593
Views
59
CrossRef citations to date
0
Altmetric
Review Article

Nano(Q)SAR: Challenges, pitfalls and perspectives

, , , , , , & show all
Pages 636-642 | Received 06 May 2014, Accepted 05 Aug 2014, Published online: 11 Sep 2014

References

  • Anderson W, Kozak D, Coleman VA, Jämting ÅK, Trau M. 2013. A comparative study of submicron particle sizing platforms: accuracy, precision and resolution analysis of polydisperse particle size distributions. J Colloid Interface Sci 405:322–30
  • Baalousha M, Lead JR. 2013. Nanoparticle dispersity in toxicology. Nat Nanotechnol 8:308–9
  • Bajorath J. 2004. Chemoinformatics: Concepts, Methods, and Tools For Drug Discovery. Totowa (NJ): Humana Press
  • Benigni R, Bossa C. 2008. Predictivity and reliability of QSAR models: the case of mutagens and carcinogens. Toxicol Mech Methods 18:137–47
  • Benigni R, Netzeva TI, Benfenati E, Bossa C, Franke R, Helma C, et al. 2007. The expanding role of predictive toxicology: an update on the (Q) sar models for mutagens and carcinogens. J Env Sci Health C 25:53–97
  • Bolcic-Tavcar M, Vracko M. 2009. Assessing the reproductive toxicity of some (Con) azole compounds using a structure-activity relationship approach. Sar QSAR Environ Res 20:7–8
  • Borders TL, Fonseca AF, Zhang H, Cho K, Rusinko Iii A. 2013. Developing descriptors to predict mechanical properties of nanotubes. J Chem Inf Model 53:773–82
  • Burello E, Worth AP. 2011a. QSAR modeling of nanomaterials. Wiley Interdiscip Rev Nanomed Nanobiotechnol 3:298–306
  • Burello E, Worth AP. 2011b. A theoretical framework for predicting the oxidative stress potential of oxide nanoparticles. Nanotoxicology 5:228–35
  • Byers D, Szerlog M. 1997. A rapid and low cost hazard categorization technique for identifying hazardous wastes. In Field Analytical Methods for Hazardous Wastes and Toxic Chemicals. Air & Waste Management Association, 35–44
  • Cardin R, Michielan L, Moro S, Sperduti A. 2009. PCA-Based Representations of Graphs for Prediction in QSAR Studies. Artificial Neural Networks–Icann 2009. Berlin, Heidelberg, Germany: Springer
  • Cassani S, Kovarich S, Papa E, Roy PP, Van Der Wal L, Gramatica P. 2013. Daphnia and fish toxicity of (benzo) triazoles: validated qsar models, and interspecies quantitative activity-activity modelling. J Hazard Mater 258–259:50–60
  • Chau YT, Yap CW. 2012. Quantitative nanostructure-activity relationship modelling of nanoparticles. Rsc Adv 2:8489–96
  • Chirico N, Gramatica P. 2011. Real external predictivity of qsar models: how to evaluate it? comparison of different validation criteria and proposal of using the concordance correlation coefficient. J Chem Inform Model 51:2320–35
  • Cronin MT, Jaworska JS, Walker JD, Comber MH, Watts CD, Worth AP. 2003a. Use of QSARS in international decision-making frameworks to predict health effects of chemical substances. Environ Health Perspect 111:1391
  • Cronin MT, Walker JD, Jaworska JS, Comber MH, Watts CD, Worth AP. 2003b. Use of QSARS in international decision-making frameworks to predict ecologic effects and environmental fate of chemical substances. Environ Health Perspect 111:1376
  • Dudek AZ, Arodz T, Galvez J. 2006. Computational methods in developing quantitative structure-activity relationships (QSAR): a review. Comb Chem High Throughput Screen 9:213–28
  • Duffus JH, Nordberg M, Templeton DM. 2007. Glossary of terms used in toxicology, (IUPAC recommendations 2007). Pure Appl Chem 79:1153–344
  • Epa VC, Burden FR, Tassa C, Weissleder R, Shaw S, Winkler DA. 2012. Modeling biological activities of nanoparticles. Nano Lett 12:5808–12
  • Eriksson L, Andersson PL, Johansson E, Tysklind M. 2006. Megavariate analysis of environmental qsar data. Part I – A basic framework founded on principal component analysis (PCA), partial least squares (PLS), and statistical molecular design (SMD). Mol Divers 10:169–86
  • Esch RK, Han L, Foarde KK, Ensor DS. 2010. Endotoxin contamination of engineered nanomaterials. Nanotoxicology 4:73–83
  • Eurachem. 1998. The Fitness for Purpose of Analytical Methods. A Laboratory Guide To Method Validation And Related Topics. Eurachem. Available at: http://www.eurachem.org/index.php/publications/guides/mv. Accessed on 23 August 2014
  • Euronews. 2013. Eu Extends Ban On Animal-Tested Cosmetics. Euronews. Available at: http://www.euronews.com/2013/03/11/eu-extends-ban-on-animal-tested-cosmetics/. Accessed on 23 August 2014
  • Fadeel B, Savolainen K. 2013. Broaden the discussion. Nat Nanotechnol 8:71
  • Fischer M. 1997. Computational neural networks: a new paradigm for spatial analysis. Environ Plann A 30:1873–91
  • Foss Hansen S, Larsen BH, Olsen SI, Baun A. 2007. Categorization framework to aid hazard identification of nanomaterials. Nanotoxicology 1:243–50
  • Fourches D, Pu D, Tassa C, Weissleder R, Shaw SY, Mumper RJ, Tropsha A. 2010. Quantitative nanostructure–activity relationship modeling. Acs Nano 4:5703–12
  • Fourches D, Pu D, Tropsha A. 2011. Exploring quantitative nanostructure-activity relationships (QNAR) modeling as a tool for predicting biological effects of manufactured nanoparticles. Comb Chem High T Scr 14:217–25
  • Frank RA, Sanderson H, Kavanagh R, Burnison BK, Headley JV, Solomon KR. 2009. Use of a (quantitative) structure–activity relationship [(Q) Sar] model to predict the toxicity of naphthenic acids. J Toxicol Environ Health A 73:319–29
  • Gajewicz A, Rasulev B, Dinadayalane TC, Urbaszek P, Puzyn T, Leszczynska D, Leszczynski J. 2012. Advancing risk assessment of engineered nanomaterials: application of computational approaches. Adv Drug Deliv Rev 64:1663–93
  • Ghorbanzadeh M, Fatemi MH, Karimpour M. 2012. Modeling the cellular uptake of magnetofluorescent nanoparticles in pancreatic cancer cells: a quantitative structure activity relationship study. Ind Eng Chem Res 51:10712–18
  • Glotzer SC, Solomon MJ. 2007. Anisotropy of building blocks and their assembly into complex structures. Nat Mater 6:557–62
  • Golbraikh A, Shen M, Xiao Z, Xiao Y-D, Lee K-H, Tropsha A. 2003. Rational selection of training and test sets for the development of validated QSAR models. J Comput Aided Mol Des 17:241–53
  • Golbraikh A, Tropsha A. 2002. Beware of Q2! J Mol Graphics Model 20:269–76
  • Goldberg L. 1983. Structure-Activity Correlation As a Predictive Tool in Toxicology. New York (NY): Hemisphere Publ Corp
  • Gubbels-Van Hal I. 2007. Non-testing in reach? Chim Oggi 25:14–16
  • Guha R, Jurs PC. 2005. Interpreting computational neural network QSAR models: a measure of descriptor importance. J Chem Inform Model 45:800–6
  • Gütlein M, Helma C, Karwath A, Kramer S. 2013. A large-scale empirical evaluation of cross-validation and external test set validation in (Q)SAR. Mol Info 32:516–28
  • Hsiao I-L, Huang Y-J. 2011. Improving the interferences of methyl thiazolyl tetrazolium and IL-8 assays in assessing the cytotoxicity of nanoparticles. J Nanosci Nanotechnol 11:5228–33
  • Hulzebos E, Janssen P, Maslankiewicz L, Meijerink M, Muller J, Pelgrom S, et al. 2001. The application of structure-activity relationships in human hazard assessment: a first approach. RIVM report 601516008, Bilthoven, the Netherlands
  • Jaworska JS, Comber M, Auer C, Van Leeuwen C. 2003. Summary of a workshop on regulatory acceptance of (Q)SARS for human health and environmental endpoints. Environ Health Perspect 111:1358
  • Johnston H, Pojana G, Zuin S, Jacobsen NR, Møller P, Loft S, et al. 2013. Engineered nanomaterial risk. lessons learnt from completed nanotoxicology studies: potential solutions to current and future challenges. Crit Rev Toxicol 43:1–20
  • Kar S, Gajewicz A, Puzyn T, Roy K. 2014. Nano-quantitative structure–activity relationship modeling using easily computable and interpretable descriptors for uptake of magnetofluorescent engineered nanoparticles in pancreatic cancer cells. Toxicol In Vitro 28:600–6
  • Kearns P, Gonzalez M, Oki N, Lee K, Rodriguez F. 2009. The safety of nanotechnologies at the OECD. In Nanomaterials: Risks and Benefits. Netherlands: Springer, 351–8
  • Konovalov DA, Llewellyn LE, Vander Heyden Y, Coomans D. 2008. Robust cross-validation of linear regression QSAR models. J Chem Info Model 48:2081–94
  • Lahl U, Gundert-Remy U. 2008. The use of (Q)SAR methods in the context of reach. Toxicol Mechan Method 18:149–58
  • Liszeková D, Polakovičová M, Beňo M, Farkaš R. 2009. Molecular determinants of juvenile hormone action as revealed by 3D QSAR analysis in Drosophila. PLoS One 4:E6001
  • Liu R, Rallo R, George S, Ji Z, Nair S, Nel AE, Cohen Y. 2011. Classification nanosar development for cytotoxicity of metal oxide nanoparticles. Small 7:1118–26
  • Liu R, Rallo R, Weissleder R, Tassa C, Shaw S, Cohen Y. 2013a. Nano-sar development for bioactivity of nanoparticles with considerations of decision boundaries. Small 9:1842–52
  • Liu R, Zhang HY, Ji ZX, Rallo R, Xia T, Chang CH, Nel A, Cohen Y. 2013b. Development of structure-activity relationship for metal oxide nanoparticles. Nanoscale 5:5644–53
  • Lowe MF, Mccall D. 2009. Globally Harmonized System of Classification and Labelling of Chemicals (GHS). Arlington: US Environmental Agency
  • Lubinski L, Urbaszek P, Gajewicz A, Cronin M, Enoch S, Madden J, et al. 2013. Evaluation criteria for the quality of published experimental data on nanomaterials and their usefulness for QSAR modelling. SAR QSAR Environ Res 24:995–1008
  • Mansouri K, Ringsted T, Ballabio D, Todeschini R, Consonni V. 2013. Quantitative structure–activity relationship models for ready biodegradability of chemicals. J Chem Info Model 53:867–78
  • Mayer J, Cheeseman M, Twaroski M. 2008. Structure–activity relationship analysis tools: validation and applicability in predicting carcinogens. Regul Toxicol Pharmacol 50:50–8
  • Mays C, Benfenati E, Pardoe S. 2012. Use and perceived benefits and barriers of QSAR models for reach: findings from a questionnaire to stakeholders. Chem Cent J 6:1–9
  • Mccall MJ, Coleman VA, Herrmann J, Kirby JK, Gardner IR, Brent PJ, Johnson CM. 2013. A tiered approach. Nat Nanotechnol 8:307–8
  • Mckinney JD, Richard A, Waller C, Newman MC, Gerberick F. 2000. The practice of structure activity relationships (SAR) in toxicology. Toxicol Sci 56:8–17
  • Mekenyan O, Dimitrov S, Pavlov T, Veith G. 2005. Pops: a QSAR system for developing categories for persistent, bioacculative and toxic chemicals and their metabolites. SAR QSAR Env Res 16:103–33
  • Monteiro-Riviere NA, Tran CL. 2014. Nanotoxicology: Progress Toward Nanomedicine. Boca Raton (FL): CRC Press
  • Murdock RC, Braydich-Stolle L, Schrand AM, Schlager JJ, Hussain SM. 2008. Characterization of nanomaterial dispersion in solution prior to in vitro exposure using dynamic light scattering technique. Toxicol Sci 101:239–53
  • Nelson BC, Petersen EJ, Marquis BJ, Atha DH, Elliott JT, Cleveland D, et al. 2013. Nist gold nanoparticle reference materials do not induce oxidative DNA damage. Nanotoxicology 7:21–9
  • Neumeyer A, Bukowski M, Veith M, Lehr C-M, Daum N. 2014. Non-invasive determination of cellular oxygen consumption as novel cytotoxicity assay for nanomaterials. Nanotoxicology 8:50–60
  • NRC. 2009. Review of Federal Strategy for Nanotechnology-Related Environmental, Health, and Safety Research. Washington, DC: The National Academies Press
  • OECD. 2007a. Guidance document on the validation of (quantitative) structure–activity relationship models. Series on Testing and Assessment, No. 69
  • OECD. 2007b. Guidance on the grouping of chemicals. Series on Testing and Assessment, No. 80
  • Panneerselvam S, Choi S. 2014. Nanoinformatics: emerging databases and available tools. Int J Mol Sci 15:7158–82
  • Pathakoti K, Huang M-J, Watts JD, He X, Hwang H-M. 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
  • Patlewicz G. 2005. Chemical Categories and Read Across. Joint Research Centre. Report no. EUR 21898 EN
  • Patlewicz G, Ball N, Booth ED, Hulzebos E, Zvinavashe E, Hennes C. 2013. Use of category approaches, read-across and (Q)SAR: general considerations. Regul Toxicol Pharmacol 67:1–12
  • Patlewicz G, Chen M, Bellin, C. 2011. Non-testing approaches under reach–help or hindrance? Perspectives from a practitioner within industry. SAR QSAR Environ Res 22:67–88
  • Petkov P, Temelkov S, Villeneuve D, Ankley G, Mekenyan O. 2009. Mechanism-based categorization of aromatase inhibitors: a potential discovery and screening tool. SAR QSAR Environ Res 20:657–78
  • Puzyn T, Gajewicz A, Leszczynska D, Leszczynski J. 2010. Nanomaterials – the next great challenge for qsar modelers. In Recent Advances in QSAR Studies. Dordrecht, The Netherlands: Springer
  • 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
  • Richarz AN, Cronin M, Madden J, Lubinski L, Mokshina E, Urbaszek P, et al. 2013. Toxicity of nanomaterials: availability and suitability of data for the development of in silico models. Toxicol Lett 221:S246
  • Roco MC, Bainbridge WS. 2005. Societal implications of nanoscience and nanotechnology: maximizing human benefit. J Nanopart Res 7:1–13
  • Shao C-Y, Chen S-Z, Su B-H, Tseng YJ, Esposito EX, Hopfinger AJ. 2013. Dependence of QSAR models on the selection of trial descriptor sets: a demonstration using nanotoxicity endpoints of decorated nanotubes. J Chem Info Model 53:142–58
  • Singh KP, Gupta S. 2014. Nano-QSAR modeling for predicting biological activity of diverse nanomaterials. Rsc Adv 4:13215–30
  • Stone V, Nowack B, Baun A, Van Den Brink N, Von Der Kammer F, Dusinska M, et al. 2010. Nanomaterials for environmental studies: classification, reference material issues, and strategies for physico-chemical characterisation. Sci Total Environ 408:1745–54
  • Sussman N, Arena V, Yu S, Mazumdar S, Thampatty B. 2003. Decision tree sar models for developmental toxicity based on an FDA/TERIS database. SAR QSAR Environ Res 14:83–96
  • Tantra R, Shard A. 2013. We need answers. Nat Nanotechnol 8:71
  • Tebby C, Mombelli E, Pandard P, Péry AR. 2011. Exploring an ecotoxicity database with the OECD (Q)SAR toolbox and dragon descriptors in order to prioritise testing on algae, daphnids, and fish. Sci Total Environ 409:3334–43
  • Toropov AA, Toropova AP, Puzyn T, Benfenati E, Gini G, Leszczynska D, Leszczynski J. 2013. QSAR a random event: modeling of nanoparticles uptake in PACA2 cancer cells. Chemosphere 92:31–7
  • Van Leeuwen C, Vermeire T, Vermeire T. 2007a. Risk Assessment of Chemicals: An Introduction. Dordrecht, The Netherlands: Springer
  • Van Leeuwen CJ, Patlewicz GY, Worth AP. 2007b. Intelligent Testing Strategies. Risk Assessment of Chemicals. Dordrecht, The Netherlands: Springer
  • Verhaar H, Van Leeuwen C, Bol J, Hermens J. 1994. Application of QSARS in risk management of existing chemicals. SAR QSAR Environ Res 2:39–58
  • Wang XZ, Yang Y, Li RF, Mcguinnes C, Adamson J, Megson IL, Donaldson K. 2013. Principal component and causal analysis of structural and acute in vitro toxicity data for nanoparticles. Nanotoxicology 8:465–76
  • Wessel MD, Jurs PC. 1994. Prediction of reduced ion mobility constants from structural information using multiple linear regression analysis and computational neural networks. Anal Chem 66:2480–7
  • Winkler DA, Burden FR. 2002. Application of neural networks to large dataset qsar, virtual screening, and library design. Combinatorial Library, 325–67
  • Winkler DA, Mombelli E, Pietroiusti A, Tran L, Worth A, Fadeel B, Mccall MJ. 2012. Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential. Toxicology 313:15–23
  • Yao X, Panaye A, Doucet J-P, Zhang R, Chen H, Liu M, et al. 2004. Comparative study of QSAR/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression. J Chem Inform Comput Sci 44:1257–66

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.