Computational ecotoxicology: simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions.

Nanotechnology has brought great advances to many fields of modern science. A manifold of applications of nanoparticles have been found due to their interesting optical, electrical, and biological/chemical properties. However, the potential toxic effects of nanoparticles to different ecosystems are of special concern nowadays. Despite the efforts of the scientific community, the mechanisms of toxicity of nanoparticles are still poorly understood. Quantitative-structure activity/toxicity relationships (QSAR/QSTR) models have just started being useful computational tools for the assessment of toxic effects of nanomaterials. But most QSAR/QSTR models have been applied so far to predict ecotoxicity against only one organism/bio-indicator such as Daphnia magna. This prevents having a deeper knowledge about the real ecotoxic effects of nanoparticles, and consequently, there is no possibility to establish an efficient risk assessment of nanomaterials in the environment. In this work, a perturbation model for nano-QSAR problems is introduced with the aim of simultaneously predicting the ecotoxicity of different nanoparticles against several assay organisms (bio-indicators), by considering also multiple measures of ecotoxicity, as well as the chemical compositions, sizes, conditions under which the sizes were measured, shapes, and the time during which the diverse assay organisms were exposed to nanoparticles. The QSAR-perturbation model was derived from a database containing 5520 cases (nanoparticle-nanoparticle pairs), and it was shown to exhibit accuracies of ca. 99% in both training and prediction sets. In order to demonstrate the practical applicability of our model, three different nickel-based nanoparticles (Ni) with experimental values reported in the literature were predicted. The predictions were found to be in very good agreement with the experimental evidences, confirming that Ni-nanoparticles are not ecotoxic when compared with other nanoparticles. The results of this study thus provide a single valuable tool toward an efficient prediction of the ecotoxicity of nanoparticles under multiple experimental conditions.

[1]  N. Monteiro-Riviere,et al.  Comprar Nanotoxicology: Characterization, Dosing and Health Effects | Nancy A. Monteiro-Riviere | 9781420045147 | Informa Healthcare , 2007 .

[2]  T. Puzyn,et al.  Toward the development of "nano-QSARs": advances and challenges. , 2009, Small.

[3]  R. Albrecht,et al.  Toxicity assessments of multisized gold and silver nanoparticles in zebrafish embryos. , 2009, Small.

[4]  Z. Chai,et al.  Phytotoxicity and biotransformation of La2O3 nanoparticles in a terrestrial plant cucumber (Cucumis sativus) , 2011, Nanotoxicology.

[5]  Humberto González-Díaz,et al.  First computational chemistry multi-target model for anti-Alzheimer, anti-parasitic, anti-fungi, and anti-bacterial activity of GSK-3 inhibitors in vitro, in vivo, and in different cellular lines , 2011, Molecular Diversity.

[6]  Maurizio Chiriva-Internati,et al.  Nanotechnology and human health: risks and benefits , 2010, Journal of applied toxicology : JAT.

[7]  Sophie Lanone,et al.  Comparative toxicity of 24 manufactured nanoparticles in human alveolar epithelial and macrophage cell lines , 2009, Particle and Fibre Toxicology.

[8]  Xiaoshan Zhu,et al.  Toxicity Assessment of Iron Oxide Nanoparticles in Zebrafish (Danio rerio) Early Life Stages , 2012, PloS one.

[9]  L. G. Pérez-Montoto,et al.  3D entropy and moments prediction of enzyme classes and experimental-theoretic study of peptide fingerprints in Leishmania parasites. , 2009, Biochimica et biophysica acta.

[10]  E. Estrada.  Structure-mutagenicity relationships in 2-furylethylene derivatives. A molecular orbital study of the role of nitro groups. , 1998, Mutation research.

[11]  Nancy A. Monteiro-Riviere,et al.  Nanotoxicology : characterization, dosing and health effects , 2007 .

[12]  K. Roy,et al.  QSTR with extended topochemical atom (ETA) indices. 9. Comparative QSAR for the toxicity of diverse functional organic compounds to Chlorella vulgaris using chemometric tools. , 2007, Chemosphere.

[13]  Baoshan Xing,et al.  Phytotoxicity of nanoparticles: inhibition of seed germination and root growth. , 2007, Environmental pollution.

[14]  Ernesto Estrada,et al.  Computer-aided knowledge generation for understanding skin sensitization mechanisms: the TOPS-MODE approach. , 2003, Chemical research in toxicology.

[15]  Xiaoshan Zhu,et al.  Toxicity and bioaccumulation of TiO2 nanoparticle aggregates in Daphnia magna. , 2010, Chemosphere.

[16]  Emilio Benfenati,et al.  Quantitative consensus of bioaccumulation models for integrated testing strategies. , 2012, Environment international.

[17]  Blaise Hanczar,et al.  Small-sample precision of ROC-related estimates , 2010, Bioinform..

[18]  Francisco Torrens,et al.  A novel approach to predict aquatic toxicity from molecular structure. , 2008, Chemosphere.

[19]  Silvana Andreescu,et al.  Toxicity and developmental defects of different sizes and shape nickel nanoparticles in zebrafish. , 2009, Environmental science & technology.

[20]  Feng Luan,et al.  Predicting multiple ecotoxicological profiles in agrochemical fungicides: a multi-species chemoinformatic approach. , 2012, Ecotoxicology and environmental safety.

[21]  Kunal Roy,et al.  First report on development of quantitative interspecies structure-carcinogenicity relationship models and exploring discriminatory features for rodent carcinogenicity of diverse organic chemicals using OECD guidelines. , 2012, Chemosphere.

[22]  A. Biffis,et al.  Catalysis by metal nanoparticles supported on functional organic polymers , 2001 .

[23]  Kaja Kasemets,et al.  Toxicity of nanoparticles of ZnO, CuO and TiO2 to yeast Saccharomyces cerevisiae. , 2009, Toxicology in vitro : an international journal published in association with BIBRA.

[24]  Kemin Wang,et al.  Immunofluorescent labeling of cancer cells with quantum dots synthesized in aqueous solution. , 2006, Analytical biochemistry.

[25]  Itamar Willner,et al.  DNA nanotechnology: from sensing and DNA machines to drug-delivery systems. , 2013, ACS nano.

[26]  A. Polman,et al.  The planar parabolic optical antenna. , 2013, Nano letters.

[27]  Jr-hau He,et al.  Probing surface band bending of surface-engineered metal oxide nanowires. , 2012, ACS nano.

[28]  Antonio Villaverde,et al.  Biomedical applications of distally controlled magnetic nanoparticles. , 2009, Trends in biotechnology.

[29]  Younan Xia,et al.  A sinter-resistant catalytic system fabricated by maneuvering the selectivity of SiO2 deposition onto the TiO2 surface versus the Pt nanoparticle surface. , 2013, Nano letters.

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

[31]  N. Wang,et al.  Nanoparticles in Biomedical Applications and Their Safety Concerns , 2011 .

[32]  Wei Bai,et al.  Effects of rare earth oxide nanoparticles on root elongation of plants. , 2010, Chemosphere.

[33]  Kerstin Hund-Rinke,et al.  Ecotoxic Effect of Photocatalytic Active Nanoparticles (TiO2) on Algae and Daphnids (8 pp) , 2006, Environmental science and pollution research international.

[34]  J. Dai,et al.  Palladium nanoparticle enhanced giant photoresponse at LaAlO3/SrTiO3 two-dimensional electron gas heterostructures. , 2013, ACS nano.

[35]  Liang Cheng,et al.  Multilayer dual-polymer-coated upconversion nanoparticles for multimodal imaging and serum-enhanced gene delivery. , 2013, ACS applied materials & interfaces.

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

[37]  Humberto González-Díaz,et al.  Entropy model for multiplex drug-target interaction endpoints of drug immunotoxicity. , 2013, Current topics in medicinal chemistry.

[38]  Edward D Rothman,et al.  Statistics, methods and applications , 1987 .

[39]  Sonia Arrasate,et al.  General theory for multiple input-output perturbations in complex molecular systems. 1. Linear QSPR electronegativity models in physical, organic, and medicinal chemistry. , 2013, Current topics in medicinal chemistry.

[40]  E Uriarte,et al.  Quantitative Structure–Toxicity Relationships Using Tops-Mode. 1. Nitrobenzene Toxicity to Tetrahymena Pyriformis , 2001, SAR and QSAR in environmental research.

[41]  Miroslav Mashlan,et al.  Multimodal action and selective toxicity of zerovalent iron nanoparticles against cyanobacteria. , 2012, Environmental science & technology.

[42]  N. Chandrasekaran,et al.  Studies on toxicity of aluminum oxide (Al2O3) nanoparticles to microalgae species: Scenedesmus sp. and Chlorella sp. , 2011 .

[43]  J. Rose,et al.  Direct and indirect CeO2 nanoparticles toxicity for Escherichia coli and Synechocystis , 2009 .

[44]  Ernesto Estrada,et al.  Quantitative Structure-Toxicity Relationships Using Tops-Mode. 2. Neurotoxicity of a Non-Congeneric Series of Solvents , 2001, SAR and QSAR in environmental research.

[45]  J. Cho,et al.  Organic field-effect transistor memory devices using discrete ferritin nanoparticle-based gate dielectrics. , 2013, Small.

[46]  Carl J. Huberty,et al.  Applied MANOVA and discriminant analysis , 2006 .

[47]  Kunal Roy,et al.  QSAR modeling of toxicity of diverse organic chemicals to Daphnia magna using 2D and 3D descriptors. , 2010, Journal of hazardous materials.

[48]  Humberto González Díaz,et al.  Computational chemistry study of 3D‐structure‐function relationships for enzymes based on Markov models for protein electrostatic, HINT, and van der Waals potentials , 2009, J. Comput. Chem..

[49]  Jianfang Wang,et al.  Loading metal nanostructures on cotton fabrics as recyclable catalysts. , 2013, Small.

[50]  K. Roy,et al.  QSTR with extended topochemical atom (ETA) indices. 12. QSAR for the toxicity of diverse aromatic compounds to Tetrahymena pyriformis using chemometric tools. , 2009, Chemosphere.

[51]  Bo-Han Su,et al.  Dependence of QSAR Models on the Selection of Trial Descriptor Sets: A Demonstration Using Nanotoxicity Endpoints of Decorated Nanotubes , 2013, J. Chem. Inf. Model..

[52]  Xiaoshan Zhu,et al.  Acute toxicities of six manufactured nanomaterial suspensions to Daphnia magna , 2009 .

[53]  Yeqing Sun,et al.  Biotoxicity of nickel oxide nanoparticles and bio-remediation by microalgae Chlorella vulgaris. , 2011, Chemosphere.

[54]  Jing Luo,et al.  Effects of particle composition and species on toxicity of metallic nanomaterials in aquatic organisms , 2008, Environmental toxicology and chemistry.

[55]  Yan Li,et al.  Comparative toxicity of several metal oxide nanoparticle aqueous suspensions to Zebrafish (Danio rerio) early developmental stage , 2008, Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering.

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

[57]  Ana García,et al.  Acute toxicity of cerium oxide, titanium oxide and iron oxide nanoparticles using standardized tests , 2011 .

[58]  Andrew P. Worth,et al.  QSAR modeling of nanomaterials. , 2011, Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology.

[59]  W. Bai,et al.  Changing exposure media can reverse the cytotoxicity of ceria nanoparticles for Escherichia coli , 2012, Nanotoxicology.

[60]  Anne Kahru,et al.  Toxicity of nanosized and bulk ZnO, CuO and TiO2 to bacteria Vibrio fischeri and crustaceans Daphnia magna and Thamnocephalus platyurus. , 2008, Chemosphere.

[61]  Jorge L Gardea-Torresdey,et al.  Ecological nanotoxicology: integrating nanomaterial hazard considerations across the subcellular, population, community, and ecosystems levels. , 2013, Accounts of chemical research.

[62]  Chunying Chen,et al.  Near‐Infrared Light‐Mediated Nanoplatforms for Cancer Thermo‐Chemotherapy and Optical Imaging , 2013, Advanced materials.