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Compilation of Data and Modelling of Nanoparticle Interactions and Toxicity in the NanoPUZZLES Project

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Modelling the Toxicity of Nanoparticles

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 947))

Abstract

The particular properties of nanomaterials have led to their rapidly increasing use in diverse fields of application. However, safety assessment is not keeping pace and there are still gaps in the understanding of their hazards. Computational models predicting nanotoxicity, such as (quantitative) structure-activity relationships ((Q)SARs), can contribute to safety evaluation, in line with general efforts to apply alternative methods in chemical risk assessment. Their development is highly dependent on the availability of reliable and high quality experimental data, both regarding the compounds’ properties as well as the measured toxic effects. In particular, “nano-QSARs” should take the nano-specific characteristics into account. The information compiled needs to be well organized, quality controlled and standardized. Integrating the data in an overarching, structured data collection aims to (a) organize the data in a way to support modelling, (b) make (meta)data necessary for modelling available, and (c) add value by making a comparison between data from different sources possible.

Based on the available data, specific descriptors can be derived to parameterize the nanomaterial-specific structure and physico-chemical properties appropriately. Furthermore, the interactions between nanoparticles and biological systems as well as small molecules, which can lead to modifications of the structure of the active nanoparticles, need to be described and taken into account in the development of models to predict the biological activity and toxicity of nanoparticles. The EU NanoPUZZLES project was part of a global cooperative effort to advance data availability and modelling approaches supporting the characterization and evaluation of nanomaterials.

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References

  1. Borders TL, Fonseca AF, Zhang H, Cho K, Rusinko A (2013) Developing descriptors to predict mechanical properties of nanotubes. J Chem Inf Model 53(4):773–782

    Article  CAS  PubMed  Google Scholar 

  2. Burello E, Worth AP (2011) QSAR modeling of nanomaterials. Wiley Interdiscip Rev Nanomed Nanobiotechnol 3(3):298–306

    Article  CAS  PubMed  Google Scholar 

  3. Fubini B, Ghiazza M, Fenoglio I (2010) Physico-chemical features of engineered nanoparticles relevant to their toxicity. Nanotoxicology 4:347–363

    Article  CAS  PubMed  Google Scholar 

  4. 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 64:1663–1693

    Article  CAS  PubMed  Google Scholar 

  5. Gallegos Saliner A, Poater A, Worth AP (2008) Toward in silico approaches for investigating the activity of nanoparticles in therapeutic development. IDrugs 11:728–732

    Google Scholar 

  6. ISA-TAB-Nano Wiki (2015) https://wiki.nci.nih.gov/display/ICR/ISA-TAB-Nano. Accessed 5 May 2015

  7. Kar S, Gajewicz A, Puzyn T, Roy K (2014) Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: a mechanistic QSTR approach. Ecotoxicol Environ Saf 107C:162–169

    Article  Google Scholar 

  8. Kellici TF, Ntountaniotis D, Leonis G, Chatziathanasiadou M, Chatzikonstantinou AV, Becker-Baldus J, Glaubitz C, Tzakos AG, Viras K, Chatzigeorgiou P, Tzimas S, Kefala E, Valsami G, Archontaki H, Papadopoulos MG, Mavromoustakos T (2015) Investigation of the interactions of silibinin with 2-hydroxypropyl-β-cyclodextrin through biophysical techniques and computational methods. Mol Pharm 12(3):954–965

    Article  CAS  PubMed  Google Scholar 

  9. Klimisch HJ, Andreae M, Tillmann U (1997) A systematic approach for evaluating the quality of experimental toxicological and ecotoxicological data. Regul Toxicol Pharmacol 25:1–5

    Article  CAS  PubMed  Google Scholar 

  10. Leonis G, Steinbrecher T, Papadopoulos MG (2013) A contribution to the drug resistance mechanism of darunavir, amprenavir, indinavir, and saquinavir complexes with HIV-1 protease due to flap mutation I50V: a Systematic MM–PBSA and thermodynamic integration study. J Chem Inf Model 53:2141–2153

    Article  CAS  PubMed  Google Scholar 

  11. Leonis G, Avramopoulos A, Salmas RE, Durdagi S, Yurtsever M, Papadopoulos MG (2014) Elucidation of conformational states, dynamics and mechanism of binding in human κ-opioid receptor complexes. J Chem Inf Model 54(8):2294–2308

    Article  CAS  PubMed  Google Scholar 

  12. Leonis G, Avramopoulos A, Papavasileiou KD, Reis H, Steinbrecher T, Papadopoulos MG (2015) A comprehensive computational study of the interaction between human serum albumin and fullerenes. J Phys Chem B 119:14971–14985

    Article  CAS  PubMed  Google Scholar 

  13. Lesar A, Milosev I (2009) Density functional study of the corrosion inhibition properties of 1,2,4-triazole and its amino derivatives. Chem Phys Lett 483:198–203

    Article  CAS  Google Scholar 

  14. Lubiński L, Urbaszek P, Gajewicz A, Cronin MTD, Enoch SJ, Madden JC, Leszczynska D, Leszczynski J, Puzyn T (2013) Evaluation criteria for the quality of published experimental data on nanomaterials and their usefulness for QSAR modelling. SAR QSAR Environ Res 24(12):995–1008

    Article  PubMed  Google Scholar 

  15. Marchese Robinson RL, Cronin MTD, Richarz A-N, Rallo R (2015) An ISA-TAB-Nano based data collection framework to support data driven modelling of nanotoxicology. Beilstein J Nanotechnol 6:1978–1999

    Article  PubMed  PubMed Central  Google Scholar 

  16. Marchese Robinson RL, Lynch I, Peijnenburg W, Rumble J, Klaessig F, Marquardt C, Rauscher H, Puzyn T, Purian R, Åberg C, Karcher S, Vriens H, Hoet P, Hoover MD, Ogilvie Hendren C, Harper SL (2016) How should the completeness and quality of curated nanomaterial data be evaluated? Nanoscale 8:9919–9943

    Article  PubMed  Google Scholar 

  17. Martin D, Maran U, Sild S, Karelson M (2007) QSPR modeling of solubility of polyaromatic hydrocarbons and fullerene in 1-octanol and n-heptane. J Phys Chem B 111(33):9853–9857

    Article  CAS  PubMed  Google Scholar 

  18. Mills KC, Murry D, Guzan KA, Ostraat ML (2014) Nanomaterial registry: database that captures the minimal information about nanomaterial physico-chemical characteristics. J Nanopart Res 16(2):1–9

    Article  Google Scholar 

  19. nanoDMS: Nanomaterial Data Management System (2015). http://biocenitc-deq.urv.cat/nanodms. Accessed 11 Sept 2015.

  20. Ostraat ML, Mills KC, Guzan KA, Murry D (2013) The Nanomaterial Registry: facilitating the sharing and analysis of data in the diverse nanomaterial community. Int J Nanomedicine 8(Suppl 1):7–13

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Petersen EJ, Henry TB, Zhao J, MacCuspie RI, Kirschling TL, Dobrovolskaia MA, Hackley V, Xing B, White JC (2014) Identification and avoidance of potential artefacts and misinterpretations in nanomaterial ecotoxicity measurements. Environ Sci Technol 48(8):4226–4246

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Petrova T, Rasulev BF, Toropov AA, Leszczynska D, Leszczynski J (2011) Improved model for fullerene C60 solubility in organic solvents based on quantum-chemical and topological descriptors. J Nanopart Res 13(8):3235–3247

    Article  CAS  Google Scholar 

  23. Puzyn T, Leszczynska D, Leszczynski J (2009) Toward the development of ‘Nano-QSAR’: advances and challenges. Small 5:2494–2509

    Article  CAS  PubMed  Google Scholar 

  24. Puzyn T, Rasulev B, Gajewicz A, Hu X, Dasari TP, Michalkova A, Hwang HM, Toropov A, Leszczynska D, Leszczynski J (2011) Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. Nat Nanotechnol 6:175–178

    Article  CAS  PubMed  Google Scholar 

  25. Richarz A-N, Madden JC, Marchese Robinson RL, Lubiński Ł, Mokshina UP, Kuz’min VE, Puzyn T, Cronin MTD (2015) Development of computational models for the prediction of the toxicity of nanomaterials. Perspect Sci 3:27–29

    Article  Google Scholar 

  26. Rocca-Serra P, Brandizi M, Maguire E, Sklyar N, Taylor C, Begley K, Field D, Harris S, Hide W, Hofmann O, Neumann S, Sterk P, Tong W, Sansone S-A (2010) ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level. Bioinformatics 26(18):2354–2356

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Sansone S-A, Rocca-Serra P, Field D, Maguire E, Taylor C, Hofmann O, Fang H, Neumann S, Tong W, Amaral-Zettler L, Begley K, Booth T, Bougueleret L, Burns G, Chapman B, Clark T, Coleman L-A, Copeland J, Das S, de Daruvar A, de Matos P, Dix I, Edmunds S, Evelo CT, Forster MJ, Gaudet P, Gilbert J, Goble C, Griffin JL, Jacob D, Kleinjans J, Harland L, Haug K, Hermjakob H, Sui SJH, Laederach A, Liang S, Marshall S, McGrath A, Merrill E, Reilly D, Roux M, Shamu CE, Shang CA, Steinbeck C, Trefethen A, Williams-Jones B, Wolstencroft K, Xenarios I, Hide W (2012) Toward interoperable bioscience data. Nat Genet 44(2):121–126

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Sayes CM, Fortner JD, Guo W, Lyon D, Boyd AM, Ausman KD, Tao YJ, Sitharaman B, Wilson LJ, Hughes JB, West JL, Colvin VL (2004) The differential cytotoxicity of water-soluble fullerenes. Nano Lett 4(10):1881–1887

    Article  CAS  Google Scholar 

  29. Schneider K, Schwarz M, Burkholder I, Kopp-Schneider A, Edler L, Kinsner-Ovaskainen A, Hartung T, Hoffmann S (2009) ToxRTool’, a new tool to assess the reliability of toxicological data. Toxicol Lett 189(2):138–144

    Article  CAS  PubMed  Google Scholar 

  30. Sizochenko N, Rasulev B, Gajewicz A, Kuzmin VE, Puzyn T, Leszczynski J (2014) From basic physics to mechanisms of toxicity: liquid drop approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles. Nanoscale 6:13986–13993

    Article  CAS  PubMed  Google Scholar 

  31. Stefaniak AB, Hackley VA, Roebben G, Ehara K, Hankin S, Postek MT, Lynch I, Fu W-E, Linsinger TPJ, Thünemann AF (2013) Nanoscale reference materials for environmental, health and safety measurements: needs, gaps and opportunities. Nanotoxicology 7(8):1325–1337

    Article  PubMed  Google Scholar 

  32. Thomas DG, Klaessig F, Harper SL, Fritts M, Hoover MD, Gaheen S, Stokes TH, Reznik-Zellen R, Freund ET, Klemm JD, Paik DS, Baker NA (2011a) Informatics and standards for nanomedicine technology. Wiley Interdiscip Rev Nanomed Nanobiotechnol 3(5):511–532

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Thomas DG, Pappu RV, Baker NA (2011b) NanoParticle ontology for cancer nanotechnology research. J Biomed Inform 44(1):59–74

    Article  PubMed  Google Scholar 

  34. Thomas DG, Gaheen S, Harper SL, Fritts M, Klaessig F, Hahn-Dantona E, Paik D, Pan S, Stafford GA, Freund ET, Klemm JD, Baker NA (2013) ISA-TAB-Nano: a specification for sharing nanomaterial research data in spreadsheet-based format. BMC Biotechnol 13:2

    Article  PubMed  PubMed Central  Google Scholar 

  35. Toropov AA, Leszczynski J (2007) A new approach to the characterization of nanomaterials: predicting Young’s modulus by correlation weighting of nanomaterials codes. Chem Phys Lett 433(1–3):125–129

    Google Scholar 

  36. Toropov AA, Toropova AP (2013) Optimal descriptor as a translator of eclectic data into endpoint prediction: mutagenicity of fullerene as a mathematical function of conditions. Chemosphere 104:262–264

    Article  PubMed  Google Scholar 

  37. Toropov AA, Leszczynska D, Leszczynski J (2007) Predicting water solubility and octanol water partition coefficient for carbon nanotubes based on the chiral vector. Comput Biol Chem 31(2):127–128

    Article  CAS  PubMed  Google Scholar 

  38. Toropov AA, Toropova AP, Puzyn T, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2013) QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells. Chemosphere 92:31–37

    Article  CAS  PubMed  Google Scholar 

  39. Toropova AP, Toropov AA (2013) Optimal descriptor as a translator of eclectic information into the prediction of membrane damage by means of various TiO2 nanoparticles. Chemosphere 93(10):2650–2655

    Article  CAS  PubMed  Google Scholar 

  40. Toropova AP, Toropov AA (2015) Mutagenicity: QSAR – quasi-QSAR – nano-QSAR. Mini Rev Med Chem 15(8):608–621

    Article  CAS  PubMed  Google Scholar 

  41. Toropova AP, Toropov AA, Puzyn T, Benfenati E, Leszczynska D, Leszczynski J (2013) Optimal descriptor as a translator of eclectic information into the prediction of thermal conductivity of micro-electro-mechanical systems. J Math Chem 51:2230–2237

    Article  CAS  Google Scholar 

  42. Tzoupis H, Leonis G, Avramopoulos A, Mavromoustakos T, Papadopoulos MG (2014) Systematic molecular dynamics, MM–PBSA and ab initio approaches to the Saquinavir resistance mechanism in HIV-1 PR due to 11 double and multiple mutations. J Phys Chem B 118(32):9538–9552

    Article  CAS  PubMed  Google Scholar 

  43. Vrontaki E, Leonis G, Avramopoulos A, Papadopoulos MG, Simčič M, Grdadolnik SG, Afantitis A, Melagraki G, Hadjikakou SK, Mavromoustakos T (2015) Stability and binding effects of silver(I) complexes at lipoxygenase-1. J Enzyme Inhib Med Chem 30:539–549

    Google Scholar 

  44. Whetzel PL, Noy NF, Shah NH, Alexander PR, Nyulas C, Tudorache T, Musen MA (2011) BioPortal: enhanced functionality via new web services from the National Center for Biomedical Ontology to access and use ontologies in software applications. Nucleic Acids Res 39(Web Server issue):W541–W545

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 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

    Article  CAS  PubMed  Google Scholar 

  46. Xia XR, Monteiro-Riviere NA, Mathur S, Song X, Xiao L, Oldenberg SJ, Fadeel B, Riviere JE (2011) Mapping the surface adsorption forces of nanomaterials in biological systems. ACS Nano 5(11):9074–9081

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Zhang H, Ji Z, Xia T, Meng H, Low-Kam C, Liu R, Pokhrel S, Lin S, Wang X, Liao Y-P, Wang M, Li L, Rallo R, Damoiseaux R, Telesca D, Mädler L, Cohen Y, Zink JI, Nel AE (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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

Funding through the European Commission 7th Framework Programme NanoPUZZLES project (FP7-NMP-2012-SMALL-6, grant agreement no.309837) is gratefully acknowledged.

Author information

Authors and Affiliations

  1. School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK

    Andrea-Nicole Richarz, Richard L Marchese Robinson & Mark TD Cronin

  2. Institute of Biology, Pharmaceutical Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece

    Aggelos Avramopoulos, Georgios Leonis & Manthos G Papadopoulos

  3. Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy

    Emilio Benfenati & Nazanin Golbamaki Bakhtyari

  4. Laboratory of Environmental Chemometrics, Institute for Environmental and Human Health Protection, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland

    Agnieszka Gajewicz & Tomasz Puzyn

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  1. Andrea-Nicole Richarz
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  2. Aggelos Avramopoulos
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  3. Emilio Benfenati
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  4. Agnieszka Gajewicz
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  7. Richard L Marchese Robinson
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  8. Manthos G Papadopoulos
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Editors and Affiliations

  1. Institute of Occupational Medicine, Edinburgh, United Kingdom

    Lang Tran

  2. Institute of Catalysis & Petrochemistry, CSIC, Madrid, Spain

    Miguel A. Bañares

  3. Universitat Rovira i Virgili, Departament d’Enginyeria Informatica i Matematiques, Tarragona, Spain

    Robert Rallo

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Richarz, AN. et al. (2017). Compilation of Data and Modelling of Nanoparticle Interactions and Toxicity in the NanoPUZZLES Project. In: Tran, L., Bañares, M., Rallo, R. (eds) Modelling the Toxicity of Nanoparticles. Advances in Experimental Medicine and Biology, vol 947. Springer, Cham. https://doi.org/10.1007/978-3-319-47754-1_10

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