205
Views
29
CrossRef citations to date
0
Altmetric
Original Article

Multi-target QSTR modeling for simultaneous prediction of multiple toxicity endpoints of nano-metal oxides

&
Pages 339-350 | Received 10 Nov 2016, Accepted 01 Mar 2017, Published online: 22 Mar 2017

References

  • Adams LK, Lyon DY, Alvarez PJ. 2006. Comparative eco-toxicity of nanoscale TiO2, SiO2, and ZnO water suspensions. Water Res 40:3527–32.
  • Alexander DL, Tropsha A, Winkler DA. 2015. Beware of R(2): simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models. J Chem Inf Model 55:1316–22.
  • Armitage JM, Arnot JA, Wania F, MaCkay D. 2013. Development and evaluation of a mechanistic bioconcentration model for ionogenic organic chemicals in fish. Environ Toxicol Chem 32:115–28.
  • Arnot JA, Gobas FAPC. 2004. A food web bioaccumulation model for organic chemicals in aquatic ecosystems. Environ Toxicol Chem 23:2343–55.
  • Auffan M, Rose J, Wiesner MR, Bottero JY. 2009. Chemical stability of metallic nanoparticles: a parameter controlling their potential cellular toxicity in vitro. Environ Pollut 157:1127–33.
  • Bai Y, Zhang Y, Zhang J, Mu Q, Zhang W, Butch ER, et al. 2010. Repeated administrations of carbon nanotubes in male mice cause reversible testis damage without affecting fertility. Nat Nanotechnol 5:683–9.
  • Basant N, Gupta S, Singh KP. 2015a. Predicting aquatic toxicities of chemical pesticides in multiple test species using nonlinear QSTR modeling approaches. Chemosphere 139:246–55.
  • Basant N, Gupta S, Singh KP. 2015b. Predicting toxicities of diverse chemical pesticides in multiple avian species using tree-based QSAR approaches for regulatory purposes. J Chem Inf Model 55:1337–48.
  • Gupta S, Basant N. 2016. Modeling the reactivity of ozone and sulphate radicals towards organic chemicals in water using machine learning approaches. RSC Adv 6:108448–108457.
  • Basant N, Gupta S. 2017. Modeling uptake of nanoparticles in multiple human cells using structure?activity relationships and intercellular uptake correlations. Nanotoxicology 11:20–30.
  • Basant N, Gupta S, Singh KP. 2016a. In silico prediction of the developmental toxicity of diverse organic chemicals in rodents for regulatory purposes. Toxicol Res 5:773–87.
  • Basant N, Gupta S, Singh KP. 2016b. QSAR modeling for predicting reproductive toxicity of chemicals in rats for regulatory purposes. Toxicol Res 5:1029–38.
  • Biau G. 2012. Analysis of a random forests model. J Mach Learn Res 13:1063–95.
  • Burello E, Worth AP. 2011a. A theoretical framework for predicting the oxidative stress potential of oxide nanoparticles. Nanotoxicology 5:228–35.
  • Burello E, Worth AP. 2011b. QSAR modeling of nanomaterials. Wiley Interdiscip Rev Nanomed Nanobiotechnol 3:298–306.
  • Burello E, Worth AP. 2012. Development and evaluation of structure–reactivity models for predicting the in vitro oxidative stress of metal oxide nanoparticles. In: Puzyn T, Leszczynski J, eds. Towards Efficient Designing of Safe Nanomaterials: Innovative Merge of Computational Approaches and Experimental Techniques. Cambridge, United Kingdom: Royal Society of Chemistry, 257–283.
  • Chai T, Draxler RR. 2014. Rootmean square error (RMSE) or mean absolute error (MAE)? arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247–50.
  • Chemicalize (https://chemicalize.com/#/calculation)
  • ChemSpider (www.Chemspider.com)
  • Clark KA, White RH, Silbergeld EK. 2011. Predictive models for nanotoxicology: current challenges and future opportunities. Regul Toxicol Pharmacol 59:361–3.
  • Dreher KL. 2004. Health and environmental impact of nanotechnology: toxicological assessment of manufactured nanoparticles. Toxicol Sci 77:3–5.
  • Eriksson L, Jaworska J, Worth AP, Cronin MTD, McDowell RM, Gramatica P. 2003. Methods for reliability and uncertainty assessment and for applicability evaluations of classification and regression-based QSARs. Environ Health Perspect 111:1361–75.
  • Gajewicz A, Schaeublin N, Rasulev B, Hussain S, Leszczynska D, Puzyn T, Leszczynski J. 2015. Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: hints from nano-QSAR studies. Nanotoxicology 9:313–25.
  • Heinlaan M, Ivask A, Bilnova I, Dubourguier HC, Kahru A. 2008. Toxicity of nanosized and bulk ZnO, CuO and TiO2 to bacteria Vibrio fischeri and crustaceans Daphnia magna and Thamnocephalus platyurus. Chemosphere 71:1308–16.
  • Kar S, Gajewicz A, Puzyn T, Roy K, Leszczynski J. 2014. Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: a mechanistic QSTR approach. Ecotoxicol Environ Saf 107:162–9.
  • Kar S, Gajewicz A, Roy K, Leszczynski J, Puzyn T. 2016. Extrapolating between toxicity endpoints of metal oxide nanoparticles: predicting toxicity to Escherichia coli and human keratinocyte cell line (HaCaT) with Nano-QTTR. Ecotoxicol Environ Saf 126:238–44.
  • Kleandrova VV, Luan F, González-Díaz H, Rusoe JM, Melo A, Speck-Planche A, et al. 2014a. Computational ecotoxicology: simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions. Environ Int 73:288–94.
  • Kleandrova VV, Luan F, González-Díaz H, Speck-Planche A, Natália M, Cordeiro MNDS. 2014b. 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 48:14686–94.
  • Linkov I, Steevens J, Adlakha-Hutcheon G, Bennett E, Chappell M, Colvin V, et al. 2009. Emerging methods and tools for environmental risk assessment, decision-making, and policy for nanomaterials: summary of NATO Advanced Research Workshop. J Nanopart Res 11:513–27.
  • Lovrić J, Cho SJ, Winnik FM, Maysinger D. 2005. Unmodified cadmium telluride quantum dots induce reactive oxygen species formation leading to multiple organelle damage and cell death. Chem Biol 12:1227–34.
  • Lowry R. 2013. Concepts and Applications of Inferential Statistics, Online resource Available at: http://vassarstats.net/textbook/html, 1998–2015.
  • Luan F, Kleandrova VV, Gonzalez-Diaz H, Juan M, Ruso JM, Andre Melo A, et al. 2014. Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR perturbation approach. Nanoscale 6:10623–30.
  • Maynard AD, Aitken RJ, Butz T, Colvin V, Donaldson K, Oberdörster G, et al. 2006. Safe handling of nanotechnology. Nature 444:267–9.
  • Meng H, Xia T, George S, Nel AE. 2009. A predictive toxicological paradigm for the safety assessment of nanomaterials. ACS Nano 3:1620–7.
  • Mitra I, Saha A, Roy K. 2010. Exploring quantitative structure–activity relationship studies of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants. Mol Simul 36:1067–79.
  • Mu Y, Wu F, Zhao Q, Ji R, Qie Y, Zhou Y, et al. 2016. Predicting toxic potencies of metal oxide nanoparticles by means of nano-QSARs. Nanotoxicology 10:1207–14.
  • Nel A, Xia T, Madler L, Li N. 2006. Toxic potential of materials at the nanolevel. Science 311:622–7.
  • Neal AL. 2008. What can be inferred from bacterium–nanoparticle interactions about the potential consequences of environmental exposure to nanoparticles? Ecotoxicology 17:362–71.
  • OECD. 2007. Environment Health and Safety Publications Series on Testing and Assessment No. 69. Guidance Document On The Validation Of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models.
  • Pan Y, Li T, Cheng J, Telesca D, Zink JI, Jiang J. 2016. Nano-QSAR modeling for predicting the cytotoxicity of metal oxide nanoparticles using novel descriptors. RSC Adv 6:25766–75.
  • Pathakoti K, Huang MJ, Watts JD, He X, Hwang HM. 2014. Using experimental data of Escherichia coli to develop a QSAR model for predicting the photo-induced cytotoxicity of metal oxide nanoparticles. J Photochem Photobiol B 130:234–40.
  • Puzyn T, Leszczynska D, Leszczynski J. 2009. Toward the development of "nano-QSARs": advances and challenges. Small 5:2494–509.
  • Puzyn T, Rasulev B, Gajewicz A, Hu X, Dasari TP, Michalkova A, et al. 2011. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. Nat Nanotechnol 6:175–8.
  • Rahman R. 2016. Multivariate Random Forest for Linearly Related Output Features, R package, Version 1.1, Date 2016-03-11.
  • Raies AB, Bajic VB. 2016. In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscip Rev Comput Mol Sci 6:147–72.
  • RDKit (http://www.scbdd.com/rdk_desc/index/)
  • Roy K, Das RN, Ambure P, Aher RB. 2016. Be aware of error measures. Further studies on validation of predictive QSAR models. Chemometr Intell Lab Syst 152:18–33.
  • Roy K, Kar S, Ambure P. 2015. On a simple approach for determining applicability domain of QSAR models. Chemometr Intell Lab Syst 145:22–9.
  • Rücker C, Rücker G, Meringer M. 2007. Y-Randomization and its variants in QSPR/QSAR. J Chem Inf Model 47:2345–57.
  • Service RF. 2004. Nanotoxicology. Nanotechnology grows up. Science 304:1732–4.
  • Singh KP, Gupta S. 2014. Nano-QSAR modeling for predicting biological activity of diverse nanomaterials. RSC Adv 4:13215–30.
  • Singh KP, Gupta S, Basant N. 2015. In silico prediction of cellular permeability of diverse chemicals using qualitative and quantitative SAR modeling approaches. Chemometr Intell Lab Syst 140:61–72.
  • Singh KP, Gupta S, Kumar A, Mohan D. 2014. Multispecies QSAR modeling for predicting the aquatic toxicity of diverse organic chemicals for regulatory toxicology. Chem Res Toxicol 27:741–53.
  • 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 6:13986–93.
  • Toropov AA, Toropova AP, Benfenati E, Gini G, Puzyn T, Leszczynska D, Leszczynski J. 2012. Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli. Chemosphere 89:1098–102.
  • Yin N, Liu Q, Liu J, He B, Cui L, Li Z, et al. 2013. Silver nanoparticle exposure attenuates the viability of rat cerebellum granule cells through apoptosis coupled to oxidative stress. Small 9:1831–41.
  • Zhang H, Ji Z, Xia T, Meng H, Low-Kam C, Liu R, et al. 2012. Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammation. ACS Nano 6:4349–68.
  • Zhao L, Dong YH, Wang H. 2010. Residues of veterinary antibiotics in manures from feedlot livestock in eight provinces of China. Sci Total Environ 408:1069–75.
  • Zhou L, Xu J, Li X, Wang F. 2006. Metal oxide nanoparticles from inorganic sources via a simple and general method. Mater Chem Phys 97:137–42.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.