The cytotoxicity of nanomaterials: Modeling multiple human cells uptake of functionalized magneto-fluorescent nanoparticles via nano-QSAR

Journal Pre-proof The cytotoxicity of nanomaterials: Modeling multiple human cells uptake of functionalized magneto-fluorescent nanoparticles via nano-QSAR Ronghua Qi, Yong Pan, Jiakai Cao, Zhenhua Jia, Juncheng Jiang PII:

S0045-6535(20)30368-4

DOI:

https://doi.org/10.1016/j.chemosphere.2020.126175

Reference:

CHEM 126175

To appear in:

ECSN

Received Date: 2 October 2019 Revised Date:

4 February 2020

Accepted Date: 9 February 2020

Please cite this article as: Qi, R., Pan, Y., Cao, J., Jia, Z., Jiang, J., The cytotoxicity of nanomaterials: Modeling multiple human cells uptake of functionalized magneto-fluorescent nanoparticles via nanoQSAR, Chemosphere (2020), doi: https://doi.org/10.1016/j.chemosphere.2020.126175. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

Author Contributions Data curation, Ronghua Qi; Formal analysis, Ronghua Qi and Jiakai Cao; Funding acquisition, Yong Pan; Methodology, Ronghua Qi and Jiakai Cao; Software, Ronghua Qi; Validation, Jiakai Cao and Yong Pan; Writing–original draft, Ronghua Qi and Yong Pan; Writing–review & editing, Zhenhua Jia, Juncheng Jiang and Yong Pan.

1

The cytotoxicity of nanomaterials: Modeling multiple human cells

2

uptake of functionalized magneto-fluorescent nanoparticles via

3

nano-QSAR

4

Ronghua Qi 1, Yong Pan1,*, Jiakai Cao1, Zhenhua Jia 2, Juncheng Jiang 1

5

1. Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science

6

and Engineering, Nanjing Tech University, Nanjing, 210009, China.

7

2. Institute of Advanced Synthesis, School of Chemistry and Molecular Engineering, Nanjing Tech

8

University, Nanjing, 210009, China.

9

* Corresponding author: Yong Pan

10

Tel.: +86-25-58139873.

11

E-mail address: [email protected] (Y. Pan).

12

Postal address: 30 South Puzhu Road, Jiangbei new district, Nanjing 211816, P.R.China.

13

Declarations of interest: None

14

I

1

The cytotoxicity of nanomaterials: Modeling multiple human cells

2

uptake of functionalized magneto-fluorescent nanoparticles via

3

nano-QSAR

4

Ronghua Qi 1, Yong Pan1,*, Jiakai Cao1, Zhenhua Jia 2, Juncheng Jiang 1

5

1. Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science

6

and Engineering, Nanjing Tech University, Nanjing, 210009, China.

7

2. Institute of Advanced Synthesis, School of Chemistry and Molecular Engineering, Nanjing Tech

8

University, Nanjing, 210009, China.

9

* Corresponding author. Tel.: +86-25-58139873.

10

E-mail address: [email protected] (Y. Pan).

11

Abstract

12

The vast majority of nanomaterials have attracted an upsurge of interest since their discovery and

13

considerable researches are being carried out about their adverse outcomes for human health and

14

the environment. In this study, two regression-based quantitative structure-activity relationship

15

models for nanoparticles (nano-QSAR) were established to predict the cellular uptakes of 109

16

functionalized magneto-fluorescent nanoparticles to pancreatic cancer cells (PaCa2) and human

17

umbilical vein endothelial cells (HUVEC) lines, respectively. The improved SMILES-based

18

optimal descriptors encoded with certain easily available physicochemical properties were

19

proposed to describe the molecular structure characteristics of the involved nanoparticles, and the

20

Monte Carlo method was used for calculating the improved SMILES-based optimal descriptors.

21

Both developed nano-QSAR models for cellular uptake prediction provided satisfactory statistical

22

results, with the squared correlation coefficient (R2) being 0.852 and 0.905 for training sets, and

1

23

0.822 and 0.885 for test sets, respectively. Both models were rigorously validated and further

24

extensively compared to literature models. Predominant physicochemical features responsible for

25

cellular uptake were identified by model interpretation. The proposed models could be reasonably

26

expected to provide guidance for synthesizing or choosing safer, more suitable surface modifiers

27

of desired properties prior to their biomedical applications.

28

Keywords

29

Cellular uptake; Nanoparticles; Cytotoxicity; Nano-QSAR; The improved SMILES-based optimal

30

descriptors

31

1. Introduction

32

With the development of nanotechnology (Abbasi et al., 2017; Mortazavi-Derazkola et al., 2017;

33

Zinatloo-Ajabshir et al., 2017; Khojasteh et al., 2019), the design and synthesis of nanoscale

34

materials with novel and extraordinary properties have been extensively advancing for over half a

35

century (Salavati-Niasari, 2004; Salavati-Niasari et al., 2006; Davar et al., 2010; Yousefi et al.,

36

2011; Mir et al., 2012; Mohandes and Salavati-Niasari, 2014; Safardoust-Hojaghan and

37

Salavati-Niasari, 2017), leading to a veritable explosion of nanotechnology in the fields of

38

electronics (Duttaa et al., 2018), catalysis (Wu et al., 2018), adsorbent (Ardekani et al., 2017;

39

Bazrafshan et al., 2017; Sadeghfar et al., 2018; Dil et al., 2019), biosensor(Bahrani et al., 2018),

40

biomedicine (El-Sayed et al., 2006; Bajpai et al., 2018), and other industrial application (Auffan et

41

al., 2009; Dong et al., 2014; Jiang et al., 2015). Metal oxide nanoparticles (MNPs) are one of the

42

most popular used nanomaterials due to their unique small-size effect, high surface reactivity, high

43

surface-to-volume ratio, and quantum-size effect. However, concerns had also been raised about

44

the potential adverse impacts on the ecosystem and living organisms when MNPs interact with

2

45

biological systems (Mattsson et al., 2016). MNPs may intrude into the human organism through

46

multiple entries such as inhalation, skin absorption and ingestion due to either intentional or

47

unintentional exposure to them. Once MNPs have entered into the human body, nanoparticles are

48

wrapped by biomacromolecules, including sugars, proteins, lipids, and nucleic acids (Shang et al.,

49

2014.), followed by cellular uptake.

50

The cellular uptake of MNPs depends on their physicochemical features, thus the intracellular

51

targeting and trafficking could be optimized by fine-tuning the MNP’s physicochemical properties,

52

which helps to synthesize efficient and novel nanomaterials (Petros et al., 2010; Pridgen et al.,

53

2015). The increased cellular uptake to normal cells may contribute to an enhanced interaction of

54

MNPs with subcellular organelles, which in turn directly induces tissue damage caused by

55

overloading and thus reinforces the cytotoxicity. The dominant reason is that the cytotoxicity

56

degree of the MNPs has some cellular absorption dependence to a great extent (Geys et al., 2008).

57

The high toxicity of MNPs appears as higher cellular uptake and vice versa (Singh and Ramarao,

58

2013). Accordingly, for the cells around treated area, lower cellular uptake is needed to exhibit low

59

toxicity, while for specific targeting of cancer cells, higher cellular uptake is required to cure

60

diseases. Consequently, owing to the desired features of functionalized MNPs and their ability for

61

cellular penetration for specific targeting of cancer cells, the MNPs have been widely employed in

62

biomedical applications for DNA structure detecting, drug-delivery carriers, molecular imaging,

63

gene delivery, fluorescent biological labels and so on (Agasti et al., 2010; Tian et al., 2016;

64

Oroojalian et al., 2017; Gai et al., 2018; Liu et al., 2019).

65

In general, experimental in vivo toxicological studies and in vitro short-term cell-based assays

66

are the basic methods to perform the preliminary evaluation of the toxic effects (Pan et al., 2016).

3

67

However, considering a great number of newly functionalized nanoparticles are widely applicable

68

for the biological fields as well as the high cost and long periodicity required for determining their

69

absorption amount in different cell lines, it is impossible to measure the toxicity for all the

70

available nanoparticles. Consequently, there is a strong demand to utilize computational in silico

71

methods flexibly to obtain certain missing data of corresponding substances. In particular, the

72

Quantitative Structure-Activity Relationship (QSAR) approach is one of the most prospective

73

methods that could predict their bioactivity accurately in a simple and efficient way prior to their

74

synthesis (Kar and Roy, 2010). QSAR is primarily based on the assumption that there is a certain

75

correlation between the variation in biological activity of chemicals and the changes in their

76

molecular structures. More recently, a great-increasing number of investigations showed that it is

77

really urgent and necessary to extend the traditional QSAR paradigm to nano-sized materials and

78

to develop “nano-QSAR” models for relating the properties of interest to structure information of

79

newly synthesized nanoparticles as well as providing theoretical guidance for designing

80

functionalized nanoparticles with expected features (Le et al., 2012; Winkler et al., 2013).

81

Current studies on biological interaction and potential risks of the omnipresent exposure to

82

MNPs are not consistent with the rapid developments of nanotechnology and are still very limited

83

(Cheng et al., 2013), but the concerns towards the potential impact of these MNPs on organisms

84

are growing. Recently, various nano-QSAR studies have been conducted on cellular uptake

85

predictions for 109 functionalized magneto-fluorescent MNPs in different cell lines. All MNPs

86

possessed one superparamagnetic core and dextran coating but bearing different biological surface

87

modifications (Weissleder et al., 2005). Fourches et al. (2010) firstly developed nano-QSAR

88

models to describe the cellular uptake to pancreatic cancer cells (PaCa2) via employing kNN

4

89

modeling approach as well as MOE molecular descriptors with the average squared correlation

90

coefficient for the test set being of 0.72. Subsequently, Kar et al. (2014) developed a more accurate

91

cellular uptake model with 6 simply computable and interpretable descriptors for the same cell line

92

by employing the partial least squares (PLS) regression method. Winkler et al. (2014) generated

93

four nano-QSAR models to predict the uptake of PaCa2 and human umbilical vein endothelial

94

cells (HUVEC) nanoparticle by employing both linear and nonlinear methods. Ojha et al. (2019)

95

established nano-QSAR models as well as quantitative inter cell line uptake specificity modeling

96

(QICLUS) using only 2D descriptors on the cellular uptake prediction of 109 MNPs towards

97

PaCa2, HUVEC, and human macrophage cells (U937) lines, respectively. Toropov et al. (2013)

98

also developed reliable nano-QSAR models by employing the SMILES-based optimal descriptors

99

that molecular descriptors were computed directly from each SMILES (Simplified Molecular

100

Input-Line Entry System) attribute fragment of chemicals in view of nanoparticles uptake in

101

PaCa2 for five described random events. However, in most existing studies, the effect of the

102

molecular descriptors on cytotoxicity is directly extracted from nano-QSAR models, except for

103

Toropov et al. (2013)’s work. Different from traditional molecular descriptors (i.e.,

104

quantum-chemical descriptors, physicochemical descriptors), the SMILES-based optimal

105

descriptors proposed by Toropova and Toropov, have been successfully applied to predict the

106

toxicity of organic and nanometer materials (Toropov et al., 2011, 2013; Toropov and Toropova,

107

2015; Toropova and Toropov, 2013, 2017; Toropova et al., 2015, 2016; Trinh et al., 2018; Choi et

108

al., 2019). In 2016, on the basis of the SMILES-based optimal descriptors, our group (Pan et al.,

109

2016) proposed the improved SMILES-based optimal descriptors by integrating codes of certain

110

basic physicochemical properties into the traditional SMILES to improve the characterization of

5

111

nanostructure information. The resulted models developed based on the improved descriptors

112

showed obvious superiority comparing with traditional models and could also imply the direct

113

effect of descriptors on cytotoxicity (Pan et al., 2016).

114

In this study, the improved SMILES-based optimal descriptors encoded with several easily

115

available physicochemical properties were employed to describe the molecular structure

116

characteristics of 109 functionalized magneto-fluorescent MNPs. Then, for the first time, two

117

nano-QSAR models for predicting cellular uptakes of 109 diverse MNPs with surface modifiers in

118

HUVEC and PaCa2 lines were developed respectively, following the Organization for Economic

119

Cooperation and Development (OECD) principles for QSAR modeling. Both developed models

120

were then rigorously validated utilizing internal and external validation procedures and their

121

applicability domains were also defined. The resulting models in this study would be expected to

122

provide guidance for synthesizing or choosing safe and suitable organic coatings with desired

123

characteristics for MNPs prior to their biomedical applications.

124

2. Materials and methods

125

2.1. Data set

126

The experimental values of the cellular uptake of 109 nanoparticles on one predominant metal

127

core platform but bearing different organic coatings (small organic molecules) were taken from the

128

previous literature (Weissleder et al., 2005) and presented in Table S1 (ESI†). All newly

129

synthesized nanoparticles in the dataset shared the same-structure core of 3-nm (Fe2O3)n(Fe3O4)m

130

monocrystalline by multivalent attachment of different small molecules. In order to test the

131

absorption of these MNPs in different cell lines, fluorescein isothiocyanate (FITC) molecules were

132

appended to their surfaces to make MNPs magneto-fluorescent. The MNPs were filtered against

6

133

three different types of cell and two different physiological states of a given cell type, including

134

human pancreatic ductal adenocarcinoma cells (PaCa2), primary resting human macrophages

135

(RestMph), granulocyte macrophage colony stimulating factor-stimulated human macrophages

136

(GMCSF_Mph), human macrophage-like cell line (U937), and human umbilical vein endothelial

137

cells (HUVEC). Among the five tested cell lines, only HUVEC and PaCa2 lines revealed

138

substantial diversity in cellular uptake of the different MNPs. Non-significant variation in cellular

139

uptake was highlighted in other three macrophage or macrophage-like cell lines. Thus in this study,

140

the data of HUVEC uptake and PaCa2 uptake were employed for the model development. The

141

selected endpoint (cellular uptake) is expressed as the decadic logarithm of the concentration (pM)

142

of nanoparticles per cell (log10[NP]/cell) (Fourches et al., 2010), which varies from 2.23 to 4.44

143

for PaCa2 line, and 2.15 to 4.76 for HUVEC line, respectively.

144

2.2. Dataset splitting

145

Division of training and test sets is a necessary step in the development of reliable predictive

146

nano-QSAR models. Prior to the QSAR modeling, all the 109 nanoparticles in the entire dataset

147

were randomly split into a training set with 87 data (80% of the dataset) and a test set with 22 data

148

(20% of the dataset). The training set was employed to develop the nano-QSAR models, while the

149

test set was used to evaluate the ability of the developed models on predicting the invisible data.

150

2.3. Molecular descriptors calculation

151

The calculation of molecular descriptors for the involved nanoparticles is crucial in the

152

nano-QSAR modeling. Since all the nanoparticles have the same metal core and different organic

153

surface modifiers belonging to the class of anhydride (mostly cyclic), amine and amino acid, their

154

differences in cellular uptake are mostly caused by the diversities of molecular species on the

7

155

nanoparticle surface. Accordingly, each nanoparticle could be represented by its structure of

156

organic surface modification.

157

In this study, based on our previous proposed descriptors, the global SMILES attributes (NOSP),

158

which are defined as indicators of the presence or the absence of four chemical atoms: nitrogen,

159

oxygen, sulfur, and phosphorus (Toropov et al., 2011), were considered and employed to further

160

improve the SMILES-based optimal descriptors (DCW), which are described as following: DCWthreshold, N  =  CWS  +  CWSS  +  CWC  +  CWCC  + CWNOSP

(1)

161

Where, Sk and SSk are local SMILES attributes obtained from the SMILES; CW(Sk) and

162

CW(SSk) are the correlation weights of the attributes. Similarly, Ck and CCk are codes of k-th

163

structure features; CW(Ck) and CW(CCk) are the correlation weights for these features. NOSP are

164

global SMILES attributes which are extracted from the SMILES notation.

165

Considered here codes of features can be defined by the following scheme:

166

(I) Standardization of feature Xk based on the formula NormX   =

minX  + X  minX  + maxX 

(2)

167

(II) After normalized calculation, each physicochemical feature was classified into one category

168

between categories 1 and 9 according to the corresponding scale (Toropova et al., 2013). (Fig. S2,

169

ESI†). Alphabet characters of A, B, D, E, M were expressed as molecular weight, mass percentage

170

of carbon atoms, topological polar surface area, ring count and atom count, respectively.

171

The local SMILES attributes of Sk (or Ck) and SSk (or CCk) could be represented as: "ABDEM":"A","B","D","E","M"; "ABDEM":"AB","BD","DE","EM";

172

The assigned codes were binary combinations of capital letters and numbers (i.e., A0, A1,… )

8

173

and the randomly selected letters couldn’t repeat the same alphabet characters for atoms in

174

SMILES structures.

175

The optimal descriptors are then calculated with the correlation weights of active

176

physicochemical features by the Monte Carlo optimization. The Monte Carlo optimization method

177

was applied to find the preferable values of threshold T* and epoch N* corresponding to the

178

maximum correlation coefficients for the test set (Toropov et al., 2011). The threshold and Nepoch

179

are parameters of the Monte Carlo optimization. The threshold is a tool to define two classes of

180

molecular features: noise and active. The Nepoch is the number of epochs of the Monte Carlo

181

optimization.

182 183

The nano-QSAR model for predicting cellular uptake can be represented by the following equation: log-. /NP0/cell = C0 + C1 × DCWT ∗ , N ∗ 

(3)

184

Where, C0 and C1 are the intercept and slope.

185

All calculations were performed in CORAL software, which can be available from

186

http://www.insilico.eu/coral for free.

187

In this study, All the SMILES came from the previous report (Fourches et al., 2010). A total of

188

five different common physicochemical properties, named as molecular weight (MW), mass

189

percentage of carbon atoms (C%), topological polar surface area (TPSA), ring count (RC), and

190

atom count (AC), were employed as codes to develop the improved SMILES-based optimal

191

descriptors. Among the five selected physicochemical properties, the molecular weight (MW) has

192

been proved to be an important parameter for effectively altering the physicochemical properties

193

of different nano-drug carriers related to intracellular drug uptake (Feng et al., 2015; Liu et al.,

9

194

2016; Zhang et al., 2017), the topological polar surface area (TPSA) is widely used in virtual

195

screening of drug targets (Monge et al., 2006; Prasanna and Doerksen, 2009), while ring count

196

(RC), atom count (AC) and mass percentage of carbon atoms (C%) are the most commonly used

197

constitutional properties for indicating the bioactivity and cytotoxicity of nanomaterials to a

198

certain degree (Jia et al., 2005; Zhang et al., 2017; Malik and Kakkar, 2018). Both MW and C%

199

were calculated with Chemdraw (version 14.0), while TPSA, RC and AC were calculated using

200

Chemicalize (https://chemaxon.com/products/chemicalize).

201

Pearson correlation coefficients between each pair of physicochemical features (including MW,

202

C%, TPSA, RC and AC) were calculated to examine whether there was any special colinearity

203

between these parameters, and the results were shown in Fig. S1 (ESI†). The resulting highest

204

correlation coefficient (0.57) was between MW and AC, due to the fact that the more atoms would

205

lead to the higher molecular weights for small organic molecules.

206

2.4. Model validation

207

The best way to measure the validity of a model is how well it could predict the properties of

208

new MNPs that were not employed to generate it. Any developed QSAR model needs to be

209

reasonably validated according to the OECD QSAR validation principles (OECD, 2014). In this

210

study, we employed various validation strategies to verify the fitness, robustness, and predictivity

211

of the proposed nano-QSAR models.

212

The most commonly used statistical parameters, the squared correlation coefficient (R2),

213

standard error (s), and root-mean-square error (RMSE), were used to evaluate the fitness of the

214

resulting nano-QSAR models, while both the internal and external validations were used to

215

indicate the predictivity. Here, the robustness and internal predictive ability of both models were

10

216

indicated by the leave-one-out cross-validation parameter (Q2LOO), which was known as the most

217

reliable internal validation method. The external validation is necessary and essential in

218

determining both the generalizability and the true predictivity of the nano-QSAR models for

219

newly synthesized MNPs. Here, the external validation was performed by randomly dividing the

220

entire available data set into a training set and an external test set. The training set is used for the

221

model development, and the test set is used for external validation. Also, both models were tested

222

on a sufficiently large number of MNPs that were not used in the model development (20% of the

223

dataset) to avoid obtaining the QSAR results by chance or obtaining non-general conclusions

224

(Tropsha et al., 2003). Moreover, Y-randomization test was performed to further ensure the model

225

reliability and robustness of the developed models (Rücker et al., 2007), while the external

226

validation criteria proposed by Golbraikh and Tropsha (2002), Roy and Roy (2008) were also

227

employed to verify the acceptability of the developed models.

228

2.5. Applicability domain (AD)

229

According to the OECD principle 3 (OECD, 2014), a nano-QSAR model should have a defined

230

domain of applicability (AD). As nano-QSAR models are probably to be employed for reliably

231

estimating the properties of newly designed MNPs, the extent to which the newly synthesized

232

MNP belonging to the AD of the model requires reasonable consideration. Here, we analyzed the

233

AD of both models by the leverage approach to detect the existence of influential nanoparticles in

234

the training set, and to verify the prediction reliability for MNPs in the test set. The leverage value

235

hi is defined as hi = xiT(XTX)-1xi, where xi is a row vector of descriptors for a particular ith MNP

236

and X is the n × m matrix of m model descriptors for n training set MNPs. A hi value greater than

237

the warning leverage value h* implies that the structure of the MNP considerably differs from

11

238

those used for the calibration. The h* value can be calculated as, h*= 3(p+1)/n, where p is the

239

number of variables used in the model, n is the data size of the training set (Puzyn et al., 2011).

240

3. Results and Discussion

241

3.1. Computational results: nano-QSAR models

242

Based on the cellular uptake data of 109 magneto-fluorescent MNPs with surface modifiers in

243

HUVEC and PaCa2 lines, two statistically significant nano-QSAR models using uptake by single

244

cell line as the dependent variable were developed, respectively. The corresponding nano-QSAR

245

models were presented as following:

246

Model 1. HUVEC model log-. /NP0/cell = −3.1057±0.0341 + 0.0954±0.0005 × DCW1,15 B

n = 87, R = 247

0.852, QBEFF

(4) = 0.845, s = 0.237, F = 489, p < 0.0001

Model 2. PaCa2 model log-. /NP0/cell = −3.0330±0.0328 + 0.0966±0.0005 × DCW1,20 B

n = 87, R = 248 249

0.905, QBEFF

(5) = 0.899, s = 0.131, F = 812, p < 0.0001

Where, n is the number of MNPs in the training set; R2 is the squared correlation coefficient; Q2LOO is the cross-validated R2; s is standard error; F is Fischer ratio; p is p-value.

250

Subsequently, both nano-QSAR models were utilized to predict the cellular uptake values of the

251

MNPs in the test set for external validation. The predicted cellular uptakes were shown in Table S1

252

(ESI†), while the main statistical parameters were presented in Table 1. Both nano-QSAR models

253

for cellular uptake prediction of the 109 MNPs yielded high correlations (R2) between the

254

experimental and predicted cellular uptake endpoints in the respective training (>0.852) and test

255

(>0.822) sets. A value of R2 in the test set exceeding 0.6 would render a model with good

12

256

predictive capability (Alexander et al., 2015). Furthermore, the difference of R2 values between

257

the training and test set (Table 1) for both models was below 0.3, a standard for good predictivity

258

of the model (Eriksson et al., 2003). The RMSE values were not only low but also as similar as

259

possible for the training and external test sets, indicating that the proposed nano-QSAR models

260

have predictive ability (low values) and generalization performance (similar values). Both the

261

good fit and high predictive capability have been verified by an excellent agreement between the

262

experimentally determined and model (nano-QSAR) predicted MNPs cellular uptake values in the

263

respective training and test sets (Fig. 1(a)-(b)).

264

Table 1 Comparisons of statistical parameters between the presented and literature models

Cell lines

HUVEC

PaCa2

Training set

Models

R

2

Test set

RMSE

n

R

2

RMSE

n

Ojha et al. (2019)

0.782

0.299

87

0.704

-

22

Epa et al. (2012)

0.55

0.38

87

0.72

0.30

21

Winkler et al.

linear

0.74

0.34

87

0.63

0.36

21

(2014)

nonlinear

0.70

0.30

87

0.66

0.33

21

Basant and Gupta (2016)

0.973

0.100

83

0.966

0.104

21

Model 1

0.852

0.235

87

0.822

0.241

22

Ojha et al. (2019)

0.814

0.198

87

0.893

-

22

Epa et al. (2012)

0.64

0.26

87

0.62

0.32

21

Winkler et al.

linear

0.76

0.19

87

0.79

0.24

21

(2014)

nonlinear

0.77

0.15

87

0.54

0.28

21

Kar et al. (2014)

0.806

0.20

89

0.879

0.12

20

Fourches et al. (2010)

-

-

87

0.72

0.18

22

Toropov et al. (2013)

0.76

0.19

91

0.86

0.14

18

Singh and Gupta (2014)

0.945

0.13

87

0.897

0.18

22

Basant and Gupta (2016)

0.974

0.067

83

0.944

0.109

21

Ghorbanzadeh et al. (2012)

0.934

0.121

90

0.943

0.214

19

Model 2

0.905

0.130

87

0.885

0.140

22

265

The developed models were also satisfied with the external validation criteria proposed by

266

Golbraikh and Tropsha (2003), Roy and Roy (2008) as given in Table S2 (ESI†). All of these

267

statistical parameters are within the acceptable limit.

268

3.2. Model validation and results analysis 13

269

In order to avoid the existence of “correlation-by-chance”, and to prove the statistical

270

significance of both established nano-QSAR models, the Y-randomization test was carried out

271

additionally on the same dataset for 10 times for each model. As expected, all the newly calculated

272

R2 values, were much lower than the ones calculated when the dependent variables were not

273

scrambled (Table S3, ESI†). Thus, the Y-randomization test clearly showed that these models were

274

not obtained by chance, and confirmed the significance of the nano-QSAR models.

275

Furthermore, a visual analysis of the residual plots between the experimental and predicted

276

values for the resulting nano-QSAR models were calculated and shown in Fig. 1(c)-(d). As most

277

of the calculated residuals are randomly distributed on both sides of the zero baseline without

278

obvious regularity, it could be reasonably concluded that no systematic errors exist in the

279

development of both nano-QSAR models.

280

All the results discussed above indicated the satisfactory stability and predictivity of the

281

developed nano-QSAR models after carrying out various rigorous model validation strategies,

282

which could be reasonably employed to predict the cytotoxicity of MNPs within their ADs.

283

Considering the limitation of existing experimental data and the complexity of current cytotoxicity

284

tests for MNPs, it was difficult to further improve the model predictions beyond the current

285

results.

286

To our delighted, this work showed that the MNPs cellular uptake to both HUVEC and PaCa2

287

lines could be successfully predicted by the QSAR approach using the improved SMILES-based

288

optimal descriptors. Once properly developed, the nano-QSAR models could be expected to

289

predict the cellular uptake for new MNPs or for other MNPs for which experimental values are

290

unknown, using only SMILES structures as well as some basic physicochemical properties. The

14

291

SMILES could be obtained from freely accessible software like ChemSketch, while the involved

292

physicochemical properties can be generated by inputting SMILES structures into software such

293

as Chemicalize, or directly obtained from structural formula.

5.0

5.0

Training set Test set

Predicted values of log(UP)

Predicted values of log(UP)

5.5

4.5 4.0 3.5 3.0

(a)

2.5 2.0 2.0

2.5

3.0

3.5

4.0

4.5

5.0

4.5 4.0 3.5 3.0 2.5 2.0 2.0

5.5

1.0

2.5

3.0

3.5

4.0

4.5

0.0

-0.5

0.5

Training set Test set

0.0

-0.5

(c) -1.0 2.0

2.5

3.0

3.5

4.0

4.5

5.0

(d) -1.0 2.0

5.5

Experimental values of log(UP)

3.5

4.0

4.5

5.0

3

Standardized residuals

Standardized residuals

3.0

4

3

Training set Test set

2 1 0 -1

(e)

-2 -3

294

2.5

Experimental values of log(UP)

4

-4 0.00

5.0

1.0

Training set Test set Residual values

Residual values

(b) Experimental values of log(UP)

Experimental values of log(UP)

0.5

Training set Test set

Training set Test set

2 1 0 -1 -2

(f)

-3

h*=0.069 0.02

0.04

0.06

0.08

Leverages

-4 0.00

h*=0.069 0.02

0.04

0.06

0.08

Leverages

295

Fig. 1 (a) Plots of experimental versus predicted cellular uptake values to HUVEC line, (b) Plots of experimental

296

versus predicted cellular uptake values to PaCa2 line, (c) Plots of the residuals for predicting cellular uptake to

297

HUVEC line, (d) Plots of the residuals for predicting cellular uptake to PaCa2 line, (e) Williams plots describing

298

applicability domains of nano-QSAR model to HUVEC line and (f) Williams plots describing applicability

299

domains of nano-QSAR model to PaCa2 line.

15

300

3.3. Applicability domain analysis

301

Once a nano-QSAR model is developed, the applicability domain (AD) of the developed model

302

should be defined, since any nano-QSAR model could properly evaluate the cytotoxicity of newly

303

synthetic MNP, only if the MNP lies within its AD.

304

In this study, the plots of the standardized residuals versus leverages (Williams plot) for both

305

models were shown in Fig. 1(e)-(f), which indicated that all the 109 magneto-fluorescent MNPs

306

were located in a squared area within ± 3 standard deviation units and a warning leverage

307

h*=0.069. That is to say, there were no obvious outliers in both the structural similarity axis and

308

the cellular uptake predictions to the corresponding cell line.

309

3.4. Comparisons with other literature models

310

The proposed models for predicting MNPs cellular uptake values in different cell lines were

311

based on the almost completely same datasets as the literature reported. Considering the

312

comparative assessment of statistical parameters of all these models as presented in Table 1, one

313

can conclude that the proposed models calculated based on the improved SMILES-based optimal

314

descriptors are generally better than or at least comparable to those reported models. More other

315

specific details of the model comparisons are further discussed as below.

316

Comparisons between model 1 and other reported models based on the same dataset

317

(cellular uptake to HUVEC)

318

Firstly, it should be noted that Epa et al.’s (2012), Winkler et al. ’s (2014) and Ojha et al.’s

319

(2019) works were developed based on different sets of interpretable 2D DRAGON descriptors,

320

Basant and Gupta ’s (2016) work used 1D and 2D theoretical descriptors calculated by PaDEL

321

program, while our model 1 employed the improved SMILES-based optimal descriptors. All types

16

322

of descriptors are conceptually simple and easy to calculate. However, the 2D DRAGON

323

descriptors have definite physical meanings, which are more interpretable than the improved

324

SMILES-based optimal descriptors. Basant and Gupta (2016) employed decision tree boost (DTB)

325

approach, which may result in the risk of overfitting, while our model 1 was a multiple linear

326

regressions (MLR) model, which is always considered to be more transparent and easier to apply.

327

Regarding the model applicability domain, the analysis of AD is missing in both Epa et al.’s (2012)

328

and Winkler et al. ’s (2014) works, and the analysis in Ojha et al.’s (2019) work is not enough.

329

Ojha et al. (2019) only checked AD of the test set by using DModX (distance to model X)

330

approach, but no further explanation was given for the training set. In this study, the AD of model

331

1 was analyzed in detail and visually defined by the Williams plot.

332

Comparisons between model 2 and other reported models based on the same dataset

333

(cellular uptake to PaCa2)

334

More nano-QSAR models are available in literature concerning cellular uptake to PaCa2

335

comparing to cellular uptake to HUVEC. Winkler et al. (2014) employed 19 interpretable

336

mechanistically 2D DRAGON descriptors as the input parameters for nano-QSAR modeling,

337

which implied the possible risk of overfitting for the resulting model. Meanwhile, only five easily

338

obtained physicochemical parameters and SMILES structures were involved in the model 2

339

developed in this study. It is well known that QSAR modeling aims at finding optimum

340

quantitative relationship between the molecular structures and desired properties with as less

341

descriptors as possible. The fewer descriptors are employed, the more robust the nano-QSAR

342

models will be considered.

343

It is worth mentioning that the traditional SMILES-based optimal descriptors were used to

17

344

characterize the structural characteristics of the nanoparticles by Toropov et al. (2013). While in

345

this work, additional physicochemical properties were included in SMILES structures to improve

346

the characterization of nanostructure information, leading to an obvious better calibration ability

347

for the training set (0.905 vs. 0.76), as well as an improved predictive ability for the test set (0.885

348

vs. 0.86).

349

Moreover, Fourches et al. (2010) employed the k nearest neighbors (kNN) modeling approach,

350

Basant and Gupta (2016) employed decision tree boost (DTB) approach, Singh and Gupta (2014)

351

employed decision tree forest (DTF) and DTB implementing bagging and boosting techniques,

352

and Ghorbanzadeh et al. (2012) used multilayered perceptron neural network (MLP-NN)

353

modeling technique to develop reliable but non-intuitive nonlinear models. Also, Ghorbanzadeh et

354

al.’s (2012) Singh and Gupta’s (2014), and Basant and Gupta’s (2016) models were superior to

355

model 2 in terms of statistical parameters for the test set. However, our model 2 was a multiple

356

linear regressions (MLR) model, which is considered to be more transparent and easier to apply.

357

As well-known, the neural network method always suffers some disadvantages, such as

358

overtraining, overfitting, and reproducibility of results. Also, the decision tree approach may result

359

in the risk of overfitting.

360

With regards to the model applicability domain, the analysis of AD is absent in Epa et al.’s

361

(2012), Winkler et al.’s (2014) and Toropov et al.’s (2013) works, and Ojha et al. (2019) only

362

checked the AD for the test set by employing DModX approach ignoring the AD for the training

363

set. In this study, the AD of model 2 was analyzed in details and visually defined by the Williams

364

plot.

365

Consequently, both the proposed models can be considered to predict the cellular uptake

18

366

endpoint of MNPs to HUVEC and PaCa2 lines with some obvious superiority in comparison to

367

the literature models. However, it should also be stated that the proposed models have some

368

limitations: The employed physicochemical properties are selected based on literature researches,

369

but these empirical parameters may be not necessarily the optimal parameters for this study, which

370

need to be further studied. In addition, the resulting models could only be used to predict the

371

cellular uptake of metal oxide nanoparticles in HUVEC and PaCa2 lines.

372

3.5. Mechanistic interpretation

373

From the developed nano-QSAR models, it can be highlighted that a lower value of the optimal

374

descriptor (DCW) resulted in a lower value of cellular uptake endpoint (i.e., lower cytotoxicity).

375

The optimal descriptor was the sum of the correlation weight (CW) of all attribute fragments of

376

improved SMILES-based optimal descriptors. Thus, the higher correlation weights always

377

correspond to the higher cytotoxicity. 4.0 HUVEC

Average absolute CW

3.5

PaCa2

3.0 2.5 2.0 1.5 1.0 0.5 0.0

378

MW

C%

TPSA

RC

AC

379

Fig. 2 Average absolute correlation weights (CW) value of each physicochemical feature

380

in both generated nano-QSAR models.

381

In this study, a total of five easily obtained physicochemical properties were combined in the

382

SMILES structures to improve the structure characterization of MNPs. Lower CW led to lower

383

cellular uptake value and thus lower cytotoxicity because positive CW implied the promotion of

19

384

an increase in cellular uptake. Among the five employed parameters, AC had the highest average

385

absolute CW value in both generated nano-QSAR models as plotted in Fig. 2. That is to say, AC

386

was found to have the most influential effects on cellular uptake of the MNPs to both cell lines.

387

Based on the correlation weight tables (Table S4-S5, ESI†), it can be concluded that, for HUVEC

388

line, the correlation weights of low MW group A1-A6 (smaller than 3.0) are lower than the

389

weights of high MW group A7-A9 (larger than 3.0), suggesting that the high MW group is more

390

toxic than low MW group. The correlation weights of C% decreases first and then increases with

391

the increase of C%, suggesting that the cytotoxicity is decreased and then enhanced with the

392

increase of C%. All the correlation weights of TPSA are low, excluding D4 and D9, implying that

393

the changes of TPSA have no significant effect on cytotoxicity. The correlation weights of low RC

394

group E0-E3 (larger than 3.0) are higher than the weights of high RC group E4-E9 (smaller than

395

3.0), suggesting that the low RC group is more toxic than high RC group. The correlation weights

396

showed a lowering trend with increase of AC, excluding M1, M5 and M6, suggesting that the

397

cytotoxicity was enhanced in a nanoparticle with low atom count. While for PaCa2 line, all the

398

correlation weights of MW are low excluding A4, implying that the changes of MW have no

399

significant effect on cytotoxicity. The correlation weights of lower C% group B2-B3 (larger than

400

3.0) are higher than the weights of higher C% group B4-B9 (smaller than 3.0) except for B6,

401

suggesting that the lower C% group exerted more toxic effects than higher C% group. All the

402

correlation weights of TPSA are low excluding D3, implying that the changes of TPSA may have

403

no significant effect on cytotoxicity. The correlation weights of lower RC group E0-E4 (larger

404

than 3.0) are higher than the weights of higher RC group E5-E9 (smaller than 3.0), suggesting that

405

the lower RC group exerted more toxic effects than higher RC group. However, it should be noted

20

406

that there are no obvious rules in the numerical changes of the correlation weights for AC.

407

Table 2 global SMILES Attributes (NOSP) chosen according to two Criteria: 1) these are not rare for models

408

of the both objects of interest; and 2) their presence in the molecules has a clear effect. HUVEC

global SMILES

PaCa2

attribute

CW

NSsa

CW

NSsa

NCvb

1

NOSP01000000

8.605

43

9

5.563

43

9

2

NOSP01100000

1.578

1

1

1.152

1

1

3

NOSP10000000

0.873

17

7

2.511

17

7

4

NOSP11000000

-2.097

26

4

2.653

26

4

NCv

b

a

NSs is the number of substances which containing a structural attribute in the training set;

b

NCv is the number of substances which containing a structural attribute in the test set.

409

Table 2 presents four mentioned global SMILES attributes involved in both models (the cellular

410

uptake to HUVEC and PaCa2 lines), which were chosen according to the following criteria: 1)

411

these attributes are not rare for models of the both endpoints; and 2) their presence in the

412

molecules has a clear effect (attributes with positive correlation weights are promoters of endpoint

413

increase and attributes with negative correlation weights are promoters of endpoint decrease)

414

(Toropov et al., 2011). It could be noted that the attributes1, 2, 3 are promoters of the increase for

415

cellular uptake of both HUVEC and PaCa2 lines, whereas the attribute 4 is a promoter of the

416

decrease of HUVEC cellular uptake and promoter of the increase of PaCa2 cellular uptake. It

417

could be concluded that the analysis of the presence of attribute 4 in the molecular structure would

418

be helpful to search for similarities and differences in the mechanisms of the cellular uptake to

419

HUVEC and PaCa2 lines.

420

4. Conclusions

421

In this study, the improved SMILES-based optimal descriptors were employed for nano-QSAR

422

modeling on the cytotoxicity of 109 MNPs to HUVEC and PaCa2 lines. Two robust and predictive

423

nano-QSAR models have been successfully developed and rigorously validated. Furthermore, the

21

424

predominant nano-structure characteristics responsible for cellular uptake of MNPs were

425

identified, and differences in the mechanisms of their cytotoxicity to different types of cells were

426

also discussed. The main findings are summarized below:

427

1) The improved SMILES-based optimal descriptors for 109 MNPs can be calculated directly

428

from SMILES and some easily available physicochemical properties without any complicated

429

calculation process. These reproducible descriptors could efficiently encode the cellular

430

uptake of MNPs leading to models with satisfactory statistical quality as well as certain

431

interpretability.

432

2) Two statistically significant and robust nano-QSAR models for predicting the cellular uptake

433

of MNPs to both HUVEC and PaCa2 lines have been successfully developed. When

434

compared with the previously reported models, the resulting models were considered to be

435

conceptually simple, easy to apply, and with acceptable model interpretability.

436

3) For HUVEC line, the atom count and molecular weight were found to be the most significant

437

factors for the cellular uptake, while for PaCa2 line, the atom count and mass percentage of

438

carbon atoms were the most important factors for the cellular uptake. The presented

439

nano-QSAR models revealed the differences in the mechanisms of cytotoxicity of MNPs to

440

HUVEC and PaCa2 lines.

441

4) This study could provide a new way for predicting the cytotoxicity of unmeasured or novel

442

surface modifiers of MNPs for which experimental values are unknown. The developed

443

models could also be used to improve the safety design and virtual screening of desired MNPs

444

prior to their biomedical applications.

445

Acknowledgements

22

446

This research was supported by National Natural Science Fund of China (No. 51974165,

447

81803274), and Natural Science Fund of Jiangsu Higher Education Institutions of China (No.

448

18KJA620002).

449

Appendix A. Supplementary materials

450

Supplementary information.docx: This file provides supplementary tables and figures.

451

23

452

References

453

Abbasi, A., Hamadanian, M., Salavati-Niasari, M., Mortazavi-Derazkola, S., 2017. Facile

454

size-controlled preparation of highly photocatalytically active ZnCr2O4 and ZnCr2O4/Ag

455

nanostructures for removal of organic contaminants. J. Colloid Interf. Sci. 500, 276–284.

456

Agasti, S.S., Rana, S., Park, M.-H., Kim, C.K., You, C.-C., Rotello, V.M., 2010. Nanoparticles for

457

detection and diagnosis. Adv. Drug Deliver. Rev. 62, 316–328.

458

Alexander, D.L., Tropsha, A., Winkler, D.A., 2015. Beware of R2: Simple, unambiguous

459

assessment of the prediction accuracy of QSAR and QSPR models. J. Chem. Inf. Model. 55,

460

1316–1322.

461

Ardekani, P.S., Karimi, H., Ghaedi, M., Asfaram,A., Purkait, M.K., 2017. Ultrasonic assisted

462

removal of methylene blue on ultrasonically synthesized zinc hydroxide nanoparticles on

463

activated carbon prepared from wood of cherry tree: Experimental design methodology and

464

artificial neural network. J. Mol. Liq. 229, 114–124.

465

Auffan, M., Rose, J., Bottero, J.-Y., Lowry, G.V., Jolivet, J.-P., Wiesner, M.R., 2009. Towards a

466

definition of inorganic nanoparticles from an environmental, health and safety perspective.

467

Nat. Nanotechnol. 4, 634–641.

468

Bahrani, S., Razmi, Z., Ghaedi, M., Asfaram, A., Javadian, H., 2018. Ultrasound-accelerated

469

synthesis of gold nanoparticles modified choline chloride functionalized graphene oxide as a

470

novel sensitive bioelectrochemical sensor: Optimized meloxicam detection using CCD-RSM

471

design and application for human plasma sample. Ultrason. Sonochem. 42, 776–786.

472

Bajpai, V.K., Shukla, S., Kang, S.-M., Hwang, S.K., Song, X., Huh, Y.S., Han Y.-K., 2018.

473

Developments of cyanobacteria for nano-marine drugs: relevance of nanoformulations in

24

474

cancer therapies. Mar. Drugs. 16, 179–201.

475

Basant, N., Gupta, S., 2016. Modeling uptake of nanoparticles in multiple human cells using

476

structure- activity relationships and intercellular uptake correlations. Nanotoxicology. 11, 20–

477

30..

478

Bazrafshan, A.A., Ghaedi, M., Hajati, S., Naghiha, R., Asfaram, A., 2017. Synthesis of

479

ZnO-nanorod-based materials for antibacterial, antifungal activities, DNA cleavage and

480

efficient ultrasound-assisted dyes adsorption. Ecotox. Environ. Safe. 142, 330–337.

481 482

Cheng, L.-C., Jiang, X., Wang, J., Chen, C., Liu, R.-S., 2013. Nano-bio effects: interaction of nanomaterials with cells. Nanoscale. 5, 3547–3569.

483

Choi, J.-S., Trinh, T.X., Yoon, T.-H., Kim, J., Byun, H.-G., 2019. Quasi-QSAR for predicting the

484

cell viability of human lung and skin cells exposed to different metal oxide nanomaterials.

485

Chemosphere. 217, 243–249.

486

Davar, F., Salavati-Niasari, M., Mir, N., Saberyan, K., Monemzadeh, M., Ahmadi, E., 2010.

487

Thermal decomposition route for synthesis of Mn3O4 nanoparticles in presence of a novel

488

precursor. Polyhedron. 29, 1747–1753.

489

Dil, E.A., Ghaedi, M., Asfaram, A., Mehrabi, F., Sadeghfar, F., 2019. Efficient adsorption of Azure

490

B onto CNTs/Zn:ZnO@Ni2P-NCs from aqueous solution in the presence of ultrasound wave

491

based on multivariate optimization. J. Ind. Eng. Chem. 74, 55–62.

492 493

Dong, J., Zink, J.I., 2014. Taking the temperature of the interiors of magnetically heated nanoparticles. ACS Nano. 8, 5199–5207.

494

Duttaa, T., Kim, K.-H., Deep, A., Szulejko, J.E., Vellingiri, K., Kumar, S., Kwon, E.E., Yun, S.-T.,

495

2018. Recovery of nanomaterials from battery and electronic wastes: A new paradigm of

25

496

environmental waste management. Renew. Sust. Energ. Rev. 82, 3694–3704.

497

El-Sayed, I.H., Huang, X., El-Sayed, M.A., 2006. Selective laser photo-thermal therapy of

498

epithelial carcinoma using anti-EGFR antibody conjugated gold nanoparticles. Cancer Lett.

499

239, 129–135.

500 501

Epa, V.C, Burden, F.R., Tassa, C., Weissleder, R., Shaw, S., Winkler, D.A., 2012. Modeling biological activities of nanoparticles. Nano Lett. 12, 5808–5812.

502

Eriksson, L., Jaworska, J., Worth, A.P., Cronin, M.T., McDowell R.M., Gramatica, P., 2003.

503

Methods for reliability and uncertainty assessment and for applicability evaluations of

504

classification- and regression-based QSARs. Environ. Health Persp. 111, 1361–1375.

505 506 507 508

Feng, S., Nie, L., Zou, P., Suo, J., 2015. Effects of drug and polymer molecular weight on drug release from PLGA-mPEG microspheres. J. Appl. Polym. Sci. 132, 41431–41438. Fourches, D., Pu, D., Tassa, C., Weissleder, R., Shaw, S.Y., Mumper, R.J., Tropsha, A., 2010. Quantitative nanostructure - activity relationship modeling. ACS Nano. 4, 5703–5712.

509

Gai, X., Jiang, Z., Liu, M., Li, Q., Wang, S., Li, T., Pan, W., Yang, X., 2018. Therapeutic effect of

510

a novel nano-drug delivery system on membranous glomerulonephritis rat model induced by

511

cationic bovine serum. AAPS PharmSciTech. 19, 2195–2202.

512

Geys, J., Nemmar, A., Verbeken, E., Smolders, E., Ratoi, M., Hoylaerts, M.F., Nemery, B., Hoet,

513

P.H., 2008. Acute toxicity and prothrombotic effects of quantum dots: impact of surface

514

charge. Environ. Health Persp. 116, 1607–1613.

515

Ghorbanzadeh, M., Fatemi, M.H., Karimpour, M., 2012. Modeling the cellular uptake of

516

magnetofluorescent nanoparticles in pancreatic cancer cells: A quantitative structure activity

517

relationship study. Ind. Eng. Chem. Res. 51, 10712–10718.

26

518

Golbraikh, A., Tropsha, A., 2002. Beware of q2!. J. Mol. Graph. Model. 20, 269–276.

519

Jia, G., Wang, H., Yan, L., Wang, X., Pei, R., Yan, T., Zhao, Y., Guo, X., 2005. Cytotoxicity of

520

carbon nanomaterials: single-wall nanotube, multi-wall nanotube, and fullerene. Environ. Sci.

521

Technol. 39, 1378–1383.

522 523 524

Jiang, S., Gao, Q., Chen, H., Roco, M.C., 2015. The roles of sharing, transfer, and public funding in nanotechnology knowledge-diffusion networks. J. Assoc. Inf. Sci. Tech. 66, 1017–1029. Kar, S., Gajewicz, A., Puzyn, T., Roy, K., 2014. Nano-quantitative structure-activity relationship

525

modeling

526

magnetofluorescent engineered nanoparticles in pancreatic cancer cells. Toxicol. in Vitro. 28,

527

600–606.

528 529

using

easily computable

and

interpretable

descriptors for

uptake

of

Kar, S., Roy, K., 2010. Predictive toxicology using QSAR: a perspective. J. Indian Chem. Soc. 87, 1455–1515.

530

Khojasteh, H., Safajo, H., Mortazavi-Derazkola, S., Salavati-Niasari, M., Heydaryan, K., Yazdani,

531

M., 2019. Economic procedure for facile and eco-friendly reduction of graphene oxide by

532

plant extracts; a comparison and property investigation. J. Clean. Prod. 229, 1139–1147.

533

Le, T., Epa, V.C., Burden, F.R., Winkler, D.A., 2012. Quantitative structure-property relationship

534

modeling of diverse materials properties. Chem. Rev. 112, 2889–2919.

535

Liu, M., Tang, F., Yang, Z., Xu, J., Yang, X., 2019. Recent progress on gold-nanocluster-based

536

fluorescent probe for environmental analysis and biological sensing. J. Anal. Methods Chem.

537

2019, 1–10.

538

Liu, R., Wang, Y., Ma, Y., Wu, Y., Guo, Y., Xu, L., 2016. Effects of the molecular weight of PLGA

539

on degradation and drug release in vitro from an mPEG-PLGA nanocarrier. Chem. Res. Chin.

27

540 541 542

Univ. 32, 848–853. Malik, P., Kakkar, R., 2018. Effects of increasing number of rings on the ion sensing ability of CdSe quantum dots: a theoretical study. J. Nanopart. Res. 20, 114–128.

543

Manganelli, S., Leone, C., Toropov, A.A., Toropova, A.P., Benfenati, E., 2016. QSAR model for

544

cytotoxicity of silica nanoparticles on human embryonic kidney cells. Mater. Today Proc. 3,

545

847–854.

546

Mattsson, K., Adolfsson, K., Ekvall, M.T., Borgström, M.T., Linse, S., Hansson, L.-A., Cedervall,

547

T., Prinz, C.N., 2016. Translocation of 40 nm diameter nanowires through the intestinal

548

epithelium of daphnia magna. Nanotoxicology. 10, 1160–1167.

549

Mir, N., Salavati-Niasari, M., Davar, F., 2012. Preparation of ZnO nanoflowers and Zn glycerolate

550

nanoplates using inorganic precursors via a convenient rout and application in dye sensitized

551

solar cells. Chem. Eng. J. 181–182, 779–789.

552

Mohandes, F., Salavati-Niasari, M., 2014. Freeze-drying synthesis, characterization and in vitro

553

bioactivity of chitosan/graphene oxide/hydroxyapatite nanocomposite. RSC Adv. 4, 25993–

554

26001.

555

Monge, A., Arrault, A., Marot, C., Morin-Allory, L., 2006. Managing, profiling and analyzing a

556

library of 2.6 million compounds gathered from 32 chemical providers. Mol. Divers. 10,

557

389–403.

558

Mortazavi-Derazkola, S., Salavati-Niasari, M., Khojasteh, H., Amiri, O., Ghoreishi, S.M., 2017.

559

Green synthesis of magnetic Fe3O4/SiO2/HAp nanocomposite for atenolol delivery and in

560

vivo toxicity study. J. Clean. Prod. 168, 39–50.

561

Ojha, P.K., Kar, S., Roy, K., Leszczynski, J., 2019. Toward comprehension of multiple human

28

562

cells uptake of engineered nano metal oxides: quantitative inter cell line uptake specificity

563

(QICLUS) modeling. Nanotoxicology. 13, 14–34.

564 565

Organisation for Economic Co-operation and Development (OECD), 2014. Guidance document on the validation of (quantitative) structure-activity relationship [(Q) SAR] models.

566

Oroojalian, F., Rezayan, A.H., Mehrnejad, F., Nia, A.H., Shier, W.T., Abnous, K., Ramezani, M.,

567

2017. Efficient megalin targeted delivery to renal proximal tubular cells mediated by

568

modified-polymyxin B-polyethylenimine based nano-gene-carriers. Mat. Sci. Eng. C. 79,

569

770–782.

570

Pan, Y., Li, T., Cheng, J., Telesca, D., Zink, J.I., Jiang, J., 2016. Nano-QSAR modeling for

571

predicting the cytotoxicity of metal oxide nanoparticles using novel descriptors. RSC Adv. 6,

572

25766–25775.

573 574 575 576 577 578

Petros, R.A., DeSimone, J.M., 2010. Strategies in the design of nanoparticles for therapeutic applications. Nat. Rev. Drug Discov. 9, 615–627. Prasanna, S., Doerksen, R.J., 2009. Topological polar surface area: A useful descriptor in 2D-QSAR. Curr. Med. Chem. 16, 21–41. Pridgen, E.M., Alexis, F., Farokhzad, O.C., 2015. Polymeric nanoparticle drug delivery technologies for oral delivery applications. Expert Opin. Drug Del. 12, 1459–1473.

579

Puzyn, T., Rasulev, B., Gajewicz, A., Hu, X., Dasari, T.P., Michalkova, A., Hwang, H.-M.,

580

Toropov, A., Leszczynska, D., Leszczynski, J., 2011. Using nano-QSAR to predict the

581

cytotoxicity of metal oxide nanoparticles. 6, 175–178.

582 583

Roy, P.P., Roy, K., 2008. On some aspects of variable selection for partial least squares regression models. QSAR Comb. Sci. 27, 302–314.

29

584 585 586

Rücker, C., Rücker, G., Meringer, M., 2007. Y-randomization and its variants in QSPR/QSAR. J. Chem. Inf. Model. 47, 2345–2357. Sadeghfar, F., Ghaedi, M., Asfaram, A., Jannesar, R., Javadian, H., Pezeshkpour, V., 2018.

587

Polyvinyl

alcohol/Fe3O4@carbon

nanotubes

nanocomposite:

Electrochemical-assisted

588

synthesis, physicochemical characterization, optical properties, cytotoxicity effects and

589

ultrasound-assisted treatment of aqueous based organic compound. J. Ind. Eng. Chem. 65,

590

349–362.

591

Safardoust-Hojaghan, H., Salavati-Niasari, M., 2017. Degradation of methylene blue as a pollutant

592

with N-doped graphene quantum dot/titanium dioxide nanocomposite. J. Clean. Prod. 148,

593

31–36.

594

Salavati-Niasari, M., 2004. Zeolite-encapsulation copper(II) complexes with 14-membered

595

hexaaza macrocycles: synthesis, characterization and catalytic activity. J. Mol. Catal.

596

A-Chem. 217, 87–92.

597

Salavati-Niasari, M., Davar, F., 2006. In situ one-pot template synthesis (IOPTS) and

598

characterization of copper(II) complexes of 14-membered hexaaza macrocyclic ligand

599

“3,10-dialkyl-dibenzo-1,3,5,8,10,12-hexaazacyclotetradecane’’. Inorg. Chem. Commun. 9,

600

175–179.

601 602 603 604 605

Shang, L., Nienhaus, K., Nienhaus, G.U., 2014. Engineered nanoparticles interacting with cells: size matters. J. Nanobiotechnol. 12, 5–15. Singh, K.P., Gupta, S., 2014. Nano-QSAR modeling for predicting biological activity of diverse nanomaterials. RSC Adv. 4, 13215–13230. Singh, R.P., Ramarao, P., 2013. Accumulated polymer degradation products as effector molecules

30

606

in cytotoxicity of polymeric nanoparticles. Toxicol. Sci. 136, 131–143.

607

Tian, Y., Guo, R., Jiao, Y., Sun, Y., Shen, S., Wang, Y., Lu, D., Jiang, X., Yang, W., 2016. Redox

608

stimuli-responsive hollow mesoporous silica nanocarriers for targeted drug delivery in cancer

609

therapy. Nanoscale Horiz. 1, 480–487.

610 611

Toropov, A.A., Toropova, A.P., 2015. Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes. Chemosphere. 124, 40–46.

612

Toropov, A.A., Toropova, A.P., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J., 2011.

613

SMILES-based QSAR approaches for carcinogenicity and anticancer activity: Comparison of

614

correlation weights for identical SMILES attributes. Anti-Cancer Agent. Me. 11, 974–982.

615

Toropov, A.A., Toropova, A.P., Puzyn, T., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.,

616

2013. QSAR as a random event: Modeling of nanoparticles uptake in PaCa2 cancer cells.

617

Chemosphere. 92, 31–37.

618

Toropova, A.P., Toropov, A.A., 2013. Optimal descriptor as a translator of eclectic information

619

into the prediction of membrane damage by means of various TiO2 nanoparticles.

620

Chemosphere. 93, 2650–2655.

621 622

Toropova, A.P., Toropov, A.A., 2017. Nano-QSAR in cell biology: Model of cell viability as a mathematical function of available eclectic data. J. Theor. Biol. 416, 113–118.

623

Toropova, A.P., Toropov, A.A., Rallo, R., Leszczynska, D., Leszczynski, J., 2015. Optimal

624

descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide

625

nanoparticles under different conditions. Ecotox. Environ. Safe. 112, 39–45.

626

Toropova, A.P., Toropov, A.A., Veselinović, A.M., Veselinović, J.B., Leszczynska, D., Leszczynski,

627

J., 2016. Monte carlo−based quantitative structure−activity relationship models for toxicity of

31

628

organic chemicals to daphnia magna. Environ. Toxicol. Chem. 35, 2691–2697.

629

Trinh, T.X., Choi, J.-S., Jeon, H., Byun, H.-G., Yoon, T.-H., Kim, J., 2018. Quasi-SMILES-based

630

nano-quantitative structure−activity relationship model to predict the cytotoxicity of

631

multiwalled carbon nanotubes to human lung cells. Chem. Res. Toxicol. 31, 183–190.

632

Tropsha, A., Gramatica, P., Gombar, V.K., 2003. The importance of being earnest: Validation is the

633

absolute essential for successful application and interpretation of QSPR models. QSAR

634

Comb. Sci. 22, 69–77.

635

Weissleder, R., Kelly, K., Sun, E.Y., Shtatland, T., Josephson, L., 2005. Cell-specific targeting of

636

nanoparticles by multivalent attachment of small molecules. Nat. Biotechnol. 23, 1418–1423.

637

Winkler, D.A., Burden, F.R., Yan, B., Weissleder, R., Tassa, C., Shaw, S., Epa, V.C., 2014.

638

Modelling and predicting the biological effects of nanomaterials. SAR QSAR Environ. Res.

639

25, 161–172.

640

Winkler, D.A., Mombelli, E., Pietroiusti, A., Tran, L., Worth, A., Fadeel, B., McCall, M.J., 2013.

641

Applying quantitative structure-activity relationship approaches to nanotoxicology: Current

642

status and future potential. Toxicology. 313, 15–23.

643 644

Wu, J., Li, S., Wei, H., 2018. Multifunctional nanozymes: enzyme-like catalytic activity combined with magnetism and surface plasmon resonance. Nanoscale Horiz. 3, 367–382.

645

Yousefi, M., Gholamian, F., Ghanbari, D., Salavati-Niasari, M., 2011. Polymeric nanocomposite

646

materials: Preparation and characterization of star-shaped PbS nanocrystals and their

647

influence on the thermal stability of acrylonitrile–butadiene–styrene (ABS) copolymer.

648

Polyhedron. 30, 1055–1060.

649

Zhang, M., Ma, Y., Wang, Z., Han, Z., Gao, W., Gu, Y., 2017. Optimizing molecular weight of

32

650

octyl chitosan as drug carrier for improving tumor therapeutic efficacy. Oncotarget. 8, 64237–

651

64249.

652 653

Zhang, M.-Q., Zhao, Y.-X., Liu, Q.-Y., Li, X.-N., He, S.-G., 2017. Does each atom count in the reactivity of vanadia nanoclusters? J. Am. Chem. Soc. 139, 342–347.

654

Zinatloo-Ajabshir, S., Mortazavi-Derazkola, S., Salavati-Niasari, M., 2017. Nd2O3 nanostructures:

655

Simple synthesis, characterization and its photocatalytic degradation of methylene blue. J.

656

Mol. Liq. 234, 430–436.

657

33

Highlights


The improved SMILES-based optimal descriptors are used to describe nanostructures




Two QSAR models are developed to predict cellular uptake of MNPs to two types of cells




Predominant structure features responsible for MNPs’ cellular uptake are identified




A new way for predicting the cellular uptake of new MNP to target cells is provided

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: