of diethylhexyl phthalate (DEHP) in surface waters of China on reproduction of fish. Environ. Pollut. 213, 482–488. https://doi.org/10.1016/j. envpol.2016.03.005. Lozano, S., Lescot, E., Halm, M.P., Lepailleur, A., Bureau, R., Rault, S., 2010. Prediction of acute toxicity in fish by using QSAR methods and chemical modes of action. J. Enzym. Inhib. Med. Chem. 25 (2), 195–203. https://doi.org/10.3109/ 14756360903169857. Maltby, L., Blake, N., Brock, T.C.M., van den Brink, P.J., 2005. Insecticide species sensitivity distributions: importance of test species selection and relevance to aquatic ecosystems. Environ. Toxicol. Chem. 24, 379–388. https://doi.org/10.1897/04025R.1. Netzeva, T.I., Pavan, M., Worth, A.P., 2008. Review of (quantitative) structure–activity relationships for acute aquatic toxicity. QSAR Comb. Sci. 27, 77–90. https://doi.org/ 10.1002/qsar.200710099. Posthuma, L., Suter, G.W., Traas, T.P. (Eds.), 2002. Species-Sensitivity Distributions in Ecotoxicology. Lewis Publishers, Boca Raton, FL, USA. PPDB, 2009. The Pesticide Properties Database (PPDB) Developed by the Agriculture & Environment Research Unit (AERU). University of Hertfordshire, funded by UK national sources and the EU-funded FOOTPRINT project (FP6-SSP-022704). http:// sitem.herts.ac.uk/aeru/footprint/. Roy, K., Kar, S., Das, R.N., 2015. Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment, first ed. Academic Press, pp. 484. Sala, S., Migliorati, S., Monti, G.S., Vighi, M., 2012. SSD-based rating system for the classification of pesticide risk on biodiversity. Ecotoxicology 21, 1050–1062. https:// doi.org/10.1007/s10646-012-0858-7. Selassie, C., Verma, R.P., 2015. QSAR of toxicology of substituted phenols. J. Pestic. Sci. 40, 1–12. https://doi.org/10.1584/jpestics.D14-097. Smith, E.P., Cairns Jr., J., 1993. Extrapolation methods for setting ecological standards for water quality: statistical and ecological concerns. Ecotoxicology 2, 203–219. Solomon, K.R., Baker, D.B., Richards, R.P., Dixon, K.R., Klaine, S.J., La Point, T.W., Kendall, R.J., Weisskopf, C.P., Giddings, J.M., Giesy, J.P., Hall Jr., L.W., Williams, W.M., 1996. Ecological risk assessment of atrazine in North American surface waters. Environ. Toxicol. Chem. 15, 31–76. Solomon, K.R., Giddings, J.M., Maund, S.J., 2001. Probabilistic risk assessment of cotton pyrethroids: I. Distributional analyses of laboratory aquatic toxicity data. Environ. Toxicol. Chem. 20, 652–659. Stephan, C.E., Mount, D.I., Hansen, D.J., Gentile, J.H., Chapman, G.A., Brungs, W.A., 1985. Guidelines for Deriving Numerical National Water Quality Criteria for the Protection of Aquatic Organisms and Their Use. Duluth: US Environmental Research Laboratories. Suter, G.W., 2002. North American history of species sensitivity distributions. In: Posthuma, L., Suter, G.W., Traas, T.P. (Eds.), Species-Sensitivity Distributions in Ecotoxicology. Lewis, Boca Raton, FL, USA, pp. 11–17. Tremolada, P., Finizio, A., Villa, S., Gaggi, C., Vighi, M., 2004. Quantitative inter-specific chemical activity relationships of pesticides in the aquatic environment. Aquat. Toxicol. (N. Y.) 67, 87–103. https://doi.org/10.1016/j.aquatox.2003.12.003.
Pseudokirchneriella subcapitata and quantitative structure-activity relationship analysis for polar narcotics. Environ. Toxicol. Chem. 26 (6), 1158–1164. https://doi. org/10.1897/06-293R.1. Chen, G., Peijnenburg, W.J.G.M., Xiao, Y., Vijver, M.G., 2018. Developing species sensitivity distributions for metallic nanomaterials considering the characteristics of nanomaterials, experimental conditions, and different types of endpoints. Food Chem. Toxicol. 112, 563–570. https://doi.org/10.1016/j.fct.2017.04.003. Coll, C., Notter, D., Gottschalk, F., Sun, T., Som, C., Nowack, B., 2016. Probabilistic environmental risk assessment of five nanomaterials (nano-TiO2, nano-Ag, nano-ZnO, CNT, and fullerenes). Nanotoxicology 10, 436–444. https://doi.org/10.3109/ 17435390.2015.1073812. Crommentuijn, T., Ploder, M., Sijm, D., de Bruijn, J., van de Plassche, E., 2000. Evaluation of the Dutch environmental risk limits for metals by application of the added risk approach. Environ. Toxicol. Chem. 19, 1692–1701. https://doi.org/10.1002/etc. 5620190628. EC (European Commission), 1994. Commission Regulation (EC) No 1488/94 of 28 June 1994 laying down the principles for the assessment of risks to man and the environment of existing substances in accordance with Council Regulation (EEC) No 793/93. Off. J. Eur. Communities 3–11 No L 161/3, 29/06/1194. EC (European Commission), 1996. Technical Guidance Document (TGD) in Support of Commission Directive 93/67/EEC on Risk Assessment for New Substances and Commission Regulation (EC) No. 1488/94 on Risk Assessment for Existing Substances. Office for Official Publications of the European Commission, Luxembourg. EC (European Commission), 2001. Directive 2001/82/EC of the European Parliament and of the Council of 6 November 2001 on the Community Code Relating to Veterinary Medicinal Products. EC (European Commission), 2006a. Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 Concerning the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH), Establishing a European Chemicals Agency, Amending Directive 1999/45/EC and Repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as Well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC. EC (European Community), 2006b. Council of 18 December 2006 Concerning the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH), Establishing a European Chemicals Agency, Amending Directive 1999/45/EC and Repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as Well as Council Directive 76/769/EEC and Commission Directives 91/ 155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC. Official Journal of the European Union L396/1 of 30.12.2006. Accessible from. http://publications.europa.eu. EC (European Community), 2006c. Directive 2006/121/EC of the European Parliament and of the Council of 18 December 2006 amending Council Directive 67/548/EEC on the approximation of laws, regulations and administrative provisions relating to the classification, packaging and labelling of dangerous substances in order to adapt it to Regulation (EC) No 1907/2006 concerning the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) and establishing a European Chemicals Agency. Official Journal of the European Union L 396/850 of 30.12.2006 Accessible from. http://publications.europa.eu. EC (European Commission), 2007a. IUCLID (International Uniform Chemical Information Database) Release 5.0. http://iuclid.eu/. EC (European Commission), 2007b. Regulation (EC) No 1107/2009 of the European Parliament and of the Council of 21 October 2009 Concerning the Placing of Plant Protection Products on the Market and Repealing Council Directives 79/117/EEC and 91/414/EEC. ECHA (European Chemicals Agency), 2008. Guidance on Information Requirements and Chemical Safety Assessment. Chapter R.6: QSARs and Grouping of Chemicals. pp. 134 Helsinki (FI). EFSA PPR Panel (EFSA Panel on Plant Protection Products and their Residues), 2014. Scientific Opinion on good modelling practice in the context of mechanistic effect models for risk assessment of plant protection products. EFSA Journal 2014 12 (3), 92–3589. https://doi.org/10.2903/j.efsa.2014.3589. Ellison, C.M., Madden, J.C., Cronin, M.T.D., Enoch, S.J., 2015. Investigation of the Verhaar scheme for predicting acute aquatic toxicity: improving predictions obtained from toxtree Ver. 2.6. Chemosphere 139, 146–154. https://doi.org/10.1016/j. chemosphere.2015.06.009. Escher, B.I., Schwarzenbach, R.P., 2002. Mechanistic studies on baseline toxicity and uncoupling of organic compounds as a basis for modeling effective membrane concentrations in aquatic organisms. Aquat. Sci. 64, 20–35. https://doi.org/10.1007/ s00027-002-8052-2. Finizio, A., Villa, S., 2002. Environmental risk assessment for pesticides: a tool for decision making. Environ. Impact Assess. Rev. 22 (3), 235–248. https://doi.org/10.1016/ S0195-9255(02)00002-1. Finizio, A., Vighi, M., 2014. Predicted No effect concentration (PNEC). In: Encyclopedia of Toxicology, 3rd ed. https://doi.org/10.1016/B978-0-12-386454-3.00572-8. Focks, A., Grisoni, F., Barsi, A., Vighi, M., 2018. Predictive models in ecotoxicology: bridging the gap between scientific progress and regulatory applicability. Integr. Environ. Assess. Manag. 14 (5), 601–603. https://doi.org/10.1002/ieam.4039. Forbes, T.L., Forbes, V.E., 1993. A critique of the use of distribution-based extrapolation methods in ecotoxicology. Funct. Ecol. 7, 249–254. Forbes, V.E., Calow, P., 2002. Species sensitivity distributions revisited: a critical appraisal. Human Ecol. Risk Assess. 8, 473–492. https://doi.org/10.1080/ 10807030290879781. Garner, K.L., Suh, S., Lenihan, H.S., Keller, A.A., 2015. Species sensitivity distributions for engineered nanomaterials. Environ. Sci. Technol. 49 (9), 5753–5759. https://doi. org/10.1021/acs.est.5b00081.
7
Ecotoxicology and Environmental Safety 190 (2020) 110068
A. Finizio, et al.
voor Volksgezondheid en Milieu. (Bilthoven, The Netherlands). Veith, G.D., Call, D.J., Brooke, L.T., 1983. Structure-toxicity relationship for the fathead minnow Pimephales promelas: narcotic industrial chemicals. Can. J. Fish. Aquat. Sci. 40, 743–748. Veith, G.D., Broderius, S.J., 1987. Structure-toxicity relation-ships for industrial chemicals causing type (II) narcosis syndrome. In: Kaiser, K.L.E. (Ed.), QSAR in Environmental Toxicology - II. D. Reidel Publishing Co., Dordrecht, pp. 385–391 1987. Verhaar, H.J.M., van Leeuwen, C.J., Hermens, J.L.M., 1992. Classifying environmental pollutants. Chemosphere 25, 471–491. https://doi.org/10.1016/0045-6535(92) 90280-5. Wagner, C., Løkke, H., 1991. Estimation of ecotoxicological protection levels from NOEC toxicity data. Water Res. 25, 1237–1242. https://doi.org/10.1016/0043-1354(91) 90062-U. Wheeler, J.R., Leung, K.M.Y., Morritt, D., Sorokin, N., Rodgers, H., Toy, R., Holt, M., Whitehouse, P., Crane, M., 2002. Freshwater to saltwater toxicity extrapolations using species sensitivity distributions. Environ. Toxicol. Chem. 21, 2459–2467.
Urrestarazu Ramos, E., Vaes, W.H.J., Verhaar, H.J.M., Hermens, J.L.M., 1997. Polar narcosis: designing a suitable training set for QSAR studies. Environ. Sci. Pollut. Res. 4, 83–90. USEPA (United States Environmental Protection Agency), 1998. Guidelines for Ecological Risk Assessment. USEPA, Washington, D.C, pp. 114 EPA/630/R-95/002F. USEPA (United States Environmental Protection Agency), 2010. ECOTOX Release 4.0. http://cfpub.epa.gov/ecotox/. Van den Brink, P.J., 2013. Assessing aquatic population and community-level risks of pesticides. Environ. Toxicol. Chem. 32, 972–973. https://doi.org/10.1002/etc.2210. Van Straalen, N.M., Denneman, G.A.J., 1989. Ecological evaluation of soil quality criteria. Ecotoxicol. Environ. Saf. 18, 241–251. https://doi.org/10.1016/0147-6513(89) 90018-3. Van Straalen, N.M., van Leeuwen, C.J., 2002. European history of species sensitivity distributions. In: Posthuma, L., Suter, G.W., Traas, T.P. (Eds.), Species-Sensitivity Distributions in Ecotoxicology. Lewis, Boca Raton, FL, USA, pp. 19–34. Van Vlaardingen, P., Traas, T.P., Aldenberg, T., 2003. Normal distribution based hazardous concentration and potentially affected fraction. ETX-2000. Rijksinstituut
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A quantitative structure-activity relationships approach to predict the toxicity of narcotic compounds to aquatic communities
T
Antonio Finizio∗, Valeria Di Nica, Cristiana Rizzi, Sara Villa Department of Earth and Environmental Sciences, University of Milano Bicocca, P.zza della Scienza 1, Milano, 20126, Italy
A R T I C LE I N FO
A B S T R A C T
Keywords: QSAR Species sensitivity distribution Hazardous concentration (HC5) Narcotics Baseline toxicity for aquatic communities
Species may vary markedly in terms of their sensitivity to toxicants, and such variation can be described through the species sensitivity distribution (SSD) approach. Using SSD cumulative functions, it is possible to calculate the hazardous concentration for 5% of the species (HC5), namely the contaminant concentration at which 5% of species will be affected. HC5 is often utilised to derive the predicted no-effect concentration, or the concentration at which a chemical will likely have no toxic effects on the different species present in an ecosystem. However, the lack of sufficient ecotoxicological data frequently obstructs the derivation of SSD curves and consequently the HC5. In the last 30 years, quantitative structure-activity relationship (QSAR) models have been widely used to predict the toxicity of chemicals to single species. The aim of this study was to evaluate the possibility of extending the applicability domain of these models from single species to the community level by predicting the HC5 values for aquatic communities and bypassing the need to derive SSD curves. This approach's practical advantage is that it would allow information on the toxicity of contaminants to be obtained on a hierarchical scale (aquatic community), which is ecologically more relevant than on the scale of single species, without the need for a robust toxicity data set. In the first part of the study, two simple QSAR models were developed for narcotic and polar narcotic compounds. Then, the QSAR model developed for narcotic compounds was utilised to define the baseline toxicity for aquatic communities and to calculate the toxicity ratios for various specifically acting compounds (insecticides and herbicides).
1. Introduction The basic aim of an environmental risk assessment (ERA) for chemicals (EC European Commission, 1994, 2001, 2006a, 2006b, 2006c; USEPA, 1998; Hommen et al., 2010) is to derive threshold concentrations for environmental contaminants to protect the structure and functional attributes of natural ecosystems. Briefly, the assessment follows a tiered approach based on the characterisation of two aspects related to environmental risk, namely exposure and effects (Finizio and Villa, 2002). In the first tier (generally based on worst-case assumptions), the effects are measured using acute and chronic standardised laboratory toxicity tests performed on non-target species chosen as representatives of trophic chains (e.g. algae, Daphnia, and fish). The outcomes of these tests are utilised to calculate the predicted no-effect concentrations using appropriate safety factors (Finizio and Vighi, 2014). If the first step of the assessment indicates concerns, then highertier approaches may be considered to refine the ERA results. Suitable approaches for the refinement of effect results include microcosm and mesocosm studies (Campbell et al., 1999; Hommen et al., 2010; EFSA,
∗
2014; Van den Brink, 2013) or additional toxicity tests on a larger number of organisms to estimate the species sensitivity distribution (SSD) curves. From SSD curves, it is possible to derive the concentration at which a specified proportion (p) of species will be affected (HCp: hazardous concentration for p% of species) given a certain level of exposure; the most frequently estimated HCp is the HC5 (Posthuma et al., 2002). SSD curves are derived by collecting single-species toxicity data and assuming that the test species are random selections of a specified distribution (Kooijman, 1987). The basic assumption of the SSD concept is that the sensitivities of a set of species can be described by some type of statistical distribution (Stephan et al., 1985; Wagner and Løkke, 1991; Van Straalen and Denneman, 1989). Since its first applications (Klaine et al., 1996; Solomon et al., 1996; Hall et al., 1998; Suter, 2002; Van Straalen and van Leeuwen, 2002), the SSD approach has been used to assess the ecological risks posed by inorganic and organic contaminants as well as nanomaterials (Crommentuijn et al., 2000; Brix et al., 2001; Solomon et al., 2001; Wheeler et al., 2002; Brock et al., 2004; Gottschalk et al., 2013; Jonsson et al., 2015; Garner et al., 2015; Coll et al., 2016; Liu et al., 2016; Boeckman and Layton,
Corresponding author. E-mail address: antonio.fi[email protected] (A. Finizio).
https://doi.org/10.1016/j.ecoenv.2019.110068 Received 17 September 2019; Received in revised form 6 December 2019; Accepted 7 December 2019 0147-6513/ © 2019 Elsevier Inc. All rights reserved.
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μmol/L. Finally, for all the investigated chemicals, relevant physicochemical properties (log Kow and pKa) were retrieved through a literature search. The selected values are reported in Tables S1–S3.
2017; Chen et al., 2018). However, despite its widespread use, this approach has been the focus of criticism (Posthuma et al., 2002) related to several areas, such as the extrapolation techniques, statistics assumptions, and data limitations (Forbes and Forbes, 1993; Hopkin, 1993; Smith and Cairns, 1993; Chapman et al., 1998; Posthuma et al., 2002; Forbes and Calow, 2002). In addition, for a large number of chemical contaminants, ecotoxicological data is lacking even in the most comprehensive available databases, such as the US EPA AQUIRE database (USEPA, 2010) and the EU IUCLID and FOOTPRINT databases (EC, 2007a, 2007b; PPDB, 2009); the shortage of ecotoxicological data frequently impedes the derivation of SSD curves. Moreover, owing to experimental, ethical, and economic reasons, it is desirable to reduce the number of ecotoxicological tests as much as possible, particularly for vertebrates (EC, 2006b; EC, 2006c), by promoting the use of alternative non-testing methods to assess the ecotoxicological effects of contaminants. In this framework, the use of QSAR models are very useful as an alternative to laboratory tests. These models allow the prediction of the environmental toxicity of contaminants to single species, starting from molecular structure or physicochemical properties (Netzeva et al., 2008; Roy et al., 2015; Klüver et al., 2016, 2019) Several authors developed QSAR models based on a series of congeneric compounds (i.e. chemical classes) or by considering a chemical's mode (and mechanism) of action (Netzeva et al., 2008; Lozano et al., 2010). For instance, several QSAR models predict the toxicity of non-polar narcotics simply from the hydrophobicity of the compound, and often use the 1-octanol-water partition coefficient (log Kow) as a predictive descriptor. The narcotic mode of action is associated with the reversibly altered structure and function of cell membranes. In principle, each organic compound can act as a non-polar narcotic. Therefore, this mode of action is considered a baseline or minimal effect, and QSAR equations for this type of chemical can be used to predict a compound's minimum toxicity to single species (Veith et al., 1983). Polar narcotic compounds (or less inert chemicals) are relatively more toxic than predicted by baseline toxicity QSAR models (based on log Kow). In general, these chemicals are characterized by hydrogen bond donor acidity (e.g. phenols and anilines). QSAR models for polar narcotics confirms that their toxicity is highly dependent hydrophobicity (log Kow), however, in many cases they requires the introduction of other descriptors to account for the ionization of the compounds (i.e. pKa) (Könemann and Musch, 1981). Within this framework, this study first aimed to analyse the possibility of extending the QSAR approach from the single-species to the community level by developing simple predictive models of the HC5 for narcotic and polar narcotic compounds. Then, the QSAR model developed for narcotic compounds was utilised to define the baseline toxicity for aquatic communities and to calculate the toxicity ratios (TRs = HC5narc/HC5 for aquatic plants, invertebrates, or vertebrates) for several specifically acting compounds (insecticides and herbicides).
2.2. Species sensitivity distribution generation The SSD curves and the estimated HC5 values (μmol/L) were generated according to the method proposed by Aldenberg and Jaworska (2000) and using ETX software (Van Vlaardingen et al., 2003). A lognormal distribution model was fitted to a minimum of seven toxicity values. The HC5 values were calculated by separately considering aquatic plants, invertebrates, and vertebrates or considering all the organisms together. All toxicity data utilised for generating the SSD curves are reported in the supporting information materials (file: data.xls). 2.3. Development of QSAR models and statistical procedure QSAR models are computational tools that relate biological activities to chemical structures. They are used to predict the effects of untested chemicals based on their physical chemical properties or molecular descriptors (ECHA, 2008). In our study, we used both single and multiple linear regressions to link physical-chemical properties of nonpolar and polar narcotic compounds to their effects to aquatic communities (HC5). The dataset of the physical chemical properties are reported in Tables S1 and S2 in SI section. Each of the two developed predictive models was subsequently subject to cross-validation procedures to estimate their robustness and predictive performance (further details in S1and S2 paragraphs of SI section). All statistical analyses were performed using the XLStat® software package (www.xlstat.com). 3. Results and discussion QSAR models are based on mathematical correlations between a chemical response (e.g. toxicity) and chemical attributes defining the features of the analysed compounds (e.g. lipophilic, electronic, and steric features). QSAR models are widely utilised to predict the negative effects of contaminants on single species (ECHA, 2008; Netzeva et al., 2008; Focks et al., 2018). In this study, we suggested that the field of application of QSAR models can be widened from single species to the community level by directly predicting the HC5 values for narcotic and polar narcotic compounds. To test this hypothesis, we selected 28 narcotic and 32 polar narcotic compounds based on the classification of the mode of toxic action defined by Verhaar et al. (1992). In the following sections (3.1 and 3.2), the results related to the obtained QSAR models are reported and discussed. In addition, consideration of specifically acting compounds is reported in Section 3.3.
2. Materials and methods 3.1. Narcotic compounds 2.1. Selection of data In QSAR models, it is widely recognised that log Kow is the best reference descriptor of the hydrophobicity of a compound; in addition, there is a general consensus that narcosis-type toxicity (also called baseline toxicity or minimum toxicity) can be modelled using log Kow as the sole descriptor (Könemann, 1981; Veith et al., 1983; Ellison et al., 2015). A previous work highlighted that species differences in baseline toxicity are explained by differences in the membrane lipid content, and the lethal membrane concentrations for algae, Daphnia, and fish are nearly identical (Escher and Schwarzenbach, 2002). This finding helps to explain the interspecies toxicity correlations reported in Tremolada et al. (2004). According to these authors, the lower the specificity of the mode of action of the compounds (narcotics or less inert chemicals), the stronger the relationships. In the absence of a specific mode of action, these authors found excellent interspecific correlations between the
The present study focused on 71 chemicals belonging to different categories of organic compounds. For these compounds, the HC5 values (μmol/L) were calculated starting with single-species acute toxicity data, which were obtained from the ECOTOX database (USEPA, 2010). The selected endpoints were the median lethal concentration (LC50) for fish, the median effect concentration (EC50) for Daphnia, and the EC50 (biomass or growth rates) for algae and plants. The selection criteria were based on the test duration (96 h for fish and algae and 48 h for Daphnia). When more than one toxicity value was available, the geometric mean was calculated. Toxicity data reported as higher-than values were not considered. Furthermore, the HC5 (μg/L) values for 16 insecticides were already available in the study by Maltby et al. (2005). In the present work, these values were log transformed and expressed in 2
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Table 1 Calculated log HC5 (hazardous concentration for 5% of species) values for non-polar narcotic compounds. The HC5 (μmol/L) values are reported for each trophic level (HC5plants, HC5inv, and HC5vert) or by considering all the organisms together (HC5aqcom = HC5 of aquatic communities). The number of data points utilised for deriving the HC5 are also reported. Chemicals
log HC5plants
log HC5inv
log HC5vert
log HC5aqcom
Data (plants)
Data (inv.)
Data (vert.)
Data (aqcom)
Methanol Ethanol Propanol Isopropanol 1-butanol Isobutanol 1-pentanol 1-hexanol 1-heptanol 1-octanol Benzene Cl-benzene 1,2Cl-benzene 1,2,3Cl-benzene 1,2,4Cl-benzene 1,2,3,4Cl-benzene HCB Acetone Toluene Dibutylester Dichloromethane Diethyl ester Ethylene glycol Tetrachloroethylene 1,1,2-trichloroethane 1,2-dichloroethane 1,2-dichloropropane 1,3-dichloropropane
– – – – – – – – – – – – – – – – – – – – – – – – – – – –
5.40 5.15 4.32 4.64 – 4.09 – – 2.24 – 2.04 – 0.53 – 0.07 – – 4.81 2.04 – – – 5.20 8.18 2.55 – – –
– 5.10 – – – 4.18 – – 2.45 – 1.63 1.17 1.08 – 0.58 – – 4.89 1.73 – – – – 1.07 – 2.89 – –
5.35 5.12 4.39 4.66 4.21 4.15 3.05 2.82 2.24 1.55 1.83 1.21 0.87 0.25 0.24 −0.25 −1.41 4.84 1.89 −0.16 2.94 1.39 5.14 0.83 2.44 2.51 2.12 2.41
2 1 1 – – 1 – 1 1 3 3 2 4 2 4 1 1 3 3 2 – 1 1 1 5 1 2 2
13 15 21 10 5 7 5 2 20 6 9 3 8 4 22 3 2 25 18 4 3 3 10 13 11 5 3 2
5 7 6 3 4 8 6 4 9 6 13 10 10 4 11 3 5 10 16 6 11 5 3 9 6 7 5 4
20 23 28 13 9 16 11 7 30 15 25 15 22 10 37 7 8 38 37 12 14 9 14 23 22 13 10 8
pesticide toxicity data of Daphnia and fish (even for fungicides with algae/fish). As the HC5 values are extrapolated from SSD curves, which are derived from acute toxicity data (LC50 and/or EC50), we hypothesised possible relationships between the HC5 values of narcotic compounds and log Kow. The HC5 values for 28 non-polar narcotic compounds are reported in Table 1. Due to the scarcity of data, it was not possible to derive HC5 values for the aquatic plants (HC5plants); however, the calculation of HC5 values for invertebrates (HC5inv) and vertebrates (HC5vert) was possible for 14 and 11 substances, respectively. The differences between HC5inv and HC5vert were very small, thereby indicating a similar degree of toxicity of these substances to the two trophic levels of the aquatic community. Although no HC5plants values were calculated, the few toxicity data points on aquatic plant species available in the literature were of the same order of magnitude as those found for invertebrates and vertebrates. The HC5 values for aquatic communities (HC5aqcom) were derived by combining all the available data (plants, invertebrates, and vertebrates). All calculated HC5aqcom values passed the Anderson-Darling test, thereby indicating a normal distribution of the toxicity data set. The HC5aqcom values reported in Table 1 were utilised to develop QSAR models. In the first step, QSAR models were derived by considering single classes of congeneric compounds; subsequently, a more general QSAR model was obtained. Fig. S1-A and S1-B reports the relationships between log 1/HC5aqcom and log Kow (Table S1) for single congeneric classes of narcotic compounds, whereas the more general QSAR model, which included all the analysed narcotic compounds, is depicted in Fig. 1. In all cases, significant correlations were found, thereby indicating that hydrophobicity was the key descriptor explaining the toxicity of non-polar narcotic compounds not only for single species, but also for aquatic communities. The following equations were obtained:
log 1/HC5aqcom (alcohols) = −4.78 − log K ow (R² adj = 0.98; n= 10; p< 0.001)
(1)
log 1/HC5aqcom (benz. der.) = −4.06 + 0.97 × log K ow (R² adj = 0.99; n= 7; p< 0.0001)
(2)
log 1/HC5aqcom (non − polar narc.) = −4.52 + 1.05 × log K ow (R² adj = 0.97; p< 0.0001)
(3)
To evaluate the predictive capability and robustness of the general QSAR model (Eq. (3)), a cross-validation method was utilised using the leave-many-out (LMO) approach (groups of seven compounds were excluded from the original data set and used for cross-validation). This process was repeated four times by randomly choosing compounds to be excluded. The results obtained confirmed the robustness and predictive capability of the relationship between log 1/HC5aqcom and log Kow. More details are reported in Section S1 of the supporting materials. 3.2. Polar narcotic compounds Polar narcotic compounds are slightly more toxic than non-polar narcotic compounds and are generally 5 to 10 times more toxic than the baseline toxicity (Verhaar et al., 1992). Several QSAR models for single species are present in the literature for these compounds (Veith and Broderius, 1987; Urrestarazu Ramos et al., 1997). In general, they are bi-parametric equations in which one of the parameters is log Kow and the other is a descriptor of the electrophilicity of the compounds (i.e. eLumo; pKa) (Chen et al., 2007; Selassie and Verma, 2015). Table 2 presents the calculated HC5 values for 32 polar narcotics compounds. For this category of chemicals, toxicity data for primary producers were scarce, whereas data for primary and secondary consumers were sufficiently available to derive the HC5 values. All HC5aqcom values, which were calculated using all available ecotoxicity data, passed the Anderson-Darling test, which indicated a normal 3
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Fig. 1. Relationship between log 1/HC5aqcom (hazardous concentration for 5% of species for aquatic communities) and log Kow for narcotic compounds.
distribution of the toxicity data set. The data reported in Tables 2 and S2 were utilised to develop QSAR models by considering both single classes of congeners (anilines and phenols) and all the substances together. Equations (4)–(6) report the relationships between log 1/HC5aqcom and lipophilicity (log Kow). The obtained equations were as follows:
log 1/HC5aqcom (phenols) = −2.5 + 0.57 × log K ow (R²adj = 0.82; p < 0.0001)
log 1/HC5aqcom (all pol. narc.) = −1.8 + 0.42 × log K ow (R²adj = 0.40; p< 0.0001)acs
(6)
Even if they were significant, the obtained results indicated that the log Kow values did not completely explain the toxicity of these compounds to aquatic communities, particularly when all the compounds were considered together. Therefore, a further descriptor (pKa) was
log 1/HC5aqcom (anilines) = − 1.6 + 0.38 × log K ow (R² adj = 0.88; p< 0.001)
(5)
(4)
Table 2 Calculated log HC5 (hazardous concentration for 5% of species) values for polar narcotic compounds. The HC5 values (μmol/L) are reported for each trophic level (HC5plants, HC5inv, and HC5vert) or for all the organisms together (HC5aqcom = HC5 aquatic communities). The number of data points utilised for deriving the HC5 values are also reported. Chemicals
log HC5plants
log HC5inv
log HC5vert
log HC5aqcom
Data (plants)
Data (inv.)
Data (vert.)
Data (aqcom)
2,3,4,6Cl-phenol 2,3,4Cl-phenol 2,3,5Cl-phenol 2,4,5-chlorophenol 2,4,6-chlorophenol 2,4Cl-phenol 2,4-dichloraniline 2,4-dimethylphenol 2,4-dinitrophenol 2,6Cl-phenol 2Cl-aniline 2Cl-phenol 2-nitrotoluene 2-phenylphenol 3,4-chloromethylaniline 3,4-dichloraniline 3Cl-aniline 4Cl-aniline 4Cl-nitrobenzene 4Cl-phenol 4-nitrophenol 4-nitrotoluene 4-nonylphenol Aniline Benzaldehyde Formalin m-cresol o-cresol p-cresol Pentachlorophenol Phenol Quinoline
– – – – – – – – – – – – – – – – – – – – – – – – – – – – – −1.15 – –
0.11 – – 0.25 – 0.94 – – – – – – – – 0.62 0.55 – – – 1.37 1.35 – −0.40 1.06 – 3.40 – 1.85 – −0.59 1.43 –
−0.12 – – 0.31 0.35 0.93 – – 0.41 – – 1.60 – – – 0.99 – – – 1.21 1.64 – −0.62 2.02 – 2.85 1.62 1.91 1.51 −0.79 1.84 –
−0.14 0.67 0.37 0.32 0.37 0.74 0.55 0.93 0.59 1.21 0.88 1.20 1.61 0.83 0.71 0.56 0.68 0.81 0.79 1.24 1.48 1.62 −0.62 1.36 1.30 2.84 1.59 1.84 1.50 −0.83 1.70 2.09
2 1 – 2 4 4 7 1 3 2 4 3 3 2 – 3 4 2 2 6 4 3 1 2 – – 1 1 1 8 4 2
9 3 2 6 3 7 3 5 4 5 2 6 3 2 7 14 3 5 3 11 11 2 17 26 5 10 2 18 2 89 8 6
7 4 6 9 11 11 5 6 14 4 3 7 4 4 1 7 3 6 3 7 12 3 21 13 6 19 7 9 12 55 11 4
18 8 8 17 18 22 15 12 21 11 9 16 10 8 8 24 10 13 8 24 27 8 39 41 11 29 10 28 15 152 23 12
4
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Fig. 2. Two parametric quantitative structure activity relationships model for predicting the HC5 (hazardous concentration for 5% of species) of polar narcotic compounds for aquatic communities.
sensitive than the others (Sala et al., 2012; Giddings et al., 2019). For this reason, the possibility of developing QSAR models for specifically acting compounds, which comprised all the aquatic organisms, was not taken into consideration in this study. According to the classification scheme proposed by Verhaar et al. (1992), four modes of action can be described, namely class I: inert chemicals; class II: relatively inert chemicals; class III: reactive chemicals; and class IV: specifically acting chemicals. The inert chemicals are chemicals that do not interact with specific receptors in the organism, and are typically associated with non-polar narcosis. This classification was based on the TR, which is the ratio between the estimated baseline toxicity and the corresponding experimental LC50 for single species (TR = LC50baseline/LC50experimental). The LC50baseline was calculated using QSAR models for single species, which were developed for narcotic compounds and based on log Kow (EC, 1996). More recently, Tremolada et al. (2004) proposed a change in the classification scheme of Verhaar et al. (1992) (narcotics: 0.5 < log TR < 0.5; less inert chemicals: 0.5 < log TR < 1.5; reactive chemicals: 1.5 < log TR < 2.5; specifically acting chemicals: 2.5 < log TR < 4; highly specifically acting chemicals: log TR > 4). Similar to these previous studies, we proposed the use of the TR for groups of species (TRgs), as follows:
introduced in the model. Equation (7) presents the two-parameter QSAR model obtained when all the polar narcotic compounds were considered, as follows:
log 1/HC5aqcom (pol. narc.) = −1.86 + 0.51 × log K ow − 0.06 × pK a (R2adj = 0.83; p< 0.0001) (7) In Fig. 2, a graphical representation of the experimental vs. predicted log 1/HC5aqcom values is depicted (QSAR models for anilines and phenols are reported in Fig. S1-C and S1-D). The robustness and predictive capability of the obtained QSAR model for polar narcotic compounds were tested using the LMO technique. The overall data set was randomly split into four training sets (Table S5) and four validation sets (Table S6); the latter represented 30% of the entire data set. Further details are reported in Section S2. 3.3. Specifically acting compounds Table 3 shows the calculated log HC5 values for the three different taxonomic groups representative of the aquatic communities for 27 pesticides (insecticides and herbicides). For specifically acting chemicals such as pesticides, combining all the organisms would result in a bimodal SSD because at least one taxonomic group would be more
TR gs = HC5aqcom /HC5gs
(8)
Table 3 Calculated log HC5 (hazardous concentration for 5% of species) values (μmol/L) for the different trophic levels of aquatic communities for specifically acting compounds (16 insecticides and 11 herbicides). Chemicals Insecticides Azinphos-CH3 Carbaryl Carbofuran Chlorpyrifos Cypermethrin Deltamethrin Diazinon Diflubenzuron Herbicides Atrazine Ametryn Alachlor Butachlor Chlorsulfuron Cyanazine a
log HC5aqplants (μmol/L)
log HC5inv (μmol/L)
log HC5vert (μmol/L)
Chemicals
log HC5aqplants (μmol/L)
log HC5inv (μmol/L)
log HC5vert (μmol/L)
– 0.52 – – – – – –
−3.90a −1.88a −2.98a −3.70a −5.14a −4.75a −2.93a −3.79a
−2.95a 0.35a −0.51a −2.78a −3.39a −3.38a −0.77a −1.13a
Fenitrothion Fenvalerate λ-cyhalothrin Lindane Methoxychlor Parathion-C2H5 Parathion-CH3 Permethrin
0.45a – – – – – – –
−2.94a −4.51a −5.18a −2.57a −2.87a −3.10a −2.93a −3.61a
−0.72a −3.34a −3.75a −1.78a −1.88a −0.50a −2.25a −3.00a
−1.24 – −1.27 – −1.74 –
1.32 – 1.32 – – –
1.45 0.94 0.97 −0.14 – 1.23
Diuron Metolachlor Simazine Terbutryn Trifluralin
– −0.76 −0.64 – –
– – −1.00 – −1.39
0.49 1.20 1.48 0.85 −0.96
Data from Maltby et al. (2005). 5
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Table 4 Calculated log TRgs (toxicity ratio for groups of species) values of pesticides. Chemicals
log TRaqplants
log TRinv
log TRvert
Insecticides Azinphos-CH3 Carbaryl Carbofuran Chlorpyrifos Cypermethrin Deltamethrin Diazinon Diflubenzuron Fenitrothion Fenvalerate λ-cyhalothrin Lindane Methoxychlor Parathion-C2H5 Parathion-CH3 Permethrin
– 1.5 – – – – – – 0.5 – – – – – – –
5.3 3.9 5.3 3.3 3.0 2.4 4.0 4.2 3.9 3.8 2.3 3.2 2.0 3.6 4.3 1.7
4.4 1.7 2.8 2.4 1.2 1.1 1.8 −1.4 1.7 2.6 0.9 2.4 1.0 1.0 0.6 1.1
Chemicals
log TRaqplants
log TRinv
log TRvert
Herbicides Alachlor Ametryn Atrazine Butachlor Chlorsulfuron Cyanazine Diuron Metolachlor Simazine Terbutryn Trifluralin
2.5 – 2.9 – 7.3 – – 2.0 2.8 – –
−0.05 – 0.40 – – – – 0.04 1.20 – 0.30
0.3 2.7 0.2 0.4 – 3.4 2.2 – 0.7 1.5 −2.0
environmental quality criteria aiming to preserve the structure and functionality of aquatic ecosystems. Although there is still a scientific debate and criticism regarding the usefulness of the HC5, this parameter is frequently used as the basis for setting environmental quality standards and for higher-tier risk assessment. In this study, two simple QSAR models were proposed to predict the HC5 of non-polar and polar narcotic compounds. The practical advantage of this approach was that one could directly estimate the hazard posed by contaminants to aquatic communities instead of single species. In addition, the QSAR model for non-polar narcotics has been used to define the baseline toxicity for aquatic communities and to derive the TRgs, which can provide information about the toxicity of specifically acting compounds to a specific group in the aquatic community.
where: HC5aqcom = calculated baseline toxicity for the aquatic community using Eq. (6). HC5gs = hazardous concentration for 5% of species belonging to a specific group of aquatic species (plants, invertebrates, and vertebrates). The TRgs could be helpful to derive toxicity range factors to be applied to estimate the first approximation of the toxicity of specifically acting compounds to a specific group in the aquatic community. Table 4 shows the calculated log TRgs values for the investigated pesticides. Based on the classification scheme of Tremolada et al. (2004), all insecticides fell into the categories of specifically or highly specifically acting chemicals for both aquatic invertebrates and vertebrates (except for methoxychlor and permethrin for both taxonomic groups and diflubenzuron for vertebrates). On the contrary, they were classified as less inert chemicals for aquatic plants. Invertebrates were the most sensitive taxonomic group to insecticides. Particularly, many phosphorodithioates (azinphos-CH3, diazinon, and parathion-CH3) and the carbamate carbofuran were highly specific to invertebrates (log TRinv range = 4.2–5.3). These findings were mostly in agreement with the considerations given in Tremolada et al. (2004) for single species. These authors highlighted that the modes of action of organophosphorus and carbamate insecticides are more specific for Daphnia than for trout. Furthermore, these authors also indicated that phyrethroids are less specific than organophosphorus and carbamates for these two species, and are slightly more specific for trout than Daphnia. Eventually, these considerations can be upscaled from single species to taxonomic groups. However, at this hierarchical level, pyrethroids seemed to be more specific to invertebrates. The majority of herbicides were classified as specifically acting chemicals for aquatic plants, whereas they were classified as narcotic or less inert narcotic compounds for invertebrates and vertebrates (with the exception of ametryn cyanazine and terbutryn, which seemed to behave as specifically acting chemicals for fish). Chlorsulfuron seemed to be the herbicide with the most specific mechanism of action for aquatic plants. This was not surprising considering that for weed control, the effective dose of sulfonylureas is at the level of g/ha (1000 times lower than the dose of other herbicides).
CRediT authorship contribution statement Antonio Finizio: Project administration; Funding acquisition; Conceptualization; Methodology; Resources; Writing – Original draft; Valeria Di Nica: Application of model software; Formal analysis; Data curation. Cristiana Rizzi: Formal analysis; Application of model software; Data curation. Sara Villa: Conceptualization; Methodology; Supervision; Writing - review & editing. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecoenv.2019.110068. References Aldenberg, T., Jaworska, J.S., 2000. Uncertainty of the hazardous concentration and fraction affected for normal species sensitivity distributions. Ecotoxicol. Environ. Saf. 46, 1–18. https://doi.org/10.1006/eesa.1999.1869. Boeckman, C.J., Layton, R., 2017. Use of species sensitivity distributions to characterize hazard for insecticidal traits. J. Invertebr. Pathol. 142, 68–70. https://doi.org/10. 1016/j.jip.2016.08.006. Brix, K.V., De Forest, D.K., Adams, W.J., 2001. Assessing acute and chronic copper risks to freshwater aquatic life using species sensitivity distributions for different taxonomic groups. Environ. Toxicol. Chem. 20, 1846–1856. https://doi.org/10.1002/etc. 5620200831. Brock, T.C.M., Crum, S.J.H., Deneer, J.W., Heimbach, F., Roijackers, R.M.M., Sinkeldam, J.A., 2004. Comparing aquatic risk assessment methods for the photosynthesis-inhibiting herbicides metribuzin and metamitron. Environ. Pollut. 130, 403–426. https://doi.org/10.1016/j.envpol.2003.12.022. Campbell, P.J., Arnold, D.J.S., Brock, T.C.M., Grandy, N.J., Heger, W., Heimbach, F., Maund, S.J., Streloke, M., 1999. Guidance Document on Higher-Tier Aquatic Risk Assessment for Pesticides (HARAP). SETAC-Europe, Brussels (BE), pp. 179. Chapman, P.M., Fairbrother, A., Brown, D., 1998. A critical evaluation of safety (uncertainty) factors for ecological risk assessment. Environ. Toxicol. Chem. 17 (1), 99–108. Chen, C.Y., Ko, C.W., Lee, P.I., 2007. Toxicity of substituted anilines to
4. Conclusions Information on aquatic toxicity is generally required in order to assess the hazards and risks of contaminants and to define 6
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A. Finizio, et al.
Giddings, J.M., Wirtz, J., Campana, D., Dobbs, M., 2019. Derivation of combined species sensitivity distributions for acute toxicity of pyrethroids to aquatic animals. Ecotoxicology 28, 242–250. https://doi.org/10.1007/s10646-019-02018-0. Gottschalk, F., Kost, E., Nowack, B., 2013. Engineered nanomaterials (ENM) in waters and soils: a risk quantification based on probabilistic exposure and effect modeling. Environ. Toxicol. Chem. 32, 1278–1287. https://doi.org/10.1002/etc.2177. Hall Jr., L.W., Scott, M.C., Killen, W.D., 1998. Ecological risk assessment of copper and cadmium in surface waters of Chesapeake Bay watershed. Environ. Toxicol. Chem. 17, 1172–1189. https://doi.org/10.1002/etc.5620170626. Hommen, U., Baveco, J.M., Galic, N., van den Brink, P.J., 2010. Potential application of ecological models in the European environmental risk assessment of chemicals. I. Review of protection goals in EU directives and regulations. Integr. Environ. Assess. Manag. 6 (3), 325–337. https://doi.org/10.1002/ieam.69. Hopkin, S.P., 1993. Ecological implications of “95% protection levels” for metals in soil. Oikos 66, 137–141. https://doi.org/10.2307/3545206. Jonsson, C.M., Silva, M.S.G.M., Macedo, V.S., Dantzger, D.D., Vallim, J.H., Marigo, A.L.S., Aoyama, H., 2015. Prediction of a low‐risk concentration of diflubenzuron to aquatic organisms and evaluation of clay and gravel in reducing the toxicity. Pan Am. J. Aquat. Sci. 10, 259–272. Klaine, S.J., Cobb, G.P., Dickerson, R.L., Dixon, K.R., Kendall, R.J., Smith, E.E., Solomon, K.R., 1996. An ecological risk assessment for the use of the biocide, dibromonitrilopropionamide (DBNPA), in industrial cooling systems. Environ. Toxicol. Chem. 15, 20–21. Klüver, N., Vogs, C., Altenburger, R., Escher, B.I., Scholz, S., 2016. Development of a general baseline toxicity QSAR model for the fish embryo acute toxicity test. Chemosphere 164, 164–173. https://doi.org/10.1016/j.chemosphere.2016.08.079. Klüver, N., Bittermann, K., Escher, B.I., 2019. QSAR for baseline toxicity and classification of specific modes of action of ionizable organic chemicals in the zebrafish embryo toxicity test. Aquat. Toxicol. 207, 110–119. https://doi.org/10.1016/j.aquatox.2018. 12.003. Könemann, H., 1981. Quantitative structure-activity relationships in fish toxicity studies Part 1: relationship for 50 industrial pollutants. Toxicology 19, 209–221. https://doi. org/10.1016/0300-483X(81)90130-X. Könemann, H., Musch, A., 1981. Quantitative structure-toxicity relationships in fish toxicity study Part 2: influence of pH on the QSAR of chlorophenols. Toxicology 19, 223–228. Kooijman, S.A.L.M., 1987. A safety factor for LC50 values allowing for differences in sensitivity among species. Water Res. 21, 269–276. https://doi.org/10.1016/00431354(87)90205-3. Liu, N., Wang, Y., Yang, Q., Lv, Y., Jin, X., Giesy, J.P., Johnson, A.C., 2016. Probabilistic assessment of risks of diethylhexyl phthalate (DEHP) in surface waters of China on reproduction of fish. Environ. Pollut. 213, 482–488. https://doi.org/10.1016/j. envpol.2016.03.005. Lozano, S., Lescot, E., Halm, M.P., Lepailleur, A., Bureau, R., Rault, S., 2010. Prediction of acute toxicity in fish by using QSAR methods and chemical modes of action. J. Enzym. Inhib. Med. Chem. 25 (2), 195–203. https://doi.org/10.3109/ 14756360903169857. Maltby, L., Blake, N., Brock, T.C.M., van den Brink, P.J., 2005. Insecticide species sensitivity distributions: importance of test species selection and relevance to aquatic ecosystems. Environ. Toxicol. Chem. 24, 379–388. https://doi.org/10.1897/04025R.1. Netzeva, T.I., Pavan, M., Worth, A.P., 2008. Review of (quantitative) structure–activity relationships for acute aquatic toxicity. QSAR Comb. Sci. 27, 77–90. https://doi.org/ 10.1002/qsar.200710099. Posthuma, L., Suter, G.W., Traas, T.P. (Eds.), 2002. Species-Sensitivity Distributions in Ecotoxicology. Lewis Publishers, Boca Raton, FL, USA. PPDB, 2009. The Pesticide Properties Database (PPDB) Developed by the Agriculture & Environment Research Unit (AERU). University of Hertfordshire, funded by UK national sources and the EU-funded FOOTPRINT project (FP6-SSP-022704). http:// sitem.herts.ac.uk/aeru/footprint/. Roy, K., Kar, S., Das, R.N., 2015. Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment, first ed. Academic Press, pp. 484. Sala, S., Migliorati, S., Monti, G.S., Vighi, M., 2012. SSD-based rating system for the classification of pesticide risk on biodiversity. Ecotoxicology 21, 1050–1062. https:// doi.org/10.1007/s10646-012-0858-7. Selassie, C., Verma, R.P., 2015. QSAR of toxicology of substituted phenols. J. Pestic. Sci. 40, 1–12. https://doi.org/10.1584/jpestics.D14-097. Smith, E.P., Cairns Jr., J., 1993. Extrapolation methods for setting ecological standards for water quality: statistical and ecological concerns. Ecotoxicology 2, 203–219. Solomon, K.R., Baker, D.B., Richards, R.P., Dixon, K.R., Klaine, S.J., La Point, T.W., Kendall, R.J., Weisskopf, C.P., Giddings, J.M., Giesy, J.P., Hall Jr., L.W., Williams, W.M., 1996. Ecological risk assessment of atrazine in North American surface waters. Environ. Toxicol. Chem. 15, 31–76. Solomon, K.R., Giddings, J.M., Maund, S.J., 2001. Probabilistic risk assessment of cotton pyrethroids: I. Distributional analyses of laboratory aquatic toxicity data. Environ. Toxicol. Chem. 20, 652–659. Stephan, C.E., Mount, D.I., Hansen, D.J., Gentile, J.H., Chapman, G.A., Brungs, W.A., 1985. Guidelines for Deriving Numerical National Water Quality Criteria for the Protection of Aquatic Organisms and Their Use. Duluth: US Environmental Research Laboratories. Suter, G.W., 2002. North American history of species sensitivity distributions. In: Posthuma, L., Suter, G.W., Traas, T.P. (Eds.), Species-Sensitivity Distributions in Ecotoxicology. Lewis, Boca Raton, FL, USA, pp. 11–17. Tremolada, P., Finizio, A., Villa, S., Gaggi, C., Vighi, M., 2004. Quantitative inter-specific chemical activity relationships of pesticides in the aquatic environment. Aquat. Toxicol. (N. Y.) 67, 87–103. https://doi.org/10.1016/j.aquatox.2003.12.003.
Pseudokirchneriella subcapitata and quantitative structure-activity relationship analysis for polar narcotics. Environ. Toxicol. Chem. 26 (6), 1158–1164. https://doi. org/10.1897/06-293R.1. Chen, G., Peijnenburg, W.J.G.M., Xiao, Y., Vijver, M.G., 2018. Developing species sensitivity distributions for metallic nanomaterials considering the characteristics of nanomaterials, experimental conditions, and different types of endpoints. Food Chem. Toxicol. 112, 563–570. https://doi.org/10.1016/j.fct.2017.04.003. Coll, C., Notter, D., Gottschalk, F., Sun, T., Som, C., Nowack, B., 2016. Probabilistic environmental risk assessment of five nanomaterials (nano-TiO2, nano-Ag, nano-ZnO, CNT, and fullerenes). Nanotoxicology 10, 436–444. https://doi.org/10.3109/ 17435390.2015.1073812. Crommentuijn, T., Ploder, M., Sijm, D., de Bruijn, J., van de Plassche, E., 2000. Evaluation of the Dutch environmental risk limits for metals by application of the added risk approach. Environ. Toxicol. Chem. 19, 1692–1701. https://doi.org/10.1002/etc. 5620190628. EC (European Commission), 1994. Commission Regulation (EC) No 1488/94 of 28 June 1994 laying down the principles for the assessment of risks to man and the environment of existing substances in accordance with Council Regulation (EEC) No 793/93. Off. J. Eur. Communities 3–11 No L 161/3, 29/06/1194. EC (European Commission), 1996. Technical Guidance Document (TGD) in Support of Commission Directive 93/67/EEC on Risk Assessment for New Substances and Commission Regulation (EC) No. 1488/94 on Risk Assessment for Existing Substances. Office for Official Publications of the European Commission, Luxembourg. EC (European Commission), 2001. Directive 2001/82/EC of the European Parliament and of the Council of 6 November 2001 on the Community Code Relating to Veterinary Medicinal Products. EC (European Commission), 2006a. Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 Concerning the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH), Establishing a European Chemicals Agency, Amending Directive 1999/45/EC and Repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as Well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC. EC (European Community), 2006b. Council of 18 December 2006 Concerning the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH), Establishing a European Chemicals Agency, Amending Directive 1999/45/EC and Repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as Well as Council Directive 76/769/EEC and Commission Directives 91/ 155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC. Official Journal of the European Union L396/1 of 30.12.2006. Accessible from. http://publications.europa.eu. EC (European Community), 2006c. Directive 2006/121/EC of the European Parliament and of the Council of 18 December 2006 amending Council Directive 67/548/EEC on the approximation of laws, regulations and administrative provisions relating to the classification, packaging and labelling of dangerous substances in order to adapt it to Regulation (EC) No 1907/2006 concerning the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) and establishing a European Chemicals Agency. Official Journal of the European Union L 396/850 of 30.12.2006 Accessible from. http://publications.europa.eu. EC (European Commission), 2007a. IUCLID (International Uniform Chemical Information Database) Release 5.0. http://iuclid.eu/. EC (European Commission), 2007b. Regulation (EC) No 1107/2009 of the European Parliament and of the Council of 21 October 2009 Concerning the Placing of Plant Protection Products on the Market and Repealing Council Directives 79/117/EEC and 91/414/EEC. ECHA (European Chemicals Agency), 2008. Guidance on Information Requirements and Chemical Safety Assessment. Chapter R.6: QSARs and Grouping of Chemicals. pp. 134 Helsinki (FI). EFSA PPR Panel (EFSA Panel on Plant Protection Products and their Residues), 2014. Scientific Opinion on good modelling practice in the context of mechanistic effect models for risk assessment of plant protection products. EFSA Journal 2014 12 (3), 92–3589. https://doi.org/10.2903/j.efsa.2014.3589. Ellison, C.M., Madden, J.C., Cronin, M.T.D., Enoch, S.J., 2015. Investigation of the Verhaar scheme for predicting acute aquatic toxicity: improving predictions obtained from toxtree Ver. 2.6. Chemosphere 139, 146–154. https://doi.org/10.1016/j. chemosphere.2015.06.009. Escher, B.I., Schwarzenbach, R.P., 2002. Mechanistic studies on baseline toxicity and uncoupling of organic compounds as a basis for modeling effective membrane concentrations in aquatic organisms. Aquat. Sci. 64, 20–35. https://doi.org/10.1007/ s00027-002-8052-2. Finizio, A., Villa, S., 2002. Environmental risk assessment for pesticides: a tool for decision making. Environ. Impact Assess. Rev. 22 (3), 235–248. https://doi.org/10.1016/ S0195-9255(02)00002-1. Finizio, A., Vighi, M., 2014. Predicted No effect concentration (PNEC). In: Encyclopedia of Toxicology, 3rd ed. https://doi.org/10.1016/B978-0-12-386454-3.00572-8. Focks, A., Grisoni, F., Barsi, A., Vighi, M., 2018. Predictive models in ecotoxicology: bridging the gap between scientific progress and regulatory applicability. Integr. Environ. Assess. Manag. 14 (5), 601–603. https://doi.org/10.1002/ieam.4039. Forbes, T.L., Forbes, V.E., 1993. A critique of the use of distribution-based extrapolation methods in ecotoxicology. Funct. Ecol. 7, 249–254. Forbes, V.E., Calow, P., 2002. Species sensitivity distributions revisited: a critical appraisal. Human Ecol. Risk Assess. 8, 473–492. https://doi.org/10.1080/ 10807030290879781. Garner, K.L., Suh, S., Lenihan, H.S., Keller, A.A., 2015. Species sensitivity distributions for engineered nanomaterials. Environ. Sci. Technol. 49 (9), 5753–5759. https://doi. org/10.1021/acs.est.5b00081.
7
Ecotoxicology and Environmental Safety 190 (2020) 110068
A. Finizio, et al.
voor Volksgezondheid en Milieu. (Bilthoven, The Netherlands). Veith, G.D., Call, D.J., Brooke, L.T., 1983. Structure-toxicity relationship for the fathead minnow Pimephales promelas: narcotic industrial chemicals. Can. J. Fish. Aquat. Sci. 40, 743–748. Veith, G.D., Broderius, S.J., 1987. Structure-toxicity relation-ships for industrial chemicals causing type (II) narcosis syndrome. In: Kaiser, K.L.E. (Ed.), QSAR in Environmental Toxicology - II. D. Reidel Publishing Co., Dordrecht, pp. 385–391 1987. Verhaar, H.J.M., van Leeuwen, C.J., Hermens, J.L.M., 1992. Classifying environmental pollutants. Chemosphere 25, 471–491. https://doi.org/10.1016/0045-6535(92) 90280-5. Wagner, C., Løkke, H., 1991. Estimation of ecotoxicological protection levels from NOEC toxicity data. Water Res. 25, 1237–1242. https://doi.org/10.1016/0043-1354(91) 90062-U. Wheeler, J.R., Leung, K.M.Y., Morritt, D., Sorokin, N., Rodgers, H., Toy, R., Holt, M., Whitehouse, P., Crane, M., 2002. Freshwater to saltwater toxicity extrapolations using species sensitivity distributions. Environ. Toxicol. Chem. 21, 2459–2467.
Urrestarazu Ramos, E., Vaes, W.H.J., Verhaar, H.J.M., Hermens, J.L.M., 1997. Polar narcosis: designing a suitable training set for QSAR studies. Environ. Sci. Pollut. Res. 4, 83–90. USEPA (United States Environmental Protection Agency), 1998. Guidelines for Ecological Risk Assessment. USEPA, Washington, D.C, pp. 114 EPA/630/R-95/002F. USEPA (United States Environmental Protection Agency), 2010. ECOTOX Release 4.0. http://cfpub.epa.gov/ecotox/. Van den Brink, P.J., 2013. Assessing aquatic population and community-level risks of pesticides. Environ. Toxicol. Chem. 32, 972–973. https://doi.org/10.1002/etc.2210. Van Straalen, N.M., Denneman, G.A.J., 1989. Ecological evaluation of soil quality criteria. Ecotoxicol. Environ. Saf. 18, 241–251. https://doi.org/10.1016/0147-6513(89) 90018-3. Van Straalen, N.M., van Leeuwen, C.J., 2002. European history of species sensitivity distributions. In: Posthuma, L., Suter, G.W., Traas, T.P. (Eds.), Species-Sensitivity Distributions in Ecotoxicology. Lewis, Boca Raton, FL, USA, pp. 19–34. Van Vlaardingen, P., Traas, T.P., Aldenberg, T., 2003. Normal distribution based hazardous concentration and potentially affected fraction. ETX-2000. Rijksinstituut
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