QSAR Model for Cytotoxicity of Silica Nanoparticles on Human Embryonic Kidney Cells1

Available online at www.sciencedirect.com

ScienceDirect Materials Today: Proceedings 3 (2016) 847 – 854

12th International Conference on Nanosciences & Nanotechnologies & 8th International Symposium on Flexible Organic Electronics

QSAR model for cytotoxicity of silica nanoparticles on human embryonic kidney cellsÕ Serena Manganellia*, Caterina Leonea, Andrey A. Toropova, Alla P. Toropovaa, Emilio Benfenatia a

IRCSS-Istituto di Ricerche Farmacologiche Mario Negri, Via Giuseppe La Masa 19, Milan 20156, Italy

Abstract A predictive model for cytotoxicity of 20 and 50 nm silica nanoparticles has been built using so-called optimal descriptors as mathematical functions of size, concentration and exposure time. These parameters have been encoded into 31 combinations ‘concentration-exposure-size’. The calculation has been carried out by means of the CORAL software (http://www.insilico.eu/coral/) using three random splits of the obtained systems into training and test sets. The statistical quality of the best model for cell viability (%) of cultured human embryonic kidney cells (HEK293) exposed to different concentrations of silica nanoparticles measured by MTT assay is satisfactory. © 2015 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of the Conference Committee Members of NANOTEXNOLOGY2015 (12th © 2016 Elsevier Ltd. All rights reserved. Selection and peer-review responsibility & of Nanotechnologies the Conference Committee Members of NANOTEXNOLOGY2015 Conferenceunder on Nanosciences & 8th International Symposium on Flexible Organic International (12th International Conference on Nanosciences & Nanotechnologies & 8th International Symposium on Flexible Organic Electronics) Electronics). Keywords: nanoQSAR; CORAL software; silica nanoparticle; cell viability; quasi-SMILES

Õ

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited. * Corresponding author. Tel.: +39 02 3901.4396; fax: +39-02-39014735. E-mail address: [email protected]

2214-7853 © 2016 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the Conference Committee Members of NANOTEXNOLOGY2015 (12th International Conference on Nanosciences & Nanotechnologies & 8th International Symposium on Flexible Organic Electronics) doi:10.1016/j.matpr.2016.02.018

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1. Introduction Given the delicate structure of the kidney filtration system, along with the major role that this organ plays in the filtration of bodily fluids and the excretion of waste products, it is possible that an inappropriate exposure to nanoparticles (NPs) may affect renal cell structure and function [1]. To investigate this possibility, the wellcharacterized human embryonic kidney (HEK293) cell line has been chosen as a test in vitro system in different studies, given the widespread use of these cells to evaluate the cytotoxic effects of chemicals [2,3]. Computational models for toxicity prediction, as well as (quantitative) structure–activity relationships ((Q)SARs), are increasingly important to support risk assessment of nanoparticles [4]. The aim of the present study is to examine the possibility to build up a model for QSAR analysis of cell viability of this cell line exposed to silica Nps, measured by MTT [3(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] assay using experimental in vitro data from the literature [5]. This model is based on the understanding that both physicochemical properties of nanoparticles and experimental conditions can be directly responsible for the cytotoxic effect. The combination of nanoparticles sizes as physicochemical property, with experimental conditions, which are specifically nanoparticles concentrations and different exposure times characterizes the ‘eclectic information’ expressed by the so-called ‘optimal descriptors’ or ‘quasi-SMILES’. The calculation was carried out with the CORAL software, which has provided satisfactory results for nanomaterials in different studies [6,7,8]. 2. Model The eclectic descriptors, also called ‘quasi-SMILES’, of nanoQSAR analysis were calculated with the CORAL software. The experimental MTT results, expressed as percentage of cell viability (%) of human embryonic kidney cells (HEK293) were taken from the literature [5]. Cells were exposed to 20 and 50 nm silica nanoparticles at 25, 50, 100 and 200 μg mL-1 for 12, 24, 36, and 48 h. Size of nanoparticles, concentrations and exposure times, defining the eclectic information, were encoded (table 1) and combined to obtain quasi-SMILES. For example, the code ‘3dy’ is a quasi-SMILES, which results from the combination of 100 μg mL-1 (3), 36 h (d) and 50 nm (y); the respective cell viability (%) is 56.070 (table 2). The 31 resulting combined systems (particle concentration-cell exposure time- particle size) were randomly split into a training set and an internal test set, respectively of 22 and 9 systems. Training and test set data are visible sets used during building up the model. An external (invisible) validation set of 9 combinations was used to check up the predictability of the model. Table 1. Codes for different sizes, concentrations and exposure times. Feature Particle size, nm

Value 20 50 25 50 100 200 0 12 24 36 48

Concentration, μg mL-1

Exposure time, hours

Code x y 1 2 3 4 a b c d e

Table 2. Some examples of quasi-SMILES and relative values of cell viability (%). Quasi-SMILES 3ay

Cell viability (%) 100.110

3dy

56.070

Serena Manganelli et al. / Materials Today: Proceedings 3 (2016) 847 – 854 2ex

62.750

3by

104.160

The encoded features with their correlation weights were used for the calculation of the so-called ‘optimal descriptors’ for nanomaterials as the following: ሺŠ”‡•Š‘Ž†ǡ ୣ୮୭ୡ୦ ሻ ൌ σ ሺ ୩ ሻDCW (Threshold, Nepoch) =∑ CW (Ck)

(1)

Where CW(Ck) are the correlation weights for codes of size, concentration and exposure time, listed in table 3 . Table 3 contains the correlation weights (CW) for codes of concentration, exposure time, and size, calculated by the Monte Carlo method; CW<1 indicate an increase of the effect, and vice versa. It is worth noticing that there is a common behavior of the codes in the three splits. Table 3. Correlation weights of codes of size (x and y), concentration (1-4), and exposure time, calculated (a-d) by the Monte Carlo method, for splits 1, 2 and 3. Split1

Split2

Split3

Ck

CW(Ck)

CW(Ck)

CW(Ck)

1

1.45441

1.30189

1.40074

2

1.3952

1.17842

1.37172

3

0.83741

0.70096

0.84833

4

0.80139

0

0.69856

a

1.49645

1.59805

1.7231

b

1.59694

1.60415

1.9011

c

0.69776

0.69898

0.498

d

0.70343

0.69814

0.49633

e

0.0

0.0

0.0

x

0.83454

0.95436

0.97374

y

1.09842

1.25187

1.14738

These are functions of the threshold (T) and number of epochs (Nepoch), which are parameters of the Monte Carlo optimization used by the CORAL software. The threshold is a tool for classifying codes as either rare (and thus likely less reliable features, probably introducing noise into the model) or not rare features, which are used by the model and labeled as active. The Nepoch is the number of cycles (sequence of modifications of correlation weight for all codes involved in model development) for the optimization [8]. The endpoint is dependent on the optimal descriptors as follows: Endpoint= C0 + C1 x DCW (T, Nepoch)

(2)

Where C0 and C1 are respectively the intercept and the slope for the training and test set. In order to obtain a model with a good predictive potential the preferable parameters of the Monte Carlo optimization, the threshold (T*) and the number of epochs (N*), which give the maximum for the correlation coefficient between experimental and calculated endpoint values for the test set, should be selected. In this case the

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preferable threshold and number of epochs, T* and N* values providing the best model statistics were N*=3 and T*=3 for split 1, N*=4 and T*=3 for split 2 and N*=4 and T*=5 for split 3. The three random splits into training and test sets for the considered systems are shown in Table 4, together with their experimental and predicted values of cell viability (%). Table 4. Quasi-SMILES and relative experimental and predicted values of cell viability (%) for splits 1, 2, and 3. Split 1

Quasi-SMILES

DCW (3, 3)

Expr

Calc

Expr-Calc

training

3ay

3.432

100.110

89.405

10.705

training

3dy

2.639

56.070

58.093

-2.023

training

2ex

2.230

62.750

41.923

20.827

training

3by

3.533

104.160

93.373

10.787

training

1cy

3.251

92.740

82.232

10.508

training

1by

4.150

101.740

117.736

-15.996

training

2cy

3.191

74.600

79.894

-5.294

training

4cx

2.334

32.980

46.028

-13.048

training

2ax

3.726

99.780

101.010

-1.230

training

2bx

3.827

98.350

104.978

-6.628

training

2dy

3.197

74.630

80.118

-5.488

training

1ax

3.785

100.040

103.348

-3.308

training

4bx

3.233

95.900

81.532

14.368

training

4by

3.497

100.900

91.951

8.949

training

1ey

2.553

71.080

54.681

16.400

training

3cx

2.370

34.670

47.450

-12.780

training

1bx

3.886

101.730

107.316

-5.586

training

3dx

2.375

34.000

47.674

-13.674

training

4cy

2.598

39.810

56.447

-16.637

training

2dx

2.933

67.150

69.698

-2.548

training

3cy

2.634

56.370

57.869

-1.499

training

3bx

3.269

96.150

82.954

13.196

test

1ex

2.289

75.010

44.261

30.749

test

3ex

1.672

26.720

19.899

6.821

test

1dy

3.256

78.400

82.456

-4.056

test

2ay

3.990

99.950

111.430

-11.480

test

3ax

3.168

100.040

78.986

21.054

test

2by

4.091

102.070

115.398

-13.328

test

1ay

4.049

99.950

113.768

-13.818

test

2cx

2.928

70.190

69.474

0.716

test

4ey

1.900

26.680

28.896

-2.216

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Split 2

Quasi-SMILES

DCW (4, 3)

Expr

Calc

Expr-Calc

training

3dy

2.651

56.070

65.729

-9.659

training

2ex

2.133

62.750

49.801

12.949

training

3by

3.557

104.160

93.578

10.582

training

1cy

3.253

92.740

84.226

8.514

training

1by

4.158

101.740

112.049

-10.309

training

2cy

3.129

74.600

80.431

-5.831

training

3ex

1.655

26.720

35.124

-8.404

training

2ax

3.731

99.780

98.922

0.858

training

2bx

3.737

98.350

99.109

-0.759

training

2dy

3.128

74.630

80.405

-5.775

training

3ax

3.253

100.040

84.245

15.795

training

1ax

3.854

100.040

102.717

-2.677

training

4bx

2.559

95.900

62.887

33.013

training

4by

2.856

100.900

72.032

28.868

training

1ey

2.554

71.080

62.741

8.339

training

3cx

2.354

34.670

56.610

-21.940

training

1bx

3.860

101.730

102.905

-1.175

training

3dx

2.353

34.000

56.584

-22.584

training

1ay

4.152

99.950

111.862

-11.912

training

4cy

1.951

39.810

44.209

-4.399

training

2dx

2.831

67.150

71.260

-4.110

training

3cy

2.652

56.370

65.755

-9.385

test

3ay

3.551

100.110

93.390

6.720

test

1ex

2.256

75.010

53.596

21.414

test

4cx

1.653

32.980

35.064

-2.084

test

1dy

3.252

78.400

84.200

-5.800

test

2ay

4.028

99.950

108.067

-8.117

test

2by

4.034

102.070

108.254

-6.184

test

2cx

2.832

70.190

71.286

-1.096

test

4ey

1.252

26.680

22.723

3.957

test

3bx

3.259

96.150

84.433

11.717

Split 3

Quasi-SMILES

DCW (4, 5)

Expr

Calc

Expr-Calc

training

3dy

2.492

56.070

54.510

1.560

training

2ex

2.345

62.750

50.115

12.635

training

3by

3.897

104.160

96.635

7.525

training

2ax

4.069

99.780

101.785

-2.005

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Serena Manganelli et al. / Materials Today: Proceedings 3 (2016) 847 – 854

training

2bx

4.247

98.350

107.123

-8.773

training

2dy

3.015

74.630

70.205

4.425

training

3ax

3.545

100.040

86.090

13.950

training

1ax

4.098

100.040

102.655

-2.615

training

4bx

3.573

95.900

86.937

8.963

training

4by

3.747

100.900

92.144

8.756

training

1ey

2.548

71.080

56.192

14.889

training

3cx

2.320

34.670

49.353

-14.683

training

1bx

4.276

101.730

107.993

-6.263

training

2by

4.420

102.070

112.330

-10.260

training

3dx

2.318

34.000

49.303

-15.303

training

1ay

4.271

99.950

107.862

-7.912

training

2cx

2.843

70.190

65.048

5.142

training

4ey

1.846

26.680

35.136

-8.456

training

4cy

2.344

39.810

50.069

-10.259

training

2dx

2.842

67.150

64.998

2.152

training

3cy

2.494

56.370

54.560

1.810

training

3bx

3.723

96.150

91.428

4.722

test

3ay

3.719

100.110

91.297

8.813

test

1ex

2.374

75.010

50.985

24.025

test

1cy

3.046

92.740

71.125

21.615

test

1by

4.449

101.740

113.200

-11.460

test

2cy

3.017

74.600

70.255

4.345

test

3ex

1.822

26.720

34.420

-7.700

test

4cx

2.170

32.980

44.862

-11.882

test

1dy

3.044

78.400

71.075

7.325

test

2ay

4.242

99.950

106.992

-7.042

Cell viability (%) as function of optimal descriptors, used for building up the model, has been calculated as follows; we also indicated the statistical parameters obtained for each split. Split1 Training set: Cell viability (%) = -46.1177394 (± 4.0947512) + 39.4848711 (± 1.2424487) * DCW(3,3) n=22;

2

R = 0.8019

2

Q =0.7495

s=11.4 %

MAE= 9.61

(3)

F= 81

Test set: Cell viability (%) = -14.74777 (± 4.0947512) + 29.68753 (± 1.2424487) * DCW(3,3) n=9;

2

R =0.8250

2

Q =0.7189

s=15.6 %

MAE= 11.6

F=33

(4) ଶ തതതത ܴ ௠ = 0.6652

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Serena Manganelli et al. / Materials Today: Proceedings 3 (2016) 847 – 854

Split2 Training set: Cell viability (%) = -15.7569693 (± 2.9599261) + 30.7381576 (± 0.8499385) * DCW(4,3) 2

n=22;

2

R =0.7096

Q =0.6584

s=14.1 %

MAE= 10.8

(5)

F= 49

Test set: Cell viability (%) = -3.47906 (± 2.9599261) + 27.29326 (± 0.8499385) * DCW(4,3) 2

n=9;

2

R =0.8996

Q =0.8430

s=10 %

MAE= 7.45

(6) ଶ തതതത ܴ ௠ = 0.8184

F=63

Split3 Training set: Cell viability (%) = -20.2188822 (± 2.1712065) + 29.9870360 (± 0.6140297) * DCW (4,5) R2= 0.8778

n=22;

Q2= 0.8503

s= 9.23 %

MAE=7.87

(7)

F= 144

Test set: Cell viability (%) = -9.30096 (± 2.1712065) + 27.46875 (± 0.6140297) * DCW (4,5) n=9;

2

2

R = 0.7734

Q = 0.6031

s= 14.0%

MAE= 11.6

(8)

F= 24

ଶ തതതത ܴ ௠ =0.6854

In Eqs. (3)–(8) n is the number of nanoparticles system in each set; R2 is the square correlation coefficient, Q is leave-one-out cross-validated correlation coefficient, s is standard error of estimation; MAE is mean absolute error; ଶ is a metric of predictability; according to the rules of QSAR/QSPR approaches a F is the variance ratio [9] ܴ௠ ଶ തതതത developed model has predictability if ܴ ௠ parameter is > 0.5 [10,11]. One more set of nine quasi-SMILES was used for the external validation, providing predicted values, as listed in table 5 for each model built on the three random splits: Table 5. Codes of systems and relative experimental and predicted values of cell viability (%) for external validation set. QuasiSMILES

Expr

DCW(3, 3)

Calc1

DCW(4, 3)

Calc2

DCW(4, 5)

Calc3

3ey

53.0100

1.93583

30.3181

1.95282

44.2692

1.99571

39.6264

4dx

27.0600

2.33936

46.2517

1.65250

35.0378

2.16864

44.8121

4ax

100.0400

3.13238

77.5640

2.55241

62.6994

3.39541

81.5993

4dy

36.0800

2.60324

56.6710

1.95000

44.1826

2.34227

50.0189

1dx

78.3000

2.99238

72.0361

2.95439

75.0555

2.87081

65.8681

1cx

89.6400

2.98671

71.8123

2.95523

75.0814

2.87247

65.9181

2ey

70.7600

2.49362

52.3425

2.43029

58.9456

2.51910

55.3215

4ex

20.3000

1.63593

18.4767

0.95436

13.5783

1.67231

29.9286

4ay

99.8600

3.39626

87.9833

2.84991

71.8442

3.56905

86.8062

The average തതതത ଶ୫ for the external validation set tested for each random split were respectively equal to 0.5945,

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Serena Manganelli et al. / Materials Today: Proceedings 3 (2016) 847 – 854

0.5945 and 0.5339.

3. Conclusions This is the first trial to build up a model for predicting cell viability (%) of human embryonic kidney cells exposed to different concentrations of nanoparticles, using quasi-SMILES. Some modifications in the optimization can be provided in the future to improve statistical results and thus increase the robustness of the model, based on the flexibility of the approach. Moreover, any intrinsic property, as well as external condition, can be easily introduced, after being encoded into a ‘quasi-SMILES’. The present paper shows the easiness to introduce descriptors of heterogeneous nature into the model, which is a great advantage in the case of nanomaterials, due to the multiple features used to characterize them and to the lack of standardization on the experimental protocols. The reasoning about the possible involvement of a feature within the endpoint to be modeled is also facilitated, since each attribute assumes an explicit meaning. This allows to put in evidence relevant factors affecting the toxicity, and also other properties of interest of the nanomaterials. Based on the present statistics, this model confirms that CORAL can provide satisfactory models for nanomaterials. Acknowledgments We thank the EC Project PreNanoTox (Project Reference 309666) and NanoPUZZLES (Project Reference 309837). References [1] V. Selvaraj, S. Bodapati, E. Murray, K. M. Rice, N. Winston, T. Shokuhfar, Y. Zhao, E. Blough, Int J Nanomedicine 9 (2014) 1379–1391. [2] A.M. Florea, F. Splettstoesser, D. Büsselberg, Toxicology and Applied Pharmacology 220 (2007) 292–301. [3] L.L., Ji, Y. Chen, Z.T. Wang, Experimental and Toxicologic Pathology 60 (2008) 87–93. [4] A. N. Richarz, J. C. Madden, R. L. Marchese Robinson, Ł. Lubiński, E. Mokshina, P. Urbaszek, V. E. Kuz‫׳‬min, T. Puzyn, M. T.D. Cronin Perspectives in Science 3 (2015) 27–29. [5] F. Wang, F. Gao, M. Lan, H. Yuan, Y. Huang, J. Liu., Toxicol. In Vitro 23 (2009), 808–815. [6] Toropova A.P., Toropov, A.A., Chemosphere 124 (2015) 40-46. [7] A.A. Toropov, A.P. Toropov, E. Benfenati, G. Gini, T. Puzyn, D. Leszczynska, J. Leszczynski Chemosphere 89 (2012) 1098-1102. [8] A.P. Toropova, A.A. Toropov, E. Benfenati, R. Korenstein, J. Nanopart. Res. 16 (2014) 2282. [9] K. Roy, P. P. Roy, Chem. Biol. Drug Des. 72 (2008) 370–382. [10] K. Roy, I. Mitra, S. Kar, P.K. Ojha, R.N. Das, H., Kabir, J. Chem. Inf. Model. 52 (2012) 396–408. [11] P.K. Ojha, I. Mitra, R.N. Das, K. Roy Chemometr. Intell. Lab. Syst. 107 (2011), 194–205.