Predicting the Toxicity of Ionic Liquids in Leukemia Rat Cell Line by

Article pubs.acs.org/IECR

Predicting the Toxicity of Ionic Liquids in Leukemia Rat Cell Line by the Quantitative Structure−Activity Relationship Method Using Topological Indexes Fangyou Yan,† Shuqian Xia,*,† Qiang Wang,*,‡ and Peisheng Ma† †

Key Laboratory for Green Chemical Technology of the State Education Ministry, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China ‡ School of Material Science and Chemical Engineering, Tianjin University of Science and Technology, 13 St. TEDA, Tianjin 300457, China S Supporting Information *

ABSTRACT: On the basis of the new topological index (TI) proposed in our previous work, a multiple linear regression (MLR) model was developed for predicting the toxicity of ionic liquids (ILs) in Leukemia Rat Cell Line (log EC50 IPC-81). The TI is derived from atom characters (e.g., atom radius, atom electronegativity, etc.) and atom position in the hydrogen-suppressed molecule structure. Because ILs are composed entirely of cations and anions, the TIs are calculated from cation and anion, respectively. A third TI was also proposed to depict the interaction of anion and cation. The toxicity of 173 ILs, which are based on imidazolium (Im), pyridinium (Py), pyrrolidinium (Pyr), ammonium (Am), phosphonium (Ph), quinolinium (Qu), piperidinium (Pi), and morpholinium (Mo), was calculated by the model. The regression coefficient (R2) and the overall average absolute error (AAE) are 0.938 and 0.226, respectively.

1. INTRODUCTION Ionic liquids (ILs) are a novel class of room temperature molten salts with melting points below 100 °C, which are composed entirely of anions and cations. ILs have been extensively investigated in recent years. ILs have a diversity of applications such as electrolytic media,1−3 catalysis,4−6 and solvents,7−10 because of their beneficial properties such as negligible vapor pressure, high heat capacity, high thermal conductivity, high thermal stability, a wide temperature range for liquids, and so on. ILs are considered as potentially environmental benign solvents due to their negligible vapor pressure, while they can also accumulate in the environment due to their significant solubility in water. Toxicity data are required to assess the hazard potential of ILs. Leukemia Rat Cell Line (IPC-81) has been frequently used in cytotoxicity assays of ILs by Ranke’s group (UFT Centre for Environmental Research and Sustainable Technology).11−14 Some toxicity data of ILs in IPC-81 can also be found free at UFT database.15 As compared to the huge number of ILs, the toxicity data in IPC-81 in the database and literature are relatively scarce. It is also time and material consuming to obtain toxicity data by experiments. To extend the applications of ILs and design the new potential ILs, it is necessary to develop a reliable mathematical model to predict the toxicity of ILs. Three literature references have explored the models for the toxicity of ILs in IPC-81. Torrecilla et al.16 developed QSAR models based on the multiple linear regression (MLR, R2 = 0.9) and neural network (NN, R2 = 0.996) for the prediction of the toxicological effect of 96 ILs in IPC-81. Torrecilla et al.17 explored the MLR (R2 = 0.867), radial basis network (RB, R2 = 0.861), and multilayer perceptron neural network (MLP, R2 = © 2012 American Chemical Society

0.982) models for the estimation of toxicity of 153 ILs in the IPC-81. Fatemi et al.18 developed QSAR models based on MLR (R2 = 0.92) and MLP (R2 = 0.99) for the prediction of the toxicological effect of 227 ILs in IPC-81. The MLP and NN models developed in the above three references are fairly good for predicting log EC50 IPC-81, while they are complex. In addition, good mathematics knowledge is needed to develop MLP and NN models. Therefore, it is necessary to develop a simple model to predict log EC50 AChE. Topological indexes (TIs) are numerical quantities derived from a graph theoretical representation of the molecular structure through mathematical invariants.19 There are two main sources of TIs, the distance (D) and adjacency (A) matrixes, which are defined as: D = (dij), dij ⎧ n if the path length between atoms i andj is n =⎨ ⎩0 otherwise

(1)

A = (aij), aij ⎧ 1 if the path length between atoms i and j is 1 =⎨ ⎩0 otherwise

(2)

Wiener proposed the first TI/Wiener index W from distance matrixes D. Wiener index W is one of the most wildly used TIs. More TIs have been developed from then on: 20

Received: Revised: Accepted: Published: 13897

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Schultz’s molecular topological indexes MTI,21 Randic’s molecular connectivity index χ,22 Pakmakar’s PI index,23 Balaban’s J index,24 and Hosoya’s Z topological index.25 The above processes only take into account the route between apexes and the adjacency relationship of the apexes. The type of atom and bond is neglected; therefore, it is difficult to show the adjacency of the C atom with other heteroatoms, which does limit its field of applications. Some TIs have been proposed for resolving the heteroatom differentiation. Ren26 derived atom-type AI topological indexes from the topological distance sums and vertex degree, which were further used to describe different structural environments of each atom-type in a molecule. Kier and Hall developed mχ index,27 which introduced the concept of valence connectivities to differentiate heteroatoms using the valence electrons of each atom in the molecule. Estrada28 proposed a possible solution to the problem of differentiation of heteroatoms in molecular graphs by using weights in the nondiagonal entries of the edge adjacency matrix. Although so many TIs have been proposed, there is no general TI that can be used for ILs separately. A general topological index (TI) was proposed on the basis of atom characters (e.g., atom radius and atom electronegativity, etc.) and atom positions in the hydrogen-suppressed molecule by our research group. It has been used for predicting the decomposition temperature of ILs29 and the toxicity of ILs in acetylcholin esterase.30 In this work, it was used for predicting the log EC50 IPC-81 of ILs.

TM = [D CV ] × [D CV ]T

ai is square root of the van der Waals radii of atom i

(6)

The three topological indices are then defined as: A 2 = λmax2 /2

A3 = λmax3 /2

(7)

where λmax1−λmax3 are the largest eigenvalues of matrixes Z1− Z3, which are defined as Z1 = [A V1 V2] × [A V1 V2]T

TI1 =

∑ tanh(λi)

(13)

TI2 =

∑ λi

(14)

TI3 = max(λi)

(15)

TI4 = mean(λi)

(16)

where λi represents the eigenvalues of TM. According to eqs 13−16, one TM will generate four TIs. For one set, there are 36 TIs obtained from 9 TMs generated from 1 D and 9 CVs. Because ILs are composed entirely of cations and anions, two sets of TIs are generated from cations and anions by the method mentioned above, respectively. The log EC50 IPC-81 of IL is not the simple sum of anion and cation contributions; therefore, another set of TI is proposed for depicting the interaction of cation and anion. The TI is defined as:

V2 = (ai)

A1 = λmax1/2

(12)

Second, TI is calculated from TM. The eigenvalues of TM are calculated first. Four TIs are then obtained from the eigenvalues. Four TIs are defined as:

(4) (5)

ai is the elements that characterize the atom i

To depict the molecule all-sidedly, eight CVs are defined using eight elements. They are defined: CV1, ai-π × van der Waal radii; CV2, ai-atom weight; CV3, ai-atom electronegativity; CV4, ai-π × atom radius; CV5, ai-exp(vertex degree, defined as the number of adjacent atoms); CV6, ai-exp(fraction of hydrogen to atom i and hydrogens adjacent to it); CV7, ai-exp(1/atom electronic shell number); CV8, ai-exp(1/atom outermost electron number) . Another CV is defined as CV9, ai-0, which means no element. The values of van der Waal radii, atom radius, and electronegativity for all atoms are shown in the Supporting Information. The TM is then defined as:

(3)

ai is square root of vertex degree of atom i

(10)

(11)

C = (aij), aij

V1 = (ai)

Z3 = [C V1 V2] × [C V1 V2]T

CV = (ai)

B = (aij), aij

⎧ 3 if the path length between atoms i and j is 3 =⎨ ⎩0 otherwise

(9)

In this work, a TI is obtained on the basis of the above method. There are two steps to generate the TI. First, the molecule information is obtained and set in a total matrix (TM), which is generated from the distance matrix D and character vector CV. Instead of matrixes A, B, and C, D is used for determining the positions of atoms in a molecule, because D contains much more position information than do A, B, and C matrixes. The CV is used for determining the characters of atoms in the hydrogen-suppressed molecule. For each TM, only one CV is used and nine CVs are defined. Every atom in the hydrogen-suppressed graph is first numbered randomly with different numbers from 1 to N, which is the total number of non-hydrogen atoms in the molecule. CV is defined as:

2. METHOD 2.1. Data Set. To develop the QSAR model, the toxicity data in IPC-81 for 173 ILs were used, which includes 75 imidazolium, 28 pyridinium, 18 pyrrolidinium, 20 ammonium, 4 phosphonium, 5 quinolinium, 13 piperidinium, 10 morpholinium cations, and 29 anions. All data are obtained from ref 13 by Ranke’s group. 2.2. Topological Index. Yao et al.31 generated three TIs from path matrixes A, B, C and two vectors (V), V1 and V2. The three TIs provided a sophisticated way to distinguish heteroatoms. They are defined from: ⎧ 2 if the path length between atoms i and jis 2 =⎨ ⎩0 otherwise

Z 2 = [B V1 V2] × [B V1 V2]T

TI5 =

(8) 13898

∑ λCa,i + ∑ λAn,i

(17)

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Table 1. Overall Results of the MLR Model chemical family no. of samples AAE

Im

Py

Pyr

Am

Ph

Qu

Pi

Mo

overall

75 0.235

28 0.254

18 0.220

20 0.204

4 0.074

5 0.210

13 0.206

10 0.241

172 0.226

Figure 1. Comparisons of the predicted log EC50 IPC-81 by the model (a) and leave-one-out cross-validation (b) with the experimental log EC50 IPC-81.

where λCa,i and λAn,i are the eigenvalues of TMs from cation and anion, respectively. According to eq 17, another set of 9 TIs are obtained from 9 TMs generated from cation and 9 TMs generated from anion. The detailed procedure for calculating the three sets of TIs is shown in the Supporting Information by the example of 1ethyl-3-methylimidazolium tetrafluoroborate.

where n is number of samples, and log EC50,exp and log EC50,cal are the experimental and calculated log EC 50 values, respectively. The overall calculation results of the model for each chemical family are shown in Table 1. For the eight kinds of chemicals, only the AAE of pyridinium-based ILs is relatively big, while the AAE values of the other seven kinds are approximate to or smaller than the overall AAE. The calculated values by eq 18 and the experimental data of log EC50 IPC-81 are compared in Figure 1a. AAE distributions are also schematically shown in Figure 2. The AAE values for most of samples are smaller than

3. RESULTS AND DISCUSSION For each ILs, three sets of TIs containing 36, 36, and 9 TIs are generated from cation, anion, and their interaction, respectively. Many calculation results show that some of 81 TIs have little valid information, and they seem to be seldom helpful for improving the prediction precision after some attempts. To simplify the model, some of these TIs containing little valid information can be omitted; only the most valid TIs are selected to develop the QSAR model. On the basis of the foregoing, 15, 10, and 2 TIs selected from cation set, anion set, and their interaction set were used to develop the QSAR model. A MLR model was developed as: 15

P = P0 + a Ncat +

10

∑ αCa,i × TICa,i + ∑ αAn,j × TIAn,j i=1

j=1

2

+

∑ αTo, h × TITo,h h=1

(18)

Figure 2. Distributions of the AAE by the model and leave-one-out cross-validation.

where N = 173, R2 = 0.938, F = 77.01, AAE = 0.226, σ = 0.281; and where P is the predicted log EC50 IPC-81; TICa,i, TIAn,j, and TITo,h are TIs generated from cation, anion, and their interaction, respectively; and Ncat is cation atom number. P0, a, αCa,i, αAn,j, and αTo,h are parameters; P0 and a are 25.8819 and −2.85936, respectively. Other parameters and the types of TIs are shown in the Supporting Information. AAE =

0.40. In all, it can be found that it is reliable for calculating the log EC50 IPC-81 by eq 18. The experimental data and the calculated values by eq 18 for log EC50 IPC-81 are shown in the Supporting Information. The TICa,i, TIAn,j, and TITo,h are also presented in the Supporting Information. The predicting ability of the model for the log EC50 IPC-81 of ILs is checked by leave-one-out cross-validation and external validation.

∑ |log EC50,exp − log EC50,cal | n

(19) 13899

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Leave-One-Out Cross-Validation. The results of leaveone-out cross-validation are shown in Table 2. The results show

Table 4. Comparisons of This Work with Refs 16−18

Table 2. Results of Predicting Ability Test by Leave-OneOut Cross-Validation status

no. of samples

R2

AAE

model leave-one-out cross-validation

173 173

0.938 0.887

0.226 0.295

that the R2 and AAE are acceptable, although they are not as good as the results calculated by eq 18. The calculated values by leave-one-out cross-validation and the experimental data of log EC50 IPC-81 are compared in Figure 1b. The AAE distributions of leave-one-out cross-validation are also compared to AAE distributions of eq 18 in Figure 2. From Figure 2, it can be found that the AAE distributions of leave-one-out crossvalidation are similar to that of eq 18, which means a good predicting ability of eq 18. External Validation. The data set is randomly divided into training set (138) and testing set (35). The QSAR model was then derived using the training set with the same descriptors used in eq 18. The samples in the testing set were predicted by this model. The R2 and AAE for the training set and testing set are calculated, and they are listed in Table 3. From Table 3, it

no. of samples

R2

AAE

training testing

138 35

0.937 0.916

0.234 0.249

no. of samples

R2

σ

ref

MLR NN MLR MLP RB MLR MLP MLR

96 96 153 153 153 227 227 173

0.90 0.996 0.867 0.861 0.982 0.92 0.99 0.938

0.34 0.12 0.07 0.07 0.03 0.29 0.12 0.28

Torrecilla16 Torrecilla16 Torrecilla17 Torrecilla17 Torrecilla17 Fatemi18 Fatemi18 this work

those in other MLRs (R2 = 0.90,16 0.867,17 0.9218) and BR (R2 = 0.84217) models. The model in this work is not as good as the MLP (R2 = 0.996,17 0.9918) and NN (R2 = 0.98216) models. Standard deviation (σ) is also compared: for the MLR models, the σ of this work (0.28) is smaller than in references (0.34,16 0.2918); the σ of this work (0.28) is bigger than the MLP and NN models in references (0.12,16 0.1218). The σ in ref 17 is much smaller than that in the other two references and this work, which is probably because of the different understandings of the standard deviation σ. In all, the MLR model in this work is more reliable than other MLR models and not as good as the MLP and NN models. Yet the MLP and NN models are difficult to use and repeat by others, especially for those not good at mathematics.

Table 3. Results of Predicting Ability Test by External Validation status

method

4. CONCLUSIONS A general topological index (TI) based on the atom characters (e.g., atom van der Waal radii, atom radius, atom electronegativity, etc.) and atom positions in the hydrogen-suppressed molecule structure was proposed by our research group for predicting properties of ILs. A MLR model for predicting the log EC50 IPC-81 of ILs was developed by the three sets of TIs generated from cation, anion, and their interaction in the work. In the model, not only the contributions of cation and anion but also the interactions of cation and anion were considered. It is distinguished from the ordinary QSAR models. The overall values of R2, AAE, and F for the model are 0.938, 0.226 and 77.01, respectively. The results show that the TI proposed in this work is not only simple but also efficient for predicting the log EC50 IPC-81 of ILs.

can be found that the R2 and AAE in the training set are approximate to the overall R2 and AAE. The AAE in the testing set is slightly bigger than the overall AAE, and the R2 in the testing set is slightly smaller than the overall R2. The calculated values both in the training set and in the testing set are compared to the experimental data of the log EC50 IPC-81 and are presented in Figure 3. The overall results show that this method has a good predictive ability. Comparisons of This Work with References. The QSAR model in this work is compared to the reference models, and the results are shown in Table 4. From Table 4, it can be found that the model in this work (R2 = 0.938) is more reliable than

Figure 3. Comparisons of the predicted log EC50 IPC-81 by the training set (a) and testing set (b) in external validation with the experimental log EC50 IPC-81. 13900

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potential of ionic liquids-basis for an eco-design of sustainable products. Green Chem. 2005, 7, 362−372. (13) Ranke, J.; Stolte, S.; Störmann, R.; Arning, J.; Jastorff, B. Design of sustainable chemical productsthe example of ionic liquids. Chem. Rev. 2007, 107, 2183−2206. (14) Stolte, S.; Arning, J.; Bottin-Weber, U.; Muller, A.; Pitner, W.-R.; Welz-Biermann, U.; Jastorff, B.; Ranke, J. Effects of different head groups and functionalised side chains on the cytotoxicity of ionic liquids. Green Chem. 2007, 9, 760−767. (15) Centre for Environmental Research and Sustainable Technology (UFT); http://www.il-eco.uft.uni-bremen.de/index.php?page= home&chent_id=&view=intro&lang=en. (16) Torrecilla, J. S.; García, J.; Rojo, E.; Rodríguez, F. Estimation of toxicity of ionic liquids in Leukemia Rat Cell Line and Acetylcholinesterase enzyme by principal component analysis, neural networks and multiple lineal regressions. J. Hazard. Mater. 2009, 164, 182−194. (17) Torrecilla, J. S.; Palomar, J.; Lemus, J.; Rodriguez, F. A quantum-chemical-based guide to analyze/quantify the cytotoxicity of ionic liquids. Green Chem. 2010, 12, 123−134. (18) Fatemi, M. H.; Izadiyan, P. Cytotoxicity estimation of ionic liquids based on their effective structural features. Chemosphere 2011, 84, 553−563. (19) Ernesto, E. Generalization of topological indices. Chem. Phys. Lett. 2001, 336, 248−252. (20) Wiener, H. Structural determination of paraffin boiling points. J. Am. Chem. Soc. 1947, 69, 17−20. (21) Schultz, H. P. Topological organic chemistry. 1. Graph theory and topological indices of alkanes. J. Chem. Inf. Comput. Sci. 1989, 29, 227−228. (22) Randic, M. Characterization of molecular branching. J. Am. Chem. Soc. 1975, 97, 6609−6615. (23) Khadikar, P. V.; Kale, P. P.; Deshpande, N. V.; Karmarkar, S.; Agrawal, V. K.; Novel, P. I. indices of hexagonal chains. J. Math. Chem. 2001, 29, 143−150. (24) Alexandru, T. B. Highly discriminating distance-based topological index. Chem. Phys. Lett. 1982, 89, 399−404. (25) Hosoya, H. Topological index: A newly proposed quantity characterizing the topological nature of structural isomers of saturated hydrocarbons. Bull. Chem. Soc. Jpn. 1971, 44, 2332−2339. (26) Biye, R. Application of novel atom-type AI topological indices to QSPR studies of alkanes. Comput. Chem. 2002, 26, 357−369. (27) Kier, L. B.; Hall, L. H. Structure-activity studies on hallucinogenic amphetamines using molecular connectivity. J. Med. Chem. 1977, 20, 1631−1636. (28) Estrada, E. Edge adjacency relationships in molecular graphs containing heteroatoms: a new topological index related to molar volume. J. Chem. Inf. Comput. 1995, 35, 701−707. (29) Yan, F.; Xia, S.; Wang, Q.; Ma, P. Predicting the decomposition temperature of ionic liquids by the Quantitative Structure−Property Relationship method using a new topological index. J. Chem. Eng. Data 2012, 57, 805−810. (30) Yan, F.; Xia, S.; Wang, Q.; Ma, P. Predicting toxicity of ionic liquids in acetylcholinesterase enzyme by the Quantitative Structure− Activity Relationship method using topological indexes. J. Chem. Eng. Data 2012, 57, 2252−2257. (31) Yao, Y.; Xu, L.; Yang, Y.; Yuan, X. Study on structure-activity relationships of organic compounds: Three new topological indexes and their applications. J. Chem. Inf. Comput. Sci. 1993, 33, 590−594.

ASSOCIATED CONTENT

S Supporting Information *

Detailed procedure for calculating the three sets of TIs by the example of 1-ethyl-3-methylimidazolium tetrafluoroborate; the values of van der Waal radii, atom radius, and electronegativity for all atoms; the parameters and the types of TIs used in eq 18; and the experimental and predicted log EC50 IPC-81 data and the TIs used in eq 18. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected] (S.X.); [email protected] (Q.W.). Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research was supported by the National Natural Science Foundation of China, No. 20976131 and U1162104, and the Programme of Introducing Talents of Discipline to Universities, No. B060006.

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