Ecotoxicology and Environmental Safety 115 (2015) 257–262
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Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv
Quantitative structure–activity relationship (QSAR) prediction of (eco)toxicity of short aliphatic protic ionic liquids Brezana Peric n, Jordi Sierra, Esther Martí, Robert Cruañas, Maria Antonia Garau Unitat d'Edafologia, Faculty of Pharmacy, University of Barcelona, Av. Joan XXIII s/n, 08028 Barcelona, Spain
art ic l e i nf o
a b s t r a c t
Article history: Received 24 September 2014 Received in revised form 14 February 2015 Accepted 17 February 2015 Available online 27 February 2015
Ionic liquids (ILs) are considered as a group of very promising compounds due to their excellent properties (practical non-volatility, high thermal stability and very good and diverse solving capacity). The ILs have a good prospect of replacing traditional organic solvents in vast variety of applications. However, the complete information on their environmental impact is still not available. There is also an enormous number of possible combinations of anions and cations which can form ILs, the fact that requires a method allowing the prediction of toxicity of existing and potential ILs. In this study, a group contribution QSAR model has been used in order to predict the (eco)toxicity of protic and aprotic ILs for five tests (Microtoxs, Pseudokirchneriella subcapitata and Lemna minor growth inhibition test, and Acetylcholinestherase inhibition and Cell viability assay with IPC-81 cells). The predicted and experimental toxicity are well correlated. A prediction of EC50 for these (eco)toxicity tests has also been made for eight representatives of the new family of short aliphatic protic ILs, whose toxicity has not been determined experimentally to date. The QSAR model applied in this study can allow the selection of potentially less toxic ILs amongst the existing ones (e.g. in the case of aprotic ILs), but it can also be very helpful in directing the synthesis efforts toward developing new “greener” ILs respectful with the environment (e.g. short aliphatic protic ILs). & 2015 Elsevier Inc. All rights reserved.
Keywords: Ionic liquids QSAR modelling Group contribution Aquatic ecotoxicity Cytotoxicity Acetylcholinesterase inhibition
1. Introduction Ionic liquids (ILs) have gained a lot of attention in the past decades because of their properties, such as negligible vapour pressure, high thermal stability and very high and versatile solving capacity. These properties make them interesting for many applications, usually aiming to replace volatile organic and toxic solvents, which have been traditionally used in laboratories and industries up to now (Petkovic et al., 2011). Even though ILs are practically non-volatile and thus cannot get into atmosphere and cause air pollution, many of them are soluble in water and can represent a danger for the environment if released with industrial and laboratory effluents (Docherty et al., 2007). There are many studies that demonstrate the aquatic toxicity of the ILs (Pham et al., 2010). So far, the investigators have been focusing mainly on aprotic ionic liquids (AILs), namely the ones derived from imidazolium and pyridinium. The protic ionic liquids (PILs) have come into focus only recently and show a good potential, both in their applications and “greenness”. The ILs are chemical products, and that means that the n
Corresponding author. Fax: þ 34 93 402 4495. E-mail address: [email protected] (B. Peric).
http://dx.doi.org/10.1016/j.ecoenv.2015.02.027 0147-6513/& 2015 Elsevier Inc. All rights reserved.
requirements of the new European Union Regulation on Chemical Substances REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) apply to ILs as well (European Union, 2006). It is considered that the possible number of ILs is about 1018, and around one million can be easily synthesized in the laboratory. It is very important to discover the hazardous potential of ILs before putting them into use, and taking into account such a large potential number of ILs, it is important to save up time and money and possibly do a pre-application screening in order to determine the potential negative effect of ILs on the environment. As in depth studies of water and terrestrial ecotoxicity of ILs are either incomplete or missing, the prediction of the environmental impact of the new and existing, but not yet tested ILs, is a necessity for the more widespread application of the ILs. In silico methods such as QSAR (quantitative structure–activity relationship) can be used for predicting toxicity and thus reduce costly and time consuming toxicity testing, either in vitro or in vivo. The use of the QSAR modelling for the prediction of toxicity is recognized by REACH regulation, stating that properties determined with the use of in silico methods are equivalent to laboratory testing. The QSAR modelling, as its name indicates, provides toxicity data based on the quantitative relationship between chemical structure and biological and/or toxicological activity, using chemical descriptors generated from the molecular structure. These descriptors are
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statistically analyzed in order to develop a model that would describe the desired activity e.g. toxicity (Amberg, 2013). The QSAR studies on toxicity of ILs have been far less frequent compared to other chemicals (Das and Roy, 2013). A new family of short aliphatic PILs has been thoroughly investigated on their (eco)toxicity and biodegradability, both in water and soil, together with comparative studies regarding the most frequently used AILs (Peric et al., 2013, 2014). The aim of this study was to apply the QSAR model described by Luis et al. (2007) on the EC50 values of ILs obtained in different studies and thus be able to predict the EC50 of the PILs that have not been tested yet. A prediction of EC50 was done for five (eco)toxicity tests (Microtoxs
test with bacteria Vibrio fischeri, green algae Pseudokirchneriella subcapitata and aquatic plant Lemna minor growth inhibition test, Acetylcholinestherase inhibition and Cell viability assay with IPC81 cells).
2. Material and methods In order to perform the QSAR modelling, a database of the EC50 values of ILs for different (eco)toxicity tests was built. The experimentally obtained EC50 values for 13 ILs, out of which 10 are PILs and three are AILs, are taken from Peric et al. (2013, 2014). The
Table 1 Ionic liquid toxicities in μmol L 1 (log EC50) for the Microtoxs test, calculated dimensionless toxicity (Ycn) and group contribution descriptors. No.
Compound
log EC50 (μmol L 1)
Ycn
A1
A2
A3
A4
Im
Py
Pyr
Amine
R1
R2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55
[C2MIM][Cl] [C2MIM][Cl] [C3MIM][BF4] [C3MIM][PF6] [C4MIM][Cl] [C4MIM][Cl] [C4MIM][Cl] [C4MIM][Cl] [C4MIM][Br] [C4MIM][Br] [C4MIM][Br] [C4MIM][BF4] [C4MIM][BF4] [C4MIM][(CF3SO2)2N] [C4MIM][(CN)2N] [C5MIM][BF4] [C6MIM][Cl] [C6MIM][Cl] [C6MIM][Cl] [C6MIM][Cl] [C6MIM][Br] [C6MIM][BF4] [C6MIM][PF6] [C6MIM][PF6] [C8MIM][Cl] [C8MIM][Cl] [C8MIM][Cl] [C8MIM][BF4] [C8MIM][PF6] [C10MIM][Cl] [C10MIM][BF4] [C4Py][Cl] [C4Py][Cl] [C4Py][Cl] [C4Py][Br] [C4Py][Br] [C4Py][(CN)2N] [C4MPy][Br] [C4MPy][Br] [C4MPy][(CN)2N] [C6MPy][Cl] [C6MPy][Br] [C4MPyr][(CF3SO2)2N] [C6MPyr][Cl] [C8MPyr][Br] 2-HEAF 2-HEAB 2-HDEAF 2-HDEAA 2-HDEAPr 2-HDEAB 2-HDEAiB 2-HDEAPe 2-HTEAB 2-HTEAPe
4.55 4.33 3.94 3.60 3.71 2.95 3.34 3.21 3.35 3.27 3.07 3.55 3.10 3.39 3.67 3.18 1.94 2.32 2.18 2.91 1.42 3.15 2.17 3.07 1.19 1.01 0.30 1.41 0.95 0.50 0.18 3.41 2.64 3.24 3.40 2.73 2.61 2.12 2.75 1.99 1.44 1.48 2.54 2.99 0.25 3.82 4.18 3.72 4.03 3.56 3.62 3.64 3.23 3.32 3.26
0.00 0.05 0.13 0.20 0.18 0.34 0.26 0.28 0.25 0.27 0.31 0.21 0.31 0.25 0.19 0.29 0.55 0.47 0.50 0.34 0.66 0.30 0.50 0.31 0.71 0.75 0.90 0.66 0.76 0.86 1.00 0.24 0.40 0.28 0.24 0.38 0.41 0.51 0.38 0.54 0.66 0.65 0.42 0.33 0.91 0.16 0.08 0.17 0.11 0.21 0.20 0.19 0.28 0.26 0.27
1 1 0 0 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 1 1 0 1 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0
0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 4 1 2 3 4 4 5 4 5
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 2 2 2 3 3
2 2 3 3 4 4 4 4 4 4 4 4 4 4 4 5 6 6 6 6 6 6 6 6 8 8 8 8 8 10 10 4 4 4 4 4 4 4 4 4 6 6 4 6 8 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0
References: 5, 15, 16, 22, 32, 35, 37 and 39 from Couling et al. (2006); 6, 9, 21, 33, 36, 38, 40 and 45 from Docherty and Kulpa (2005); 7, 11, 13, 18, 25 and 29 from Garcia et al. (2005); 1, 17, 41, 42 and 44 from Luis et al. (2007); 2, 14, 26 and 43 from Stolte et al. (2007); 3, 4, 10, 12, 19, 20, 23, 24, 28, 30 and 31 from Ranke et al. (2007b); 8, 27, 34 and 46–55 from Peric et al. (2013).
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remaining 42 EC50 values were taken from the literature (Table 1). The structures of PILs and AILs included in this study are shown in Supplementary material Figs. S1 and S2 respectively. The Fig. S3 of Supplementary material shows cations and anions of different aprotic ILs whose EC50 values were taken from the literature and were also used in the process of QSAR modelling. These ILs are derived from disubstituted imidazolium, pyridinium and pyrrolidinium and were selected in order to have a larger database of EC50 values. The PILs belong to a family of short aliphatic ionic liquids, composed of substituted amines (mono-, di- and triethanolamine) as cations and aliphatic organic acids (formic, acetic, propionic, butyric, isobutyric and pentanoic acid) as anions: 2-hydroxyethanolamine formate (2-HEAF), 2-hydroxyethanolamine butanoate (2-HEAB), 2-hydroxydiethanolamine formate (2-HDEAF), 2-hydroxydiethanolamine acetate (2-HDEAA), 2-hydroxydiethanolamine propionate (2-HDEAPr), 2-hydroxydiethanolamine butanoate (2-HDEAB), 2-hydroxydiethanolamine isobutanoate (2-HDEAiB), 2-hydroxydiethanolamine pentanoate (2-HDEAPe), 2-hydroxytriethanolamine butanoate (2-HTEAB) and 2-hydroxytriethanolamine pentanoate (2-HTEAPe). The analyzed AILs are: 1-butyl-3-methylimidazolium chloride ([C4MIM][Cl]), 1-methyl-3octylimidazolium chloride ([C8MIM][Cl]) and N-butylpyridinium chloride ([C4Py][Cl]). Eight remaining ILs from the new family of PILs are: 2-hydroxyethanolamine acetate (2-HEAA), 2-hydroxyethanolamine propionate (2-HEAPr), 2-hydroxyethanolamine isobutanoate (2-HEAiB), 2-hydroxyethanolamine pentanoate (2HEAPe), 2-hydroxytriethanolamine formate (2-HTEAF), 2-hydroxytriethanolamine acetate (2-HTEAA) and 2-hydroxytriethanolamine propionate (2-HTEAPr), 2-hydroxytriethanolamine isobutanoate (2-HTEAiB).
3. Results and discussion The QSAR model was based on the group contribution method, proposed by Luis et al. (2007) where molecular properties can be viewed as a sum of the contributions of its atoms and/or fragments. Every ILs has been divided into basic fragments/groups, which can contribute to toxicity. The groups were encoded in a Boolean manner, meaning that they are equal to 1 if the group is present in the ILs molecule and 0 if not. A detailed overview of all of the molecular descriptors can be found in Tables 1 and 3. Toxicity data has been expressed on molar basis in order to be comparable between structurally different ILs. The EC50 values were converted into a logarithmic scale and the toxicity was expressed as the calculated dimensionless toxicity (Ycn), with values between 0 and 1 according to the following formula:
Yc⁎ =
log EC50 _ max − log EC50 log EC50 _ max − log EC50 _ min
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the logarithm of the experimentally obtained EC50 value. The predicted dimensionless toxicity (Ypn) used in this model represents the sum of the contributions of all of the groups present in the ILs molecule, as presented in the following equation:
Yp⁎ =
∑ ai ⋅Ai + ∑ c j ⋅C j + ∑ sk ⋅Sk i
j
k
The structure of the ILs is described by three main groups: anions (A), cations (C) and substitutions (S) of the cation. The Ai and Cj are variables that have value of 1–0, depending if the anion or cation are present or not in the molecule. For example, the value will be 1 if pyridinium is present in the ILs. On the other hand, the Sk has the value of the number of C atoms in the alkyl chains (R1 and R2). The ai, cj and sk are the coefficients of regression representing the contribution of each group to ILs toxicity. Table 1 shows the group descriptors used in the QSAR modelling, the experimentally obtained value for the log EC50 in μmol L 1 for the ILs in the inhibition of the V. fischeri luminescence assay (Microtoxs) and the calculated dimensionless toxicity (Ycn). Anions include the ones who appear in the AILs molecule: chloride (Cl ), bromide (Br ), tetrafluoroborate (BF4 ), hexafluorophosphate (PF6 ), dicyanamide (N(CN2)2 ), bis-trifluoromethan-sulfonimide (N(CF3SO2)2 ); and anions belonging to the PILs molecule: formate, acetate, propionate, butanoate, isobutanoate and pentanoate. The anions were grouped into four main descriptors, based on the similar contribution: A1 (Cl and Br ), A2 (BF4 and PF6 ), A3 (N(CN2)2 and N(CF3SO2)2 ) and A4 (number of C atoms in the PILs anions) in order to avoid the overfitting that could be caused by an excess of descriptors. The AILs cations are imidazolium, pyridinium, and pyrrolidinium (Im, Py and Pyr, respectively) and the PILs cations are monoethanolamine, diethanolamine or triethanolamine (Amine). Alkyl side chain substituents of the heterocyclic ring, from propyl to decyl (R1) and the methyl substituent (R2) were also considered. The data set of 55 EC50 values was fitted to the QSAR model by multiple linear regression using IBM SPSS Statistics 20 software. The calculated dimensionless toxicity obtained from experimental EC50 data and the predicted dimensionless toxicity obtained after QSAR modelling were plotted and the data plot graph is shown in Fig. 1. A reasonably good fitting was achieved, with n¼ 55, R2 ¼0.9184 and 10 descriptors. Residuals were calculated as the differences between the calculated dimensionless toxicity and the one predicted by the QSAR method (taking into account the absolute values). The distribution of the residuals in Table 2 and the plot graph in Fig. 1 indicate that there are practically no outliners and that 84% of the residuals are lower than 0.10. Both the most and the least toxic ILs were well predicted by the model. This means a deviation of 710% in the predicted log EC50 (mmol L 1) which was calculated applying the equation:
(
)
log EC50 μmol L−1 = log EC50 _ max – (log EC50 _ max − log EC50 _ min ) ·Yp⁎
where log EC50_max and log EC50_min are the highest and the lowest EC50 value in the EC50 database, respectively. The log EC50 is
All of the molecular descriptors are presented and explained in Table 3, together with the corresponding group contributions and
Table 2 Distribution of residuals between the calculated and predicted dimensionless toxicity and the mean residual for Microtoxs test, Algal and Lemna minor growth inhibition test, Acetylcholinesterase inhibition and cell viability assay with IPC-81 cells. Microtox
Algae
Lemna minor
AChE
IPC-81
Range
Residuals
%
Residuals
%
Residuals
%
Residuals
%
Residuals
%
o0.10 0.10–0.20 40.20 o0.28 Mean residual
46 8 1 55 0.0647 0.051
84 14 2 100
23 4 5 32 0.0717 0.060
72 12 16 100
20 4 0 24 0.0767 0.069
83 17 0 100
53 8 0 61 0.0517 0.040
87 13 0 100
41 3 1 45 0.053 7 0.045
91 7 2 100
260
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Fig. 1. Calculated and predicted dimensionless toxicity data plot for Microtoxs test.
confidence intervals for the QSAR model. The analysis of the contributions of each of the descriptors provides information on the influence of the structural groups on ILs toxicity. The anions of the AILs have a negative contribution to toxicity. The contributions of anions show very small differences between three groups of AILs anions. The contribution of cations is positive, which means that the presence of either of these cations in the AILs molecules leads to an increase in toxicity. Many studies confirmed that the influence of cations on ILs toxicity is stronger than the one of anions (Ranke et al., 2004; Couling et al., 2006; Matzke et al., 2007; Stolte et al., 2007). Imidazolium, pyridinium and pyrrolidinium cation contribute around 27%, 17% and 3% to the toxicity, respectively. The imidazolium's higher contribution to toxicity and lower toxicity of pyrrolidinium cation compared to imidazolium and piridinium have been also found by other authors (Stolte et al., 2007; Pham et al., 2008; Latała et al., 2009). The contribution for the number of carbon atoms in long alkyl side chains indicates that with every carbon added to the side chain, an increase of about 13% in toxicity of AILs is produced. The presence of the short chain also has a positive influence on toxicity. These two results are in agreement with other authors findings that the substitution of the cation ring and the increase of the side alkyl chain length have a positive influence on toxicity (Ranke et al., 2004; Wells and Coombe, 2006; Pham et al., 2010; Radošević et al., 2013). On the other hand, cations and anions of the PILs do not exhibit a high overall influence on the ILs toxicity, if they are analyzed in the present set of ILs. This coincides with the fact that
the PILs showed much lower toxicity than the AILs in the Microtoxs test. The amines have a stronger influence on toxicity towards V. fischeri in the Microtoxs test as the contribution of the amine is five times higher than the one for the acid part of the PILs molecule. These results are in agreement with those reported by Peric et al. (2013). In summary, the here presented QSAR model has 10 descriptors and could allow the prediction of (eco)toxicity of existing and potential ILs, resulting from the possible combinations of 11 types of anions, 6 types of cations and 8 types of different substituents of the cationic moiety. The same QSAR model was used for four more tests in order to predict the ILs (eco)toxicity: aquatic ecotoxicity (toxic effect on algae and aquatic plant L. minor), inhibition of acetylcholinesterase (AChE) and cytotoxicity (IPC-81 cells). The ILs toxicities in μ mol L 1 (log EC50) for these tests and calculated dimensionless toxicities (Ycn) can be found in Supplementary material Table S1. The fitting plots for the calculated and predicted Yn can be seen in Fig. S4 of Supplementary material. In case of these four (eco)toxicity tests a reasonably good fitting was also achieved, with R2 being 0.9918 for the Acetylcholinestherase inhibition test (n ¼61), 0.9799 for the Cytotoxicity test with IPC-81 cell line (n ¼ 45), 0.866 for the Test of inhibition of algal growth (n ¼32) and 0.901 for the L. minor growth inhibition test (n ¼24), The distribution of residuals for the QSAR modelling and the mean residual for these four (eco)toxicity tests are presented in Table 2. The group contributions and confidence intervals are shown in Table 4. All but one (eco)toxicity test (Acetylcholinestherase inhibition) show negative, very low or no contribution of the anions to the toxicity, meaning that the selected anions do not play a key role in AILs toxicity, which, as previously mentioned, coincides with the literature findings. The contribution of cations is the strongest in the case of the Acetylcholinesterase inhibition test, both for AILs and PILs. There is also a positive influence of anions for this test. This can be explained with the different toxicity mode of action between the Acetylcholinesterase inhibition test and the other three tests. While in Algal and L. minor growth inhibition test and Cell viability assay with IPC-81 the toxicity is based on the membrane disruption (Docherty and Kulpa, 2005), the Acetylcholinesterase inhibition test involves binding to the enzyme's active centre (Stock et al., 2004). As initially found by Stock et al. (2004), acetylcholinesterase can be inhibited by ILs containing a cation with positively charged nitrogen and certain lipophilicity.
Table 3 Significance and contribution of the descriptors used in the QSAR modelling. Group
Molecular descriptor
Comments
Contribution
Confidence interval (95%)
Anion
A1
Influence of anions: chloride (Cl ), bromide (Br ). Value is 1 if it exists and 0 if not. Influence of anions: hexafluorophosphate (PF6 ), tetrafluoroborate (BF4 ). Value is 1 if it exists and 0 if not. Influence of anions: dicyanamide (N(CN2)2 ), bis-trifluoromethan-sulfonimide (N(CF3SO2)2 ). Value is 1 if it exists and 0 if not. Influence of number of carbons in PILs anion. Value of A4: 1–5
0.269
0.400
0.139
0.300
0.442
0.158
0.251
0.382
0.120
0.016
0.025
0.056
A2 A3
A4 Cation
Im – imidazolium Py – pyridinium Pyr – pyrrolidinium Amine
Influence of imidazolium cation. Value is 1 if it exists and 0 if not. Influence of pyridinium cation. Value is 1 if it exists and 0 if not. Influence of pyrrolidinium cation. Value is 1 if it exists and 0 if not. Influence of amine. Value: 1, 2 or 3 in function of the complexity of the PILs cation
0.274 0.175 0.035 0.085
0.139 0.092 0.073 0.015
0.399 0.259 0.144 0.155
Cation subsitution
R1
Influence of Value of R1: Influence of Value of R1:
0.132
0.087
0.127
0.112
0.097
0.126
R2
number of carbons in long chains of the AILs molecule. 0–10 number of carbons in short chains of the AILs molecule. 1 if methyl group exists and 0 if not.
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Table 4 The group contributions to calculated dimensionless toxicity. Algae
Lemna minor
AChE
IPC-81
Group
Molecular descriptor
Contribution Confidence interval (95%)
Contribution Confidence interval Contribution Confidence inter(95%) val (95%)
Contribution Confidence interval (95%)
Anion
A1 A2 A3 A4
0.098 0.091 0.051 0.032
0.225 0.233 0.182 0.048
0.029 0.041 0.080 0.111
0.244 0.231 0.419 0.087
0.357 0.349 0.541 0.003
0.131 0.113 0.297 0.174
0.023 0.019 0.000 0.014
0.031 0.037 0.000 0.020
0.077 0.074 0.000 0.047
0.103 0.019 0.000 0.081
0.023 0.095 0.000 0.044
0.182 0.057 0.000 0.118
Cation
Im Py Pyr Amine
0.075 0.000 0.000 0.010
0.064 0.000 0.000 0.146
0.214 0.000 0.000 0.126
0.005 0.048 0.085 0.057
0.023 0.061 0.035 0.206
0.033 0.157 0.140 0.092
0.361 0.699 0.000 0.084
0.312 0.629 0.000 0.027
0.410 0.770 0.000 0.141
0.067 0.019 0.000 0.015
0.002 0.057 0.000 0.079
0.133 0.140 0.000 0.048
0.086 0.036
0.048 0.187
0.124 0.114
0.141 0.000
0.057 0.000
0.224 0.000
0.018 0.000
0.010 0.000
0.026 0.000
0.084 0.008
0.072 0.040
0.097 0.057
Substitution R1 R2
The study of Arning et al. (2008) revealed that the pyridinium derived ILs show stronger inhibition than the imidazolium ones, which has also been confirmed in this study, with the contribution to toxicity of pyridinium being two times higher than the one for imidazolium. The anions do not show inhibitory effect on the enzyme activity with only exception of the fluoride and fluoride containing anions (Matzke et al., 2007), also confirmed by the results of this study. The statistical models applied by Roy et al. (2014) also demonstrated that aromatic ILs are more toxic than non-aromatic and aliphatic ones. The influence of the long side alkyl chains is predominant, as the short alkyl chains show negative, very low o zero influence on the AILs toxicity. This influence also coincides with the strong relationship between the (eco) toxicity of ILs and its hydrophobic character described in the literature (Garcia et al., 2005; Ranke et al., 2007a; Cvjetko Bubalo et al., 2014), meaning that longer the alkyl chain, the higher the hydrophobic character of the ionic liquid, the fact that facilitates the ILs molecule's penetration through cell membranes and thus potentiates their toxicity. A correlation between an increasing chain length of the side chains connected to the cationic head groups and an enhanced inhibitory potential of the ILs on the acetylcholinesterase, found in other authors' studies (Stock et al., 2004; Arning et al., 2008) was also confirmed in this study. The cations and anions of the PILs do not display a high influence on toxicity and the PILs once again showed much lower toxicity than the AILs. The amines have a stronger influence on toxicity towards acetylcholinesterase, and the acids in the anionic moiety have a stronger contribution to the toxicity towards P. subcapitata and IPC-81. These results are also in agreement with those reported by Peric et al. (2013) where good correlation between the acid moieties and toxicity was found for these tests. It can be seen that both PILs cation and anion contribute to toxicity towards L. minor, which coincides with findings that L. minor is the Table 5 The predicted values of log EC50 (mmol L 1) for Microtoxs test, Algal and Lemna minor growth inhibition test, Acetylcholinestherase inhibition and Cell viability assay with IPC-81 cells for eight representatives of the new family of PILs.
2-HEAA 2-HEAPr 2-HEAiB 2-HEAPe 2-HTEAF 2-HTEAA 2-HTEAPr 2-HTEAiB
Microtox
Algae
L. minor
AChE
IPC-81
4.19 4.11 4.03 3.96 4.26 3.35 3.27 3.19
3.94 3.68 3.43 3.18 4.19 4.10 3.85 3.59
3.22 2.94 2.67 2.40 3.49 3.57 3.30 3.03
4.36 4.31 4.26 4.21 4.41 3.74 3.69 3.64
4.12 3.81 3.49 3.17 4.44 4.25 3.93 3.61
most sensitive species in the case of the aquatic ecotoxicity tests (Peric et al., 2013). The EC50 values for the previously untested eight representatives of the new family of short aliphatic PILs were predicted for these five (eco)toxicity tests. The results can be found in Table 5. The model proposed by Luis et al. (2007), although initially designed for the AILs, seems to be adequate for the PILs too.
4. Conclusions The model based on group contribution used in this work allows the prediction of ionic liquids' (eco)toxicity, with well correlated experimental and predicted toxicity data. The EC50 values for eight new short aliphatic protic ILs, previously untested, were predicted. So, this QSAR model, which was first proposed for aprotic ILs, proved to be useful for predicting the toxicity of protic ILs as well. Literature findings of the predominant influence of cations on toxicity and the increase of toxicity with the elongation of the side alkyl chains in aprotic ILs molecule were confirmed by the results of this study. In the case of new protic ILs, both anionic and cationic part of the molecule have influence on (eco)toxicity, and it is the elongation of the acid in the anionic moiety which has more influence on the overall toxicity in three out of the five (eco) toxicity tests (cytotoxicity and algal and aquatic plants growth inhibition). Apart from EC50 prediction, this model can also help to determine the positive or negative influence of different structural groups on the (eco)toxicity and in that way “guide” the synthesis of new ionic liquids towards more sustainable ones.
Acknowledgements B. Peric would like to thank the Spanish Ministry of Foreign Affairs and Cooperation and the Spanish Agency for International Cooperation for Development (Grant MAEC-AECID II-A). The authors would like to thank Francesc Fàbrega for his advice on the initial QSAR modelling and Dr. Jesús Gómez for his valuable suggestions and comments.
Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.ecoenv.2015.02. 027.
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References Amberg, A., 2013. In silico methods In: Vogel, H.G., Maas, J., Hock, F.J., Mayer, D. (Eds.), Drug Discovery and Evaluation: Safety and Pharmacokinetic Assays. Springer, Berlin Heidelberg, pp. 1273–1296. Arning, J., Stolte, S., Böschen, A., Stock, F., Pitner, W.-R., Welz-Biermann, U., Jastorff, B., Ranke, J., 2008. Qualitative and quantitative structure activity relationships for the inhibitory effects of cationic head groups, functionalized side chains and anions of ionic liquids on acetylcholinesterase. Green Chem. 10, 47–58. Couling, D.J., Bernot, R.J., Docherty, K.M., Dixon, J.K., Maginn, E.J., 2006. Assessing the factors responsible for ionic liquid toxicity to aquatic organisms via quantitative structure-property relationship modeling. Green Chem. 8, 82–90. Cvjetko Bubalo, M., Radošević, K., Radojčić Redovniković, I., Halambek, J., Gaurina Srček, V., 2014. A brief overview of the potential environmental hazards of ionic liquids. Ecotoxicol. Environ. Saf. 99, 1–12. Das, R.N., Roy, K., 2013. Advances in QSPR/QSTR models of ionic liquids for the design of greener solvents of the future. Mol. Divers. 17, 151–196. Docherty, K.M., Dixon, J.K., Kulpa, C.F., 2007. Biodegradability of imidazolium and pyridinium ionic liquids by an activated sludge microbial community. Biodegradation 18, 481–493. Docherty, K.M., Kulpa, C.F., 2005. Toxicity and antimicrobial activity of imidazolium and pyridinium ionic liquids. Green Chem. 7, 185–189. European Union, 2006. Regulation (EC) no 1907/2006 of the European Parliament and of the Council concerning the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH). Off. J. Eur. Union 396, 1–856. Garcia, M.T., Gathergood, N., Scammells, P.J., 2005. Biodegradable ionic liquids. Part II: effect of the anion and toxicology. Green Chem. 7, 9–14. Latała, A., Nędzi, M., Stepnowski, P., 2009. Toxicity of imidazolium and pyridinium based ionic liquids towards algae. Chlorella vulgaris, Oocystis submarina (green algae) and Cyclotella meneghiniana, Skeletonema marinoi (diatoms). Green Chem. 11, 580–588. Luis, P., Ortiz, I., Aldaco, R., Irabien, A., 2007. A novel group contribution method in the development of a QSAR for predicting the toxicity (Vibrio fischeri EC50) of ionic liquids. Ecotoxicol. Environ. Saf. 67, 423–429. Matzke, M., Stolte, S., Thiele, K., Juffernholz, T., Arning, J., Ranke, J., Welz-Biermann, U., Jastorff, B., 2007. The influence of anion species on the toxicity of 1-alkyl-3methylimidazolium ionic liquids observed in an (eco)toxicological test battery. Green Chem. 9, 1198–1207.
Peric, B., Sierra, J., Martí, E., Cruañas, R., Garau, M.A., Arning, J., Bottin-Weber, U., Stolte, S., 2013. (Eco)toxicity and biodegradability of short aliphatic protic ionic liquids. J. Hazard. Mater. 261, 99–105. Peric, B., Sierra, J., Martí, E., Cruañas, R., Garau, M.A., 2014. A comparative study of the terrestrial ecotoxicity of selected protic and aprotic ionic liquids. Chemosphere 108, 418–425. Petkovic, M., Seddon, K.R., Rebelo, L.P.N., Pereira, C.S., 2011. Ionic liquids: a pathway to environmental acceptability. Chem. Soc. Rev. 40, 1383–1403. Pham, T.P.T., Cho, C.-W., Min, J., Yun, Y.-S., 2008. Alkyl-chain length effects of imidazolium and pyridinium ionic liquids on photosynthetic response of Pseudokirchneriella subcapitata. J. Biosci. Bioeng. 105, 425–428. Pham, T.P.T., Cho, C.W., Yun, Y.S., 2010. Environmental fate and toxicity of ionic liquids: a review. Water Res. 44, 252–272. Radošević, K., Cvjetko, M., Kopjar, N., Novak, R., Dumić, J., Gaurina Srček, V., 2013. In vitro cytotoxicity assessment of imidazolium ionic liquids: biological effects in fish Channel Catfish Ovary (CCO) cell line. Ecotoxicol. Environ. Saf. 92, 112–118. Ranke, J., Mölter, K., Stock, F., Bottin-Weber, U., Poczobutt, J., Hoffmann, J., Ondruschka, B., Filser, J., Jastorff, B., 2004. Biological effects of imidazolium ionic liquids with varying chain length in acute Vibrio fischeri and WST-1 cell viability assays. Ecotoxicol. Environ. Saf. 58, 396–404. Ranke, J., Müller, A., Bottin-Weber, U., Stock, F., Stolte, S., Arning, J., Störman, R., Jastroff, B., 2007a. Lipophilicity parameters for ionic liquid cations and their correlation to in vitro cytotoxicity. Ecotoxicol. Environ. Saf. 67, 430–438. Ranke, J., Stolte, S., Störmann, R., Arning, J., Jastorff, B., 2007b. Design of sustainable chemical products – the example of ionic liquids. Chem. Rev. 107, 2183–2206. Roy, K., Das, R.N., Popelier, P.L.A., 2014. Quantitative structure–activity relationship for toxicity of ionic liquids to Daphnia magna: aromaticity vs. lipophilicity. Chemosphere 112, 120–127. Stock, F., Hoffmann, J., Ranke, J., Störmann, R., Ondruschka, B., Jastorff, B., 2004. Effects of ionic liquids on the acetylcholinesterase – a structure-activity relationship consideration. Green Chem. 6, 286–290. Stolte, S., Matzke, M., Arning, J., Böschen, A., Pitner, W.-R., Welz-Biermann, U., Jastorff, B., Ranke, J., 2007. Effects of different head groups and functionalized side chains on the aquatic toxicity of ionic liquids. Green Chem. 9, 1170–1179. Wells, A.S., Coombe, V.T., 2006. On the freshwater ecotoxicity and biodegradation properties of some common ionic liquids. Org. Process Res. Dev. 10, 794–798.
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Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv
Quantitative structure–activity relationship (QSAR) prediction of (eco)toxicity of short aliphatic protic ionic liquids Brezana Peric n, Jordi Sierra, Esther Martí, Robert Cruañas, Maria Antonia Garau Unitat d'Edafologia, Faculty of Pharmacy, University of Barcelona, Av. Joan XXIII s/n, 08028 Barcelona, Spain
art ic l e i nf o
a b s t r a c t
Article history: Received 24 September 2014 Received in revised form 14 February 2015 Accepted 17 February 2015 Available online 27 February 2015
Ionic liquids (ILs) are considered as a group of very promising compounds due to their excellent properties (practical non-volatility, high thermal stability and very good and diverse solving capacity). The ILs have a good prospect of replacing traditional organic solvents in vast variety of applications. However, the complete information on their environmental impact is still not available. There is also an enormous number of possible combinations of anions and cations which can form ILs, the fact that requires a method allowing the prediction of toxicity of existing and potential ILs. In this study, a group contribution QSAR model has been used in order to predict the (eco)toxicity of protic and aprotic ILs for five tests (Microtoxs, Pseudokirchneriella subcapitata and Lemna minor growth inhibition test, and Acetylcholinestherase inhibition and Cell viability assay with IPC-81 cells). The predicted and experimental toxicity are well correlated. A prediction of EC50 for these (eco)toxicity tests has also been made for eight representatives of the new family of short aliphatic protic ILs, whose toxicity has not been determined experimentally to date. The QSAR model applied in this study can allow the selection of potentially less toxic ILs amongst the existing ones (e.g. in the case of aprotic ILs), but it can also be very helpful in directing the synthesis efforts toward developing new “greener” ILs respectful with the environment (e.g. short aliphatic protic ILs). & 2015 Elsevier Inc. All rights reserved.
Keywords: Ionic liquids QSAR modelling Group contribution Aquatic ecotoxicity Cytotoxicity Acetylcholinesterase inhibition
1. Introduction Ionic liquids (ILs) have gained a lot of attention in the past decades because of their properties, such as negligible vapour pressure, high thermal stability and very high and versatile solving capacity. These properties make them interesting for many applications, usually aiming to replace volatile organic and toxic solvents, which have been traditionally used in laboratories and industries up to now (Petkovic et al., 2011). Even though ILs are practically non-volatile and thus cannot get into atmosphere and cause air pollution, many of them are soluble in water and can represent a danger for the environment if released with industrial and laboratory effluents (Docherty et al., 2007). There are many studies that demonstrate the aquatic toxicity of the ILs (Pham et al., 2010). So far, the investigators have been focusing mainly on aprotic ionic liquids (AILs), namely the ones derived from imidazolium and pyridinium. The protic ionic liquids (PILs) have come into focus only recently and show a good potential, both in their applications and “greenness”. The ILs are chemical products, and that means that the n
Corresponding author. Fax: þ 34 93 402 4495. E-mail address: [email protected] (B. Peric).
http://dx.doi.org/10.1016/j.ecoenv.2015.02.027 0147-6513/& 2015 Elsevier Inc. All rights reserved.
requirements of the new European Union Regulation on Chemical Substances REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) apply to ILs as well (European Union, 2006). It is considered that the possible number of ILs is about 1018, and around one million can be easily synthesized in the laboratory. It is very important to discover the hazardous potential of ILs before putting them into use, and taking into account such a large potential number of ILs, it is important to save up time and money and possibly do a pre-application screening in order to determine the potential negative effect of ILs on the environment. As in depth studies of water and terrestrial ecotoxicity of ILs are either incomplete or missing, the prediction of the environmental impact of the new and existing, but not yet tested ILs, is a necessity for the more widespread application of the ILs. In silico methods such as QSAR (quantitative structure–activity relationship) can be used for predicting toxicity and thus reduce costly and time consuming toxicity testing, either in vitro or in vivo. The use of the QSAR modelling for the prediction of toxicity is recognized by REACH regulation, stating that properties determined with the use of in silico methods are equivalent to laboratory testing. The QSAR modelling, as its name indicates, provides toxicity data based on the quantitative relationship between chemical structure and biological and/or toxicological activity, using chemical descriptors generated from the molecular structure. These descriptors are
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statistically analyzed in order to develop a model that would describe the desired activity e.g. toxicity (Amberg, 2013). The QSAR studies on toxicity of ILs have been far less frequent compared to other chemicals (Das and Roy, 2013). A new family of short aliphatic PILs has been thoroughly investigated on their (eco)toxicity and biodegradability, both in water and soil, together with comparative studies regarding the most frequently used AILs (Peric et al., 2013, 2014). The aim of this study was to apply the QSAR model described by Luis et al. (2007) on the EC50 values of ILs obtained in different studies and thus be able to predict the EC50 of the PILs that have not been tested yet. A prediction of EC50 was done for five (eco)toxicity tests (Microtoxs
test with bacteria Vibrio fischeri, green algae Pseudokirchneriella subcapitata and aquatic plant Lemna minor growth inhibition test, Acetylcholinestherase inhibition and Cell viability assay with IPC81 cells).
2. Material and methods In order to perform the QSAR modelling, a database of the EC50 values of ILs for different (eco)toxicity tests was built. The experimentally obtained EC50 values for 13 ILs, out of which 10 are PILs and three are AILs, are taken from Peric et al. (2013, 2014). The
Table 1 Ionic liquid toxicities in μmol L 1 (log EC50) for the Microtoxs test, calculated dimensionless toxicity (Ycn) and group contribution descriptors. No.
Compound
log EC50 (μmol L 1)
Ycn
A1
A2
A3
A4
Im
Py
Pyr
Amine
R1
R2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55
[C2MIM][Cl] [C2MIM][Cl] [C3MIM][BF4] [C3MIM][PF6] [C4MIM][Cl] [C4MIM][Cl] [C4MIM][Cl] [C4MIM][Cl] [C4MIM][Br] [C4MIM][Br] [C4MIM][Br] [C4MIM][BF4] [C4MIM][BF4] [C4MIM][(CF3SO2)2N] [C4MIM][(CN)2N] [C5MIM][BF4] [C6MIM][Cl] [C6MIM][Cl] [C6MIM][Cl] [C6MIM][Cl] [C6MIM][Br] [C6MIM][BF4] [C6MIM][PF6] [C6MIM][PF6] [C8MIM][Cl] [C8MIM][Cl] [C8MIM][Cl] [C8MIM][BF4] [C8MIM][PF6] [C10MIM][Cl] [C10MIM][BF4] [C4Py][Cl] [C4Py][Cl] [C4Py][Cl] [C4Py][Br] [C4Py][Br] [C4Py][(CN)2N] [C4MPy][Br] [C4MPy][Br] [C4MPy][(CN)2N] [C6MPy][Cl] [C6MPy][Br] [C4MPyr][(CF3SO2)2N] [C6MPyr][Cl] [C8MPyr][Br] 2-HEAF 2-HEAB 2-HDEAF 2-HDEAA 2-HDEAPr 2-HDEAB 2-HDEAiB 2-HDEAPe 2-HTEAB 2-HTEAPe
4.55 4.33 3.94 3.60 3.71 2.95 3.34 3.21 3.35 3.27 3.07 3.55 3.10 3.39 3.67 3.18 1.94 2.32 2.18 2.91 1.42 3.15 2.17 3.07 1.19 1.01 0.30 1.41 0.95 0.50 0.18 3.41 2.64 3.24 3.40 2.73 2.61 2.12 2.75 1.99 1.44 1.48 2.54 2.99 0.25 3.82 4.18 3.72 4.03 3.56 3.62 3.64 3.23 3.32 3.26
0.00 0.05 0.13 0.20 0.18 0.34 0.26 0.28 0.25 0.27 0.31 0.21 0.31 0.25 0.19 0.29 0.55 0.47 0.50 0.34 0.66 0.30 0.50 0.31 0.71 0.75 0.90 0.66 0.76 0.86 1.00 0.24 0.40 0.28 0.24 0.38 0.41 0.51 0.38 0.54 0.66 0.65 0.42 0.33 0.91 0.16 0.08 0.17 0.11 0.21 0.20 0.19 0.28 0.26 0.27
1 1 0 0 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 1 1 0 1 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0
0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 4 1 2 3 4 4 5 4 5
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 2 2 2 3 3
2 2 3 3 4 4 4 4 4 4 4 4 4 4 4 5 6 6 6 6 6 6 6 6 8 8 8 8 8 10 10 4 4 4 4 4 4 4 4 4 6 6 4 6 8 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0
References: 5, 15, 16, 22, 32, 35, 37 and 39 from Couling et al. (2006); 6, 9, 21, 33, 36, 38, 40 and 45 from Docherty and Kulpa (2005); 7, 11, 13, 18, 25 and 29 from Garcia et al. (2005); 1, 17, 41, 42 and 44 from Luis et al. (2007); 2, 14, 26 and 43 from Stolte et al. (2007); 3, 4, 10, 12, 19, 20, 23, 24, 28, 30 and 31 from Ranke et al. (2007b); 8, 27, 34 and 46–55 from Peric et al. (2013).
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remaining 42 EC50 values were taken from the literature (Table 1). The structures of PILs and AILs included in this study are shown in Supplementary material Figs. S1 and S2 respectively. The Fig. S3 of Supplementary material shows cations and anions of different aprotic ILs whose EC50 values were taken from the literature and were also used in the process of QSAR modelling. These ILs are derived from disubstituted imidazolium, pyridinium and pyrrolidinium and were selected in order to have a larger database of EC50 values. The PILs belong to a family of short aliphatic ionic liquids, composed of substituted amines (mono-, di- and triethanolamine) as cations and aliphatic organic acids (formic, acetic, propionic, butyric, isobutyric and pentanoic acid) as anions: 2-hydroxyethanolamine formate (2-HEAF), 2-hydroxyethanolamine butanoate (2-HEAB), 2-hydroxydiethanolamine formate (2-HDEAF), 2-hydroxydiethanolamine acetate (2-HDEAA), 2-hydroxydiethanolamine propionate (2-HDEAPr), 2-hydroxydiethanolamine butanoate (2-HDEAB), 2-hydroxydiethanolamine isobutanoate (2-HDEAiB), 2-hydroxydiethanolamine pentanoate (2-HDEAPe), 2-hydroxytriethanolamine butanoate (2-HTEAB) and 2-hydroxytriethanolamine pentanoate (2-HTEAPe). The analyzed AILs are: 1-butyl-3-methylimidazolium chloride ([C4MIM][Cl]), 1-methyl-3octylimidazolium chloride ([C8MIM][Cl]) and N-butylpyridinium chloride ([C4Py][Cl]). Eight remaining ILs from the new family of PILs are: 2-hydroxyethanolamine acetate (2-HEAA), 2-hydroxyethanolamine propionate (2-HEAPr), 2-hydroxyethanolamine isobutanoate (2-HEAiB), 2-hydroxyethanolamine pentanoate (2HEAPe), 2-hydroxytriethanolamine formate (2-HTEAF), 2-hydroxytriethanolamine acetate (2-HTEAA) and 2-hydroxytriethanolamine propionate (2-HTEAPr), 2-hydroxytriethanolamine isobutanoate (2-HTEAiB).
3. Results and discussion The QSAR model was based on the group contribution method, proposed by Luis et al. (2007) where molecular properties can be viewed as a sum of the contributions of its atoms and/or fragments. Every ILs has been divided into basic fragments/groups, which can contribute to toxicity. The groups were encoded in a Boolean manner, meaning that they are equal to 1 if the group is present in the ILs molecule and 0 if not. A detailed overview of all of the molecular descriptors can be found in Tables 1 and 3. Toxicity data has been expressed on molar basis in order to be comparable between structurally different ILs. The EC50 values were converted into a logarithmic scale and the toxicity was expressed as the calculated dimensionless toxicity (Ycn), with values between 0 and 1 according to the following formula:
Yc⁎ =
log EC50 _ max − log EC50 log EC50 _ max − log EC50 _ min
259
the logarithm of the experimentally obtained EC50 value. The predicted dimensionless toxicity (Ypn) used in this model represents the sum of the contributions of all of the groups present in the ILs molecule, as presented in the following equation:
Yp⁎ =
∑ ai ⋅Ai + ∑ c j ⋅C j + ∑ sk ⋅Sk i
j
k
The structure of the ILs is described by three main groups: anions (A), cations (C) and substitutions (S) of the cation. The Ai and Cj are variables that have value of 1–0, depending if the anion or cation are present or not in the molecule. For example, the value will be 1 if pyridinium is present in the ILs. On the other hand, the Sk has the value of the number of C atoms in the alkyl chains (R1 and R2). The ai, cj and sk are the coefficients of regression representing the contribution of each group to ILs toxicity. Table 1 shows the group descriptors used in the QSAR modelling, the experimentally obtained value for the log EC50 in μmol L 1 for the ILs in the inhibition of the V. fischeri luminescence assay (Microtoxs) and the calculated dimensionless toxicity (Ycn). Anions include the ones who appear in the AILs molecule: chloride (Cl ), bromide (Br ), tetrafluoroborate (BF4 ), hexafluorophosphate (PF6 ), dicyanamide (N(CN2)2 ), bis-trifluoromethan-sulfonimide (N(CF3SO2)2 ); and anions belonging to the PILs molecule: formate, acetate, propionate, butanoate, isobutanoate and pentanoate. The anions were grouped into four main descriptors, based on the similar contribution: A1 (Cl and Br ), A2 (BF4 and PF6 ), A3 (N(CN2)2 and N(CF3SO2)2 ) and A4 (number of C atoms in the PILs anions) in order to avoid the overfitting that could be caused by an excess of descriptors. The AILs cations are imidazolium, pyridinium, and pyrrolidinium (Im, Py and Pyr, respectively) and the PILs cations are monoethanolamine, diethanolamine or triethanolamine (Amine). Alkyl side chain substituents of the heterocyclic ring, from propyl to decyl (R1) and the methyl substituent (R2) were also considered. The data set of 55 EC50 values was fitted to the QSAR model by multiple linear regression using IBM SPSS Statistics 20 software. The calculated dimensionless toxicity obtained from experimental EC50 data and the predicted dimensionless toxicity obtained after QSAR modelling were plotted and the data plot graph is shown in Fig. 1. A reasonably good fitting was achieved, with n¼ 55, R2 ¼0.9184 and 10 descriptors. Residuals were calculated as the differences between the calculated dimensionless toxicity and the one predicted by the QSAR method (taking into account the absolute values). The distribution of the residuals in Table 2 and the plot graph in Fig. 1 indicate that there are practically no outliners and that 84% of the residuals are lower than 0.10. Both the most and the least toxic ILs were well predicted by the model. This means a deviation of 710% in the predicted log EC50 (mmol L 1) which was calculated applying the equation:
(
)
log EC50 μmol L−1 = log EC50 _ max – (log EC50 _ max − log EC50 _ min ) ·Yp⁎
where log EC50_max and log EC50_min are the highest and the lowest EC50 value in the EC50 database, respectively. The log EC50 is
All of the molecular descriptors are presented and explained in Table 3, together with the corresponding group contributions and
Table 2 Distribution of residuals between the calculated and predicted dimensionless toxicity and the mean residual for Microtoxs test, Algal and Lemna minor growth inhibition test, Acetylcholinesterase inhibition and cell viability assay with IPC-81 cells. Microtox
Algae
Lemna minor
AChE
IPC-81
Range
Residuals
%
Residuals
%
Residuals
%
Residuals
%
Residuals
%
o0.10 0.10–0.20 40.20 o0.28 Mean residual
46 8 1 55 0.0647 0.051
84 14 2 100
23 4 5 32 0.0717 0.060
72 12 16 100
20 4 0 24 0.0767 0.069
83 17 0 100
53 8 0 61 0.0517 0.040
87 13 0 100
41 3 1 45 0.053 7 0.045
91 7 2 100
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Fig. 1. Calculated and predicted dimensionless toxicity data plot for Microtoxs test.
confidence intervals for the QSAR model. The analysis of the contributions of each of the descriptors provides information on the influence of the structural groups on ILs toxicity. The anions of the AILs have a negative contribution to toxicity. The contributions of anions show very small differences between three groups of AILs anions. The contribution of cations is positive, which means that the presence of either of these cations in the AILs molecules leads to an increase in toxicity. Many studies confirmed that the influence of cations on ILs toxicity is stronger than the one of anions (Ranke et al., 2004; Couling et al., 2006; Matzke et al., 2007; Stolte et al., 2007). Imidazolium, pyridinium and pyrrolidinium cation contribute around 27%, 17% and 3% to the toxicity, respectively. The imidazolium's higher contribution to toxicity and lower toxicity of pyrrolidinium cation compared to imidazolium and piridinium have been also found by other authors (Stolte et al., 2007; Pham et al., 2008; Latała et al., 2009). The contribution for the number of carbon atoms in long alkyl side chains indicates that with every carbon added to the side chain, an increase of about 13% in toxicity of AILs is produced. The presence of the short chain also has a positive influence on toxicity. These two results are in agreement with other authors findings that the substitution of the cation ring and the increase of the side alkyl chain length have a positive influence on toxicity (Ranke et al., 2004; Wells and Coombe, 2006; Pham et al., 2010; Radošević et al., 2013). On the other hand, cations and anions of the PILs do not exhibit a high overall influence on the ILs toxicity, if they are analyzed in the present set of ILs. This coincides with the fact that
the PILs showed much lower toxicity than the AILs in the Microtoxs test. The amines have a stronger influence on toxicity towards V. fischeri in the Microtoxs test as the contribution of the amine is five times higher than the one for the acid part of the PILs molecule. These results are in agreement with those reported by Peric et al. (2013). In summary, the here presented QSAR model has 10 descriptors and could allow the prediction of (eco)toxicity of existing and potential ILs, resulting from the possible combinations of 11 types of anions, 6 types of cations and 8 types of different substituents of the cationic moiety. The same QSAR model was used for four more tests in order to predict the ILs (eco)toxicity: aquatic ecotoxicity (toxic effect on algae and aquatic plant L. minor), inhibition of acetylcholinesterase (AChE) and cytotoxicity (IPC-81 cells). The ILs toxicities in μ mol L 1 (log EC50) for these tests and calculated dimensionless toxicities (Ycn) can be found in Supplementary material Table S1. The fitting plots for the calculated and predicted Yn can be seen in Fig. S4 of Supplementary material. In case of these four (eco)toxicity tests a reasonably good fitting was also achieved, with R2 being 0.9918 for the Acetylcholinestherase inhibition test (n ¼61), 0.9799 for the Cytotoxicity test with IPC-81 cell line (n ¼ 45), 0.866 for the Test of inhibition of algal growth (n ¼32) and 0.901 for the L. minor growth inhibition test (n ¼24), The distribution of residuals for the QSAR modelling and the mean residual for these four (eco)toxicity tests are presented in Table 2. The group contributions and confidence intervals are shown in Table 4. All but one (eco)toxicity test (Acetylcholinestherase inhibition) show negative, very low or no contribution of the anions to the toxicity, meaning that the selected anions do not play a key role in AILs toxicity, which, as previously mentioned, coincides with the literature findings. The contribution of cations is the strongest in the case of the Acetylcholinesterase inhibition test, both for AILs and PILs. There is also a positive influence of anions for this test. This can be explained with the different toxicity mode of action between the Acetylcholinesterase inhibition test and the other three tests. While in Algal and L. minor growth inhibition test and Cell viability assay with IPC-81 the toxicity is based on the membrane disruption (Docherty and Kulpa, 2005), the Acetylcholinesterase inhibition test involves binding to the enzyme's active centre (Stock et al., 2004). As initially found by Stock et al. (2004), acetylcholinesterase can be inhibited by ILs containing a cation with positively charged nitrogen and certain lipophilicity.
Table 3 Significance and contribution of the descriptors used in the QSAR modelling. Group
Molecular descriptor
Comments
Contribution
Confidence interval (95%)
Anion
A1
Influence of anions: chloride (Cl ), bromide (Br ). Value is 1 if it exists and 0 if not. Influence of anions: hexafluorophosphate (PF6 ), tetrafluoroborate (BF4 ). Value is 1 if it exists and 0 if not. Influence of anions: dicyanamide (N(CN2)2 ), bis-trifluoromethan-sulfonimide (N(CF3SO2)2 ). Value is 1 if it exists and 0 if not. Influence of number of carbons in PILs anion. Value of A4: 1–5
0.269
0.400
0.139
0.300
0.442
0.158
0.251
0.382
0.120
0.016
0.025
0.056
A2 A3
A4 Cation
Im – imidazolium Py – pyridinium Pyr – pyrrolidinium Amine
Influence of imidazolium cation. Value is 1 if it exists and 0 if not. Influence of pyridinium cation. Value is 1 if it exists and 0 if not. Influence of pyrrolidinium cation. Value is 1 if it exists and 0 if not. Influence of amine. Value: 1, 2 or 3 in function of the complexity of the PILs cation
0.274 0.175 0.035 0.085
0.139 0.092 0.073 0.015
0.399 0.259 0.144 0.155
Cation subsitution
R1
Influence of Value of R1: Influence of Value of R1:
0.132
0.087
0.127
0.112
0.097
0.126
R2
number of carbons in long chains of the AILs molecule. 0–10 number of carbons in short chains of the AILs molecule. 1 if methyl group exists and 0 if not.
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Table 4 The group contributions to calculated dimensionless toxicity. Algae
Lemna minor
AChE
IPC-81
Group
Molecular descriptor
Contribution Confidence interval (95%)
Contribution Confidence interval Contribution Confidence inter(95%) val (95%)
Contribution Confidence interval (95%)
Anion
A1 A2 A3 A4
0.098 0.091 0.051 0.032
0.225 0.233 0.182 0.048
0.029 0.041 0.080 0.111
0.244 0.231 0.419 0.087
0.357 0.349 0.541 0.003
0.131 0.113 0.297 0.174
0.023 0.019 0.000 0.014
0.031 0.037 0.000 0.020
0.077 0.074 0.000 0.047
0.103 0.019 0.000 0.081
0.023 0.095 0.000 0.044
0.182 0.057 0.000 0.118
Cation
Im Py Pyr Amine
0.075 0.000 0.000 0.010
0.064 0.000 0.000 0.146
0.214 0.000 0.000 0.126
0.005 0.048 0.085 0.057
0.023 0.061 0.035 0.206
0.033 0.157 0.140 0.092
0.361 0.699 0.000 0.084
0.312 0.629 0.000 0.027
0.410 0.770 0.000 0.141
0.067 0.019 0.000 0.015
0.002 0.057 0.000 0.079
0.133 0.140 0.000 0.048
0.086 0.036
0.048 0.187
0.124 0.114
0.141 0.000
0.057 0.000
0.224 0.000
0.018 0.000
0.010 0.000
0.026 0.000
0.084 0.008
0.072 0.040
0.097 0.057
Substitution R1 R2
The study of Arning et al. (2008) revealed that the pyridinium derived ILs show stronger inhibition than the imidazolium ones, which has also been confirmed in this study, with the contribution to toxicity of pyridinium being two times higher than the one for imidazolium. The anions do not show inhibitory effect on the enzyme activity with only exception of the fluoride and fluoride containing anions (Matzke et al., 2007), also confirmed by the results of this study. The statistical models applied by Roy et al. (2014) also demonstrated that aromatic ILs are more toxic than non-aromatic and aliphatic ones. The influence of the long side alkyl chains is predominant, as the short alkyl chains show negative, very low o zero influence on the AILs toxicity. This influence also coincides with the strong relationship between the (eco) toxicity of ILs and its hydrophobic character described in the literature (Garcia et al., 2005; Ranke et al., 2007a; Cvjetko Bubalo et al., 2014), meaning that longer the alkyl chain, the higher the hydrophobic character of the ionic liquid, the fact that facilitates the ILs molecule's penetration through cell membranes and thus potentiates their toxicity. A correlation between an increasing chain length of the side chains connected to the cationic head groups and an enhanced inhibitory potential of the ILs on the acetylcholinesterase, found in other authors' studies (Stock et al., 2004; Arning et al., 2008) was also confirmed in this study. The cations and anions of the PILs do not display a high influence on toxicity and the PILs once again showed much lower toxicity than the AILs. The amines have a stronger influence on toxicity towards acetylcholinesterase, and the acids in the anionic moiety have a stronger contribution to the toxicity towards P. subcapitata and IPC-81. These results are also in agreement with those reported by Peric et al. (2013) where good correlation between the acid moieties and toxicity was found for these tests. It can be seen that both PILs cation and anion contribute to toxicity towards L. minor, which coincides with findings that L. minor is the Table 5 The predicted values of log EC50 (mmol L 1) for Microtoxs test, Algal and Lemna minor growth inhibition test, Acetylcholinestherase inhibition and Cell viability assay with IPC-81 cells for eight representatives of the new family of PILs.
2-HEAA 2-HEAPr 2-HEAiB 2-HEAPe 2-HTEAF 2-HTEAA 2-HTEAPr 2-HTEAiB
Microtox
Algae
L. minor
AChE
IPC-81
4.19 4.11 4.03 3.96 4.26 3.35 3.27 3.19
3.94 3.68 3.43 3.18 4.19 4.10 3.85 3.59
3.22 2.94 2.67 2.40 3.49 3.57 3.30 3.03
4.36 4.31 4.26 4.21 4.41 3.74 3.69 3.64
4.12 3.81 3.49 3.17 4.44 4.25 3.93 3.61
most sensitive species in the case of the aquatic ecotoxicity tests (Peric et al., 2013). The EC50 values for the previously untested eight representatives of the new family of short aliphatic PILs were predicted for these five (eco)toxicity tests. The results can be found in Table 5. The model proposed by Luis et al. (2007), although initially designed for the AILs, seems to be adequate for the PILs too.
4. Conclusions The model based on group contribution used in this work allows the prediction of ionic liquids' (eco)toxicity, with well correlated experimental and predicted toxicity data. The EC50 values for eight new short aliphatic protic ILs, previously untested, were predicted. So, this QSAR model, which was first proposed for aprotic ILs, proved to be useful for predicting the toxicity of protic ILs as well. Literature findings of the predominant influence of cations on toxicity and the increase of toxicity with the elongation of the side alkyl chains in aprotic ILs molecule were confirmed by the results of this study. In the case of new protic ILs, both anionic and cationic part of the molecule have influence on (eco)toxicity, and it is the elongation of the acid in the anionic moiety which has more influence on the overall toxicity in three out of the five (eco) toxicity tests (cytotoxicity and algal and aquatic plants growth inhibition). Apart from EC50 prediction, this model can also help to determine the positive or negative influence of different structural groups on the (eco)toxicity and in that way “guide” the synthesis of new ionic liquids towards more sustainable ones.
Acknowledgements B. Peric would like to thank the Spanish Ministry of Foreign Affairs and Cooperation and the Spanish Agency for International Cooperation for Development (Grant MAEC-AECID II-A). The authors would like to thank Francesc Fàbrega for his advice on the initial QSAR modelling and Dr. Jesús Gómez for his valuable suggestions and comments.
Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.ecoenv.2015.02. 027.
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