Abstract
The production of nanomaterials for biomedical research and applications increases exponentially. Interestingly, there is an increase in the use of nanoparticles in pharmaceutical sciences for diagnosis and treatment purposes, and therefore, nano-toxicity becomes one of the major role aspects in the future of pharmaceutical nanotechnology. This study focused on discerning and identifying the main variables that govern a group of metal oxide nanoparticles’ toxicity in human keratinous cells (HaCaT), combining computational simulation and semiempirical calculations with the available experimental data allowed revealing and explaining the nanoparticle toxicity for the corresponding cell line, through the development and validation of an interpretive nano-QSAR model with acceptable statistical quality by applying a multivariate linear regression with a coupled genetic algorithm. This function included only two descriptors, orthogonal to each other: the enthalpy of a standard formation of metal oxide nanocluster\( {\Delta \mathrm{H}}_{\mathrm{f}}^{\mathrm{c}} \) and the absolute value of Fermi energy from the cluster\( {\upepsilon}_{\mathrm{Fermi}}^{\mathrm{c}} \).The values of statistical indices obtained for this model showed its quality and robustness, for example, R2 = 0.90; \( {\mathrm{Q}}_{\mathrm{cv}}^2 \) = 0.86 and F = 37.15. This study demonstrated the need to use quantum-mechanical descriptors to explain the toxicity of metal oxide nanoparticles, capable of characterizing the electronic state of nanostructures. Regularization methods based on LASSO and Ridge regression have been employed in the model selection and validation. Furthermore, we propose a mechanism for toxicological effects applicable to a relevant group of nanoparticles, as well as their generalization to other toxicity studies not available in the literature, with potential nanopharmaceutical applications.
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References
Beyer M, Williams ER, Bondybey VE (1999) Unimolecular reactions of dihydrated alkaline earth metal dications M2+(H2O)2, M = Be, Mg, Ca, Sr, and Ba: salt-bridge mechanism in the proton-transfer reaction M2+(H2O)2 → MOH+ + H3O+. J Am Chem Soc 121(7):1565–1573. https://doi.org/10.1021/ja982653+
De Oliveira DB, Gaudio AC (2000) BuildQSAR: a new computer program for QSAR analysis. Quant Struct-Act Relat 19(6):599–601
Hourahine B, Aradi B, Blum V, Bonafé F, Buccheri A, Camacho C, Cevallos C, Deshaye MY, Dumitric˘a T, Dominguez A, Ehlert S, Elstner M, van der Heide T, Hermann J, Irle S, Kranz JJ, Köhler C, Kowalczyk T, Kubaˇr T, Lee IS, Lutsker V, Maurer RJ, Min SK, Mitchell I, Negre C, Niehaus TA, Niklasson AMN, Page AJ, Pecchia A, Penazzi G, Persson MP, Rˇ ezácˇ J, Sánchez CG, Sternberg M, Stöhr M, Stuckenberg F, Tkatchenko A, Yu VW-z, Frauenheim T, (2016) DFTB+, a software package for efficient approximate density functional theory based atomistic simulations. J Chem Phys 152(12)
Enoch SJ (2010) The use of quantum mechanics derived descriptors in computational toxicology. In: Puzyn T, Leszczynski J, Cronin M (eds) Recent Advances in QSAR Studies. Challenges and Advances in Computational Chemistry and Physics, vol 8. Springer, Dordrecht, New York, pp 13–28. https://doi.org/10.1007/978-1-4020-9783-6_2
Etienne T, Michaux C, Monari A, Assfeld X, Perpète EA (2014) Theoretical computation of Betain B30 solvatochromism using a Polarizable Continuum Model. Dyes Pigments 100:24–31. https://doi.org/10.1016/j.dyepig.2013.07.017
Fan G, Zhu S, Ni K, Xu H (2017) Theoretical study of the adsorption of aromatic amino acids on a single-wall boron nitride nanotube with empirical dispersion correction. Can J Chem 95(6):710–716
Foresman JB, Keith TA, Wiberg KB, Snoonian J, Frisch MJ (1996) Solvent effects 5. The influence of cavity shape, truncation of electrostatics, and electron correlation on ab initio reaction field calculations. J Phys Chem 100:16098–16104. https://doi.org/10.1021/jp960488j
Gomaa EA, Tahoon MA, Negm A (2017) Aqueous micro-solvation of Li+ ions: thermodynamics and energetic studies of Li+-(H2O)n (n=1–6) structures. J Mol Liq 241:595–602. https://doi.org/10.1016/j.molliq.2017.06.061
Gramatica P (2010) Chemometric methods and theoretical molecular descriptors in predictive QSAR modeling of the environmental behavior of organic pollutants. In: Puzyn T, Leszczynski J, Cronin M (eds) Recent Advances in QSAR Studies. Challenges and Advances in Computational Chemistry and Physics, vol 8. Springer, Dordrecht, New York, pp 327–366. https://doi.org/10.1007/978-1-4020-9783-6_12
Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR (2012) Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminformatics 4(1):17
Hourahine B, Aradi B, Blum V, Bonafe F, Buccheri A, Camacho C, Cevallos C, Deshaye M, Dumitrică T, Dominguez A (2020) DFTB+, a software package for efficient approximate density functional theory based atomistic simulations. J Chem Phys 152(12):124101
Kurban H, Dalkilic M, Temiz S, Kurban M (2020) Tailoring the structural properties and electronic structure of anatase, brookite and rutile phase TiO2 nanoparticles: DFTB calculations. Comput Mater Sci 183:109843. https://doi.org/10.1016/j.commatsci.2020.109843
Lei XL, Pan BC (2012) The geometries and proton transfer of hydrated divalent lead ion clusters [Pb(H2O)n]2+(n = 1–17). J Theor Comput Chem 11(05):1149–1164. https://doi.org/10.1142/s0219633612500769
Maheshwary S, Patel N, Sathyamurthy N, Kulkarni AD, Gadre SR (2001) Structure and stability of water clusters (H2O)n, n = 8−20: an ab initio investigation. J Phys Chem A 105(46):10525–10537. https://doi.org/10.1021/jp013141b
Mananghaya M, Rodulfo E, Santos GN, Villagracia AR (2012) Theoretical investigation on the solubilization in water of functionalized single-wall carbon nanotubes. J Nanotechnol. https://doi.org/10.1155/2012/780815
Mu Y, Wu F, Zhao Q, Ji R, Qie Y, Zhou Y, Hu Y, Pang C, Hristozov D, Giesy JP (2016) Predicting toxic potencies of metal oxide nanoparticles by means of nano-QSARs. Nanotoxicology 10(9):1207–1214
Pérez C, Muckle MT, Zaleski DP, Seifert NA, Temelso B, Shields GC, Kisiel Z, Pate BH (2012) Structures of cage, prism, and book isomers of water hexamer from broadband rotational spectroscopy. Science 336(6083):897–901. https://doi.org/10.1126/science.1220574
Portier J, Hilal H, Saadeddin I, Hwang S, Subramanian M, Campet G (2004) Thermodynamic correlations and band gap calculations in metal oxides. Prog Solid State Chem 32(3-4):207–217
Puzyn T, Rasulev B, Gajewicz A, Hu X, Dasari TP, Michalkova A, Hwang H-M, Toropov A, Leszczynska D, Leszczynski J (2011) Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. Nat Nanotechnol 6(3):175. https://doi.org/10.1038/NNANO.2011.10
QSAR Toolbox (n.d.) https://qsartoolbox.org. Accessed 1 Dec 2019
R Development Core Team (2008) R: A Language and Environment for Statistical Computing 3.6.1 edn., Vienna, Austria. https://www.r-project.org. Accessed 29 Dec 2019
Saiyed M, Patel R, Patel S (2011) Toxicology perspective of nanopharmaceuticals: a critical review. Int J Pharm Sci Nanotechnol 4(1):1287–1295
Schmidt MW, Baldridge KK, Boatz JA, Elbert ST, Gordon MS, Jensen JH, Koseki S, Matsunaga N, Nguyen KA, Su S, Windus TL, Dupuis M, Montgomery JA Jr (1993) General atomic and molecular electronic structure system. J Comput Chem 14(11):1347–1363. https://doi.org/10.1002/jcc.540141112
Sizochenko N, Rasulev B, Gajewicz A, Kuz'min V, Puzyn T, Leszczynski J (2014) From basic physics to mechanisms of toxicity: The “liquid drop” approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles. Nanoscale 6(22):13986–13993
Tibshirani R (1996) Regression Shrinkage and Selection Via the Lasso. J R Stat Soc Ser B Methodol 58(1):267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
Uudsemaa M, Tamm T (2001) Calculations of hydrated titanium ion complexes: structure and influence of the first two coordination spheres. Chem Phys Lett 342(5):667–672. https://doi.org/10.1016/S0009-2614(01)00617-0
Wander MCF, Clark AE (2008) Hydration properties of aqueous Pb(II) ion. Inorg Chem 47(18):8233–8241. https://doi.org/10.1021/ic800750g
Weigend F, Ahlrichs R (2005) Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy. Phys Chem Chem Phys 7(18):3297–3305. https://doi.org/10.1039/B508541A
Wendumu TB, Seifert G, Lorenz T, Joswig J-O, Enyashin A (2014) Optical properties of triangular molybdenum disulfide nanoflakes. J Phys Chem Lett 5(21):3636–3640
Williams T, Kelley C (n.d.) GNUplot: An Interactive Plotting Program. 5.2 edn., http://www.gnuplot.info. Accessed 21 Jan 2020
Winkler DA, Mombelli E, Pietroiusti A, Tran L, Worth A, Fadeel B, McCall MJ (2013) Applying quantitative structure–activity relationship approaches to nanotoxicology: current status and future potential. Toxicology 313(1):15–23
Yang N, Yang D, Chen L, Liu D, Cai M, Fan X (2017) A first-principle theoretical study of mechanical and electronic properties in graphene single-walled carbon nanotube junctions. Materials 10(11):1300
Zhang H, Ji Z, Xia T, Meng H, Low-Kam C, Liu R, Pokhrel S, Lin S, Wang X, Liao Y-P (2012) Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammation. ACS Nano 6(5):4349–4368
Zheng Z, Brédas J-L, Coropceanu V (2016) Description of the Charge Transfer States at the Pentacene/C60 Interface: combining Range-Separated Hybrid Functionals with the Polarizable Continuum Model. J Phys Chem Lett 7(13):2616–2621. https://doi.org/10.1021/acs.jpclett.6b00911
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Summary of geometries implemented in Avogadro software v1.2.0 for the construction of a metal oxide cluster. (PDF 218 kb)
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Summary of the calculated thermodynamic properties. (XLSX 31 kb)
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Sifonte, E.P., Castro-Smirnov, F.A., Jimenez, A.A.S. et al. Quantum mechanics descriptors in a nano-QSAR model to predict metal oxide nanoparticles toxicity in human keratinous cells. J Nanopart Res 23, 161 (2021). https://doi.org/10.1007/s11051-021-05288-0
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DOI: https://doi.org/10.1007/s11051-021-05288-0