Nano-QSAR modeling for ecosafe design of heterogeneous TiO 2 -based nano-photocatalysts

Mikolajczyk, Alicja; Gajewicz, Agnieszka; Mulkiewicz, Ewa; Rasulev, Bakhtiyor; Marchelek, Martyna; Diak, Magdalena; Hirano, Seishiro; Zaleska-Medynska, Adriana; Puzyn, Tomasz

doi:10.1039/C8EN00085A

Title: Nano-QSAR modeling for ecosafe design of heterogeneous TiO ₂ -based nano-photocatalysts

Journal Article · Mon Jan 01 00:00:00 EST 2018 · Environmental Science: Nano

DOI:https://doi.org/10.1039/C8EN00085A· OSTI ID:1434091

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Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, 80-308 Gdansk, Poland
Department of Environmental Analytics, Faculty of Chemistry, University of Gdansk, 80-308 Gdansk, Poland
Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, USA
Department of Environmental Technology, Faculty of Chemistry, University of Gdansk, 80-308 Gdansk, Poland
Center for Environmental Risk Research, National Institute for Environmental Studies, Tsukuba, Japan

The human health and environmental risk assessment of engineered nanomaterials (NPs) is nowadays of high interest.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0001717

OSTI ID:: 1434091

Journal Information:: Environmental Science: Nano, Journal Name: Environmental Science: Nano Vol. 5 Journal Issue: 5; ISSN 2051-8153

Publisher:: Royal Society of Chemistry (RSC)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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Cited by: 44 works

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References (64)

Generalized Gradient Approximation Made Simple Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias Physical Review Letters, Vol. 77, Issue 18, p. 3865-3868 https://doi.org/10.1103/PhysRevLett.77.3865	journal	October 1996
A Risk Assessment Tool for the Virtual Screening of Metal Oxide Nanoparticles through Enalos InSilicoNano Platform Melagraki, Georgia; Afantitis, Antreas Current Topics in Medicinal Chemistry, Vol. 15, Issue 18 https://doi.org/10.2174/1568026615666150506144536	journal	June 2015
Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models Sizochenko, Natalia; Gajewicz, Agnieszka; Leszczynski, Jerzy Nanoscale, Vol. 8, Issue 13 https://doi.org/10.1039/C5NR08279J	journal	January 2016
Zeta Potential for Metal Oxide Nanoparticles: A Predictive Model Developed by a Nano-Quantitative Structure–Property Relationship Approach Mikolajczyk, Alicja; Gajewicz, Agnieszka; Rasulev, Bakhtiyor Chemistry of Materials, Vol. 27, Issue 7 https://doi.org/10.1021/cm504406a	journal	March 2015
Use of QSAR models in predicting joint effects in multi-component mixtures of organic chemicals Xu, S.; Nirmalakhandan, N. Water Research, Vol. 32, Issue 8 https://doi.org/10.1016/S0043-1354(98)00006-2	journal	August 1998
Ionic Liquids As Tunable Toxicity Storage Media for Sustainable Chemical Waste Management Seitkalieva, Marina M.; Kashin, Alexey S.; Egorova, Ksenia S. ACS Sustainable Chemistry & Engineering, Vol. 6, Issue 1 https://doi.org/10.1021/acssuschemeng.7b03036	journal	November 2017
Relationships between optical band gap and thermodynamic properties of binary oxides Portier, Josik; Campet, Guy; Kwon, Chai Won International Journal of Inorganic Materials, Vol. 3, Issue 7 https://doi.org/10.1016/S1466-6049(01)00075-7	journal	November 2001
Development of a novel in silico model of zeta potential for metal oxide nanoparticles: a nano-QSPR approach Wyrzykowska, Ewelina; Mikolajczyk, Alicja; Sikorska, Celina Nanotechnology, Vol. 27, Issue 44 https://doi.org/10.1088/0957-4484/27/44/445702	journal	September 2016
Visible-Light Photocatalysis in Nitrogen-Doped Titanium Oxides Asahi, R. Science, Vol. 293, Issue 5528 https://doi.org/10.1126/science.1061051	journal	July 2001
Ab Initio Studies of Anatase TiO₂ (101) Surface-supported Au₈ Clusters Mikolajczyk, A.; Pinto, H. P.; Gajewicz, A. Current Topics in Medicinal Chemistry, Vol. 15, Issue 18 https://doi.org/10.2174/1568026615666150506151826	journal	June 2015
Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles Puzyn, Tomasz; Rasulev, Bakhtiyor; Gajewicz, Agnieszka Nature Nanotechnology, Vol. 6, Issue 3 https://doi.org/10.1038/nnano.2011.10	journal	February 2011
Towards chromate-free corrosion inhibitors: structure–property models for organic alternatives Winkler, D. A.; Breedon, M.; Hughes, A. E. Green Chem., Vol. 16, Issue 6 https://doi.org/10.1039/C3GC42540A	journal	January 2014
Quantitative Structure−Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure Zhu, Hao; Martin, Todd M.; Ye, Lin Chemical Research in Toxicology, Vol. 22, Issue 12 https://doi.org/10.1021/tx900189p	journal	December 2009
From ultrasoft pseudopotentials to the projector augmented-wave method Kresse, G.; Joubert, D. Physical Review B, Vol. 59, Issue 3, p. 1758-1775 https://doi.org/10.1103/PhysRevB.59.1758	journal	January 1999
Mechanism of Photogenerated Reactive Oxygen Species and Correlation with the Antibacterial Properties of Engineered Metal-Oxide Nanoparticles Li, Yang; Zhang, Wen; Niu, Junfeng ACS Nano, Vol. 6, Issue 6 https://doi.org/10.1021/nn300934k	journal	May 2012
How safe are nanomaterials? Valsami-Jones, E.; Lynch, I. Science, Vol. 350, Issue 6259 https://doi.org/10.1126/science.aad0768	journal	October 2015
Size-dependent cytotoxicity of silver nanoparticles in human lung cells: the role of cellular uptake, agglomeration and Ag release Gliga, Anda R.; Skoglund, Sara; Odnevall Wallinder, Inger Particle and Fibre Toxicology, Vol. 11, Issue 1 https://doi.org/10.1186/1743-8977-11-11	journal	January 2014
Quantitative structure-activity relationships for joint toxicity of substituted phenols and anilines to Scenedesmus obliquus Wang, Chao; Lu, Guanghua; Tang, Zhuyun Journal of Environmental Sciences, Vol. 20, Issue 1 https://doi.org/10.1016/S1001-0742(08)60018-2	journal	January 2008
Absolute electronegativity and hardness: application to inorganic chemistry Pearson, Ralph G. Inorganic Chemistry, Vol. 27, Issue 4 https://doi.org/10.1021/ic00277a030	journal	February 1988
Joint toxicity of mixtures of 8 and 24 chemicals to the guppy (Poecilia reticulata) Hermens, Joop; Leeuwangh, Peter Ecotoxicology and Environmental Safety, Vol. 6, Issue 3 https://doi.org/10.1016/0147-6513(82)90020-3	journal	June 1982
Excess electron states in reduced bulk anatase TiO2: Comparison of standard GGA, GGA+U, and hybrid DFT calculations Finazzi, Emanuele; Di Valentin, Cristiana; Pacchioni, Gianfranco The Journal of Chemical Physics, Vol. 129, Issue 15 https://doi.org/10.1063/1.2996362	journal	October 2008
Toxicity assessment of anatase and rutile titanium dioxide nanoparticles: The role of degradation in different pH conditions and light exposure De Matteis, Valeria; Cascione, Mariafrancesca; Brunetti, Virgilio Toxicology in Vitro, Vol. 37 https://doi.org/10.1016/j.tiv.2016.09.010	journal	December 2016
Negligible particle-specific toxicity mechanism of silver nanoparticles: The role of Ag+ ion release in the cytosol De Matteis, Valeria; Malvindi, Maria Ada; Galeone, Antonio Nanomedicine: Nanotechnology, Biology and Medicine, Vol. 11, Issue 3 https://doi.org/10.1016/j.nano.2014.11.002	journal	April 2015
Quantitative Nanostructure−Activity Relationship Modeling Fourches, Denis; Pu, Dongqiuye; Tassa, Carlos ACS Nano, Vol. 4, Issue 10 https://doi.org/10.1021/nn1013484	journal	September 2010
Be aware of error measures. Further studies on validation of predictive QSAR models Roy, Kunal; Das, Rudra Narayan; Ambure, Pravin Chemometrics and Intelligent Laboratory Systems, Vol. 152 https://doi.org/10.1016/j.chemolab.2016.01.008	journal	March 2016
Classification NanoSAR Development for Cytotoxicity of Metal Oxide Nanoparticles Liu, Rong; Rallo, Robert; George, Saji Small, Vol. 7, Issue 8 https://doi.org/10.1002/smll.201002366	journal	March 2011
Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: A mechanistic QSTR approach Kar, Supratik; Gajewicz, Agnieszka; Puzyn, Tomasz Ecotoxicology and Environmental Safety, Vol. 107 https://doi.org/10.1016/j.ecoenv.2014.05.026	journal	September 2014
A Comparison of Two Approaches to the Construction of Models for Quantal Responses to Mixtures of Drugs Plackett, R. L.; Hewlett, P. S. Biometrics, Vol. 23, Issue 1 https://doi.org/10.2307/2528279	journal	March 1967
Real External Predictivity of QSAR Models: How To Evaluate It? Comparison of Different Validation Criteria and Proposal of Using the Concordance Correlation Coefficient Chirico, Nicola; Gramatica, Paola Journal of Chemical Information and Modeling, Vol. 51, Issue 9 https://doi.org/10.1021/ci200211n	journal	August 2011
The joint effects on Photobacterium phosphoreum of metal oxide nanoparticles and their most likely coexisting chemicals in the environment Wang, Dali; Gao, Ya; Lin, Zhifen Aquatic Toxicology, Vol. 154 https://doi.org/10.1016/j.aquatox.2014.05.023	journal	September 2014
Molecular Descriptors for Chemoinformatics: Volume I: Alphabetical Listing / Volume II: Appendices, References Todeschini, Roberto; Consonni, Viviana Methods and Principles in Medicinal Chemistry https://doi.org/10.1002/9783527628766	book	July 2009
Investigating the influence of data splitting on the predictive ability of QSAR/QSPR models Puzyn, Tomasz; Mostrag-Szlichtyng, Aleksandra; Gajewicz, Agnieszka Structural Chemistry, Vol. 22, Issue 4 https://doi.org/10.1007/s11224-011-9757-4	journal	February 2011
Titanium Dioxide Nanomaterials: Synthesis, Properties, Modifications, and Applications Chen, Xiaobo; Mao, Samuel S. Chemical Reviews, Vol. 107, Issue 7 https://doi.org/10.1021/cr0500535	journal	July 2007
Correlation of Biological Activity of Phenoxyacetic Acids with Hammett Substituent Constants and Partition Coefficients Hansch, Corwin; Maloney, Peyton P.; Fujita, Toshio Nature, Vol. 194, Issue 4824 https://doi.org/10.1038/194178b0	journal	April 1962
What Contributes to the Combined Effect of a Complex Mixture? Altenburger, Rolf; Walter, Helge; Grote, Matthias Environmental Science & Technology, Vol. 38, Issue 23 https://doi.org/10.1021/es049528k	journal	December 2004
On a simple approach for determining applicability domain of QSAR models Roy, Kunal; Kar, Supratik; Ambure, Pravin Chemometrics and Intelligent Laboratory Systems, Vol. 145 https://doi.org/10.1016/j.chemolab.2015.04.013	journal	July 2015
Size-dependent electronic properties of nanomaterials: How this novel class of nanodescriptors supposed to be calculated? Jagiello, Karolina; Chomicz, Bartłomiej; Avramopoulos, Aggelos Structural Chemistry, Vol. 28, Issue 3 https://doi.org/10.1007/s11224-016-0838-2	journal	September 2016
Degenerate semiconductors in the light of electronegativity and chemical hardness Portier, Josik; Campet, Guy; Poquet, Armel International Journal of Inorganic Materials, Vol. 3, Issue 7 https://doi.org/10.1016/S1466-6049(01)00074-5	journal	November 2001
In Vitro Toxicity of Silver Nanoparticles at Noncytotoxic Doses to HepG2 Human Hepatoma Cells Kawata, Koji; Osawa, Masato; Okabe, Satoshi Environmental Science & Technology, Vol. 43, Issue 15 https://doi.org/10.1021/es900754q	journal	August 2009
Evaluating the toxicity of TiO ₂ -based nanoparticles to Chinese hamster ovary cells and Escherichia coli: a complementary experimental and computational approach Mikolajczyk, Alicja; Sizochenko, Natalia; Mulkiewicz, Ewa Beilstein Journal of Nanotechnology, Vol. 8 https://doi.org/10.3762/bjnano.8.216	journal	January 2017
From basic physics to mechanisms of toxicity: the “liquid drop” approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles Sizochenko, Natalia; Rasulev, Bakhtiyor; Gajewicz, Agnieszka Nanoscale, Vol. 6, Issue 22 https://doi.org/10.1039/C4NR03487B	journal	January 2014
Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set Kresse, G.; Furthmüller, J. Computational Materials Science, Vol. 6, Issue 1, p. 15-50 https://doi.org/10.1016/0927-0256(96)00008-0	journal	July 1996
Quantitative aspects of the synergism between carbaryl and some 1,3-benzodioxole (methylenedioxyphenyl) compounds in houseflies Hewlett, P. S.; Wilkinson, C. F. Journal of the Science of Food and Agriculture, Vol. 18, Issue 7 https://doi.org/10.1002/jsfa.2740180703	journal	July 1967
Toward the Development of â€œNano-QSARsâ€: Advances and Challenges Puzyn, Tomasz; Leszczynska, Danuta; Leszczynski, Jerzy Small, Vol. 5, Issue 22 https://doi.org/10.1002/smll.200900179	journal	November 2009
Modeling Biological Activities of Nanoparticles Epa, V. Chandana; Burden, Frank R.; Tassa, Carlos Nano Letters, Vol. 12, Issue 11 https://doi.org/10.1021/nl303144k	journal	October 2012
Existing and Developing Approaches for QSAR Analysis of Mixtures Muratov, Eugene N.; Varlamova, Ekaterina V.; Artemenko, Anatoly G. Molecular Informatics, Vol. 31, Issue 3-4 https://doi.org/10.1002/minf.201100129	journal	March 2012
Enalos InSilicoNano platform: an online decision support tool for the design and virtual screening of nanoparticles Melagraki, Georgia; Afantitis, Antreas RSC Adv., Vol. 4, Issue 92 https://doi.org/10.1039/C4RA07756C	journal	January 2014
Semiconductor Photocatalysis — Past, Present, and Future Outlook Serpone, N.; Emeline, A. V. The Journal of Physical Chemistry Letters, Vol. 3, Issue 5 https://doi.org/10.1021/jz300071j	journal	February 2012
The ecotoxicology of nanoparticles and nanomaterials: current status, knowledge gaps, challenges, and future needs Handy, Richard D.; Owen, Richard; Valsami-Jones, Eugenia Ecotoxicology, Vol. 17, Issue 5 https://doi.org/10.1007/s10646-008-0206-0	journal	April 2008
Evaluation of Toxicity Ranking for Metal Oxide Nanoparticles via an in Vitro Dosimetry Model Liu, Rong; Liu, Haoyang Haven; Ji, Zhaoxia ACS Nano, Vol. 9, Issue 9 https://doi.org/10.1021/acsnano.5b04420	journal	August 2015
Toxicokinetic modeling of mixtures of organic chemicals McCarty, L. S.; Dixon, D. G.; Ozburn, G. W. Environmental Toxicology and Chemistry, Vol. 11, Issue 7 https://doi.org/10.1002/etc.5620110716	journal	July 1992
Computer-based QSARs for predicting mixture toxicity of benzene and its derivatives Zhang, Li; Zhou, Pei-jiang; Yang, Feng Chemosphere, Vol. 67, Issue 2 https://doi.org/10.1016/j.chemosphere.2006.09.018	journal	February 2007
Beware of q2! Golbraikh, Alexander; Tropsha, Alexander Journal of Molecular Graphics and Modelling, Vol. 20, Issue 4 https://doi.org/10.1016/S1093-3263(01)00123-1	journal	January 2002
Best Practices for QSAR Model Development, Validation, and Exploitation Tropsha, Alexander Molecular Informatics, Vol. 29, Issue 6-7 https://doi.org/10.1002/minf.201000061	journal	July 2010
The ecotoxicology and chemistry of manufactured nanoparticles Handy, Richard D.; von der Kammer, Frank; Lead, Jamie R. Ecotoxicology, Vol. 17, Issue 4 https://doi.org/10.1007/s10646-008-0199-8	journal	March 2008
A theoretical framework for predicting the oxidative stress potential of oxide nanoparticles Burello, Enrico; Worth, Andrew P. Nanotoxicology, Vol. 5, Issue 2 https://doi.org/10.3109/17435390.2010.502980	journal	July 2010
How the toxicity of nanomaterials towards different species could be simultaneously evaluated: a novel multi-nano-read-across approach Sizochenko, Natalia; Mikolajczyk, Alicja; Jagiello, Karolina Nanoscale, Vol. 10, Issue 2 https://doi.org/10.1039/C7NR05618D	journal	January 2018
Structure– and Property–Activity Relationship Models for Prediction of Microbial Toxicity of Organic Chemicals to Activated Sludge Nirmalakhandan, N.; Egemen, E.; Trevizo, C. Ecotoxicology and Environmental Safety, Vol. 39, Issue 2 https://doi.org/10.1006/eesa.1997.1615	journal	February 1998
The “double cross-validation” software tool for MLR QSAR model development Roy, Kunal; Ambure, Pravin Chemometrics and Intelligent Laboratory Systems, Vol. 159 https://doi.org/10.1016/j.chemolab.2016.10.009	journal	December 2016
Principles of QSAR models validation: internal and external Gramatica, Paola QSAR & Combinatorial Science, Vol. 26, Issue 5 https://doi.org/10.1002/qsar.200610151	journal	May 2007
Nano-quantitative structure–activity relationship modeling using easily computable and interpretable descriptors for uptake of magnetofluorescent engineered nanoparticles in pancreatic cancer cells Kar, Supratik; Gajewicz, Agnieszka; Puzyn, Tomasz Toxicology in Vitro, Vol. 28, Issue 4 https://doi.org/10.1016/j.tiv.2013.12.018	journal	June 2014
The expected effect of a combination of agents: the general solution Berenbaum, Morris C. Journal of Theoretical Biology, Vol. 114, Issue 3 https://doi.org/10.1016/S0022-5193(85)80176-4	journal	June 1985
Application and validation of approaches for the predictive hazard assessment of realistic pesticide mixtures Junghans, M.; Backhaus, T.; Faust, M. Aquatic Toxicology, Vol. 76, Issue 2 https://doi.org/10.1016/j.aquatox.2005.10.001	journal	February 2006
A DFT study of adsorption of perylene on clean and altered anatase (101) TiO2 Ikäläinen, Suvi; Laasonen, Kari Physical Chemistry Chemical Physics, Vol. 15, Issue 28 https://doi.org/10.1039/c3cp51295a	journal	January 2013

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