Quantitative structure-activity relationship between the toxicity of amine surfactant and its molecular structure

Science of the Total Environment 702 (2020) 134593

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Quantitative structure-activity relationship between the toxicity of amine surfactant and its molecular structure Wengang Liu a,b,c,⇑, Xinyang Wang a,*, Xiaotong Zhou b,c, Hao Duan a, Panxing Zhao a, Wenbao Liu a a

School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China Guangdong Institute of Resources Comprehensive Utilization, Guangzhou 510650, China c Guangdong Provincial Key Laboratory of Development and Comprehensive Utilization of Mineral Resources, Guangzhou 510650, China b

h i g h l i g h t s

g r a p h i c a l a b s t r a c t


Tested the acute toxicity of 20 amine

surfactants on Daphnia magna and determined the 24 h EC50 values.
Calculated 35 molecular structure descriptors of amine surfactants by DFT computer simulation method.
Developed a new quantitative structure activity relationship for toxicity of amine surfactants through GFA method.
The influence of key molecular features of amine surfactants on the acute toxicity.

a r t i c l e

i n f o

Article history: Received 9 August 2019 Received in revised form 15 September 2019 Accepted 20 September 2019 Available online 03 November 2019 Editor: Daqiang Yin Keywords: Amine surfactants Toxicity QSAR GFA Molecular structure

a b s t r a c t With the extensive applications and ongoing world demand, more and more amine surfactants are discharged into natural environment. However, the database about toxicity of amine surfactants is incomplete, which is not beneficial to environmental protection process. In this paper, the toxicity of 20 amine surfactants on Daphnia magna were tested to extend the toxicity data of amine surfactants. Besides, 35 molecular structure descriptors including quantum parameters, physicochemical parameters and topological indices were chosen and calculated as independent variables to develop the quantitative structure-activity relationship (QSAR) model between the toxicity of amine surfactants and their molecular structure by genetic function approximation (GFA) algorithm. According to statistical analysis, a robust model was built with the determination coefficient of (R2) was 0.962 and coefficient determinations of cross-validation (R2cv ) was 0.794. Meanwhile, external validation was implemented to evaluate the QSAR model. The result of coefficient determinations of cross-validation (R2ext ) for external validation was calculated as 0.942, illustrating the model has great goodness-of-fit and good prediction ability. Ó 2019 Elsevier B.V. All rights reserved.

⇑ Corresponding authors at: School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China (W. Liu). E-mail addresses: [email protected] (W. Liu), [email protected] (X. Wang). https://doi.org/10.1016/j.scitotenv.2019.134593 0048-9697/Ó 2019 Elsevier B.V. All rights reserved.

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W. Liu et al. / Science of the Total Environment 702 (2020) 134593

1. Introduction Amine surfactants are consisted of at least one hydrophobic long alkyl chain attached a basic nitrogen atom with a lone pair of electrons (Poste et al., 2014) and have been widely used in household detergents, toiletries, institutional cleaning, flotation reagents, etc. (Calgaroto et al., 2016; Liu et al., 2019a; Rubingh, 1990). With the ongoing world demand and social economic development, amine surfactants consumption tends to increase constantly every year. It is estimated that approximately 5500 tons of amine surfactants are used for flotation in Brazil per year (Araujo et al., 2010). Consequently, lots of amine surfactants will be released and resided in natural environment. For instance, quaternary ammonium compounds (QACs), as a common amine surfactant, was discovered in various levels in sewage effluents, treated sludge or sediment (Table 1). The influence of amine surfactants on environment has drawn more and more attentions of researchers who study in environmental and surfactant exploitative field. The surfactants and their by-products are mainly discharged into sewage-treatment plants and then dispersed into water environment after reuse and cycling (Lechuga et al., 2016). Besides, the presence of the hydrophobic chain and positively charged N atoms facilitates the adsorption and accumulation of amine surfactants on sediments and sludge (Li et al., 2014). Due to the formation of several potentially toxic compounds, including N-nitrosamines and N-nitramines, most amine surfactants are harmful to aquatic plant and animals (Van de Voorde et al., 2012). Thus, it is important for us to explore and predict their toxicity in aquatic systems (Ma et al., 2012). Up to now, the majority research of aquatic toxicity for amine surfactants was focused on quaternary ammonium compounds (QACs) and systematic study on different types of amine surfactant with their aquatic toxicity was relatively rare (Nałe˛cz Jawecki et al., 2003; Zhang et al., 2015). Quantitative Structure Activity Relationship (QSAR) methods are widely applied to reveal the relevance between molecular structure and their chemical reactivity or physicochemical properties, then to predict the uncertain molecular properties of a certain molecular structure based on the relationship (Hopfinger, 1980; Philipp et al., 2007). Recently, several QSAR models have been established to reveal the relationship between molecular structures and physicochemical properties of amine surfactants (Wang et al., 2018). Newsome et al. (Newsome et al., 1993) developed two QSAR models respectively using algae 96-h EC50 data and Daphnia 48-h EC50 values of 18 amines. The result showed that n-octanol/water partition was positive related with EC50 and Daphnia was more sensitive than algae to amine surfactants. The in vivo toxicity of long chain primary, secondary and tertiary amines to the algae Amphora sp. [cf. coffeaeformis] and Dunaliella parva, and to the nauplii larvae of Artemia salina was established by Finlay and Callow (Finlay and Callow, 1996). The developed QSAR model indicated that toxicity of primary, secondary and dimethyl tertiary amines increased with increasing chain length up to a region of maximum activity with Dunaliella and Artemia. Additionally, with the development of computation chemistry technology, density functional theory (DFT) have been applied into calculating quan-

tum chemistry properties of amine surfactants to provide accurate structure parameters for building QSAR model. Zhu et al. (Zhu et al., 2010) derived a QSAR model to correlate the toxicity of quaternary ammonium compounds (QACs) on green alga Chlorella vulgaris with the quantum chemistry parameters calculated by DFT method, such as energy of the lowest unoccupied molecular orbital (ELUMO ) and energy of the highest occupied molecular orbital (EHOMO ). And the obtained QSAR model could be used for predicting the toxicity of QACs. Recently, a QSAR model of EC50 on Daphnia magna developed to predict untested biocides toxicities by using simple and interpretable 2D descriptors and the model was validated using stringent tests (Khan et al., 2019). Hammer et al. (Hammer et al., 2018) applied a QSAR model to determine the influence of anionic surfactants with different carbon chain lengths on the aquatic ecotoxicity. Thence QSAR model between toxicity of amine surfactants and its molecular structures also can be build based on the relationship between toxicity and the molecular structures. However, the toxicity data about amine surfactants were relatively less, which lead to the inconvenience and restriction to establish QSAR model. Therefore, the purpose of this study is to extend toxicity data of amine surfactants with different molecular structures and develop a reliable QSAR model between the toxicity of different types of amine surfactants on Daphnia magna and their structural parameters. 2. Materials and methods 2.1. Materials The following 20 amine surfactants are used in this work: Dodecylamine (DDA), Tetradecylamine (TDA), Hexadecylamine (HAD), Dodecylmethylamine (DDMA), Dimethylaminododecane (DMAD), Dodecyltrimethylammonium Chloride (DTAC), Dodecyltrimethylammonium Bromide (DTAB), Hexadecanyltrimethylammonium bromide (HTAB), Dodecyldimethylethylammonium bromide (DMAB) and Bis(2-hydroxyethyl)dodecylmethylammonium bromide (2HDMAB) were provided from Sinopharm Chemical Reagent Co. Ltd, while N-Dodecyl-1,3-propanediamine (NP), Bis(2-hydro xyethyl)dodecylamine (2HDDA), N,N-Bis(2-hydroxyethyl)dodeca namide (2HDDCA), Dodecanamide (DDCA), N-(2-Hydroxyethyl) dodecanamide (HDDCA), 3-(Dodecyloxy)propanamine (DOPA) and Dimethyldodecylamine oxide (OA-12) were provided from Tokyo Chemical Industry Co.,Ltd without further purification (purity  99%); N-dodecylethylene-diamine (ND) was synthesized in laboratory (Duan et al., 2019; Liu et al., 2009); N,N-DimethylN-dodecanoyl-1,3-diaminopropane (DMDDP) with high purity (98%) was purchased from Wuhan FengyaoTonghui Chemical Products Co., Ltd; N,N-Dimethylaminopropyldodecylamide oxide (LAO) was also synthesized in the laboratory by the oxidation of DMDDP and the purity is enough to use this experiment according to liquid chromatography combined with mass spectrometry (LC/MS) analysis (shown in supporting information). Molecular structures and the corresponding CAS numbers for the amine surfactants were displayed in Table 2. 2.2. Daphnia magna toxicity tests on amine surfactants

Table 1 QACs levels detected in the environment (Ivankovic´ and Hrenovic´, 2010). Location

Level

Sewage effluent Treated sludge Sediment

0.062 mg/L 5870 mg /kg (0.022–0.206) mg /L

Daphnia magna, a kind of common zooplankton in freshwater lakes and ponds, is frequently applied in ecotoxicity tests as one of most sensitive organisms (Alberdi et al., 1996; Cassani et al., 2013). Moreover, the median effective inhibition concentration (EC50) of Daphnia magna is commonly used as a representative test specie to determine the ecotoxicological evaluation of industrial chemicals and build QSAR model (Ha et al., 2019; Levet et al.,

W. Liu et al. / Science of the Total Environment 702 (2020) 134593

3

Table 2 Molecular Structures and CAS numbers of amine structures. NO.

surfactants

CAS NO.

1

DDA

124-22-1

2

TDA

2016-42-4

3

HAD

143-27-1

4

DDMA

7311-30-0

5

DMAD

112-18-5

6

DTAC

112-00-5

7

DTAB

1119-94-4

8

HTAB

57-09-0

9

DMAB

68207-00-1

10

2HDMAB

57122-49-3

11

ND

–

12

NP

5538-95-4

13

2HDDA

1541-67-9

14

2HDDCA

120-40-1

15

DDCA

1120-16-7

16

HDDCA

142-78-9

17

DOPA

7617-74-5

18

DMDDP

3179-80-4

19

LAO

61792-31-2

20

OA-12

1643-20-5

2016). Thus, the EC50 values of amine surfactants will be tested in our work to establish QSAR model between the acute toxicity and molecular structure. Daphnia magna used in test was purified though three generations of cultivation in M4 media at 20 °C with a photoperiod of 12 h light and 12 h dark. (Elendt and Bias, 1990). They were fed with Scenedesmus algae once three days and Scenedesmus algae was provided from Freshwater Algae Culture Collection at the Institute of Hydrobiology (FACHB), the Chinese Academy of Sciences. The tests were implemented in 100 ml glass beakers with 50 ml media liquid in each and the media liquid was the medium in standard OECD guideline 202 that was consisted of 266 mg/L CaCl22H2O, 112 mg/L MgSO47H2O, 5 mg/L KCl and 65 mg/L NaHCO3 respectively (OECD 2004). Tap water was used in all experiment. The oxygen saturation of tap water without chlorine was 7.5 ± 0.2 mg/L after aeration for 2 days and the pH was 7.2– 7.5. Ten neonates of Daphnia magna (<24 h) were transferred to each beaker containing different concentrations of an amine surfactant and three parallel samples were prepared for each concentration. All beakers were closed with medical gauze and placed in the constant temperature incubator with a natural light–dark cycle. Notably, the neonates can be separated from adults according to their smaller size (<1.5 mm). In addition, the reference

Structures

toxicant K2Cr2O7 was performed the acute toxicity test to check whether the sensitivity of Daphnia magna was within the limits (24-h EC50 = 0.6–2.1 mg/L) as set by OECD 2004. For each amine surfactant, at least five concentrations were used for calculating EC50 values (Sandbacka et al., 2000). There was no aeration and feeding during the toxicity test. All tests were carried out under static conditions. Moreover, blank control without any amine surfactant was used to detect aqueous environment. The test endpoint for acute toxicity was inhibition of mobility of Daphnia magna and the concentration with 50% immobilization of crustaceans was determined visually after 24 h. Then, probit method was utilized to calculate the median effective inhibition concentration (EC50) and its 95% confidence intervals. 2.3. Structural parameter calculation Generally, chemical toxicity is caused by two key reaction steps which are partitioning of toxicant into/through the biological membrane and the interactions of the toxicant with the site of action (Ding et al., 2009). The toxicity of organic compound is about hydrophobic, electronegativity and chemical activity sites of molecular and so forth. Thus, it is very important to select suitable molecular structural descriptors to characterize reaction pro-

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W. Liu et al. / Science of the Total Environment 702 (2020) 134593

cesses in QSAR studies. In this work, quantum parameters, physicochemical parameters and topological indices were chosen to describe different characterizes of amine surfactants caused by the structural differences. Quantum parameters such as Dipole moment (l), energy of the lowest unoccupied molecular orbital (ELUMO ), energy of the highest occupied molecular orbital (EHOMO ), hardness (g), electronegativity (v), softness (S) and electrophilicity index (x) were calculated as molecular descriptors by the Gaussian 09 suite of programs (Frisch et al., 2015) using DFT methods with a 6-311G(d) basis set and correlation functional of Lee, Yang and Parr (B3LYP) (Lee et al., 1988; Parr, 1980). Examination of the vibrational frequencies showed that all calculated compounds were in minimum energy configuration. Besides, g, v, and S can be defined using Koopmans’ theorem for closed-shell molecules as following equations (Koopmans, 1933):

g¼

v¼

ðEhomo þ Elumo Þ ðIP  EAÞ ¼ 2 2

ð1Þ

ðEhomo  Elumo Þ ðIP þ EAÞ ¼ 2 2

ð2Þ

1 1 S¼ ¼ 2g IP  EA

ð3Þ

where IP and EA are the ionization potential and electron affinity of the chemical species, respectively. The electrophilicity index x is calculated by (Parr et al., 1999):

x¼

v2 2g

ð4Þ

Two NBO charges, namely the most positive net atomic charge on a hydrogen atom (qþ H ), the minimum atomic net charge on nitrogen atom (qN ), were also selected as molecular descriptors owing to the correlation with above parameters. In addition, thermodynamic parameters under the conditions of T = 298.150 K and P = 101 325 Pa were computed as follows: internal energy (Eth), constant-volume heat capacity (C0v ), entropy (So), total energy (ET), enthalpy (Ho), free energy (Go) and zero-point vibrational energy (ZPVE). It was known that Fukui function can used to express the preferred region where electronic density was easier to be changed by the electrons of some chemical species. Therefore, these descriptors indicated the deform tendency of the electronic density at a given position to accept or donate electrons (Ayers and Parr, 2000). In order to determine the active sites of amine surfactants, Fukui function of all atoms in amine surfactants were calculated by DMol3 module in Materials Studio 2018. The minimum energy configurations were imported to Materials Studio 2018 software from Gaussian 09. All Electron relativistic was applied for the core treatment and Double Numerical plusd-functions (DND) was basic set in this paper. For self-consistent electronic minimization, the Pulay density mixing method was used with a SCF convergence tolerance of 1.0  106. And convergence criteria fixed for the energy, maximum force and maximum displacement tolerance were 1.0  105 Ha, 0.002 Ha/Å and 0.005 Å, respectively (Liu et al., 2019b). Normally, there are three different types of Fukui funcþ  tions, namely f for nucleophilic attack, f for electrophilic attack 0

and f for radical attack (Langenaeker et al., 1990). For a better comparison of the ability to gain or lose electrons on atoms, f

ð2Þ

þ

¼f f



The higher f

was applied to simplify the number of parameters. ð2Þ

is, the easier of nucleophilic attack on the atom. ð2Þ

The maximum value on a hydrogen atom (f Hmax ), the minimum ð2Þ

value on a nitrogen atom (f Nmin ), the maximum value on a molecu-

ð2Þ

ð2Þ

lar atom (f max ) and the minimum value on a molecular atom (f min ) were chosen as quantum parameters to reflect the activity of amine surfactants and build the QSAR model. Enormous research results revealed that the toxicity was closely related to molecule topological index (Netzeva et al., 2008; Sabljic, 1983). Thus, molecular connectivity index ( n v), Shape index ( n K) and valence connectivity index ( n dv ) were chosen as topological indices in our work because they can characterize the molecular structure quantitatively. Furthermore, several physicochemical descriptors including the alkyl chain lengths (CL) of amine surfactants, the number of nitrogen atom-containing compounds (amines and quaternary ammonium compounds) (N), the number of rotatable bonds (R), Hydrogen Bond Acceptor (HBA), Hydrogen Bond Donor (HBD), Molecular surface area (MSA) and octanol/ water partition coefficient were used in the QSAR study. R, HBA, HBD and MSA were computed by Materials studio. The octanol/ water partition coefficients, logKow, was estimated using software KOWWIN (Version 1.67, EPI Suite, US EPA). 2.4. GFA analysis In this study, the Genetic Function Approximation (GFA) algorithm was applied to build the QSAR model based on its overfitting resistance, accuracy and celerity compared with other methods (Khaled, 2011; Rogers and Hopfinger, 1994). The population and maximum generations were 200 and 2000, respectively. Friedman lack-of-fit measure (Friedman LOF) was used to evaluate the quality of model, and the smoothness parameter was 0.5. Notably, in this work, the acute toxicity of 20 amine surfactants were tested and 18 amine surfactants among the data were split into a training set to develop the QSAR model. The others consisting of 2 amine surfactants were used for external validation of the model. After building QSAR model, the coefficient of determination (R2) of regression, adjusted R-squared (R2ad ), F-value, coefficient determinations of cross-validation (R2cv ) and external validation (R2ext ), mean bias deviation (MBD) and root mean-square error (RMSE) for cross-validation and external validation were used to evaluate the fitness and prediction ability of the model in our statistics study. The RMSE and MBD were calculated using following eq.5 and 6 (Wang et al., 2016).

RMSE ¼

MAE ¼

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  PN  b 2 i¼1 yi  yi N

 PN  yi  ybi  i¼1

N

ð5Þ

ð6Þ

where yi and ybi are experimental and predicted values, respectively, and N is the number of tested amine surfactants in the training set or external validation set. 3. Results and discussion 3.1. Toxicity of amine surfactants The acute toxicity of 20 amine surfactants on Daphnia magna are displayed on Table 3. The wide range of 24 h EC50, from 0.3236 to 401.7288 lmol/L, can be observed. By comparing the EC50 of DDA, TDA and HAD, the toxicity increased with the increasing length of the alkyl chain. And there was the same rule between DTAB and HTAB. The substitution of H atom with methyl group (ACH3) on the N atom can decline the toxicity of amine surfactants resulting in order of toxicity as DDA > DDMA > DMAD > DTAB. Besides, adding either N atom or O atom in the main alkyl chain of molecular structures can reduce the toxicity of amine surfac-

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W. Liu et al. / Science of the Total Environment 702 (2020) 134593 Table 3 Tested and predicted 24 h EC50 of amine surfactants on Daphnia magna. EC50 (lmol/L)

Compounds

DDA TDA HAD DDMA DMAD DTAC DTAB HTAB DMAB 2HDMAB ND NP 2HDDA 2HDDCA DDCA HDDCA DOPA DMDDP d LAO d OA-12 a b c d

Confident intervala

0.3401 0.3243 0.3236 0.7307 1.5091 3.6956 3.9684 1.4819 3.6595 11.3811 0.8294 2.9443 3.3443 19.3989 36.6214 20.3091 249.3803 9.0847 1094.788 172.9144

log (EC50)

[0.3200–0.3601] [0.3056–0.3433] [0.3016–0.3468] [0.6240–0.8332] [1.1824–1.7859] [3.3333–4.0593] [3.7529–4.1792] [1.3533–1.6012] [2.9241–4.2860] [11.0164–11.7484] [0.7481–0.9088] [2.8011–3.0896] [3.1406–3.5521] [16.9784–21.5592] [33.5944–40.1653] [18.4166–22.2467] [232.0455–266.7824] [7.0830–10.8893] [1004.992–1169.908] [151.1812–193.1282]

Tested

Predicatedb

Residualsc

0.468425 0.488994 0.490022 0.136234 0.178718 0.567690 0.598617 0.170829 0.563416 1.056185 0.081232 0.468983 0.524304 1.287776 1.563735 1.307690 2.396862 0.958310 3.039330 2.237831

0.324453 0.413563 0.502788 0.217985 0.049767 0.808275 0.349802 0.145880 0.538501 1.152379 0.118368 0.269383 0.720413 1.067442 1.374420 1.565791 2.214864 1.182493 3.314201 2.341235

0.143972 0.075431 0.012766 0.081751 0.128951 0.240585 0.248816 0.024949 0.024915 0.096193 0.199600 0.199600 0.196109 0.220334 0.189315 0.258100 0.181998 0.224183 0.274871 0.103404

Confident interval: the 95% confident interval (lmol/L) was calculated with the probit method. Predicted: predicted values by the QSAR model of this study. Residuals = log(EC50)(tested)-log(EC50) (predicted). Amine surfactants were used for external validation.

Table 4 The quantum parametersa used in the construction of QSAR models.

a

Amine

l

ELUMO

EHOMO

v

g

S

x

f Hmax

f Nmin

f max

f min

DDA TDA HAD DDMA DMAD DTAC DTAB HTAB DMAB 2HDMAB ND NP 2HDDA 2HDDCA DDCA HDDCA DOPA DMDDP LAO OA-12

1.3704 1.3698 1.3694 0.9427 0.4558 12.6700 12.8575 12.8519 12.7000 12.6012 1.8457 2.1838 1.2876 4.5748 3.5059 4.8335 2.0460 4.0874 6.4047 4.1649

0.0411 0.0411 0.0411 0.0417 0.0412 0.0055 0.0063 0.0063 0.0018 0.0147 0.0389 0.0392 0.0323 0.0141 0.0251 0.0177 0.0395 0.0246 0.0163 0.0278

0.2342 0.2342 0.2342 0.2208 0.2137 0.1885 0.1754 0.1753 0.1746 0.1879 0.2208 0.2199 0.2180 0.2448 0.2519 0.2528 0.2365 0.2257 0.2063 0.1945

0.1376 0.1376 0.1376 0.1313 0.1275 0.0915 0.0846 0.0845 0.0864 0.0866 0.1298 0.1296 0.1252 0.1294 0.1385 0.1353 0.1380 0.1252 0.1113 0.1112

0.0965 0.0965 0.0965 0.0895 0.0863 0.0970 0.0909 0.0908 0.0882 0.1013 0.0910 0.0903 0.0929 0.1153 0.1134 0.1175 0.0985 0.1005 0.0950 0.0834

0.0483 0.0483 0.0483 0.0448 0.0431 0.0485 0.0454 0.0454 0.0441 0.0506 0.0455 0.0452 0.0464 0.0577 0.0567 0.0588 0.0493 0.0503 0.0475 0.0417

0.0981 0.0981 0.0981 0.0962 0.0942 0.0432 0.0393 0.0393 0.0423 0.0370 0.0926 0.0929 0.0843 0.0726 0.0846 0.0778 0.0967 0.0779 0.0652 0.0741

0.148 0.125 0.114 0.141 0.061 0.16 0.167 0.166 0.218 0.531 0.168 0.143 0.399 0.034 0.041 0.057 0.124 0.083 0.129 0.175

0.514 0.498 0.492 0.369 0.236 0.075 0.077 0.076 0.055 0.02 0.298 0.279 0.236 0.01 0.026 0.019 0.384 0.158 0.019 0.02

0.148 0.125 0.114 0.141 0.061 0.16 0.167 0.166 0.218 0.531 0.168 0.143 0.399 0.087 0.137 0.057 0.124 0.203 0.129 0.175

0.514 0.498 0.492 0.369 0.236 0.075 0.077 0.076 0.083 0.216 0.298 0.279 0.236 0.163 0.054 0.048 0.384 0.158 0.43 0.455

ð2Þ

ð2Þ

ð2Þ

ð2Þ

l in unit of Debye, ELUMO , EHOMO , g, v, S and x in unit of a.u.

tants effectively in basis of the lower toxicity of ND, NP and DOPA than DDA. OA-12 and LAO with N ? O group have less toxicity than DDA and DMDDP, indicating toxicity decrease with adding N ? O group. It can be summarized that functional groups play an important role in reducing the toxicity of tested amine surfactants. However, experimental toxicity data provided an insight into the influence of functional groups on the toxicity of amine surfactants in qualitative level rather in a quantitate way. Thus, building a reliable QSAR model to predict the toxicity of amine surfactants is important for novel surfactant exploitation and environmental risk assessment.

tively. And physicochemical parameters and topological indices are shown in Table 6. 3.3. QSAR model establishment GFA analysis was applied to establish and optimize the QSAR model between log(EC50) values set as a dependent variable and 35 structural descriptors as independent variables. Subsequently, an analytical QSAR equation can be obtained as the following Eq. (7):

logðEC50 Þ ¼ 1:76095  N  0:24580  CL þ 67:94781g þ 159:40167ZPVE  5:91202 

3.2. Molecular structure descriptors

0

v þ 11:65269  2 v

v

 12:10013  d  5:29310 2

ð7Þ

where n = 18 is the number of amine surfactants in the training set, The calculation results of the quantum parameters including thermodynamic parameters are listed in Tables 4 and 5, respec-

R2 = 0.961828, R2ad = 0.935108, R2cv =0.793514, RMSEtra = 0.165519, MAEtra = 0.148434.

F = 35.996480,

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W. Liu et al. / Science of the Total Environment 702 (2020) 134593

Table 5 The thermodynamic parametersa used in the construction of QSAR models.

a

Amine

Eth

C0v

So

ET

Ho

Go

ZPVE

qþ H

qN

DDA TDA HAD DDMA DMAD DTAC DTAB HTAB DMAB 2HDMAB ND NP 2HDDA 2HDDCA DDCA HDDCA DOPA DMDDP LAO OA-12

0.3963 0.4553 0.5150 0.4249 0.4540 0.5011 0.5010 0.6202 0.5311 0.5731 0.4739 0.5037 0.5256 0.5081 0.3772 0.4428 0.4908 0.5441 0.5495 0.4595

0.0625 0.0722 0.0819 0.0671 0.0721 0.0837 0.0838 0.1036 0.0888 0.1001 0.0763 0.0811 0.0894 0.0910 0.0647 0.0777 0.0803 0.0926 0.0962 0.0757

0.1394 0.1539 0.1684 0.1469 0.1527 0.1717 0.1739 0.2037 0.1797 0.1946 0.1610 0.1683 0.1837 0.1829 0.1453 0.1685 0.1689 0.1857 0.1895 0.1563

528.044 607.065 685.709 567.737 607.053 1110 3220 3380 3260 3450 662.417 701.739 836.149 910.217 602.489 756.356 721.613 854.435 929.602 682.222

248.8638 286.3104 323.7570 267.2423 285.4777 315.0503 314.9606 389.7935 333.8649 360.2040 297.9444 316.6461 330.3917 319.4485 237.2586 278.4490 308.5964 342.0031 345.4199 288.9591

207.3051 240.4138 273.5343 223.4422 239.9444 263.8663 263.1177 329.0463 280.3001 302.187 249.9475 266.4805 275.6233 264.9098 193.931 228.2006 258.2243 286.643 288.9177 242.3722

0.3779 0.4349 0.4919 0.4059 0.4337 0.4776 0.4773 0.5911 0.5061 0.5449 0.4522 0.4806 0.5002 0.4823 0.3587 0.4205 0.4679 0.5179 0.5227 0.4386

0.334 0.334 0.334 0.33 0.191 0.281 0.277 0.277 0.276 0.487 0.337 0.335 0.437 0.448 0.347 0.374 0.335 0.381 0.384 0.231

0.82 0.82 0.82 0.652 0.504 0.309 0.309 0.309 0.314 0.321 0.823 0.819 0.542 0.501 0.77 0.579 0.818 0.504 0.017 0.016

In units of Hartee for Eth , ET, H0, G0, ZPVE; Kcal/ (mol K) for C0v and S0 ; e for qþ H and qN .

Table 6 Physicochemical parametersa and topological indices used in the construction of QSAR models.

a

Amine

CL

N

R

HBA

HBD

MSA

logKow

0

DDA TDA HAD DDMA DMAD DTAC DTAB HTAB DMAB 2HDMAB ND NP 2HDDA 2HDDCA DDCA HDDCA DOPA DMDDP LAO OA-12

12 14 16 12 12 12 12 16 12 12 12 12 12 12 12 12 12 12 12 12

1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 2 2 1

10 12 14 11 11 11 11 15 12 17 13 14 17 17 10 14 14 15 15 11

1 1 1 1 1 0 0 0 0 2 2 2 3 3 1 2 2 2 2 1

2 2 2 1 0 0 0 0 0 2 3 3 2 2 2 2 2 1 1 0

304.5790 348.1371 391.6905 326.8742 345.3634 394.3726 400.5756 490.6307 418.7188 429.6501 364.9087 387.0464 409.6502 419.092 305.9429 361.046 382.0034 430.0435 440.2079 354.3798

4.76 5.75 6.73 5.23 5.44 1.22 1.22 3.18 1.71 0.32 4.25 4.74 3.9 2.89 3.75 3.24 4.98 4.4 3.64 4.67

9.78 11.19 12.61 10.49 11.36 12.28 12.28 15.11 12.99 15.11 11.90 12.61 14.18 15.05 10.65 12.77 12.61 15.05 15.98 12.28

v

1

v

6.41 7.41 8.41 6.91 7.27 7.56 7.56 9.56 8.12 9.68 7.91 8.41 9.35 9.76 6.77 8.31 8.41 9.66 9.95 7.56

2

v

4.18 4.89 5.60 4.54 5.36 6.49 6.49 7.91 6.45 7.24 5.24 5.60 6.41 6.93 5.01 5.86 5.60 7.40 8.53 6.49

3

K path

2.71 3.21 3.71 2.96 3.14 3.28 3.28 4.28 4.16 4.84 3.46 3.71 4.36 4.89 2.89 3.85 3.71 4.53 4.67 3.28

3

K cluster

0 0 0 0 0.41 1.56 1.56 1.56 1.21 0.93 0 0 0.20 0.40 0.41 0.29 0 0.70 1.85 1.56

0

dv

9.36 10.77 12.18 10.28 11.23 12.23 12.23 15.05 12.93 13.95 11.27 11.98 12.95 13.15 9.56 11.34 11.89 14.05 14.46 11.63

1

dv

6.12 7.12 8.12 6.56 6.92 7.36 7.36 9.36 7.94 8.74 7.32 7.82 8.29 8.25 6.05 7.18 7.69 8.58 8.76 7.10

2

dv

3.97 4.68 5.38 4.29 4.99 6.20 6.20 7.61 6.16 6.42 4.72 5.07 5.47 5.41 4.04 4.65 4.94 6.09 6.58 5.48

MSA in units of Å2.

According to the developed QSAR model as shown in Eq. (7), R2 and R2ad were 0.962 and 0.935, which demonstrated the QSAR model has above 90% accuracy with less dangerous of overestimating. Since R2cv value was calculated by the CV method, it was obvious that the higher R2cv value was, the more robust the model would be. This model was identified to have good predict ability according to R2cv = 0.794 (more than 0.6). Meanwhile, the F value of this model was far bigger than threshold value (3.143149), which meant the significance of the model was very well. The RMSE and MAE values were a measure of prediction precision, and hence a lower RMSE value corresponds to a better prediction precision. The small values of RMSEtra and MAEtra for internal validation on 18 training amine surfactants were 0.166 and 0.148, indicating the model performed great goodness-of-fit. In order to further test and verify the prediction ability of this model on the toxicity of amine surfactants, two toxicity values, DMDDP and LAO, were randomly chosen to use for external verifi-

cation. The experimental and predicted results of DMDDP and LAO were shown in Table 3. For DMDDP, there was less deviation between the experimental result of 0.958 and the predicted result of 1.182. And the results of LAO have the same tendency as DMDDP that the experimental and predicted results respectively 3.039 and 3.314. The coefficient determinations of cross-validation, RMSE and MAE on external verification were also calculated to determine deviation of this QSAR model. The results were respectivelyR2ext = 0.942, RMSEext = 0.251 and MAEext = 0.250, proving this model with good prediction ability. Furthermore, for 20 tested amine surfactants both the training set and external validation set, the correlation between tested and predicted log(EC50) values was very significant (shown as Fig. 1) according to R2 = 0.969 and F = 560.527. AsR2ad = 0.967 is remarkably larger than 0.5, this model is clearly robust and have great prediction ability. According to the established model, increasing CL value of amine surfactants leads to the reduction on the value of log(EC50)

W. Liu et al. / Science of the Total Environment 702 (2020) 134593

7

and 2 dv also have correlations with the toxicity of amine surfactants. 0 v and 2 v values are the sigma electron count, while 2 dv is interpreted in terms of the information encoded, describing both the volume and electronic characteristics of bonds in molecules (Hall and Kier, 2001; Kier and Hall, 1981). They can intuitively discuss and demonstrate the relationship between toxicity and molecular structure of amine surfactant. Firstly, the high 0 v value indicated the amine surfactant with a long chain length and thence 0 v also has negative relation with log(EC50) that was similar with CL. And the value of 2 v more closely reflected the statue of the branched chain, which has positively assisted with log(EC50). The result was also consistent with our experimental data that the substitution of H atom with methyl group (ACH3) on the N atom will reduce the toxicity of amine surfactants. Besides, the value of 2 dv taking account of the effect of electronic characteristics on molecular structure has the negative relationship with log(EC50) of amine surfactants. It revealed amine surfactants with more net charge has more negative influence on environment and larger toxicity. Fig. 1. Linear correlation equation between actual values and predicted values (y = 1.00955x + 0.01767, R2 = 0.96889, R2ad = 0.96716, F = 560.5271).

and larger toxicity. The alkyl chain length determines some physical–chemical properties (water solubility, octanol/water partition coefficient, adsorption/partition coefficient on sediments, sludges and soils) of a surfactant (Garcıa et al., 2001). As Table 6 displayed, amine surfactants with larger CL value have higher octanol/water partition coefficient, which may promote the partitioning of toxicant into the biological membrane. Thus, the long alkyl chain length can enlarge toxicity of amine surfactants. The result was consistent with our experimental data. N is the number of nitrogen atom-containing compounds (amines and quaternary ammonium compounds), which is the most toxic compounds than other compounds (Versteeg et al., 1997). More amines or quaternary ammonium compounds, containing a net positive charge at environmental pH, will be increase molecular volume of amine surfactants and the effect on charge of biological membrane, leading to amine surfactants with larger toxicity. Therefore, N has the negative relationship with log(EC50) which is displayed from Eq. (7). Notably, ZPVE played a decisive role in the QSAR model. The positive relationship between ZPVE and log(EC50) could be found from Eq. (7), indicating larger ZPVE value with less toxicity of amine surfactants. The thermodynamic parameter — zero-point vibrational energy (ZPVE) is associated with bond-length, bondangle, torsional and out-of-plane vibrations at 0 K based on the Heisenberg uncertainty principle and it is an important correction coefficient for entropy, internal energy and other thermodynamic parameters (Truhlar et al., 2004). It could be concluded from Table 5 that amine surfactants with larger ZPVE will have higher internal energy and less stability, which may improve the transform of molecular structure and decrease the acute toxicity before partitioning of toxicant into the biological membrane. Besides, hardness (g) is of course used extensively to make predictions about chemical behavior, which is related with HOMO energy and LUMO energy of amine surfactants. A small HOMO-LUMO energy gap means a little excitation energy on the manifold excited states and a soft molecular with a lower gap has more polarizable than hard one (Pearson, 1986). The model demonstrated that hardness (g) of the amine surfactants was positive correlated with log (EC50). Amine surfactant with high polarizable may be easier to across membrane phospholipids. Thus, the amine surfactants with higher polarizable will have larger toxicity in water environment. Molecular topological index was universally applied in QSAR studies of toxicity (Juric´ et al., 1992). We can find that 0 v, 2 v

4. Conclusions In this study, the toxicity of 20 amine surfactants on Daphnia magna were tested and a reliable QSAR model between the toxicity for amine surfactants and their molecular structures was established with GFA algorithm. The experimental results for EC50 of 20 types amine surfactants revealed that the toxicity of amine surfactants was positive correlation with alkyl chain length and the substitution of H atom with methyl group (ACH3) on the N atom also declined the toxicity of amine surfactants. Besides, introducing of function group, such as N ? O, effectively reduces the toxicity of amine surfactants. Moreover, the best-fitted QSAR model on the toxicity of amine surfactants were established with R2 of 0.962 and R2cv of 0.794. Further study would be needed to increase the toxicity data of amine surfactants and develop better prediction ability of this model. Declaration of Competing Interest The authors declare that there is no conflict of interest. Acknowledgements This work was supported by the National Natural Science Foundation of China under Grant number 51874074, the open fund of Guangdong Provincial Key Laboratory of Development and Comprehensive Utilization of Mineral Resources (2017B030314046), and the young and middle-aged science and technology innovation talent support program of Shenyang (RC170556). Appendix A. Supplementary material Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2019.134593. References Alberdi, J., Sáenz, M., Di Marzio, W., Tortorelli, M., 1996. Comparative acute toxicity of two herbicides, paraquat and glyphosate, to Daphnia magna and D. spinulata. B Environ. Contam. Tox. 57, 229–235. Araujo, D.M., Yoshida, M.I., Takahashi, J.A., Carvalho, C.F., Stapelfeldt, F., 2010. Biodegradation studies on fatty amines used for reverse flotation of iron ore. Int. Biodeter. Biodegr. 64, 151–155. Ayers, P.W., Parr, R.G., 2000. Variational principles for describing chemical reactions: the Fukui function and chemical hardness revisited. J. Am. Chem. Soc. 122, 2010–2018. Calgaroto, S., Azevedo, A., Rubio, J., 2016. Separation of amine-insoluble species by flotation with nano and microbubbles. Miner. Eng. 89, 24–29.

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