Cronin, MTD

Kuz׳min, VE

Lubiński, Ł

Madden, JC

Marchese Robinson, RL

Mokshina, E

Puzyn, T

Richarz, A-N

Urbaszek, P

Richarz, Andrea-Nicole

Madden, Judith C.

Marchese Robinson, Richard L.

Lubiński, Łukasz

Mokshina, Elena

Urbaszek, Piotr

Kuz׳min, Victor E.

Puzyn, Tomasz

Cronin, Mark T.D.

Elsevier - Publisher Connector 

Available online at www.sciencedirect.comPerspectives in Science (2015) 3, 27–29http://dx.doi.org/1
2213-0209/& 2014 P
(http://creativecom
☆This article is p
elements – Diagnost
nCorresponding au
E-mail address: awww.elsevier.com/locate/piscEXTENDED ABSTRACTDevelopment of computational models for the
prediction of the toxicity of nanomaterials$
Andrea-Nicole Richarza,n, Judith C. Maddena,
Richard L. Marchese Robinsona, Łukasz Lubińskib,
Elena Mokshinac, Piotr Urbaszekb, Victor E. Kuz'minc,
Tomasz Puzynb, Mark T.D. Cronina
aSchool of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF,
England
bLaboratory of Environmental Chemometrics, Department of Chemistry, Sobieskiego 18, 80-952 Gdańsk,
Poland
cDepartment of Molecular Structure and Chemoinformatics, A.V. Bogatsky Physico-Chemical Institute of
the National Academy of Sciences of Ukraine, 86 Lustdorfskaya Doroga, Odessa 65080, UkraineReceived 30 July 2014; accepted 31 August 2014
Available online 3 December 2014Nanomaterials are deﬁned as material consisting of parti-
cles, in an unbound state/as an aggregate/agglomerate,
which for Z50% of the particles in the number size
distribution have a size between 1 and 100 nm, according
to the European Commission Recommendation on the deﬁ-
nition of nanomaterial (European Commission, 2011). They
comprise different types of substances such as metal (e.g.
Au, Ag, Fe) and metal oxide nanoparticles, as well as
fullerenes, carbon nanotubes or quantum dots. Speciﬁc
characteristics include the particle shape, surface area/
charge/chemistry, state of dispersion, state of agglomera-
tion, particle size distribution, solubility and porosity. These
characteristics contribute to their properties, which has
enabled the increasing use of nanomaterials in many ﬁelds
of technology, a trend which is foreseen to considerably
expand in the future.0.1016/j.pisc.2014.11.015
ublished by Elsevier GmbH. This is an open access a
mons.org/licenses/by-nc-nd/3.0/).
art of a special issue entitled “Tracing the
ic and medical importance of trace elements”.
thor.
.richarz@ljmu.ac.uk (A.-N. Richarz).The hazard and risk assessment of nanoparticles, how-
ever, is not yet developed adequately to ensure safety of
their use. One reason is the lack of consistent, comparable
and publicly accessible toxicity data. Efforts in nanotoxicity
research are undertaken globally, however, they are dis-
perse, as is the publication of data. Inconsistent data are
due, for example, to poorly characterised nanomaterials or
arbitrary experimental conditions. Moreover, the organisa-
tion and representation of the data is inconsistent.
Computational models for toxicity prediction are increas-
ingly important to support risk assessment. They include the
formation of categories of chemicals and subsequent read-
across, i.e. prediction by interpolation of activities, as well
as (quantitative) structure–activity relationships ((Q)SARs).
The models are based on the understanding that chemical
structure and thus the physico-chemical properties of a
molecule are directly responsible for biological activity, and
effects may be predicted from this relationship (see
Figure 1). Their development relies on high quality experi-
mental data and chemical structure information.
Therefore, in order to build a suitable foundation of
toxicological data for the development of in silico modelsrticle under the CC BY-NC-ND license
Figure 1 General scheme showing the correlation between a
physico-chemical descriptor and biological activity/toxicity.
The QSAR is built in the attempt to ﬁt a line of best ﬁt of the
type y=ax+c, where a is the slope and c is the intercept.
Usually, several descriptors are included.
Figure 2 Workﬂow of data collection in view of the develop-
ment of computational models for nanotoxicity prediction.
Data collection taking into account standardisation efforts is
followed by a step of quality assessment to obtain the ﬁnal
datasets, which can be integrated into overarching databases
and feed into the development of in silico models predicting
nanoparticle toxicity, both supporting the safety evaluation of
nanomaterials.
A.-N. Richarz et al.28for nanomaterial toxicity prediction, available data relating
to the physical, chemical and toxicological properties of
nanoparticles and their structures have been, and are
being, collected from different sources and through a
comprehensive literature review within the EU NanoPUZ-
ZLES and NanoBRIDGES projects.
Generally, a cross-institutional, cross-project, even glo-
bal effort is needed to overcome the fragmentation of work
to generate and evaluate toxicity data for nanoparticle risk
assessment. Data collection needs to be coordinated in
order to make valid conclusions on nanoparticle toxicity for
risk assessment. Thus, this work is integrated in the on-
going research within the Nanosafety Modelling Cluster
comprising ﬁve research projects developing in silico mod-
els for nanotoxicity, and generally within the NanoSafety
Cluster for collaboration across EU projects (www.nanosafe
tycluster.eu) and the US-EU initiative for a dialogue
between US and EU researchers (us-eu.org). A standardised
data exchange format to share data and a uniﬁed ontology
for data collection is needed to ensure a certain quality
standard in terms of completeness, avoid duplication, allow
a comparison between data from different sources and
make it possible to integrate different datasets into one
database. ISA-TAB-nano (Thomas et al., 2013) has been
identiﬁed as a suitable standard format, deﬁning a set of
linked spreadsheet ﬁles (Investigation, Study, Assay and
Material), with a pre-deﬁned ﬁle structure and syntax for
(meta)data.
Furthermore, the quality and suitability of the data for
developing predictive toxicology models are being assessed.
Evaluation criteria were developed to assess the relative
quality of literature nanoparticle data sets and their
usefulness for building computational models for nanopar-
ticles based on Klimisch et al. (1997) criteria (Lubiński
et al., 2013). The successful development of nano-QSAR
models depends not only on the quality of experimental
data, but also on the availability of sufﬁciently large data
sets. The quality assessment approach is being extended in
ongoing work.
The overall approach pursued in this project to collect
data in view of the development of computational models
for prediction of nanotoxicity is summarised in Figure 2.The data compilation performed has allowed for the
development of QSAR models for nanoparticle toxicity. As
a ﬁrst step, “nanodescriptors” have to be deﬁned to reﬂect
the speciﬁc intrinsic nanoparticle properties, e.g. stucture
and electronic states resulting from quantum effects of the
nanosize.
There are various types of methodologies to develop
nano-QSAR models for the prediction of toxicity. One
example of such a model for metal oxide nanoparticles is
that developed by Puzyn et al. (2011). It describes the
cytotoxicity of 17 metal oxide nanoparticles (ZnO, CuO,
V2O3, Y2O3, Bi2O3, In2O3, Sb2O3, Al2O3, Fe2O3, SiO2, ZrO2,
SnO2, TiO2, CoO, NiO, Cr2O3, La2O3) to the bacterium E.
coli, based on experimental testing. The toxicity decreased
in the order Me2+4Me3+4Me4+. The model is based on the
enthalpy of formation of a gaseous cation which has the
same oxidation state as the metal ion in the oxide structure.
Another example is the model for genotoxicity of metal
oxide nanoparticles developed by Golbamaki Bakhtyari
et al. (2013). Chromosomal aberrations, oxidative DNA
damage, DNA strand breaks, and mutations have been found
in the literature to be caused by metal nanoparticles. The
authors collected in vivo and in vitro data on nano metal
oxide (Al2O3, NiO, Co3O4, CuO, Fe2O3, Fe3O4, TiO2, ZnO,
SiO2, V2O3, V2O5, MnO2) genotoxic effects and developed a
nano-QSAR model for genotoxicity prediction, correlating
genotoxicity to the electronegativity of oxygen, the core–
core repulsion in eV – reﬂecting the binding energy between
atoms in the cluster – and the enthalpy of detachment of
metal cations from the cluster surface.
Regarding environmental effects, Mokshina et al. (2013)
evaluated acute toxicity data for metal oxide nanoparticles
towards the aquatic organisms Daphnia magna and Para-
mecium multimicronucleatum. They used descriptors based
on the liquid-drop and surface-area-difference models
applying statistical approaches such as random forest and
neural network methods to develop models predicting the
acute aquatic toxicity of metal oxide nanoparticles to these
organisms.
29Prediction of the toxicity of nanomaterialsFurther in silico models for metal oxides in the literature
include a model for predicting the oxidative stress potential
of metal oxide nanoparticles (TiO2, CuO, ZnO, FeO, Fe2O3,
Fe3O4), using reactivity descriptors to characterise their
energy structure (Burello and Worth, 2011). The oxidative
stress potential is predicted through their ability to transfer
electrons and perturb the overall intracellular redox state.
Another nano-SAR model was based on high-throughput
in vitro toxicity screening assay data for bronchial epithelial
(BEAS-2B) cells and ranked the cytotoxicity of metal oxide
nanoparticles (Al2O3, CeO2, Co3O4, TiO2, ZnO, CuO, SiO2,
Fe3O4, WO3) based on the descriptors atomisation energy of
the metal oxide, period of the nanoparticle metal, nano-
particle primary size, and nanoparticle volume fraction (Liu
et al., 2011).
In conclusion, a global effort is needed to overcome the
fragmentation of efforts to generate and evaluate toxicity
data for the risk assessment of nanoparticles. The aim of the
present work was to bridge the gaps between experimental
and computational approaches and to compile data to create
a dataset suitable for the development of in silico models for
nanoparticle toxicity assessment. Nano-QSAR models can
predict the toxicity for new nanoparticles and thus support
the hazard and risk assessment of nanomaterials.
Conﬂict of interest
The authors declare that there is no conﬂict of interest.
Acknowledgements
Funding through the European Commission 7th Framework
Programme NanoBRIDGES (FP7-PEOPLE-2011-IRSES, Grant
agreement no. 295128) and NanoPUZZLES (FP7-NMP-2012-
SMALL-6, Grant agreement no. 309837) projects is gratefully
acknowledged.References
Burello, E., Worth, A.P., 2011. A theoretical framework for pre-
dicting the oxidative stress potential of oxide nanoparticles.
Nanotoxicology 5, 228–235.
European Commission, 2011. Commission Recommendation of 18
October 2011 on the deﬁnition of nanomaterial. Off. J. Eur.
Union (L275/38–40).
Golbamaki Bakhtyari, N., Rasulev, B., Benfenati, E., Cronin, M.,
Leszczynski, J., 2013. Genotoxicity of metal oxide nanoparti-
cles: a new predictive (Q)SAR model. Poster presentation at the
Environmental Mutagenesis and Genomics Society (EMGS) 44th
Annual Meeting, 22 September 2013. Monterey, California, USA.
Klimisch, H.J., Andreae, M., Tillmann, U., 1997. A systematic
approach for evaluating the quality of experimental toxicologi-
cal and ecotoxicological data. Regul. Toxicol. Pharmacol. 25,
1–5.
Liu, R., Rallo, R., George, S., Ji, Z., Nair, S., Nel, A.E., Cohen, Y.,
2011. Classiﬁcation NanoSAR development for cytotoxicity of
metal oxide nanoparticles. Small 7, 1118–1126.
Lubiński, Ł., Urbaszek, P., Gajewicz, A., Cronin, M.T.D., Puzyn, T.,
Enoch, S.J., Madden, J.C., Leszczynska, D., Leszczynski, J.,
2013. Evaluation criteria for the quality of published experi-
mental data on nanomaterials and their usefulness for QSAR
modelling. SAR QSAR Environ. Res. 24, 995–1008.
Mokshina, E., Richarz, A., Cronin, M., Kuz'min, V., 2013. NanoQSAR:
metal oxides nanoparticles toxicity assessment. Poster Presen-
tation at the MACC-5 – Methods and Applications of Computa-
tional Chemistry, Fifth Symposium, 4 July 2013. Kharkiv,
Ukraine.
Puzyn, T., Rasulev, B., Gajewicz, A., Hu, X., Dasari, T.P., Michalk-
ova, A., Hwang, H.M., Toropov, A., Leszczynska, D., Leszc-
zynski, J., 2011. Using nano-QSAR to predict the cytotoxicity of
metal oxide nanoparticles. Nat. Nanotechnol. 6, 175–178.
Thomas, D.G., Gaheen, S., Harper, S.L., Fritts, M., Klaessig, F.,
Hahn-Dantona, E., Paik, D., Pan, S., Stafford, G.A.,
Freund, E.T., Klemm, J.D., Baker, N.A., 2013. ISA-TAB-Nano: a
speciﬁcation for sharing nanomaterial research data in
spreadsheet-based format. BMC Biotechnol. 132, 2.


Development of computational models for the prediction of the toxicity of nanomaterials 

Andrea-Nicole Richarz

Judith C. Madden

Richard L. Marchese Robinson

Łukasz Lubiński

Elena Mokshina

Piotr Urbaszek

Victor E. Kuz׳min

Tomasz Puzyn

Mark T.D. Cronin

Crossref

Perspectives in Science

Development of computational models for the prediction of the toxicity of nanomaterials

LJMU Research Online (Liverpool John Moores University)

Available online at www.sciencedirect.comPerspectives in Science (2015) 3, 27–29http://dx.doi.org/12213-0209/& 2014 P(http://creativecom☆This article is pelements – DiagnostnCorresponding auE-mail address: awww.elsevier.com/locate/piscEXTENDED ABSTRACTDevelopment of computational models for theprediction of the toxicity of nanomaterials$Andrea-Nicole Richarza,n, Judith C. Maddena,Richard L. Marchese Robinsona, Łukasz Lubińskib,Elena Mokshinac, Piotr Urbaszekb, Victor E. Kuz'minc,Tomasz Puzynb, Mark T.D. CroninaaSchool of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF,EnglandbLaboratory of Environmental Chemometrics, Department of Chemistry, Sobieskiego 18, 80-952 Gdańsk,PolandcDepartment of Molecular Structure and Chemoinformatics, A.V. Bogatsky Physico-Chemical Institute ofthe National Academy of Sciences of Ukraine, 86 Lustdorfskaya Doroga, Odessa 65080, UkraineReceived 30 July 2014; accepted 31 August 2014Available online 3 December 2014Nanomaterials are deﬁned as material consisting of parti-cles, in an unbound state/as an aggregate/agglomerate,which for Z50% of the particles in the number sizedistribution have a size between 1 and 100 nm, accordingto the European Commission Recommendation on the deﬁ-nition of nanomaterial (European Commission, 2011). Theycomprise different types of substances such as metal (e.g.Au, Ag, Fe) and metal oxide nanoparticles, as well asfullerenes, carbon nanotubes or quantum dots. Speciﬁccharacteristics include the particle shape, surface area/charge/chemistry, state of dispersion, state of agglomera-tion, particle size distribution, solubility and porosity. Thesecharacteristics contribute to their properties, which hasenabled the increasing use of nanomaterials in many ﬁeldsof technology, a trend which is foreseen to considerablyexpand in the future.0.1016/j.pisc.2014.11.015ublished by Elsevier GmbH. This is an open access amons.org/licenses/by-nc-nd/3.0/).art of a special issue entitled “Tracing theic and medical importance of trace elements”.thor..richarz@ljmu.ac.uk (A.-N. Richarz).The hazard and risk assessment of nanoparticles, how-ever, is not yet developed adequately to ensure safety oftheir use. One reason is the lack of consistent, comparableand publicly accessible toxicity data. Efforts in nanotoxicityresearch are undertaken globally, however, they are dis-perse, as is the publication of data. Inconsistent data aredue, for example, to poorly characterised nanomaterials orarbitrary experimental conditions. Moreover, the organisa-tion and representation of the data is inconsistent.Computational models for toxicity prediction are increas-ingly important to support risk assessment. They include theformation of categories of chemicals and subsequent read-across, i.e. prediction by interpolation of activities, as wellas (quantitative) structure–activity relationships ((Q)SARs).The models are based on the understanding that chemicalstructure and thus the physico-chemical properties of amolecule are directly responsible for biological activity, andeffects may be predicted from this relationship (seeFigure 1). Their development relies on high quality experi-mental data and chemical structure information.Therefore, in order to build a suitable foundation oftoxicological data for the development of in silico modelsrticle under the CC BY-NC-ND licenseFigure 1 General scheme showing the correlation between aphysico-chemical descriptor and biological activity/toxicity.The QSAR is built in the attempt to ﬁt a line of best ﬁt of thetype y=ax+c, where a is the slope and c is the intercept.Usually, several descriptors are included.Figure 2 Workﬂow of data collection in view of the develop-ment of computational models for nanotoxicity prediction.Data collection taking into account standardisation efforts isfollowed by a step of quality assessment to obtain the ﬁnaldatasets, which can be integrated into overarching databasesand feed into the development of in silico models predictingnanoparticle toxicity, both supporting the safety evaluation ofnanomaterials.A.-N. Richarz et al.28for nanomaterial toxicity prediction, available data relatingto the physical, chemical and toxicological properties ofnanoparticles and their structures have been, and arebeing, collected from different sources and through acomprehensive literature review within the EU NanoPUZ-ZLES and NanoBRIDGES projects.Generally, a cross-institutional, cross-project, even glo-bal effort is needed to overcome the fragmentation of workto generate and evaluate toxicity data for nanoparticle riskassessment. Data collection needs to be coordinated inorder to make valid conclusions on nanoparticle toxicity forrisk assessment. Thus, this work is integrated in the on-going research within the Nanosafety Modelling Clustercomprising ﬁve research projects developing in silico mod-els for nanotoxicity, and generally within the NanoSafetyCluster for collaboration across EU projects (www.nanosafetycluster.eu) and the US-EU initiative for a dialoguebetween US and EU researchers (us-eu.org). A standardiseddata exchange format to share data and a uniﬁed ontologyfor data collection is needed to ensure a certain qualitystandard in terms of completeness, avoid duplication, allowa comparison between data from different sources andmake it possible to integrate different datasets into onedatabase. ISA-TAB-nano (Thomas et al., 2013) has beenidentiﬁed as a suitable standard format, deﬁning a set oflinked spreadsheet ﬁles (Investigation, Study, Assay andMaterial), with a pre-deﬁned ﬁle structure and syntax for(meta)data.Furthermore, the quality and suitability of the data fordeveloping predictive toxicology models are being assessed.Evaluation criteria were developed to assess the relativequality of literature nanoparticle data sets and theirusefulness for building computational models for nanopar-ticles based on Klimisch et al. (1997) criteria (Lubińskiet al., 2013). The successful development of nano-QSARmodels depends not only on the quality of experimentaldata, but also on the availability of sufﬁciently large datasets. The quality assessment approach is being extended inongoing work.The overall approach pursued in this project to collectdata in view of the development of computational modelsfor prediction of nanotoxicity is summarised in Figure 2.The data compilation performed has allowed for thedevelopment of QSAR models for nanoparticle toxicity. Asa ﬁrst step, “nanodescriptors” have to be deﬁned to reﬂectthe speciﬁc intrinsic nanoparticle properties, e.g. stuctureand electronic states resulting from quantum effects of thenanosize.There are various types of methodologies to developnano-QSAR models for the prediction of toxicity. Oneexample of such a model for metal oxide nanoparticles isthat developed by Puzyn et al. (2011). It describes thecytotoxicity of 17 metal oxide nanoparticles (ZnO, CuO,V2O3, Y2O3, Bi2O3, In2O3, Sb2O3, Al2O3, Fe2O3, SiO2, ZrO2,SnO2, TiO2, CoO, NiO, Cr2O3, La2O3) to the bacterium E.coli, based on experimental testing. The toxicity decreasedin the order Me2+4Me3+4Me4+. The model is based on theenthalpy of formation of a gaseous cation which has thesame oxidation state as the metal ion in the oxide structure.Another example is the model for genotoxicity of metaloxide nanoparticles developed by Golbamaki Bakhtyariet al. (2013). Chromosomal aberrations, oxidative DNAdamage, DNA strand breaks, and mutations have been foundin the literature to be caused by metal nanoparticles. Theauthors collected in vivo and in vitro data on nano metaloxide (Al2O3, NiO, Co3O4, CuO, Fe2O3, Fe3O4, TiO2, ZnO,SiO2, V2O3, V2O5, MnO2) genotoxic effects and developed anano-QSAR model for genotoxicity prediction, correlatinggenotoxicity to the electronegativity of oxygen, the core–core repulsion in eV – reﬂecting the binding energy betweenatoms in the cluster – and the enthalpy of detachment ofmetal cations from the cluster surface.Regarding environmental effects, Mokshina et al. (2013)evaluated acute toxicity data for metal oxide nanoparticlestowards the aquatic organisms Daphnia magna and Para-mecium multimicronucleatum. They used descriptors basedon the liquid-drop and surface-area-difference modelsapplying statistical approaches such as random forest andneural network methods to develop models predicting theacute aquatic toxicity of metal oxide nanoparticles to theseorganisms.29Prediction of the toxicity of nanomaterialsFurther in silico models for metal oxides in the literatureinclude a model for predicting the oxidative stress potentialof metal oxide nanoparticles (TiO2, CuO, ZnO, FeO, Fe2O3,Fe3O4), using reactivity descriptors to characterise theirenergy structure (Burello and Worth, 2011). The oxidativestress potential is predicted through their ability to transferelectrons and perturb the overall intracellular redox state.Another nano-SAR model was based on high-throughputin vitro toxicity screening assay data for bronchial epithelial(BEAS-2B) cells and ranked the cytotoxicity of metal oxidenanoparticles (Al2O3, CeO2, Co3O4, TiO2, ZnO, CuO, SiO2,Fe3O4, WO3) based on the descriptors atomisation energy ofthe metal oxide, period of the nanoparticle metal, nano-particle primary size, and nanoparticle volume fraction (Liuet al., 2011).In conclusion, a global effort is needed to overcome thefragmentation of efforts to generate and evaluate toxicitydata for the risk assessment of nanoparticles. The aim of thepresent work was to bridge the gaps between experimentaland computational approaches and to compile data to createa dataset suitable for the development of in silico models fornanoparticle toxicity assessment. Nano-QSAR models canpredict the toxicity for new nanoparticles and thus supportthe hazard and risk assessment of nanomaterials.Conﬂict of interestThe authors declare that there is no conﬂict of interest.AcknowledgementsFunding through the European Commission 7th FrameworkProgramme NanoBRIDGES (FP7-PEOPLE-2011-IRSES, Grantagreement no. 295128) and NanoPUZZLES (FP7-NMP-2012-SMALL-6, Grant agreement no. 309837) projects is gratefullyacknowledged.ReferencesBurello, E., Worth, A.P., 2011. A theoretical framework for pre-dicting the oxidative stress potential of oxide nanoparticles.Nanotoxicology 5, 228–235.European Commission, 2011. Commission Recommendation of 18October 2011 on the deﬁnition of nanomaterial. Off. J. Eur.Union (L275/38–40).Golbamaki Bakhtyari, N., Rasulev, B., Benfenati, E., Cronin, M.,Leszczynski, J., 2013. Genotoxicity of metal oxide nanoparti-cles: a new predictive (Q)SAR model. Poster presentation at theEnvironmental Mutagenesis and Genomics Society (EMGS) 44thAnnual Meeting, 22 September 2013. Monterey, California, USA.Klimisch, H.J., Andreae, M., Tillmann, U., 1997. A systematicapproach for evaluating the quality of experimental toxicologi-cal and ecotoxicological data. Regul. Toxicol. Pharmacol. 25,1–5.Liu, R., Rallo, R., George, S., Ji, Z., Nair, S., Nel, A.E., Cohen, Y.,2011. Classiﬁcation NanoSAR development for cytotoxicity ofmetal oxide nanoparticles. Small 7, 1118–1126.Lubiński, Ł., Urbaszek, P., Gajewicz, A., Cronin, M.T.D., Puzyn, T.,Enoch, S.J., Madden, J.C., Leszczynska, D., Leszczynski, J.,2013. Evaluation criteria for the quality of published experi-mental data on nanomaterials and their usefulness for QSARmodelling. SAR QSAR Environ. Res. 24, 995–1008.Mokshina, E., Richarz, A., Cronin, M., Kuz'min, V., 2013. NanoQSAR:metal oxides nanoparticles toxicity assessment. Poster Presen-tation at the MACC-5 – Methods and Applications of Computa-tional Chemistry, Fifth Symposium, 4 July 2013. Kharkiv,Ukraine.Puzyn, T., Rasulev, B., Gajewicz, A., Hu, X., Dasari, T.P., Michalk-ova, A., Hwang, H.M., Toropov, A., Leszczynska, D., Leszc-zynski, J., 2011. Using nano-QSAR to predict the cytotoxicity ofmetal oxide nanoparticles. Nat. Nanotechnol. 6, 175–178.Thomas, D.G., Gaheen, S., Harper, S.L., Fritts, M., Klaessig, F.,Hahn-Dantona, E., Paik, D., Pan, S., Stafford, G.A.,Freund, E.T., Klemm, J.D., Baker, N.A., 2013. ISA-TAB-Nano: aspeciﬁcation for sharing nanomaterial research data inspreadsheet-based format. BMC Biotechnol. 132, 2.

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