Predicting Toxicity of Ionic Liquids in Acetylcholinesterase Enzyme by

Article pubs.acs.org/jced

Predicting Toxicity of Ionic Liquids in Acetylcholinesterase Enzyme 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: A new topological index (TI) was proposed based on atom characters (e.g., atom radius, atom electronegativity, etc.) and atom positions in the hydrogen-suppressed molecule structure in our previous work. In this work, the TI was used for predicting the toxicity of ILs in acetylcholin esterase (log EC50 AChE) by the multiple linear regression (MLR) method. For ILs composed entirely of cations and anions, the TIs are calculated from cations and anions, respectively. The 221 ILs used in the MLR model are based on imidazolium (Im), pyridinium (Pyi), pyrrolidinium (Pyo), ammonium (Am), phosphonium (Ph), quinolinium (Qu), piperidinium (Pi), and morpholinium (Mo). The regression coefficient (R2) and the overall average absolute error (AAE) are 0.877 and 0.153, respectively.

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INTRODUCTION Ionic liquids (ILs) are a novel class of room temperature molten salts with melting points near ambient temperature, which are composed entirely of anions and cations. ILs have attracted much attention in recent years, for 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. The most interesting character is that the properties of ILs could be altered by modifying the structures of the cations or anions, which increases the unique features and applicability of ILs further. Due to the advantages mentioned above, ILs have diversities of applications: electrolytic media,1−3 catalysis,4−6 and solvents.7−10 Although ILs can lessen the risk of air pollution due to their negligible vapor pressure, they can also accumulate in the environment for their significant solubility in water. With their increasing applications, toxicity data are required to evaluate the environmental fate of ILs. The enzyme acetylcholin esterase (AChE) plays the most important role in nerve response and function, which catalyzes the degradation of the neurotransmitter acetylcholine. An inhibition of AChE leads to various adverse effects in neuronal processes, such as heart diseases or myasthenia in humans.11 AChE represents the main target in the development of potent insecticides based on phosphoric acid esters (e.g., Parathione1) and carbamates (e.g., Carbendazim1). Therefore, the activity pattern of this enzyme © XXXX American Chemical Society

in different biological matrices and tissues is used as an established biomarker to monitor the pesticide burden in nontarget species.12 AChE has been frequently used in cytotoxicity assays of ILs by Ranke’s group (UFT Centre for Environmental Research and Sustainable Technology).12−15 Compared to the huge number of ILs, the toxicity data in literature are relatively scarce. Therefore, it is necessary to develop a feasible mathematical model to predict the toxicity of ILs. A few quantitative structure−activity relationship method (QSAR) models have been reported in the literatures for predicting the toxicity of ILs. Some QSAR models were developed for predicting the Vibrio f ischeri toxicity of ILs.16−18 ́ Garcia-Lorenzo et al.19 developed a QSAR model for predicting the ecotoxicity of 15 imidazolium-derived ILs in the human Caco-2 cell line. Torrecilla et al.20 developed QSAR models based on MLR and neural network for the prediction of the toxicological effect of 96 ILs on the leukemia rat cell line. Other methods were also used for estimating the ecotoxicity of ILs. Lacrămă et al.21 developed a spectral−structure activity relationship model for the anionic−cationic interaction of ILs in V. f ischeri ecotoxicity. Alvarez-Guerra et al.22 estimated the ecotoxicity of ILs in V. f ischeri, by means of the application of Received: February 17, 2012 Accepted: June 20, 2012

A

dx.doi.org/10.1021/je3002046 | J. Chem. Eng. Data XXXX, XXX, XXX−XXX

Journal of Chemical & Engineering Data

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58 pyridinium, 24 pyrrolidinium, 17 ammonium, 3 phosphonium, 4 quinolinium, 16 piperidinium, and 9 morpholinium. All of the data are obtained from ref 15 by Ranke’s group. The experimental data can be found in the Supporting Information. The approximate experimental confidence regions of the cytotoxicity values were established to be about 0.15.15 Topological Index. Yao et al.35 generated three TIs from path matrixes A, B, and C and two vectors (V), V1 and V2. The three TIs provided a sophisticated way to distinguish heteroatoms. They are defined as:

partial least squares-discriminant analysis. Only Torrecilla et al.23 explored the models for toxicity of ILs in the AChE. They developed the multiple linear regression (MLR) (R2 = 0.867, 0.814), radial basis network (RB; R2 = 0.861, 0.842), and multilayer perceptron neural network (MLP; R2 = 0.982, 0.973) models for the estimation of toxicity of 153 ILs in the leukemia rat cell line and AChE. The MLP model developed by Torrecilla is fairly good for predicting log EC50 AChE, while it is much more complex. In addition good mathematics knowledge is needed to develop the MLP model. 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.24 There are two main sources of TIsthe distance (D) and adjacency (A) matrices, which are defined as:

B = (aij) ⎧ 2 if the path length between atoms i and j is 2 aij = ⎨ ⎩0 otherwise (3)

C = (aij)

D = (dij)

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

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

(4) (1)

V1 = (ai)

A = (aij)

ai is the square root of vertex degree of atom i

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

(5)

V2 = (ai) ai is the square root of the van der Waals radii of atom i

(2)

(6)

Wiener proposed the first TI/Wiener index W from distance matrices D. More and more TIs have been developed from then on: Schultz’s molecular topological indexes MTI,26 Randic’s molecular connectivity index χ,27 Pakmakar’s PI index,28 Balaban’s J index,29 and Hosoya’s Z topological index.30 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. Biye31 derived atom-type AI topological indexes from the topological distance sums and vertex degree which were further used to describe different structural environment of each atom-type in a molecule. Kier and Hall developed an mχ index32 which introduced the concept of valence connectivities to differentiate heteroatoms using the valence electrons of each atom in the molecule. Estrada33 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 based on 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 ILs.34 Based on the topological index, a MLR model was developed for predicting the log EC50 AChE of ILs. 25

Then three topological indices are defined as A1 = λmax1/2

A 2 = λmax2 /2

A3 = λmax3 /2 (7)

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

(8)

Z 2 = [B V1 V2] × [A V1 V2]T

(9)

Z3 = [C V1 V2] × [A V1 V2]T

(10)

In this work a TI is obtained based on the above method. There are two steps to generate the TI: First, obtain the information of a molecule and set it in a total matrix (TM), which is generated from distance matrix D and character vector CV. Instead of using 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 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 atom in the molecule. CV is defined as: CV = (ai)

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ai are the elements that characterize the atom i

METHOD Data Set. To develop the QSAR model, the toxicity data in AChE for 221 ILs were used, which includes 90 imidazolium,

(11)

To depict the molecule all-sidedly, eight CVs are defined using eight elements. They are defined as: CV1, ai: π × van der Waals radii; B



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Table 1. van der Waals Radii, Atom Radius, and Electronegativity for All Atoms Used in This Work atom

B

C

N

O

F

S

P

Cl

Br

I

van der Waals radii (Å) atom radius (Å) electronegativity

0.98 1.17 2.04

1.8 0.91 2.55

1.5 0.75 3.04

1.4 0.65 3.44

1.35 0.57 3.98

1.85 1.09 2.58

1.9 1.23 2.19

1.85 0.97 3.16

1.95 1.12 2.96

2.15 1.32 2.66

that the TIs from the interaction of cation and anion cannot improve the prediction accuracy obviously, and the log EC50 AChE of IL is mainly based on the cation contribution. Therefore, 15 and 1 TIs selected from the cation and anion set were used to develop the QSAR model. This result is consistent with the conclusion that the cation contribution to toxicities is dominant for most of the commercially available ILs.14,20 The model can be shown as follows:

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 Waals radii, atom radius, and electronegativity for all atoms are listed in Table 1. Then the TM is defined as: TM = [D CV] × [D CV]T

i=1

P = P0 + a Ncat +

15

n = 221

∑ tanh(λi) ∑ λi

(12)

(13) (14)

TI3 = max(λi)

(15)

TI4 = mean(λi)

(16)

∑ λCa,i + ∑ λAn,i

AAE = 0.153

∑ |log EC50,exp − log EC50,cal | (19)

n

where n is the number of the sample; log EC50,exp and log EC50,cal are the experimental and calculated log EC50 values, respectively. The overall calculation results of the model for each chemical family are shown in Table 3. For the eight kinds of chemicals only the AAE values of ammonium- and morpholinium-based ILs are relatively large. The calculated values by eq 18 and the experimental data of log EC50 AChE are compared in Figure 1a.

where λi is the eigenvalues of TM. According to eqs 13 to16, 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. Another set of TI is proposed for depicting the interactions of cations and anions. The TI is defined as: TI5 =

F = 84.6

(18)

AAE = TI 2 =

R2 = 0.877

1

where P is the predicted log EC50 AChE; TICa,i and TIAn,j are TIs generated from cation and anion, respectively; Ncat is the cation atom number. P0, a, αCa,i, and αAn,j are parameters; P0 and a are 11.283 and −4.595, respectively; F is the Fisher− Snedecor distribution. Other parameters and the types of TIs are shown in Table 2.

Second, calculate TI from TM. The eigenvalues of TM are calculated first. Then four TIs are obtained from the eigenvalues. Four TIs are defined as: TI1 =

j=1

∑ αCa,i·TICa,i + ∑ αAn,j·TIAn,j

Table 2. Parameters and the Types of TIs for Equation 18a TI types

(17)

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

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RESULTS AND DISCUSSION For each ILs, three sets of TIs containing 36, 36, and 9 TIs are generated from cations, anions, and their interaction, respectively. After many calculations, it was found that, for the 81 TIs, some of them contain little valid information and they cannot improve the predicting precisely. 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. After attempting many calculations, it was found

i

αCa,i

m

k

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 j 1

1.491732 −0.02341 0.746748 −0.00505 0.95298 −2.26115 1.882796 0.000115 −2.15873 0.874397 −0.52337 −34.6882 −0.87186 63.46710 −27.4838 αAn,j 3.94·10−6

2 2 3 3 4 4 5 5 6 6 6 7 8 8 9

3 4 1 3 1 3 1 2 1 2 3 3 2 3 3

5

4

a

m: TI is obtained from TM generated from CVm and D; k(1, 2, 3, 4): TICa,i or TIAn,i is defined as eqs 13, 14, 15, and 16. C



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Table 3. Overall Results of the MLR Model chemical family

Im

Pyi

Pyo

Am

Ph

Qu

Pi

Mo

overall

no. samples AAE

90 0.144

58 0.140

24 0.161

17 0.217

3 0.091

4 0.128

16 0.147

9 0.216

221 0.153

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

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

good as the results calculated by eq 18. The calculated values by leave-one-out cross-validation and the experimental data of log EC50 AChE are compared in Figure 1b. The AAE distributions of leave-one-out cross-validation are also compared with AAE

The AAE distributions are also schematically shown in the Figure 2. The AAE values for most of the samples are smaller than 0.25. In summary, it can be found that it is reliable for calculating the log EC50 AChE by eq 18. The experimental data and the calculated values by eq 18 for log EC50 AChE 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 was checked by leaveone-out cross-validation and external validation. Leave-One-Out Cross-Validation. The results of leaveone-out cross-validation are shown in Table 4. The results show that the R2 and AAE are acceptable although they are not as

Table 4. Results of Predicting Ability Test by Leave-OneOut Cross-Validation

D

status

no. samples

R2

AAE

model leave-one-out cross-validation

221 221

0.877 0.838

0.153 0.171



Article

distributions of eq 18, and they are shown in Figure 2. From Figure 2, it can be found that the AAE distributions of leaveone-out cross-validation are similar to that of eq 18, which means a good predicting ability of eq 18. External Validation. The data set was randomly divided into the training set (177) and testing set (44). The QSAR model was then derived using the training set and with the same descriptors used to derive eq 18. The R2 and AAE for the training set and testing set were calculated, and they are listed in Table 5. From Table 5, it can be found that the R2 and AAE

Table 6. Comparisons of This Work with Reference 23

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no. samples

R2

AAE

training testing

177 44

0.884 0.823

0.150 0.179

no. sample

R2

ref

MLR MLP RB MLR

153 153 153 221

0.814 0.973 0.842 0.877

Torrecilla et al.23 Torrecilla et al.23 Torrecilla et al.23 this work

CONCLUSIONS A general topological index (TI) based on the atom characters (e.g., atom van der Waals radii, atom radius, atom electronegativity, etc.) and atom positions in the hydrogen-suppressed molecule structure was proposed by our research group for predicting the properties of ILs. An MLR model for predicting the log EC50 AChE of ILs was developed by two sets of TIs generated from cations and anions in the work. It was found that the log EC50 AChE of ILs is mainly based on the cation contribution. The overall values of R2, AAE, and F for the model are 0.877, 0.153, and 84.60, respectively. The results show that the TI proposed in this work is not only simple but also efficient for predicting the log EC50 AChE of ILs.

Table 5. Results of Predicting Ability Test by External Validation status

method

in the training set are approximate to the overall R2 and AAE. The AAE in the testing set are relatively larger than the overall AAE and the R2 in the testing set are relatively smaller than the overall R2, but they are acceptable. The calculated values both in the training and testing sets are compared with the experimental data of the log EC50 AChE in Figure 3. The overall results show that this method has a good predictive ability. Comparisons of This Work with Reference 23. The QSAR model is compared with ref models, and the result are shown in Table 6. From Table 6, it can be found that the model in this work with more samples (R2 = 0.877) is more reliable than other MLR (R2 = 0.814) and BR (R2 = 0.842) models. The model in this work is not as good as the MLP model, but the MLP model is difficult to use and repeat for others, especially those not good at mathematics.

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ASSOCIATED CONTENT

S Supporting Information *

Full list of cations and anions, log EC50 AChE data, experimental status, and TI values (xls). 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] or [email protected]. Funding

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

Figure 3. Comparisons of the predicted log EC50 AChE by the training set (a) and testing set (b) in external validation with the experimental log EC50 AChE. E



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Notes

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The authors declare no competing financial interest.

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Predicting Toxicity of Ionic Liquids in Acetylcholinesterase Enzyme by

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