Summary and Analysis of the Currently Existing Literature Data on Metal-based Nanoparticles Published for Selected Aquatic Organisms: Applicability for Toxicity Prediction by (Q)SARs

This review establishes an inventory of existing toxicity data on nanoparticles (NPs) with the purpose of developing (Quantitative) Structure–Activity Relationships for NPs (nano-[Q]SARs), and also of maximising the use of scientific sources for NP risk assessment. From a data search carried out on 27 February 2014, a total of 910 publications were retrieved from the Web of Science™ Core Collection, and a database comprising 886 records of toxicity endpoints, based on these publications, was built. The test organisms mainly comprised bacteria, algae, yeast, protozoa, nematoda, crustacea and fish. The NPs consisted mostly of metals, metal oxides, nanocomposites and quantum dots. The data were analysed further, in order to: a) categorise each toxicity endpoint and the biological effects triggered by the NPs; b) survey the characterisation of the NPs used; and c) assess whether the data were suitable for nano-(Q)SAR development. Despite the efforts of numerous scientific programmes on nanomaterial safety and design, our study concluded that lack of data consistency prevents the use of experimental data in developing and validating nano-(Q)SARs. Finally, an outlook on the future of nano-(Q)SAR development is provided.

[1]  Chun Wei Yap,et al.  Quantitative Nanostructure–Activity Relationship modelling of nanoparticles , 2012 .

[2]  A. Tropsha,et al.  Quantitative nanostructure-activity relationship modeling. , 2010, ACS nano.

[3]  Kavitha Pathakoti,et al.  Using experimental data of Escherichia coli to develop a QSAR model for predicting the photo-induced cytotoxicity of metal oxide nanoparticles. , 2014, Journal of photochemistry and photobiology. B, Biology.

[4]  R. Weissleder,et al.  Modeling biological activities of nanoparticles. , 2012, Nano letters.

[5]  Bo Wang,et al.  Mesoporous carbon-coated LiFePO4 nanocrystals co-modified with graphene and Mg2+ doping as superior cathode materials for lithium ion batteries. , 2014, Nanoscale.

[6]  Shikha Gupta,et al.  Nano-QSAR modeling for predicting biological activity of diverse nanomaterials , 2014 .

[7]  R. Weissleder,et al.  Cell-specific targeting of nanoparticles by multivalent attachment of small molecules , 2005, Nature Biotechnology.

[8]  Kirk G Scheckel,et al.  Impact of environmental conditions (pH, ionic strength, and electrolyte type) on the surface charge and aggregation of silver nanoparticles suspensions. , 2010, Environmental science & technology.

[9]  Kevin Kendall,et al.  Aggregation and surface properties of iron oxide nanoparticles: Influence of ph and natural organic matter , 2008, Environmental toxicology and chemistry.

[10]  Qasim Chaudhry,et al.  Considerations for environmental fate and ecotoxicity testing to support environmental risk assessments for engineered nanoparticles. , 2009, Journal of chromatography. A.

[11]  Lutz Mädler,et al.  Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammation. , 2012, ACS nano.

[12]  T. Xia,et al.  Development of structure-activity relationship for metal oxide nanoparticles. , 2013, Nanoscale.

[13]  Manuela Pavan,et al.  Review of QSAR Models for Ready Biodegradation , 2006 .

[14]  H. Marquart,et al.  A proposal for evaluation of exposure data. , 2002, The Annals of occupational hygiene.

[15]  Willie Peijnenburg,et al.  The Application of QSAR Approaches to Nanoparticles , 2014, Alternatives to laboratory animals : ATLA.

[16]  S. Muresan,et al.  Chemical predictive modelling to improve compound quality , 2013, Nature Reviews Drug Discovery.

[17]  Jerzy Leszczynski,et al.  Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli. , 2012, Chemosphere.

[18]  Alexander Tropsha,et al.  Exploring quantitative nanostructure-activity relationships (QNAR) modeling as a tool for predicting biological effects of manufactured nanoparticles. , 2011, Combinatorial chemistry & high throughput screening.

[19]  Sang Hyup Lee,et al.  Acute toxicity of Ag and CuO nanoparticle suspensions against Daphnia magna: the importance of their dissolved fraction varying with preparation methods. , 2012, Journal of hazardous materials.

[20]  Dale A Pelletier,et al.  Relating nanomaterial properties and microbial toxicity. , 2013, Nanoscale.

[21]  Erik Tielemans,et al.  Excluding exposure data of very poor quality is a core principle for regulatory risk assessment. , 2002, The Annals of occupational hygiene.

[22]  Jerzy Leszczynski,et al.  From basic physics to mechanisms of toxicity: the "liquid drop" approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles. , 2014, Nanoscale.

[23]  Jerzy Leszczynski,et al.  Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. , 2011, Nature nanotechnology.

[24]  R. Brain,et al.  Probabilistic hazard assessment of environmentally occurring pharmaceuticals toxicity to fish, daphnids and algae by ECOSAR screening. , 2003, Toxicology letters.

[25]  A. Nel,et al.  Classification NanoSAR development for cytotoxicity of metal oxide nanoparticles. , 2011, Small.

[26]  C. Oksel,et al.  Current situation on the availability of nanostructure–biological activity data , 2015, SAR and QSAR in environmental research.

[27]  Feng Luan,et al.  nanotoxicology : assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach † , 2014 .

[28]  Jerzy Leszczynski,et al.  Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: a mechanistic QSTR approach. , 2014, Ecotoxicology and environmental safety.

[29]  U. Tillmann,et al.  A systematic approach for evaluating the quality of experimental toxicological and ecotoxicological data. , 1997, Regulatory toxicology and pharmacology : RTP.

[30]  Thomas Hartung,et al.  "ToxRTool", a new tool to assess the reliability of toxicological data. , 2009, Toxicology letters.

[31]  Feng Luan,et al.  Computational ecotoxicology: simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions. , 2014, Environment international.

[32]  U. Karsten,et al.  Prevention of biofilm growth on man-made surfaces: evaluation of antialgal activity of two biocides and photocatalytic nanoparticles , 2010, Biofouling.

[33]  Tomasz Puzyn,et al.  Nano-quantitative structure-activity relationship modeling using easily computable and interpretable descriptors for uptake of magnetofluorescent engineered nanoparticles in pancreatic cancer cells. , 2014, Toxicology in vitro : an international journal published in association with BIBRA.

[34]  Kirk G Scheckel,et al.  Surface charge-dependent toxicity of silver nanoparticles. , 2011, Environmental science & technology.

[35]  Lang Tran,et al.  Safe handling of nanotechnology , 2006, Nature.

[36]  A. Kahru,et al.  From ecotoxicology to nanoecotoxicology. , 2010, Toxicology.

[37]  Mark Crane,et al.  The ecotoxicology and chemistry of manufactured nanoparticles , 2008, Ecotoxicology.

[38]  Saber M Hussain,et al.  Metal-based nanoparticles and their toxicity assessment. , 2010, Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology.

[39]  J C Madden,et al.  Evaluation criteria for the quality of published experimental data on nanomaterials and their usefulness for QSAR modelling , 2013, SAR and QSAR in environmental research.

[40]  Guangchao Chen,et al.  Comparative study of biodegradability prediction of chemicals using decision trees, functional trees, and logistic regression , 2014, Environmental toxicology and chemistry.

[41]  Aravind Subramanian,et al.  Perturbational profiling of nanomaterial biologic activity , 2008, Proceedings of the National Academy of Sciences.

[42]  Mohammad Hossein Fatemi,et al.  Modeling the cellular uptake of magnetofluorescent nanoparticles in pancreatic cancer cells : a quantitative structure activity relationship study , 2012 .

[43]  Jerzy Leszczynski,et al.  Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: Hints from nano-QSAR studies , 2015, Nanotoxicology.

[44]  F. Gagné,et al.  Ecotoxicity of selected nano‐materials to aquatic organisms , 2008, Environmental toxicology.

[45]  Frank A. P. C. Gobas,et al.  A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms , 2006 .

[47]  K R Przybylak,et al.  Assessing toxicological data quality: basic principles, existing schemes and current limitations , 2012, SAR and QSAR in environmental research.

[48]  ANNE KAHRU,et al.  Mapping the dawn of nanoecotoxicological research. , 2013, Accounts of chemical research.

[49]  R. Brüggemann,et al.  Henry's law constants for a diverse set of organic chemicals: Experimental determination and comparison of estimation methods , 1999 .

[50]  Jerzy Leszczynski,et al.  QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells. , 2013, Chemosphere.

[51]  Andrew Worth,et al.  Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential. , 2013, Toxicology.

[52]  Michael St J Warne,et al.  Evaluation of Criteria Used to Assess the Quality of Aquatic Toxicity Data , 2005, Integrated environmental assessment and management.

[53]  Peng Wang,et al.  In vitro evaluation of cytotoxicity of engineered metal oxide nanoparticles. , 2009, The Science of the total environment.

[54]  Jerzy Leszczynski,et al.  Advancing risk assessment of engineered nanomaterials: application of computational approaches. , 2012, Advanced drug delivery reviews.

[55]  C. Tso,et al.  Stability of metal oxide nanoparticles in aqueous solutions. , 2010, Water science and technology : a journal of the International Association on Water Pollution Research.

[56]  W. Russell,et al.  Ethical and Scientific Considerations Regarding Animal Testing and Research , 2011, PloS one.

[57]  Katre Juganson,et al.  Mechanisms of toxic action of Ag, ZnO and CuO nanoparticles to selected ecotoxicological test organisms and mammalian cells in vitro: A comparative review , 2014, Nanotoxicology.

[58]  M. Mortimer,et al.  Toxicity of Ag, CuO and ZnO nanoparticles to selected environmentally relevant test organisms and mammalian cells in vitro: a critical review , 2013, Archives of Toxicology.

[59]  Farooq Ahmad,et al.  Particle‐specific toxic effects of differently shaped zinc oxide nanoparticles to zebrafish embryos (Danio rerio) , 2014, Environmental toxicology and chemistry.