Computational Tool for Risk Assessment of Nanomaterials: Novel

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Computational tool for risk assessment of nanomaterials: Novel QSTRperturbation model for simultaneous prediction of ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under multiple experimental conditions Alejandro Speck-Planche, Valeria V. Kleandrova, Feng Luan, Humberto Gonzalez-Diaz, Juan M. Ruso, and M. Natalia Dias Soeiro Cordeiro Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es503861x • Publication Date (Web): 10 Nov 2014 Downloaded from http://pubs.acs.org on November 12, 2014

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Environmental Science & Technology

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Computational tool for risk assessment of nanomaterials: Novel QSTR-perturbation model for

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simultaneous prediction of ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under

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multiple experimental conditions

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Valeria V. Kleandrova a, Feng Luan a,b, Humberto González-Díaz c,d, Juan M. Ruso e,

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Alejandro Speck-Planche a,e*, and M. N. D. S. Cordeiro a*

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a

REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal.

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b

Department of Applied Chemistry, Yantai University, Yantai 264005, People's Republic of China.

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c

Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Bilbao, Spain.

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d

IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain.

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e

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Compostela, Spain.

Department of Applied Physics, University of Santiago de Compostela (USC), 15782, Santiago de

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ABSTRACT

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Nanomaterials have revolutionized modern science and technology due to their multiple applications in

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engineering, physics, chemistry, and biomedicine. Nevertheless, the use and manipulation of nanoparticles

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(NPs) can bring serious damages to living organisms and their ecosystems. For this reason, ecotoxicity and

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cytotoxicity assays are of special interest, in order to determine the potential harmful effects of NPs. Processes

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based on ecotoxicity and cytotoxicity tests can significantly consume time and financial resources. In this sense,

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alternative approaches such as quantitative structure-activity/toxicity relationships (QSAR/QSTR) modeling

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have provided important insights for the better understanding of the biological behavior of NPs that may be

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responsible for causing toxicity. Until now, QSAR/QSTR models have predicted ecotoxicity or cytotoxicity

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separately against only one organism (bio-indicator species or cell line), and have not reported information

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regarding the quantitative influence of characteristics other than composition or size. In this work, we

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developed a unified QSTR-perturbation model to simultaneously probe ecotoxicity and cytotoxicity of NPs

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under different experimental conditions, including diverse measures of toxicities, multiple biological targets,

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compositions, sizes and conditions to measure those sizes, shapes, times during which the biological targets

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were exposed to NPs, and coating agents. The model was created from 36488 cases (NP-NP pairs) and

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exhibited accuracies higher than 98% in both training and prediction sets. The model was used to predict 1

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toxicities of several NPs that were not included in the original dataset. The results of the predictions suggest

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that the present QSTR-perturbation model can be employed as a highly promising tool for the fast and efficient

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assessment of ecotoxicity and cytotoxicity of NPs.

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Keywords: nanoparticle, ecotoxicity, cytotoxicity, moving average approach, perturbation, QSTR, prediction.

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To whom correspondence should be addressed:

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E-mail: [email protected]; Fax: +351 220402659 (ASP)

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E-mail: [email protected]; Fax: +351 220402659 (MNDSC)

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INTRODUCTION

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Nanotechnology has emerged as a promising cross-disciplinary science, speeding up the development of

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science, and providing great benefits to mankind. In this sense, the 21st century has been characterized by the

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huge number of applications of nanoparticles (NPs) in different areas such as those associated to electronics,1-3

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catalysis,4-8 magnetism,9 optics and photonics,5, 10-11 as well as biomedical research.12-14

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Despite the growing applications of NPs, the appearance of toxic effects on human health and the remarkable

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damage to the ecosystems remain as two of the most serious problems. Thus, the need for evaluating the

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potential adverse effects of NPs on both human beings and ecosystems is of utmost importance. Towards that

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end, powerful experimental techniques based on high throughput/content screening (HTS/HCS) could

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definitely play an essential role for the fast and efficient assessment of ecotoxicity and cytotoxicity of NPs.15

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However, the real possibility of covering and filtering with such techniques the vast chemical diversity and

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biological behavior of NPs is very limited. This is indeed a very difficult task combining multi-factors such as

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chemical compositions, sizes and conditions under which the sizes were determined, shapes, coating agents,

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types of measures of the toxicity, biological targets (cell lines, crustaceans, algae, bacteria, fungi, plants, fish,

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etc), and times during which the biological targets were exposed to NPs.

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Owing to this, as well as to the high cost involved in both financial resources and time, there has been a

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considerable upsurge of interest in alternative computer-aided methods, which can help to rationalize the

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evaluation of NPs and further allow the search for safer nanoentities. Among them, quantitative-structure 2


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activity/toxicity relationships (QSAR/QSTR) models have provided important insights regarding several key

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characteristics of NPs, which may be responsible for triggering toxic effects.16-21 However, these classical

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QSAR/QSTR models have attempted to predict the toxicity of NPs against only one biological target, by

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considering only one type of toxicity test, and there is no information regarding the role of the other factors

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mentioned above. Therefore, such classical models are unable to gather a detailed knowledge on how the

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chemical diversity and the dissimilar experimental conditions may affect the toxic behavior of the NPs. In

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addition, the different toxic effects have been studied separately.

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Great advances have been realized for creating innovative and evolved QSAR/QSTR models. For instance,

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very recently, Gonzalez-Diaz et al have developed a promising perturbation approach of wide applicability.22

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Thus, several rigorously validated QSAR/QSTR perturbation models were created for modeling diverse

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processes and/or phenomena like organic reactions, parameters associated with the absorption, distribution,

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metabolism and elimination (ADME), and the self-aggregation of drugs into micellar NPs. To the best of our

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knowledge, there is yet no computational model able to simultaneously probe ecotoxicity and cytotoxicity of

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NPs. In fact, such a model would be of crucial importance in nanotechnology and related disciplines, because it

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would help to establish more precise guidelines regarding the hazard of nanomaterials for the environment and

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human health. To fill that gap, this work describes the development of a novel unified QSTR-perturbation

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model for predicting both the ecotoxicity and cytotoxicity of NPs under different experimental conditions.

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MATERIALS AND METHODS

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Dataset, descriptors, and generation of the QSTR-perturbation model

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We retrieved 229 NPs/cases from the literature,23-77 and these were the results of combining 32 diverse

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chemical compositions of nanomaterials, in which the toxicity assays were carried out by considering at least 1

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out of 5 measures of toxic effects (me), against at least 1 out of 50 biological targets (bt) with different

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complexities (algae, bacteria, cell lines, crustaceans, plants, fish, and others). Moreover, the NPs exhibited at

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least 1 out of 11 possible shape labels (ns), and their sizes were measured in at least 1 out of 8 conditions (dm).

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As final requirement, we considered at least 1 out of 10 assay times (ta) during which the biological targets

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were exposed to NPs. Some NPs of our dataset have been reported in their bare forms, while other NPs were

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coated by different organic molecules, specifically using 12 different coating agents (sc). We would like to

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point out that the combination of the first five elements/factors defines a unique experimental condition cj, 3



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under which a NP is tested. Thus, cj can be represented as an ontology with the form cj => (me, bt, ns, dm, ta),

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while the sixth element sc can be considered an external factor. Each of the 229 NPs/cases was assigned to 1 of

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2 possible classes or groups called ‘positive’ and ‘negative. These classes were related with the toxic effect of a

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NP i in a defined experimental condition [TEi(cj)]. Thus, a case was chosen as non-toxic [TEi(cj)=1] when it

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exhibited a high value of measured toxicity, otherwise, the compound was considered as toxic [TEi(cj)= −1].

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All these assignments were realized according to cutoff values, which are represented in Table 1. It should be

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noticed that the assignments were limited by the test times, i.e. the periods of time during which the diverse

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biological targets were exposed to the nanoparticles; the largest test time taken in this study being thus 120h.

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For each NP/case, four different descriptors were considered, namely: molar volume (V), electronegativity (E),

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polarizability (P), and the size of the NP (L). The first three descriptors are physicochemical properties that

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were extracted from the public source Chemicool Periodic Table,78 whereas descriptor L was based on

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available experimental data. Furthermore, it should be emphasized here that for those NPs formed by two or

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more elements, we have normalized the first three descriptors. That is, each physicochemical property was

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expressed as the sum of the properties of all the atoms which formed the molecule − representing the chemical

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composition of the NP, and after, the results were divided by total number of atoms.

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The QSAR-perturbation model should be focused on two principal objectives. First, the model needs to be

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sensitive to the changes in both the chemical compositions of the NPs, and the dissimilar sets of experimental

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conditions according to the ontology cj. But, if the original descriptors (V, E, P, L) are used, they will not be

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able to discriminate the ecotoxicity or cytotoxicity of a NP when the different elements of cj are varied.

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Consequently, we generated new descriptors by applying the moving average approach (MAA) to our data:79

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∆Di(cj) = Di –Di(cj)avg

(1)

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where Di is a descriptor/property (V, E, P, L), and Di(cj)avg describes each set nj of NPs/cases that were tested

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under the same experimental condition/ontology cj. For instance, for the case of me, Di(cj)avg is defined as the

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average of all the Di values of the NPs/cases, which were tested by considering the same measure of toxic

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effect. This procedure was applied to all elements of the ontology cj. In any case, we only used the descriptors

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of type ∆Di(cj) because they characterize both the chemical composition and the combination of the different 4


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elements of the experimental condition/ontology cj. Here, it should be pointed out that Eq. 1 was not applied to

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the element sc, because this element represents the presence of coating agents, which have their own chemical

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structures. In fact, to characterize the chemical structure of each coating agent, we used the following

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expression:

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Gµ k ( PP ) = µ k ( PP ) ⋅ 2 NMU

( 2)

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where the symbol µk(PP) is the spectral moment of order k of the bond adjacency matrix. These molecular

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descriptors were calculated from order 1 to 6, being weighted by physicochemical properties (PP) such as

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hydrophobicity and polar surface area. The µk(PP) descriptors were calculated with the software

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MODELSLAB.80 Further, the term NMU in Eq. 2 refers to the number of monomer units. For the case of an

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organic molecule, NMU=1, and for the case of a polymer, NMU is the ratio between the molar mass of the

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polymer and the sum of the molar masses of the monomers in that polymer. Finally, the term Gµk(PP) stands

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for the general spectral moment of order k of the bond adjacency matrix, being Gµk(PP) = 0 for uncoated NPs.

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The second aspect to be considered by the QSAR-perturbation model is an alternative to overcome certain

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limitations of the classical QSAR/QSTR models applied to date. The hypothesis underpinning classical

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QSAR/QSTR models is that changes in the structures and/or compositions of the chemicals will produce the

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corresponding variations in their toxicities. Thus, the intercept of a QSAR/QSTR equation is usually

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interpreted as a magnitude of the biological effect (activity, toxicity, etc) of a reference chemical. Nevertheless,

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this idea is very limited since, in principle, more than one chemical can be used as reference. Therefore, to

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overcome such limitation, we applied the aforementioned perturbation approach reported by Gonzalez-Diaz et

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al,22 according to the following steps. Firstly, random NP-NP pairs (36488 in total) were generated, and for

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each of these pairs, one of the NP was always used as the reference (initial) state, and the other as the new

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(output or final) state to be predicted. The QSTR-perturbation equation can be expressed in the following way:

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[

TE i ( c j ) nw = a 0 ⋅ TE i (c j ) rf + ∑ b j ⋅ ∆Di (c j ) nw − ∆ Di (c j ) rf

[

]

+ ∑ d j ⋅ G µ k ( PP ) nw − G µ k ( PP ) rf + e0

] (3)

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A close inspection of Eq. 3 reflects that the toxic effect TEi(cj)nw of a NP in the final or new state (new

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experimental condition), can be predicted if the toxic effect of the NP of the reference TEi(cj)rf is known, as 5



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well as the differences between the two NPs related to their chemical compositions, sizes, conditions under

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which the sizes were measured, shapes, measures of toxic effects, biological targets used for the toxicity tests,

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and time during which such biological targets were exposed to the NPs. Those differences are accounted for by

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the second term of Eq. 3, involving the descriptors ∆Di(cj)nw and ∆Di(cj)rf. Additionally, a0, bj, and d0 represent

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the coefficients which can be statistically determined by resorting to classification/regression techniques such

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as linear discriminant analysis (LDA) or multiple linear regression (MLR). A more compacted or abbreviated

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form of Eq. 3 can be written in the following way:

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( 4)

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where ∆∆D(cj) are the perturbation terms, and they characterize both the variations in the physicochemical

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properties between any two NPs, and the changes involving the first five elements of the experimental

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condition/ontology cj. On the other hand, the term ∆Gµk(PP) describes the differences between the chemical

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structures of the coating agents used for each NP.

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Our final dataset that comprises 36488 cases (NP-NP pairs), was randomly split into two series: training and

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prediction (validation or test) sets. The training set was employed to generate the QSTR-perturbation model,

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being formed by 27347 cases, 17560 of them assigned as non-toxic and 9787 toxic. The prediction (validation or

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test) set was used to assess the predictive power of the model. This set encompasses 9141 cases, 5880 non-

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toxic and 3261 toxic. It should be specifically detailed here that all the cases belonging to the prediction set

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were never used in the training set. We employed LDA as pattern recognition (classification) technique in

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order to search for the QSTR-perturbation model, and the program STATISTICA was used to accomplish this

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task,81 by applying a forward stepwise procedure as variable selection strategy.

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The statistical quality and predictive power of the model was analyzed by examining different indices such as

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Wilks’s lambda (λ), the chi-square (χ2), and the p-level.79 Additionally, we calculated the percentages of correct

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classification for non-toxic cases (sensitivity) and toxic cases (specificity), the overall percentage of correct

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classification (accuracy), the Mathew’s correlation coefficient (MCC),82 and the areas under the receiving

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operating characteristic (ROC) curves.83 All the percentages of correct classification, as well as MCC and ROC,

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were calculated for both training and prediction sets. The sequential steps involving the development of the

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QSTR-perturbation model are depicted in Figure 1.

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RESULTS AND DISCUSSION

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QSTR-perturbation model

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The best model found by us contains nine descriptors, which are the result of applying the variable selection

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strategy together with the principle of parsimony. Consequently, the present QSTR-perturbation model is that

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with the most appropriate combination of high statistical quality and the lowest number of variables: TEi (c j ) nw = 0.771TEi (c j ) rf − 0.092∆∆V (me ) + 1.226∆∆E (bt ) + 0.186∆∆P(bt )

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− 0.220∆∆P (ns ) + 1.569∆∆E ( d m ) + 0.028∆∆L(t a ) − 8.820 ⋅ 10 −3 ∆Gµ3 ( Hyd ) + 8.860 ⋅ 10 −9 ∆Gµ5 ( PSA) − 0.403 N = 27347 λ = 0.331 χ 2 = 30235.35

p − level < 10 −16

(5)

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In Eq. 5, we can observe small value of λ, a large χ2, and the remarkable small p-level, altogether indicating the

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good statistical quality of the QSTR-perturbation model, which is convergent with the high percentages of

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correct classification of cases, that is, sensitivity, specificity, and accuracy. Thus, for the training set, our model

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correctly classified 17288 of 17560 non-toxic cases with sensitivity of 98.45%, while the specificity was 98.16%

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(9607 of 9787 toxic cases were rightly classified). The accuracy for the training set was 98.35%. In the

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prediction set, the model exhibited a sensitivity of 98.95% (5818 of 5880 non-toxic cases were correctly

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classified), the specificity had a value of 98.34% (3207 of 3261 toxic cases were rightly classified), and the

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accuracy was 98.73%. Specific details regarding the chemical and toxicological data associated to each

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NP/case, together with their corresponding classifications can be found in Supporting Information files from 1

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to 5 (SI1-SI5) file. Additionally, the MCC values were 0.964 and 0.972 for training and prediction sets

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respectively, indicating in both cases the presence of strong correlation between the observed and the predicted

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toxic effects of the NPs. As final proof of the good performance of the QSTR-perturbation model, we

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calculated the areas under the ROC curves (Figure 2). By definition, the ROC curve going along the diagonal

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from bottom left to upper right represents pure-chance performance. For this reason, the area under the ROC

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curve can contribute in a very important way to the assessment of the quality and predictive ability of any

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model used as classifier. The value of this statistical index was 0.999 for both training and prediction sets. This

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means that a randomly selected NP/case from the class named “positive” will have a larger value of probability

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than a randomly selected NP/case from the class named “negative” in 99.9% of the time. Thus, we can deduce

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that our QSTR-perturbation model does not behave as a random classifier, for which the area equals 0.5.

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To sum up, all these statistical indices, including Eq. 5, clearly show that the present QSTR-perturbation

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model is able to integrate dissimilar data focused on ecotoxicity with those based on cytotoxicity, and displays

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a very good quality and predictive power, comparable with other models reported in the literature.22

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In addition, one should recall here that the uncertainty of the data can definitely affect the performance and

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reliability of the QSAR/QSTR models. In fact, a handicap commonly found in classical QSAR/QSTR models

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is that the variables used to construct them do not contain information about the experimental conditions under

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which the chemical species (drug, NPs, etc.) were tested, thereby increasing the uncertainty of the data. As can

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be seen in Eq. 5, our QSTR-perturbation model considers the most relevant factors which influence the toxic

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behavior of the NPs. This comes from the fact that the descriptors that entered in the model are derived from at

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least one element of the ontology cj. The use of the ontology cj, allows us to personalize/specify each toxicity

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test, thus helping us to efficiently control the uncertainty of the data.

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Physicochemical interpretation of the descriptors

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A very important aspect in this work is that the present QSTR-perturbation model (Eq. 5) can be interpreted in

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terms of the physicochemical information embodied in the different descriptors (see Table 2). Towards that

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goal, the descriptors will now be explained based on how the physicochemical properties should vary in the NP

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to be predicted (new state) in order to diminish its toxic effects. In reality, for a correct classification of a NP in

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the new (output or final) state belonging to a specific group, the NP to be employed as reference should

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preferably belong to the same class. For example, to classify a NP as non-toxic according to its observed value

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of toxic effect, the use of a reference NP assigned as non-toxic is preferred. In any case, the perturbations were

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carried out by considering NP-NP pairs resulting from mixing NPs/cases belonging to any of the groups of

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classification This, combined with the high percentages of correct classification (>98%) for both non-toxic and

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toxic cases, permit to deduce that our QSTR-perturbation model can account for aspects regarding the

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similarity/dissimilarity, involving chemical compositions, as well as experimental conditions characterized by 8


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the ontology cj. By analyzing Eq. 5, we can observe that the diminution of the toxic effect of a NP is

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influenced by the increment in the electronegativity of the atoms, and this property is described by ∆∆E(bt).

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This descriptor accounts for the changes in the chemical compositions between any two NPs, depending also

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on the biological targets against which the NPs were tested. A similar effect is observed for the case of

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∆∆E(dm), involving the same physicochemical property, but with the difference that this last descriptor

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depends on the conditions under which the sizes of the NPs were measured. In both cases, the presence of

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oxygen is a key element in the decrease of the toxicity of the NPs. This indicates that metal oxides (and in less

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degree salts) NPs will have a tendency to exhibit a lower toxicity than metallic NPs.

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The toxic effect of a NP can be also decreased by increasing the polarizability of the NP, which is encoded by

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∆∆P(bt), characterizing both the difference in chemical compositions between any two NPs, and the biological

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targets against which the NPs were tested. However, this descriptor is constrained by ∆∆P(ns), which describes

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the diminution of the polarizability, depending on the difference in chemical composition between any two

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NPs, and their shapes. Accordingly, the variation of the polarizability is controlled by both ∆∆P(bt) and

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∆∆P(ns) in such a way, that the increment of this property in the NPs may not cause toxicity for certain

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biological targets, but the excessive increment can lead to the release of ions to the solution, with possible

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devastating toxic effects to biological systems.

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Another factor with influence on the toxic effect of the NPs is the molar volume that is described by ∆∆V(m e).

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This descriptor will depend on the differences in the chemical compositions between any two NPs, and

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simultaneously, there will be a dependency on the measures of toxicity, which were used in the assays, and its

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diminution will decrease the toxicity of the NPs. By considering the chemical compositions, we need to say

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that ∆∆V(m e) will have a lower value with the decrease in the number of oxygen atoms, constraining the

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descriptors ∆∆E(bt) and ∆∆E(dm), which were explained above.

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A descriptor of tremendous importance is ∆∆L(ta), which expresses the difference in size between any two NPs,

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depending also on the interval of times during which they were tested against the biological targets. An

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increment in the value of ∆∆L(ta) will cause a reduction in the toxic effects, since it will lead to an increment in

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the size of the NPs, with the possible occurrence of agglomeration. This will prevent the penetration of the NPs

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through the cell membranes. 9



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Finally, the presence of coating agents is of particular interest, and in Eq. 5 there are two descriptors

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characterizing the chemical structures of the organic molecules attached to the surface of the NPs. The

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descriptor ∆Gµ3(Hyd) encodes the decrease in the hydrophobicity in regions formed by three bonds or less

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(three-membered rings included) and considers the difference in the chemical structures between the coating

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agents used for any two NPs. Converging with the information provided by ∆Gµ3(Hyd), the descriptor

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∆Gµ5(PSA) indicates the increment in the polar surface area in molecular regions formed by five bonds or less

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(including five-membered rings), depending on the variations of the chemical structures between the coating

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agents used for any two NPs. Thus, the interpretation of the descriptors ∆Gµ3(Hyd) and ∆Gµ5(PSA) allow us

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to conclude that the larger the polar regions (major number of polar groups) of the coating agents, the greater

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will be their influences in the decrease of the toxic effect of the NPs.

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Virtual prediction of ecotoxicity and cytotoxicity of new NPs

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The analysis of all the statistical indices obtained, including the classification results (SI1-SI5), demonstrated

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the ability of the QSTR-perturbation model to integrate dissimilar kinds of chemical data with toxicological

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profiles (i.e. ecotoxicity and cytotoxicity) under multiple sets of experimental conditions. This forwards us to

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envisage that the QSTR-perturbation model might serve as a powerful computational tool to perform virtual

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screening of NPs, related to their diverse ecotoxic and cytotoxic effects. For this reason, and in order to show

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how the model works in practice, we attempted to predict the toxicities of several types of NPs, namely of the

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following materials: silver with sizes 43.4nm, 62.6nm, and 46.3nm (Ag-43.4nm, Ag-62.6nm, and Ag-46.3nm

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respectively); nickel ferrite with size 97nm (NiFe2O4-97nm), and iron(III) oxide with size 30nm (Fe2O3-30nm).

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It should be emphasized here that all these NPs were not considered to build our dataset, i.e. they do not

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belong to either the training or the prediction sets, and were collected from external sources.84-86

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As commented before, QSAR/QSTR models have several limitations. For instance, in these models, each case

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(chemical, drug, NP) constitute one single prediction in the data set, and the intercept of the equation is often

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considered as the value of a chemical species used as references.87 The fact that any case can be used as

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reference, has been an aspect often neglected or underestimated during many years, and it can affect the

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performance and reliability of the model. With the generation of the present QSTR-perturbation model, we

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have considered random NP-NP pairs, where each NP was used many times in both cases, i.e., as NP in the 10


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initial state (reference NP) and as NP in the new (output or final) state. That is to say, each NP was used as

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reference and participated in many predictions of other NPs, and at the same time, the same NP was predicted

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many times from the others. This route breaks thus the boundaries of the classical QSAR/QSTR models,

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allowing us to obtain more precise and realistic results.

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Moreover, the decision of predicting a NP to belong to the group of non-toxic or toxic species was based on

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consensus predictions. Therefore, we used our QSTR-perturbation model to predict each new NP mentioned

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above 229 times, which means that we employed the original 229 NPs/cases as references. Each new NP was

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considered to really belong to its observed group (prediction of TEi(cj)nw), if its predictions were correct more

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than 50% of the times. If the percentage of correct classification was lower than 50%, then the prediction of

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TEi(cj)nw performed by the model was considered to be incorrect, and for a percentage equal 50%, we

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considered that the model performed a random prediction. To do so, we calculated a statistical index called

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consensus percentage (%CCT), defined as the percentage of times in which any NP was correctly predicted as

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positive or negative according to its experimental value of toxicity. The explicit results of these predictions can

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be found in the Supporting Information (file SI6), while a summary of them is depicted in Table 3.

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First, we attempted to predict spherical Ag NPs (Ag-43.4nm, Ag-62.6nm, and Ag-46.3nm).84 The cytotoxicity

307

of these NPs was evaluated against RAW264.7 cells (mouse) during a period of 4h. The sizes of these NPs

308

were measured in aqueous suspension; the Ag-43.4nm NPs was coated with polydiallyldimethylammonium

309

chloride (PDADMAC), and the Ag-46.3nm NPs was coated with oleate, whereas the Ag-62.6nm NPs were

310

uncoated. The CC50 values for Ag-43.4nm, Ag-62.6nm, and Ag-46.3nm were 0.1µg/mL (0.927µM), 4.9µg/mL

311

(45.426µM), and 1.1µg/mL (10.198µM), respectively. According to these values of cytotoxicity and taking

312

into account the cutoffs in Table 1, all the Ag NPs should be predicted as toxic. The results from Table 3

313

confirm that our theoretical predictions are in clear agreement with the experimental evidences. For all the

314

cases, the Ag NPs were correctly predicted as toxic more than 50% of times. Notice that the Ag NPs with the

315

lowest degree of cytotoxicity are those belonging to Ag-62.6nm, and this can be easily explained because these

316

uncoated NPs have a considerable larger size due to aggregation. Another interesting detail is that both the Ag-

317

43.4nm and Ag-46.3nm have very similar sizes, but the percentages of correct classification as toxic NPs are

318

100% and 89.96%, respectively. According to Eq. 5, the hydrophobicity should diminish and the polar surface

319

area should be increased in order to diminish the toxic effects of the NPs. Consequently, this difference in 11



Page 12 of 27

320

percentages of correct classification can be explained by the difference in the chemical structures of the

321

coating agents. The PDADMAC used for Ag-43.4nm has a larger hydrophobic region than the oleate used for

322

Ag-46.3nm. At the same time, the carboxylate group present in oleate has a higher polar surface area than the

323

cationic nitrogen in the structure of PDADMAC. The large hydrophobic regions and very low polar surface in

324

this last coating agent are detrimental to the toxic effect of Ag-43.4nm.

325 326

A second case of study was devoted to predict the cytotoxicity of nickel ferrite NPs (NiFe2O4-97nm).85 These

327

uncoated NPs with irregular shape were tested during 24h against A549 human cells. The size of NiFe2O4-

328

97nm NPs was determined in DMEM culture medium. The reported CC50 value for these NPs was higher than

329

100µg/mL (426.652µM). Then, by applying the same procedure as in the case of Ag NPs, we found that the

330

NiFe2O4-97nm NPs should be predicted as non-toxic. An analysis of Table 3 demonstrates that these NPs were

331

effectively predicted as non-toxic in 98.69% of times. As principal factors contributing to the low toxicity of

332

NiFe2O4-97nm, one can mention its very large size, its high electronegativity and its low polarizability mainly

333

due to the presence of oxygen atoms.

334 335

The prediction of ecotoxicity of Fe2O3-30nm NPs constituted our final case of study.86 No shape was reported

336

for these NPs, and the ecotoxic effects were assessed against Danio rerio (embryos). The size of Fe2O3-30nm

337

was determined for the dry powder without coating agent. The LC50 value was reported to be 53.35mg/L

338

(334.08µM) for a period of 168h. The higher assay time reported in our dataset is 120h, which means that in

339

principle it would not be possible to predict these NPs. However, the authors of this work reported toxicity

340

assays of Fe2O3-30nm during many periods of time, including 24h, 48h, 72h, 96h and 120h, and they

341

concluded that the ecotoxic effect of Fe2O3-30nm is time-dependent. This clearly indicates that the LC50 values

342

of Fe2O3-30nm against Danio rerio (embryos) for periods of time inferior to 168h are equal or higher than

343

53.35mg/L (334.08µM). Therefore, for the five exposure times mentioned above and contemplated within our

344

dataset, Fe2O3-30nm should be predicted as non-toxic by the QSTR-perturbation model. The results of the

345

predictions demonstrate that the NPs in this last case of study were correctly predicted far more than 50% of

346

times (see Table 3). Notice that despite the relative small size of Fe2O3-30nm, its high electronegativity and

347

small value of polarizability are the main physicochemical properties that influence the low ecotoxicity of

348

these NPs.

349 12


Page 13 of 27


350

To conclude, we have illustrated some representative results that are consistent with other experimental data

351

found in the literature regarding the toxicity profiles of NPs. In fact, the model confirmed to be a very

352

promising in silico tool for the computational assessment of the cytotoxicity of Ag and NiFe2O4 NPs, as well as

353

for the prediction of the ecotoxicity of Fe2O3 NPs. The present QSTR-perturbation model can thus be viewed

354

as an encouraging alternative approach focused on giving new and deeper insights of the overall toxicological

355

behavior of NPs, playing also an important role in their virtual screening for both ecotoxicity and cytotoxicity.

356

Therefore, new horizons towards the improvement of the regulatory framework for nanomaterials safety can be

357

opened with this in silico approach.

358 359

ACKNOWLEDGMENTS

360

This work received financial support from the European Union (FEDER funds through COMPETE) and

361

National Funds (FCT, Fundação para a Ciência e Tecnologia) through project Pest-C/EQB/LA0006/2013. The

362

work also received financial support from the European Union (FEDER funds) under the framework of QREN

363

through Project NORTE-07-0124-FEDER-000067-NANOCHEMISTRY, and MICINN-Spain (Project No.

364

MAT2011-25501). ASP and FL acknowledge also FCT and the European Social Fund for financial support

365

(Grants SFRH/BD/77690/2011 and SFRH/BPD/63666/2009, respectively). To all financing sources the

366

authors are greatly indebted.

367 368

Supporting Information Available

369

Chemical and toxicological data of the nanoparticles understudy along with their classification. This

370

information is available free of charge via the Internet at http://pubs.acs.org/.

371 372 373 374 375 376 377

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charge and concentration dependent ecotoxicity of three organo-coated silver nanoparticles: comparison

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between general linear model-predicted and observed toxicity. Sci. Total Environ. 2014, 468–469, 968–976.

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survival in two daphnid species. PLoS One 2013, 8 (8), e71260.

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Captions

600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630

Figure 1. Essential steps involving in the development of the QSTR-perturbation model. Figure 2. Areas under the ROC curves.

21



631

Page 22 of 27

Table 1. Conditions under which a nanoparticle was considered as non-toxic Measure of toxicity Cutoff Concept (Units) value Cytotoxic concentration of the nanoparticle CC50 (µM) ≥188.57 leading to 50% reduction in cell viability assays Effective concentration of the nanoparticle EC50 (µM) which inhibits at 50% the growth of the living ≥168.45 system. Concentration of the nanoparticle which IC50 (µM)p inhibits the root elongation of the living system ≥177.62 (plant) at 50%. Concentration which causes toxic effects in TC50 (µM) ≥126.52 50% of the living system. Lethal concentration which causes mortality in ≥250.00 LC50 (µM) 50% of the living system.

632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 22


Page 23 of 27


676

Table 2. Final variables (descriptors) that entered in the QSTR-perturbation model Descriptor Concept Binary (classification) variable expressing the toxic effect of the TEi(cj)rf nanoparticle used as reference. Perturbation term that characterizes the variations in the molar volume between the new (output or final state) nanoparticle and the other used as ∆∆V(m e) reference, also depending on the measures of the toxic effects. Perturbation term that describes the changes in the electronegativity between the new (output or final state) nanoparticle and the other used as ∆∆E(bt) reference, also depending on the biological targets. Perturbation term that accounts for the variations in the polarizability between the new (output or final state) nanoparticle and the other used as ∆∆P(bt) reference, also depending on the biological targets. Perturbation term that characterizes the changes in the polarizability between the new (output or final state) nanoparticle and the other used as ∆∆P(ns) reference, also depending on the shapes of the nanoparticles. Perturbation term that describes the variations in the electronegativity between the new (output or final state) nanoparticle and the other used as ∆∆E(dm) reference, also depending on the conditions under which the sizes of the nanoparticles were measured. Perturbation term that accounts for the changes in the size between the new (output or final state) nanoparticle and the other used as reference, ∆∆L(ta) also depending on the intervals of time during which the biological targets were exposed to the nanoparticles. Perturbation spectral moment of order 3, weighted by the hydrophobicity, and characterizing the differences between the chemical structure of the ∆Gµ3(Hyd) coating agent used in the new (output or final state) nanoparticle, and the coating agent used for the reference nanoparticle. Perturbation spectral moment of order 5, weighted by the polar surface area, and describing the differences between the chemical structure of the ∆Gµ5(PSA) coating agent used in the new (output or final state) nanoparticle, and the coating agent used for the reference nanoparticle.

677 678 679 680 681 682 683 684 685 686 687 688 689 690 23



691

Page 24 of 27

Table 3. Results of the consensus predictions performed by the QSTR-perturbation model me bt ns dm te sca TEi(cj)nw %CCTb NP Ag-43.4nm

CC50 (µM)

RAW264.7 (M)

spherical

H2O

4

Ag-62.6nm

CC50 (µM)

RAW264.7 (M)

spherical

H2O

Ag-46.3nm

CC50 (µM)

RAW264.7 (M)

spherical

H2O

A549 (H)

irregular

NiFe2O4-97nm CC50 (µM)

PDADMAC

-1

100

4

UC

-1

73.80

4

oleate

-1

89.96

DMEM

24

UC

1

98.69

Fe2O3-30nm

LC50 (µM) Danio rerio (embryos)

N/A

Dry

24

UC

1

78.17

Fe2O3-30nm


N/A

Dry

48

UC

1

86.03

Fe2O3-30nm


N/A

Dry

72

UC

1

85.15

Fe2O3-30nm


N/A

Dry

96

UC

1

85.15

Fe2O3-30nm LC50 (µM) Danio rerio (embryos) N/A Dry 120 UC 1 79.04 692 a PDADMAC is the abbreviation used for polydiallyldimethylammonium chloride; UC is referred to uncoated 693 nanoparticles. b Consensus percentage, i.e., the percentage of times that the QSTR-perturbation model correctly 694 predicted the nanoparticle according to their observed value TEi(cj)nw.

695 696 697 698

24


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TOC Art 252x159mm (96 x 96 DPI)



Essential steps involving in the development of the QSTR-perturbation model. 80x61mm (300 x 300 DPI)


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Areas under the ROC curves. 221x166mm (96 x 96 DPI)


Computational Tool for Risk Assessment of Nanomaterials: Novel

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