Ecotoxicology and Environmental Safety 107 (2014) 162–169

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Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv

Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: A mechanistic QSTR approach Supratik Kar a,b, Agnieszka Gajewicz b, Tomasz Puzyn b, Kunal Roy a,n, Jerzy Leszczynski c a

Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India Laboratory of Environmental Chemometrics, Institute for Environmental and Human Health Protection, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland c Department of Chemistry and Biochemistry, Jackson State University Jackson, MS 39217-0510, USA b

art ic l e i nf o

a b s t r a c t

Article history: Received 24 April 2014 Received in revised form 20 May 2014 Accepted 22 May 2014

Nanotechnology has evolved as a frontrunner in the development of modern science. Current studies have established toxicity of some nanoparticles to human and environment. Lack of sufficient data and low adequacy of experimental protocols hinder comprehensive risk assessment of nanoparticles (NPs). In the present work, metal electronegativity (χ), the charge of the metal cation corresponding to a given oxide (χox), atomic number and valence electron number of the metal have been used as simple molecular descriptors to build up quantitative structure–toxicity relationship (QSTR) models for prediction of cytotoxicity of metal oxide NPs to bacteria Escherichia coli. These descriptors can be easily obtained from molecular formula and information acquired from periodic table in no time. It has been shown that a simple molecular descriptor χox can efficiently encode cytotoxicity of metal oxides leading to models with high statistical quality as well as interpretability. Based on this model and previously published experimental results, we have hypothesized the most probable mechanism of the cytotoxicity of metal oxide nanoparticles to E. coli. Moreover, the required information for descriptor calculation is independent of size range of NPs, nullifying a significant problem that various physical properties of NPs change for different size ranges. & 2014 Elsevier Inc. All rights reserved.

Keywords: Escherichia coli Metal oxide nanoparticle Nanotoxicity QSTR

1. Introduction Nanoparticles (NPs) are becoming an integral part of modern life and have a wide range of applications (Gajewicz et al., 2012). Nanomaterials are now used as a vital constituents in various electronic systems, space technology, cosmetics and sunscreens, medicine, pharmacy, solar batteries, environmental engineering, stain-resistant clothing, self-cleaning windows, environmental monitoring and so on (Artiles et al., 2011; Puzyn et al., 2010; Venkataraman et al., 2011). Among NPs, there are two easily noticeable most representative classes, which are (i) carbon nanoparticles and (ii) metal/metal oxide nanoparticles (Gajewicz et al., 2012). Metal oxides are an important group of engineered NPs, as they are extensively utilized in cosmetics and sunscreens, self-cleaning coatings and textiles as well as water-treatment agents (Puzyn et al., 2009). Metal oxide nanoparticles exhibit unique physical and chemical properties due to their limited size and a high density of n

Corresponding author. Fax: þ91 33 2837 1078. E-mail addresses: [email protected], [email protected] (K. Roy). URL: http://sites.google.com/site/kunalroyindia/ (K. Roy).

http://dx.doi.org/10.1016/j.ecoenv.2014.05.026 0147-6513/& 2014 Elsevier Inc. All rights reserved.

corner or edge surface sites. It has been shown that nanosized metal oxides are toxic to some organisms (Dreher, 2004). The characterization of the risks posed by NPs is extraordinarily complex because these materials can have a wide range of sizes, shapes, chemical compositions and surface modifications, all of which may affect toxicity (Clark et al., 2011). Therefore, there is an urgent need for developing rapid methods for predicting the properties, toxic behavior and environmental impact of these NPs as early as possible before it is too late to cope up this situation. Predictive toxicity models could form an integral component of such approach. One of the most promising methods that could provide such information is the quantitative structure–activity/toxicity/property relationship (QSAR/QSTR/QSPR) approach (Kar and Roy, 2010, 2012). Many investigations confirm that there is a strong need to extend the traditional QSAR paradigm to NPs and to develop “nano-QSAR” models which can accurately assess the toxicity of NPs (Gil et al., 2010; Puzyn et al., 2009; Puzyn et al., 2011). QSPR models have been developed for solubility predictions of C60 in various solvents (Toropov et al., 2008, 2009). Martin et al. (2007) have proposed two models predicting solubility of C60 fullerene in n-octanol and n-heptane . Nano-QSAR models have been established to predict the cellular uptake of 109 NPs in PaCa2 cell by various group of

S. Kar et al. / Ecotoxicology and Environmental Safety 107 (2014) 162–169

authors (Epa et al., 2012; Fourches et al., 2010; Ghorbanzadeh et al., 2012; Kar et al., 2014; Toropov et al., 2013). Rational nano quantitative structure–properties relationships (nano-QSPR) models have been established from the point of view of catastrophe theory by Carbo-Dorca and Besalù (2011). A very few QSAR models have been established using metal oxides NPs till now. Liu et al. (2013) developed a nano-QSAR model for toxicity of metal oxide nanoparticles (NPs) using metrics based on the dose–response analysis and consensus self-organizing map clustering for human bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cells. Puzyn et al. (2011) established a model to describe the cytotoxicity of metal oxide NPs to bacteria Escherichia coli based on quantum chemical calculations leading to model characterized by n¼17, r2 ¼0.862. The calculations of twelve descriptors have been performed at the semi-empirical level of the theory with use of PM6 method available in MOPAC 2009 software package (Stewart, 2009). The obtained results indicated that ΔHMe þ (representing the enthalpy of formation of a gaseous cation having the same oxidation state as that in the metal oxide structure) can be utilized as an efficient descriptor of the chemical stability (or reactivity) of metal oxides, and, therefore, their cytotoxicity in E. coli in vitro tests. Further, using the same dataset, Toropov et al. (2012) applied the web based software CORAL as a reliable tool to build up QSAR models with SMILES based optimal descriptors. Six random splits of the data into training and test sets were examined with a good internal as well as external predictivity pattern with R2(Training) and R2(Test) values falling within the ranges 0.740–0.837 and 0.83–0.96, respectively. We have explored in the present study predictive model development using the same dataset due to the following reasons. The first QSAR report (Puzyn et al., 2011) mentioned above uses quantum chemical descriptors for which one needs to have quantum-chemical background to be able to perform the calculations and the descriptors are little bit computationally demanding. In case of second work as cited above (Toropov et al., 2012), SMILES based descriptors are used; however, interpretability of such models is limited. Hence, we have attempted here to develop QSTR models for the cytotoxicity of metal oxide NPs to bacteria E. coli and the models have been validated extensively using multiple strategies. It is important to point out that all 17 nano-sized metal oxides have size ranged from 15 to 90 nm. Interestingly, Adams et al. (2006) revealed that the particle size did not affect antibacterial activity for the investigated sizes, since all nanopowders resulted in similarly sized aggregated particles in water suspension, regardless of the powder size. Thus, the size effects of NPs are nullified here. Table 1 Numerical values of the calculated descriptors along with cytotoxicity values for metal oxide nanoparticles. ID No. Metal oxide pEC50 χ

∑χ

∑χ=nO MW

NMetal NOxygen χox

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1.65 1.9 3.26 2.44 4.04 3.56 4.1 3.22 3.66 1.9 1.33 1.96 1.54 1.88 1.91 3.32 2.2

1.650 1.900 1.087 0.813 1.347 1.187 1.367 1.073 1.220 0.950 0.665 0.980 0.770 1.880 1.910 1.107 0.733

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

ZnO CuO V2O3 Y2O3 Bi2O3 In2O3 Sb2O3 Al2O3 Fe2O3 SiO2 ZrO2 SnO2 TiO2 CoO NiO Cr2O3 La2O3

3.45 3.2 3.14 2.87 2.82 2.81 2.64 2.49 2.29 2.2 2.15 2.01 1.74 3.51 3.45 2.51 2.87

1.65 1.9 1.63 1.22 2.02 1.78 2.05 1.61 1.83 1.9 1.33 1.96 1.54 1.88 1.91 1.66 1.1

81.38 79.546 149.88 225.82 465.96 277.62 291.52 101.96 159.6 60.08 123.2 150.7 79.86 74.93 74.69 151.98 325.8

1 1 3 3 3 3 3 3 3 2 2 2 2 1 1 3 3

2 2 3 3 3 3 3 3 3 4 4 4 4 2 2 3 3

163

Most importantly, we have tried to identify promising simple molecular descriptors for modeling toxicity of metal oxides NPs leading to models with high statistical quality as well as interpretability. It is worth to mention here that fundamental properties like electronegativity and the charge of the metal cation corresponding to a given oxide (χox), which can be easily obtained from molecular formula and information acquired from periodic table in no time, are used here to build up QSTR models for prediction of cytotoxicity of metal oxide NPs to bacteria Escherichia coli, which is the strong advantage of the current models. Significantly, such basic properties are independent of size range of NPs nullifying a typical problem that many physical properties of NPs change for different size ranges. Thus, the major aim of this study is to establish these simple descriptors for prediction of toxicity of metal oxide NPs. Based on the developed model and previously published experimental results, we have hypothesized the most probable mechanism of the cytotoxicity of metal oxide nanoparticles to E. coli. 2. Material and methods 2.1. Dataset The cytotoxicity data of seventeen metal oxides to bacteria E. coli (negative logarithm of concentration for 50percent effect, pEC50) have been taken from the literature (Puzyn et al., 2011) to develop and validate QSTR models (Table 1). 2.2. Descriptors calculation To develop the QSTR models, we have used some basic information of metal oxides to encode their cytotoxicity to bacteria E. coli. The used simple molecular descriptors are metal electronegativity (χ), sum of metal electronegativity for individual metal oxide (∑χ ), sum of metal electronegativity for individual metal oxide divided by the number of oxygen atoms present in a particular metal oxide (∑χ =nO), number of metal atoms (NMetal), number of oxygen atoms (NOxygen), the charge of the metal cation corresponding to a given oxide (χox) and molecular weight (MW). Using only periodic table information and molecular formula, these seven descriptors are obtained in no time which is very time effective as well as cost efficient as no software is used for the descriptor calculation. Numerical values of all seven descriptors are presented in Table 1. 2.3. Splitting of the dataset The selection of training and test sets plays a crucial role in the development of QSTR models. In the present work, the dataset was divided randomly into ten different combinations of training and test sets comprising eleven and six compounds respectively. Splitting of the dataset was performed based on random selection. Compounds present in the training and the test sets for each model is demonstrated in Table S1 in Supplementary material section. 2.4. Chemometric tools The models were developed using stepwise multiple linear regression (MLR) (Darlington, 1990; Snedecor and Cochran, 1967) and partial least squares (PLS) (Wold, 1995). The selection of the significant descriptors for developing the stepwise MLR model was done according to the ‘stepping criteria’ (F) (Darlington, 1990; Snedecor and Cochran, 1967). The F-value used for inclusion or exclusion (here, F-inclusion¼ 4 and F-exclusion ¼ 3.9) of a variable in the stepwise regression process is a test for partial regression coefficient and it is obtained by dividing the difference between reductions of sum of squares with and without the variable being included or excluded with error mean square of the equation. In case of PLS regression, to avoid overfitting, a strict test for the significance of each consecutive PLS component is necessary and then stopping when the components are nonsignificant. 2.5. Validation metrics The fitting potential of the regression based models was determined based on several statistical metrics like determination coefficient (R2), cross-validated squared correlation coefficient of the model (Q2 or r2cv) and root-mean-square error (RMSETr). Internal validation of the models was followed by the calculation of the conventional R2pred metric and RMSETest for external validation of the developed

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model. The external predictive potential of the models was also checked based on additional validation metric viz. Q2ext(F2) (Schüürmann et al., 2008). The r2m metrics namely r m 2 and Δ

r2m

developed by the Roy et al. (2012) were also employed for the

present work. The calculation of the r2m metrics for the test set data (r 2m ðtestÞ, Δrm 2(test)) additionally estimated the closeness between the values of the predicted and the corresponding observed activity data of the test set (Ojha et al., 2011; Roy et al., 2012). The models were also validated externally based on the parameters suggested by Golbraikh and Tropsha (2002). The regression based QSTR models were also subjected to a randomization test (Wold et al., 1998). In an ideal case, the squared average correlation coefficients (R2r ) for the randomized models should be much lower than the squared correlation coefficient (R2) of the original model. Accordingly, we have calculated the metric c 2 Rp using the following formula (Mitra et al., 2010): qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi c 2 Rp ¼ R  R2  R2r ð1Þ For an acceptable model, the value of c R2p should be more than 0.5. Technically, applicability domain (AD) represents the chemical space defined by the structural information of the chemicals used in model development, i.e., the training set compounds in a QSTR analysis. Here, we have used leverage approach (Gramatica, 2007) to assess the AD.

3. Result and discussions Based on the toxicity data and calculated seven descriptors, we have developed a simple but statistically significant QSTR equation with stepwise MLR, using all seventeen metal oxides to successfully predict the cytotoxicity. The obtained mono-parametric equation is as follows: pEC50 ¼ 4:781  ð1:380  χ OX Þ N ¼ 17; R2 ¼ 0:84; R2adj ¼ 0:83; Q 2LOO ¼ 0:81; Q 2L  10percent  OUT ¼ 0:82; Q 2L  20percent  OUT ¼ 0:83; Q 2L  25percent  OUT ¼ 0:80; c R2P ¼ 0:82

ð2Þ

Eq. (2) could explain 83percent of the variance (adjusted coefficient of variation) while it could predict 81percent of the variance (leave-one-out LOO predicted variance). To check the model reliability in terms of internal validation, the leave-manyout (LMO) cross-validation (10percent, 25percent and 50percent) methods were also applied. LMO cross-validation for 10percent, 20percent and 25percent removal could predict 82percent, 83percent and 80percent variance, respectively. Ten random splits of the data into the training and test sets were examined to check their statistical quality and interpretability. Table S2 contains statistical qualities of the ten random splits in Supplementary material showing good internal as well as external predictivity pattern with R2(Training) and r2(Test) values falling within the ranges 0.81–0.90 and 0.73–0.96, respectively, which are highly comparable with the previous results (Liu et al., 2013). Along with R2pred , metrics like r 2mðtestÞ ,Δr 2mðtestÞ and Q 2F2 also support high predictive ability of our developed models. All ten models are comprised of only one descriptor (χox). The Y-randomization test of the models also confirms their robustness indicating that the models are not obtained by chance. The list of metal oxides used in this study along with the quantitative values of their observed and calculated/predicted cytotoxicity to bacteria E. coli are represented in Table S3 . The leverage value h (critical value is 0.55) is noted to study any deviation of the structure(s) of some compound(s) in the test set from those used for the QSTR development for each model. It is noteworthy to mention that all the test compounds are remaining in the applicability domain of the developed respective QSTR models. Leverage values of all compounds for individual models are summarized in Table S4. In terms of mechanistic interpretation, the evolved descriptor is highly interpretable. For all models, χox has a negative coefficient. Cytotoxicity of metal oxide NPs is inversely proportional with the

value of χox. If we plot the observed cytotoxicity with the value of χox for each metal oxide, then we can easily introspect that as the value of χox increases, cytotoxicity decreases accordingly. It is true for top four most cytotoxic NPs (CoO, NiO, ZnO and CuO) in our case study except V2O3. Again, if we inspect the calculated or predicted cytotoxicity of V2O3, it is clearly seen that the difference between the observed and the calculated/predicted cytotoxicity for this oxide is high. Our models suggested that V2O3 should be classified as moderately cytotoxic compound as the value of χox is three while its experimental cytotoxicity value is quite high. The ratio of χox for the four most (CoO, NiO, CuO and ZnO) and least cytotoxic (SiO2, ZrO2, SnO2 and TiO2) compounds is two and four, respectively. All other metal oxides have χox ratio of three which is no doubt a significant observation. After a critical analysis of the results obtained from the developed stepwise MLR models, we found limited ability of the used descriptor to describe the endpoint, as different metal oxides containing same descriptor value has same predicted values for the developed models. So, to discriminate between the predicted toxicity values of two metal oxides having same χox value, one would need additional features predicting the metal oxides more accurately. So, PLS is performed using the same divisions using all seven descriptors initially and then omitting the less significant ones based on standardized coefficients. This is done to use additional descriptor(s) in the models to add further interpretation regarding metal oxides cytotoxicity. Using all seventeen metal oxides, the obtained final PLS equation (with one latent variable) is as follows: pEC50 ¼ 4:401  ð1:324  χ OX Þ þ ð0:176  χ Þ N ¼ 17; LV ¼ 1; R2 ¼ 0:82; Q 2LOO ¼ 0:75; Q 2L  10percent  OUT ¼ 0:76; Q 2L  20percent  OUT ¼ 0:74; Q 2L  25percent  OUT ¼ 0:76; c R2P ¼ 0:79

ð3Þ

Eq. (3) could explain 82percent of the variance while it could predict 75percent of the variance (LOO predicted variance). The equation contains only two descriptors with one latent variable (LV). To check the model reliability in terms of internal validation, the leave-many-out (LMO) cross-validation (10percent, 25percent and 50percent) methods were also applied. LMO cross-validation for 10percent, 20percent and 25percent removal could predict 76percent, 74percent and 76percent of the variance, respectively. We have checked the inter-correlation between the evolved descriptors (χ and χox) from the QSTR model and the correlation coefficient value is 0.038. The correlation matrix is presented in Table S5. Ten random splits were then examined with the PLS tool to check the statistical quality of the models and further interpretability (Table 2). Ten random splits of the data into the training and test sets were examined showing good internal as well as external predictivity pattern. The average R2pred for ten models is 0.78 which supports the high predictability of the developed models. Along with R2pred , metrics like r2mðtestÞ , Δr 2mðtestÞ and Q 2F2 also support high predictive ability of our developed models. All ten models are comprised of only two descriptors (χox and χ) with one LV. The Yrandomization test of the models also confirms their robustness indicating that the models are not obtained by chance. Fig. 1 represents graphically the scatter plots for each split. The list of metal oxides used in this study along with the quantitative values of their observed and calculated/predicted cytotoxicity to bacteria E. coli is represented in Table 3. The leverage value h (critical value is 0.82) is noted to study any deviation of the structure(s) of some compound(s) in the test set from those used for the QSTR development for each model. It is noteworthy to mention that all the test compounds are within the applicability domain of the

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165

Table 2 Statistical qualities of all ten models developed from PLS statistical tool. Model

1 2 3 4 5 6 7 8 9 10 Average a

Descriptors

χox and χ using one latent variable

Training set

Test set

Randomization

R2

Q2

RMSE

r2

R2pred

RMSE

r2mðtestÞ

Δr 2mðtestÞ

Q2F2

GTCa

R2r

RMSEr

c

0.84 0.85 0.77 0.82 0.84 0.73 0.82 0.87 0.85 0.78 0.82

0.78 0.77 0.65 0.61 0.75 0.55 0.71 0.65 0.73 0.63 0.68

0.21 0.20 0.26 0.24 0.22 0.27 0.24 0.20 0.20 0.25 0.23

0.96 0.88 0.95 0.82 0.88 0.82 0.88 0.77 0.70 0.87 0.85

0.80 0.68 0.83 0.79 0.87 0.76 0.83 0.65 0.67 0.88 0.78

0.22 0.28 0.19 0.18 0.18 0.25 0.19 0.28 0.29 0.18 0.22

0.63 0.64 0.58 0.75 0.79 0.58 0.74 0.70 0.44 0.82 0.67

0.11 0.15 0.16 0.13 0.08 0.19 0.11 0.01 0.29 0.10 0.13

0.79 0.68 0.83 0.79 0.85 0.76 0.80 0.64 0.67 0.86 0.77

Passed Passed Passed Passed Passed Passed Passed Passed Passed Passed Passed

0.13 0.08 0.10 0.06 0.10 0.00 0.08 0.13 0.10 0.10 0.09

0.54 0.57 0.49 0.52 0.60 0.51 0.55 0.59 0.58 0.51 0.55

0.77 0.81 0.72 0.79 0.79 0.73 0.78 0.80 0.80 0.73 0.77

R2p

Golbraikh and Tropsha's criteria, RMSEr: RMSE value for Y-scrambled models.

developed respective QSTR models. Leverage values of all compounds for individual model are summarized in Table 4. For all ten models, χox has a negative coefficient and this descriptor is already identified as one of the most important ones to interpret and predict the cytotoxicity of metal oxides. Those oxides having low χox (like CoO, NiO, CuO and ZnO) usually exhibit the strong reductive properties (easy detachment of the metal cation), which enhance their cytotoxicity to the bacteria. The outcome of our computational work with higher number of metal oxides strongly supports the in vitro work of Hu et al. (2009) where they have described that the cytotoxicity decreased with an increase in the cation charge for seven metal oxide NPs. From the PLS models, electronegativity of metal is identified as another important variable for our studied compounds. The PLS models could predict the individual metal oxides in a more discrete way than the previously developed models with stepwise regression. The positive coefficient of χ indicates the energy required to detach the metal cation from the metal oxides during the toxicity effects. Recent contributions show that large, size-dependent changes of the electron affinity for such metal oxides such as ZnO and TiO2 occur below about 5 nm; the changes between 15 and 90 nm are negligible (Kukreja et al., 2004; Labat et al., 2008; Zhang and Tang, 2004). Therefore, the molecular parameters of the studied oxides reliably described predicted characteristics. The mechanism of cytotoxicity of nano-sized metal oxides to the bacteria cells is related to lipid peroxidation by reactive oxygen species (ROS) as established by different group of authors (Lovric et al., 2005; Neal, 2008). ROS, such as superoxide (O2 ) and hydroxyl radicals (OH) can be formed in a series of reactions initiated by the electron detachment from the metal oxide nanoparticles (Burello and Worth, 2011; Neal, 2008). The energy required to detach the electron can be derived by solar radiation. For example, we can analyzed the case of TiO2 hν

TiO2 ⟹TiO2þ þ e e þ O2 -O2   O2   þ 2H þ þ e-H2 O2 O2   þ H2 O2 -OH þ OH  þ O2 H þ þ H2 O⟹OH þ H þ The presented mechanism is also supported by the experimental results in which after adding two enzymes: superoxide dismutase (catalyzing the dismutation of O2 into O2 and H2O2) and catalase (catalyzing decomposition of H2O2 to H2O and O2) to

Al2O3 nanoparticle suspension, damages to E. coli membranes have been significantly reduced (Chang et al., 2007). Daoud et al. (2005) and confirmed that nanoparticles of TiO2 presented as a coating on cellulose fibers still showed toxicity (but reduced one) in the absence of light. The latter observation can explain and fully support the importance of the χox descriptor used in our model. The commonly accepted ‘nanoparticle’ has size range of 100 nm or less. Thus, the products available at the market in the form of metal oxides NPs have the particles of size higher than 15 nm. However, as mentioned earlier, at this level of size, specific sizerelated changes of such electronic properties are not significant, and from this viewpoint, the particles behave similarly to a bulk (Kukreja et al., 2004; Labat et al., 2008; Zhang and Tang, 2004). On the other hand, ironically, they are more toxic to bacteria/microorganism/human than the particles of more than 100 nm. This toxicity is clearly related to the reductive potential, i.e., the detachment of the electron from the metal oxides. In addition, there are significant differences in toxicity between the individual oxides even though the particle size is almost the same. So, the main question here is which factors are responsible for the difference in toxicity and why the experimental results contradict each other. As we have hypothesized in this study that for the most toxic metal oxides, the small fragments detach an electron much easier than the same fragments in the crystal structure. Strongly reductive fragments initiate formation of ROS and the response of bacteria, so-called the oxidative stress. We hope that this hypothesis will further be introspected by experimental and additional theoretical studies. The obtained results provide an important clue for mechanism of action of cytotoxicity to E. coli and the developed models may give theoretical predictions for the cytotoxicity of untested metal oxide NPs in near future. Comparison of the present work has been performed with other previously reported studies on modeling toxicity of metal oxide NPs to E. coli as demonstrated in Table 5. Analyzing the comparison of statistical qualities among different studies, there is no doubt that the present models obtained from simple descriptors are statistically robust enough providing more insight of the most probable mechanism of cytotoxicity of the metal oxide nanoparticles to E. coli. Again, in our study, the used simple molecular descriptors are more interpretable mechanistically and less computationally demanding as well as reproducible than ones used in the previously reported models.

4. Conclusion We have successfully developed interpretative QSTR models which have been successfully applied to predict cytotoxicity of

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Fig. 1. Scatter plots for observed versus calculated/predicted cytotoxicity to bacteria Escherichia coli of metal oxide particles from the QSTR models with PLS tool for the ten random splits of the data into the training and test sets.

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Table 3 The list of metal oxides used in this study along with the quantitative values of their observed and calculated/predicted cytotoxicity to bacteria Escherichia coli performed with PLS statistical tool. ID No.

Metal oxide

Observed Cytotoxicity

Calculated/predicted cytotoxicity Model 1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 a

ZnO CuO V2O3 Y2O3 Bi2O3 In2O3 Sb2O3 Al2O3 Fe2O3 SiO2 ZrO2 SnO2 TiO2 CoO NiO Cr2O3 La2O3

3.45 3.2 3.14 2.87 2.82 2.81 2.64 2.49 2.29 2.2 2.15 2.01 1.74 3.51 3.45 2.51 2.87

Model 2

3.47 3.42a 2.68 2.77 2.60a 2.65a 2.59 2.69 2.64 1.83a 1.95a 1.82 1.91 3.42a 3.42 2.68 2.80

Model 3

3.48 3.53a 2.78 2.70 2.85 2.81a 2.86 2.77 2.82a 2.13 2.02 2.14a 2.06 3.53 3.53a 2.78a 2.68

Model 4

Model 5

a

3.35 3.42 2.74 2.63 2.84 2.78a 2.85a 2.73a 2.79 2.20 2.05 2.21a 2.10 3.41a 3.42 2.75a 2.60

3.45 3.43 2.71 2.74 2.69 2.70a 2.68 2.71a 2.70 1.96a 2.00a 1.96 1.98 3.43a 3.43 2.71a 2.74

3.38 3.42 2.69 2.61 2.76 2.72a 2.76 2.68a 2.72 2.05 1.95a 2.06 1.99 3.42 3.42 2.69a 2.59a

Model 6 a

3.22 3.33 2.67a 2.50 2.83a 2.73 2.85 2.66 2.75 2.24a 2.00 2.26a 2.09 3.32 3.33 2.68a 2.45

Model 7 3.44 3.48a 2.71 2.65a 2.77 2.73a 2.77 2.71 2.74 2.02a 1.94 2.03 1.97 3.48 3.48a 2.71a 2.63

Model 8 a

3.49 3.53a 2.74 2.67a 2.81 2.76 2.81a 2.73 2.77a 2.04 1.94a 2.05 1.98 3.53 3.53 2.74 2.64

Model 9

Model 10

3.32 3.39 2.71a 2.59 2.82 2.75a 2.83 2.70 2.77a 2.18 2.02 2.20a 2.08 3.39a 3.39 2.72a 2.56

3.37 3.43a 2.71 2.61a 2.80 2.74a 2.80 2.70a 2.75 2.11 1.98 2.12a 2.03 3.42a 3.43 2.71 2.59

Signifies predicted cytotoxicity as these compounds are present in the corresponding test sets.

Table 4 Leverage values of all compounds for individual PLS models. Id No.

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Model 9

Model 10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

0.34 0.29* 0.09 0.31 0.20* 0.10* 0.28 0.09 0.13 0.30* 0.31* 0.46 0.35 0.28* 0.41 0.09 0.45

0.34 0.27* 0.09 0.27 0.24 0.10* 0.27 0.09 0.12* 0.38 0.31 0.30* 0.26 0.36 0.27* 0.09* 0.39

0.27 0.28 0.09 0.29 0.26 0.10* 0.22* 0.08* 0.13 0.42 0.30 0.32* 0.26 0.22* 0.29 0.08* 0.42

0.25* 0.26 0.14 0.66 0.17 0.08* 0.19 0.13* 0.09 0.29 0.36* 0.33 0.34 0.26 0.26 0.11* 0.48*

0.27 0.28 0.10 0.33 0.19 0.09* 0.21 0.09* 0.11 0.28* 0.30* 0.43 0.35 0.22* 0.28 0.09* 0.46

0.25* 0.26 0.08* 0.33 0.23* 0.12 0.34 0.09 0.15 0.41* 0.34 0.44* 0.36 0.26 0.26 0.09* 0.48

0.35 0.26* 0.10 0.27* 0.21 0.09* 0.24 0.10 0.11 0.26* 0.36 0.40 0.27 0.35 0.26* 0.09* 0.51

0.26* 0.27* 0.11 0.32* 0.22 0.10 0.20* 0.11 0.10* 0.32 0.32* 0.35 0.30 0.36 0.37 0.10 0.67

0.27 0.27 0.08* 0.28 0.22 0.10* 0.24 0.09 0.11* 0.38 0.30 0.30* 0.26 0.21* 0.28 0.08* 0.40

0.36 0.26* 0.10 0.27* 0.22 0.09* 0.24 0.09* 0.11 0.37 0.35 0.29* 0.26 0.25* 0.35 0.09 0.54

n

Signifies compounds present in the corresponding test sets,

a

The critical value of h is 0.82.

Table 5 Comparison of the statistical qualities of the present models with previously reported studies on modeling toxicity of metal oxide NPs to E. coli. Dataset

Training set 2

Puzyn et al. (2010) Toropov et al. (2012) Present study with Stepwise-MLR Present study with PLS

Test set 2

R

Q

0.85 0.74–0.83 0.81–0.90 0.73–0.87

0.77 – 0.74–0.85 0.55–0.78

RMSE

c

0.20 0.17–0.23 0.16–0.22 0.19–0.27

– 0.74–0.85 0.77–0.86 0.72–0.81

R2p

metal oxide nanoparticles to bacteria Escherichia coli. Here, we have attempted to examine the applicability of the QSTR approach to model metal oxide NPs with simple molecular descriptors as many other computational approaches, like ab initio quantum chemistry methods and 3D descriptors, are computationally demanding for complex systems. It is interesting to point out that the major aim of this work is to establish the simple periodic table derived descriptors as useful ones for metal oxide nanoparticles for future use. We strongly believe that not only for metal oxide nanoparticles, one can use these descriptors for other inorganic

R2

R2pred

RMSE

r2mðtestÞ

Δr 2mðtestÞ

Q 2F2

GTC

– 0.83–0.96 0.73–0.96 0.70–0.96

0.83 – 0.72–0.91 0.65–0.88

0.19 0.14–0.33 0.15–0.26 0.17–0.29

– – 0.57–0.86 0.43–0.82

– – 0.01–0.19 0.004–0.28

– – 0.72–0.89 0.64–0.86

– – Passed Passed

compounds to develop QSAR/QSTR models. Here formal oxidation states and elctronegativity can explain the toxicity very well without performing any exhaustive descriptor calculation process, which brings the simplicity to this work. The major findings of this particular study are summarized below a) Mechanistic approach: The χox and χ are two significant descriptors for cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli. These values are also independent of size range of NPs obviating a common problem that many physical properties

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of NPs change for different size ranges. Metal oxides having low χox usually exhibit the strong reductive properties (easy detachment of the metal cation and electron from the metal oxides), which enhances their cytotoxicity to the bacteria E. coli and vice versa. Metal electronegativity indicates the energy required to detach the electron from the metal oxides during the toxicity effects. Thus, the major success of this study is to establish these simple descriptors as promising ones for future prediction of cytotoxicity of metal oxide NPs with probable mechanistic interpretation. b) Simplicity of model development: Calculations of the descriptors are not computationally demanding; one can easily obtain these descriptors from molecular formula and periodic table without having any quantum-chemical background. c) Cost effective: In this study, we have used simple descriptors to make the calculation easy and reproducible as well as time effective, and these descriptors can be used for modeling various responses of diverse metal oxide NPs successfully in future.

Conflict of interest The authors declare no conflict of interest.

Acknowledgment The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/20072013) under grant agreement n° 309837 (NanoPUZZLES project). SK is grateful to the European Commission for the financial support through Marie Curie IRSES program, NanoBRIDGES project (FP7-PEOPLE-2011-IRSES, Grant Agreement Number 295128). SK also thanks the Department of Science and Technology (DST), Government of India for awarding him a Research fellowship under the INSPIRE scheme. This paper was prepared with financial support of the Foundation for Polish Science (FOCUS Programme). A.G. thanks the European Social Fund for granting her with a fellowship in frame of the project Development Program of the University of Gdańsk in areas of Europe 2020 (UG 2020)” supported by Human Capital Operational Programme, Action 4.3, Strengthening of didactic potential of universities in key areas in the context of the goals of the Europe 2020 strategy (Grant no. UDA – POKL. 04.03.00-00-047/12).

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