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Modeling to predict the cytotoxicity of SiO2 and TiO2 nanoparticles

Yıl 2019, Cilt: 23 Sayı: 2, 267 - 274, 27.06.2025

Samira Jafari Ali Shayanfar

Öz

The objective of the current study was to design a suitable model to predict the cytotoxicity induced by SiO2 and TiO2 nanoparticles in different conditions using computational models. To achieve this, we employed various statistical approaches such as linear regression, as well as artificial neural networks and support vector machine (nonlinear models). The effective input parameters of the SiO2 nanoparticles were particle size, particle concentration, and cell exposure time. In the case of the TiO2 nanoparticles, the particle size and concentration served as input variables. Cell viability was considered the output response for both nanoparticles. The modeling was performed using both linear and non-linear methods. In addition, an external validation analysis was conducted to evaluate the predictability of the models by splitting the data into training and test data. The best models to predict cell viability were the models developed by artificial neural network. The results of this investigation indicate that non-linear models could be superior to linear models in predicting cell viability for SiO2 and TiO2 nanoparticles.

Anahtar Kelimeler

Artificial neural network cytotoxicity modeling nanoparticles

Kaynakça

  • [1] Barar J. Bioimpacts of nanoparticle size: why it matters? BioImpacts. 2015; 5(3): 113-115. [CrossRef]
  • [2] Fard JK, Jafari S, Eghbal MA. A review of molecular mechanisms involved in toxicity of nanoparticles. Adv Pharm Bull. 2015; 5(4): 447-454. [CrossRef]
  • [3] Barratt MD, Rodford RA. The computational prediction of toxicity. Curr Opin Chem Biol. 2001; 5(4): 383-388. [CrossRef]
  • [4] Muster W, Breidenbach A, Fischer H, Kirchner S, Müller L, Pähler A. Computational toxicology in drug development. Drug Discov Today. 2008; 13(7): 303-310. [CrossRef]
  • [5] Puzyn T, Rasulev B, Gajewicz A, Hu X, Dasari TP, Michalkova A, Hwang H-M, Toropov A, Leszczynska D, Leszczynski J. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. Nat Nanotechnol. 2011; 6(3): 175-178. [CrossRef]
  • [6] Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz'Min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A. QSAR modeling: where have you been? Where are you going to? J Med Chem. 2014; 57(12): 4977-5010. [CrossRef]
  • [7] Dearden JC. Whither QSAR? Pharm Sci. 2017; 23(2): 82-83. [CrossRef]
  • [8] Le T, Epa VC, Burden FR, Winkler DA. Quantitative structure–property relationship modeling of diverse materials properties. Chem Rev. 2012; 112(5): 2889-2919. [CrossRef]
  • [9] Rowe RC, Roberts RJ. Artificial intelligence in pharmaceutical product formulation: knowledge-based and expert systems. Pharm Sci Technol Today. 1998; 1(4): 153-159. [CrossRef]
  • [10] Shahsavari S, Bagheri G, Mahjub R, Bagheri R, Radmehr M, Rafiee-Tehrani M, Dorkoosh FA. Application of artificial neural networks for optimization of preparation of insulin nanoparticles composed of quaternized aromatic derivatives of chitosan. Drug Res. 2014; 64(3): 151-158. [CrossRef]
  • [11] Byvatov E, Fechner U, Sadowski J, Schneider G. Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J Chem Inf Comput Sci. 2003; 43(6): 1882-1889. [CrossRef]
  • [12] Norinder U. Support vector machine models in drug design: Applications to drug transport processes and QSAR using simplex optimisations and variable selection. Neurocomputing. 2003; 55(1-2): 337-346. [CrossRef]
  • [13] Tatardar S, Jouyban A, Soltani S, Zakariazadeh M. QSAR analysis of cyclooxygenase inhibitors selectivity index (COX1/COX2): Application of SVM-RBF and MLR methods. Pharm Sci. 2015; 21(2): 86-93. [CrossRef]
  • [14] Richarz A-N, Madden JC, Robinson RLM, Lubiński Ł, Mokshina E, Urbaszek P, Kuz VE, Puzyn T, Cronin MT. Development of computational models for the prediction of the toxicity of nanomaterials. Perspect Sci. 2015; 3(1): 27-29. [CrossRef]
  • [15] Winkler DA, Mombelli E, Pietroiusti A, Tran L, Worth A, Fadeel B, McCall MJ. Applying quantitative structure–activity relationship approaches to nanotoxicology: current status and future potential. Toxicol. 2013; 313(1): 15-23. [CrossRef]
  • [16] Manganelli S, Leone C, Toropov AA, Toropova AP, Benfenati E. QSAR model for predicting cell viability of human embryonic kidney cells exposed to SiO2 nanoparticles. Chemosphere. 2016; 144: 995-1001. [CrossRef]
  • [17] Chen G, Peijnenburg W, Xiao Y, Vijver MG. Current knowledge on the use of computational toxicology in hazard assessment of metallic engineered nanomaterials. Int J Mol Sci. 2017; 18(7): pii: E1504. [CrossRef]
  • [18] Kleandrova VV, Luan F, González-Díaz H, Ruso JM, Melo A, Speck-Planche A, Cordeiro MND. Computational ecotoxicology: Simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions. Environ Int. 2014; 73: 288-294. [CrossRef]
  • [19] Kleandrova VV, Luan F, González-Díaz H, Ruso JM, Speck-Planche A, Cordeiro MND. Computational tool for risk assessment of nanomaterials: novel QSTR-perturbation model for simultaneous prediction of ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under multiple experimental conditions. Environ Sci Technol. 2014; 48(24): 14686-14694. [CrossRef]
  • [20] Wang F, Gao F, Lan M, Yuan H, Huang Y, Liu J. Oxidative stress contributes to silica nanoparticle-induced cytotoxicity in human embryonic kidney cells. Toxicol In Vitro. 2009; 23(5): 808-815. [CrossRef]
  • [21] Zhang J, Song W, Guo J, Zhang J, Sun Z, Li L, Ding F, Gao M. Cytotoxicity of different sized TiO2 nanoparticles in mouse macrophages. Toxicol Ind Health. 2013; 26(6): 523-533. [CrossRef]
  • [22] Baharifar H, Amani A. Cytotoxicity of chitosan/streptokinase nanoparticles as a function of size: An artificial neural networks study. Nanomed Nanotechnol Biol Med. 2016; 12(1): 171-180. [CrossRef]
  • [23] Dearden J, Cronin M, Kaiser K. How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR). SAR QSAR Environ Res. 2009; 20(3-4): 241-266. [CrossRef]
  • [24] Fjodorova N, Novic M, Gajewicz A, Rasulev B. The way to cover prediction for cytotoxicity for all existing nanosized metal oxides by using neural network method. Nanotoxicology. 2017; 11(4): 475-483. [CrossRef]
  • [25] Baharifar H, Amani A. Size, loading efficiency, and cytotoxicity of albumin-loaded chitosan nanoparticles: An artificial neural networks study. J Pharm Sci. 2017; 106(1): 411-417. [CrossRef]
  • [26] Gramatica P. Principles of QSAR models validation: internal and external. QSAR Comb Sci. 2007; 26(5): 694-701. [CrossRef]
  • [27] Gramatica P, Giani E, Papa E. Statistical external validation and consensus modeling: A QSPR case study for Koc prediction. J Mol Graph Model. 2007; 25(6): 755-767. [CrossRef]
  • [28] Shayanfar A, Shayanfar S. Is regression through origin useful in external validation of QSAR models? Eur J Pharm Sci. 2014; 59(1): 31-35. [CrossRef]
  • [29] Toropova AP, Toropov AA, Rallo R, Leszczynska D, Leszczynski J. Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions. Ecotoxicol Environ Saf. 2015; 112: 39-45. [CrossRef]
  • [30] Toropov AA, Toropova AP, Benfenati E, Gini G, Puzyn T, Leszczynska D, Leszczynski J. Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli. Chemosphere. 2012; 89(9): 1098-1102. [CrossRef]

Yıl 2019, Cilt: 23 Sayı: 2, 267 - 274, 27.06.2025

Samira Jafari Ali Shayanfar

Öz

Kaynakça

  • [1] Barar J. Bioimpacts of nanoparticle size: why it matters? BioImpacts. 2015; 5(3): 113-115. [CrossRef]
  • [2] Fard JK, Jafari S, Eghbal MA. A review of molecular mechanisms involved in toxicity of nanoparticles. Adv Pharm Bull. 2015; 5(4): 447-454. [CrossRef]
  • [3] Barratt MD, Rodford RA. The computational prediction of toxicity. Curr Opin Chem Biol. 2001; 5(4): 383-388. [CrossRef]
  • [4] Muster W, Breidenbach A, Fischer H, Kirchner S, Müller L, Pähler A. Computational toxicology in drug development. Drug Discov Today. 2008; 13(7): 303-310. [CrossRef]
  • [5] Puzyn T, Rasulev B, Gajewicz A, Hu X, Dasari TP, Michalkova A, Hwang H-M, Toropov A, Leszczynska D, Leszczynski J. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. Nat Nanotechnol. 2011; 6(3): 175-178. [CrossRef]
  • [6] Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz'Min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A. QSAR modeling: where have you been? Where are you going to? J Med Chem. 2014; 57(12): 4977-5010. [CrossRef]
  • [7] Dearden JC. Whither QSAR? Pharm Sci. 2017; 23(2): 82-83. [CrossRef]
  • [8] Le T, Epa VC, Burden FR, Winkler DA. Quantitative structure–property relationship modeling of diverse materials properties. Chem Rev. 2012; 112(5): 2889-2919. [CrossRef]
  • [9] Rowe RC, Roberts RJ. Artificial intelligence in pharmaceutical product formulation: knowledge-based and expert systems. Pharm Sci Technol Today. 1998; 1(4): 153-159. [CrossRef]
  • [10] Shahsavari S, Bagheri G, Mahjub R, Bagheri R, Radmehr M, Rafiee-Tehrani M, Dorkoosh FA. Application of artificial neural networks for optimization of preparation of insulin nanoparticles composed of quaternized aromatic derivatives of chitosan. Drug Res. 2014; 64(3): 151-158. [CrossRef]
  • [11] Byvatov E, Fechner U, Sadowski J, Schneider G. Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J Chem Inf Comput Sci. 2003; 43(6): 1882-1889. [CrossRef]
  • [12] Norinder U. Support vector machine models in drug design: Applications to drug transport processes and QSAR using simplex optimisations and variable selection. Neurocomputing. 2003; 55(1-2): 337-346. [CrossRef]
  • [13] Tatardar S, Jouyban A, Soltani S, Zakariazadeh M. QSAR analysis of cyclooxygenase inhibitors selectivity index (COX1/COX2): Application of SVM-RBF and MLR methods. Pharm Sci. 2015; 21(2): 86-93. [CrossRef]
  • [14] Richarz A-N, Madden JC, Robinson RLM, Lubiński Ł, Mokshina E, Urbaszek P, Kuz VE, Puzyn T, Cronin MT. Development of computational models for the prediction of the toxicity of nanomaterials. Perspect Sci. 2015; 3(1): 27-29. [CrossRef]
  • [15] Winkler DA, Mombelli E, Pietroiusti A, Tran L, Worth A, Fadeel B, McCall MJ. Applying quantitative structure–activity relationship approaches to nanotoxicology: current status and future potential. Toxicol. 2013; 313(1): 15-23. [CrossRef]
  • [16] Manganelli S, Leone C, Toropov AA, Toropova AP, Benfenati E. QSAR model for predicting cell viability of human embryonic kidney cells exposed to SiO2 nanoparticles. Chemosphere. 2016; 144: 995-1001. [CrossRef]
  • [17] Chen G, Peijnenburg W, Xiao Y, Vijver MG. Current knowledge on the use of computational toxicology in hazard assessment of metallic engineered nanomaterials. Int J Mol Sci. 2017; 18(7): pii: E1504. [CrossRef]
  • [18] Kleandrova VV, Luan F, González-Díaz H, Ruso JM, Melo A, Speck-Planche A, Cordeiro MND. Computational ecotoxicology: Simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions. Environ Int. 2014; 73: 288-294. [CrossRef]
  • [19] Kleandrova VV, Luan F, González-Díaz H, Ruso JM, Speck-Planche A, Cordeiro MND. Computational tool for risk assessment of nanomaterials: novel QSTR-perturbation model for simultaneous prediction of ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under multiple experimental conditions. Environ Sci Technol. 2014; 48(24): 14686-14694. [CrossRef]
  • [20] Wang F, Gao F, Lan M, Yuan H, Huang Y, Liu J. Oxidative stress contributes to silica nanoparticle-induced cytotoxicity in human embryonic kidney cells. Toxicol In Vitro. 2009; 23(5): 808-815. [CrossRef]
  • [21] Zhang J, Song W, Guo J, Zhang J, Sun Z, Li L, Ding F, Gao M. Cytotoxicity of different sized TiO2 nanoparticles in mouse macrophages. Toxicol Ind Health. 2013; 26(6): 523-533. [CrossRef]
  • [22] Baharifar H, Amani A. Cytotoxicity of chitosan/streptokinase nanoparticles as a function of size: An artificial neural networks study. Nanomed Nanotechnol Biol Med. 2016; 12(1): 171-180. [CrossRef]
  • [23] Dearden J, Cronin M, Kaiser K. How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR). SAR QSAR Environ Res. 2009; 20(3-4): 241-266. [CrossRef]
  • [24] Fjodorova N, Novic M, Gajewicz A, Rasulev B. The way to cover prediction for cytotoxicity for all existing nanosized metal oxides by using neural network method. Nanotoxicology. 2017; 11(4): 475-483. [CrossRef]
  • [25] Baharifar H, Amani A. Size, loading efficiency, and cytotoxicity of albumin-loaded chitosan nanoparticles: An artificial neural networks study. J Pharm Sci. 2017; 106(1): 411-417. [CrossRef]
  • [26] Gramatica P. Principles of QSAR models validation: internal and external. QSAR Comb Sci. 2007; 26(5): 694-701. [CrossRef]
  • [27] Gramatica P, Giani E, Papa E. Statistical external validation and consensus modeling: A QSPR case study for Koc prediction. J Mol Graph Model. 2007; 25(6): 755-767. [CrossRef]
  • [28] Shayanfar A, Shayanfar S. Is regression through origin useful in external validation of QSAR models? Eur J Pharm Sci. 2014; 59(1): 31-35. [CrossRef]
  • [29] Toropova AP, Toropov AA, Rallo R, Leszczynska D, Leszczynski J. Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions. Ecotoxicol Environ Saf. 2015; 112: 39-45. [CrossRef]
  • [30] Toropov AA, Toropova AP, Benfenati E, Gini G, Puzyn T, Leszczynska D, Leszczynski J. Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli. Chemosphere. 2012; 89(9): 1098-1102. [CrossRef]
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Eczacılık ve İlaç Bilimleri (Diğer)
Bölüm Articles
Yazarlar

Samira Jafari

Ali Shayanfar

Yayımlanma Tarihi 27 Haziran 2025
Yayımlandığı Sayı Yıl 2019 Cilt: 23 Sayı: 2

Kaynak Göster

APA Jafari, S., & Shayanfar, A. (2025). Modeling to predict the cytotoxicity of SiO2 and TiO2 nanoparticles. Journal of Research in Pharmacy, 23(2), 267-274.
AMA Jafari S, Shayanfar A. Modeling to predict the cytotoxicity of SiO2 and TiO2 nanoparticles. J. Res. Pharm. Haziran 2025;23(2):267-274.
Chicago Jafari, Samira, ve Ali Shayanfar. “Modeling to Predict the Cytotoxicity of SiO2 and TiO2 Nanoparticles”. Journal of Research in Pharmacy 23, sy. 2 (Haziran 2025): 267-74.
EndNote Jafari S, Shayanfar A (01 Haziran 2025) Modeling to predict the cytotoxicity of SiO2 and TiO2 nanoparticles. Journal of Research in Pharmacy 23 2 267–274.
IEEE S. Jafari ve A. Shayanfar, “Modeling to predict the cytotoxicity of SiO2 and TiO2 nanoparticles”, J. Res. Pharm., c. 23, sy. 2, ss. 267–274, 2025.
ISNAD Jafari, Samira - Shayanfar, Ali. “Modeling to Predict the Cytotoxicity of SiO2 and TiO2 Nanoparticles”. Journal of Research in Pharmacy 23/2 (Haziran 2025), 267-274.
JAMA Jafari S, Shayanfar A. Modeling to predict the cytotoxicity of SiO2 and TiO2 nanoparticles. J. Res. Pharm. 2025;23:267–274.
MLA Jafari, Samira ve Ali Shayanfar. “Modeling to Predict the Cytotoxicity of SiO2 and TiO2 Nanoparticles”. Journal of Research in Pharmacy, c. 23, sy. 2, 2025, ss. 267-74.
Vancouver Jafari S, Shayanfar A. Modeling to predict the cytotoxicity of SiO2 and TiO2 nanoparticles. J. Res. Pharm. 2025;23(2):267-74.