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Toropova AP, Toropov AA. Nanomaterials: Quasi-SMILES as a flexible basis for regulation and environmental risk assessment. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 823:153747. [PMID: 35149067 DOI: 10.1016/j.scitotenv.2022.153747] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 02/04/2022] [Accepted: 02/04/2022] [Indexed: 06/14/2023]
Abstract
Basic principles and problems of the systematization of data on nanomaterials are discussed. The eclectic character of nanomaterials is defined as the key difference between nanomaterials and traditional substances. The quasi-SMILES technique is described and discussed. The possible role of the approach is bridging between experimentalists and developers of models for endpoints related to nanomaterials. The use of models on the possible impact of nanomaterials on the environment and human health has been collected and compared. The new criteria of the predictive potential for the above models are discussed. The advantage of the statistical criteria sensitive simultaneously to both the correlation coefficient and the root mean square error noted. The rejection of the border between the effect of the biochemical reality of substances at a molecular level and the effect of experiment conditions at the macro level gives the possibility to develop models that are epistemologically more reliable in the comparison with traditional models based exclusively on the molecular structure-biological activity interdependence (without taking into account experimental conditions). Models of the physicochemical and biochemical behaviour of nanomaterials are necessary in order to develop and apply new industrial achievements, everyday comfort species, medicine, cosmetics, and foods without negative effects on ecology and human health. The CORAL (abbreviation CORrelation And Logic) software provides the user with the possibility to build up nano-QSAR models as a mathematical function of so-called correlation weights of fragments of quasi-SMILES. These models are built up via the Monte Carlo method. Apparently, the quasi-SMILES is a universal representation of nano-reality since there is no limitation to choose the list of eclectic data able to have an impact on nano-phenomena. This paradigm is a convenient language to the conversation of experimentalists and developers of models for nano-phenomena.
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Affiliation(s)
- Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
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Varsou DD, Ellis LJA, Afantitis A, Melagraki G, Lynch I. Ecotoxicological read-across models for predicting acute toxicity of freshly dispersed versus medium-aged NMs to Daphnia magna. CHEMOSPHERE 2021; 285:131452. [PMID: 34265725 DOI: 10.1016/j.chemosphere.2021.131452] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 06/29/2021] [Accepted: 07/04/2021] [Indexed: 06/13/2023]
Abstract
Nanoinformatics models to predict the toxicity/ecotoxicity of nanomaterials (NMs) are urgently needed to support commercialization of nanotechnologies and allow grouping of NMs based on their physico-chemical and/or (eco)toxicological properties, to facilitate read-across of knowledge from data-rich NMs to data-poor ones. Here we present the first ecotoxicological read-across models for predicting NMs ecotoxicity, which were developed in accordance with ECHA's recommended strategy for grouping of NMs as a means to explore in silico the effects of a panel of freshly dispersed versus environmentally aged (in various media) Ag and TiO2 NMs on the freshwater zooplankton Daphnia magna, a keystone species used in regulatory testing. The dataset used to develop the models consisted of dose-response data from 11 NMs (5 TiO2 NMs of identical cores with different coatings, and 6 Ag NMs with different capping agents/coatings) each dispersed in three different media (a high hardness medium (HH Combo) and two representative river waters containing different amounts of natural organic matter (NOM) and having different ionic strengths), generated in accordance with the OECD 202 immobilization test. The experimental hypotheses being tested were (1) that the presence of NOM in the medium would reduce the toxicity of the NMs by forming an ecological corona, and (2) that environmental ageing of NMs reduces their toxicity compared to the freshly dispersed NMs irrespective of the medium composition (salt only or NOM-containing). As per the ECHA guidance, the NMs were grouped into two categories - freshly dispersed and 2-year-aged and explored in silico to identify the most important features driving the toxicity in each group. The final predictive models have been validated according to the OECD criteria and a QSAR model report form (QMRF) report included in the supplementary information to support adoption of the models for regulatory purposes.
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Affiliation(s)
| | - Laura-Jayne A Ellis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT, Birmingham, UK
| | | | - Georgia Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, Vari, Greece.
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT, Birmingham, UK.
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Toropova AP, Toropov AA, Leszczynska D, Leszczynski J. Application of quasi-SMILES to the model of gold-nanoparticles uptake in A549 cells. Comput Biol Med 2021; 136:104720. [PMID: 34364261 DOI: 10.1016/j.compbiomed.2021.104720] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/27/2021] [Accepted: 07/27/2021] [Indexed: 11/30/2022]
Abstract
Cell death is critical to human health and is associated with a variety of medical conditions. Therefore, new controllers of cell death are needed for the treatment of diverse diseases. In particular, nanoparticles (NP) are now regularly used in various applications, including a variety of products and medicines. Gold nanoparticles (GNPs) are widely used in the medical field against A549 lung carcinoma cells. The present study is devoted to developing computational models of the cellular uptake potentials by A549 cells of gold nanoparticles (GNPs) under various conditions. Simplified molecular input-line entry system (SMILES) is an efficient tool to represent the molecular structure by a sequence of symbols. Quasi-SMILES represents an extended version of SMILES where symbols to denote physicochemical and/or biochemical conditions are added. In other words, the quasi-SMILES represents a biochemical (medical) phenomenon related to the whole matter (not only molecular structure). We developed models for the cellular utpake potential of gold nanoparticles (GNPs) in A549 [10-11 g Au/Cell] under various conditions based on quasi-SMILES using the Monte Carlo method. The statistical quality of these models is quite good.
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Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy.
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
| | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch Street, Jackson, MS, 39217-0510, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, 1325 Lynch Street, Jackson, MS, 39217, USA
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Saleemi MA, Hosseini Fouladi M, Yong PVC, Chinna K, Palanisamy NK, Wong EH. Toxicity of Carbon Nanotubes: Molecular Mechanisms, Signaling Cascades, and Remedies in Biomedical Applications. Chem Res Toxicol 2020; 34:24-46. [PMID: 33319996 DOI: 10.1021/acs.chemrestox.0c00172] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Carbon nanotubes (CNTs) are the most studied allotropic form of carbon. They can be used in various biomedical applications due to their novel physicochemical properties. In particular, the small size of CNTs, with a large surface area per unit volume, has a considerable impact on their toxicity. Despite of the use of CNTs in various applications, toxicity is a big problem that requires more research. In this Review, we discuss the toxicity of CNTs and the associated mechanisms. Physicochemical factors, such as metal impurities, length, size, solubilizing agents, CNTs functionalization, and agglomeration, that may lead to oxidative stress, toxic signaling pathways, and potential ways to control these mechanisms are also discussed. Moreover, with the latest mechanistic evidence described in this Review, we expect to give new insights into CNTs' toxicological effects at the molecular level and provide new clues for the mitigation of harmful effects emerging from exposure to CNTs.
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Affiliation(s)
- Mansab Ali Saleemi
- School of Biosciences, Faculty of Health and Medical Sciences, Taylor's University Lakeside Campus, 47500 Subang Jaya, Selangor Darul Ehsan, Malaysia
| | - Mohammad Hosseini Fouladi
- School of Engineering, Faculty of Innovation and Technology, Taylor's University Lakeside Campus, 47500 Subang Jaya, Selangor Darul Ehsan, Malaysia
| | - Phelim Voon Chen Yong
- School of Biosciences, Faculty of Health and Medical Sciences, Taylor's University Lakeside Campus, 47500 Subang Jaya, Selangor Darul Ehsan, Malaysia
| | - Karuthan Chinna
- School of Medicine, Faculty of Health and Medical Sciences, Taylor's University Lakeside Campus, 47500 Subang Jaya, Selangor Darul Ehsan, Malaysia
| | - Navindra Kumari Palanisamy
- Department of Medical Microbiology and Parasitology, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh Campus, 47000 Sungai Buloh, Selangor, Malaysia
| | - Eng Hwa Wong
- School of Medicine, Faculty of Health and Medical Sciences, Taylor's University Lakeside Campus, 47500 Subang Jaya, Selangor Darul Ehsan, Malaysia
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Toropov AA, Toropova AP. Correlation intensity index: Building up models for mutagenicity of silver nanoparticles. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 737:139720. [PMID: 32554036 DOI: 10.1016/j.scitotenv.2020.139720] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/21/2020] [Accepted: 05/24/2020] [Indexed: 06/11/2023]
Abstract
Nanomaterials become significant component of economics. Consequently, nanomaterials become object of environmental sciences. There is a traditional list of endpoints which are indicators of the ecological risk. Mutagenicity is one of important component in this list. The quasi-SMILES approach, that in contrast to majority of work dedicated to modelling behaviour of nanomaterials gives possibility to consider experimental conditions as well as other circumstances which can impact the behaviour of nanomaterials is suggested. This is carried out via so-called quasi-SMILES. The quasi-SMILES is a line on of codes that contains all the above available eclectic data. Modelling process aimed to build up a model involves Correlation Intensity Index (CII) that is a new criterion of predictive potential of models. The scheme of calculation of CII is described in this work in the first time. The applying of CII together with Index of Ideality Correlation (IIC) in modelling of mutagenicity of silver nanoparticles by the Monte Carlo method using the CORAL software (http://www.insilico.eu/coral) indicates that application of the CII improves the predictive potential of these models for three random splits into the training set (75%) and validation set (25%).
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Affiliation(s)
- Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
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Ahmadi S, Toropova AP, Toropov AA. Correlation intensity index: mathematical modeling of cytotoxicity of metal oxide nanoparticles. Nanotoxicology 2020; 14:1118-1126. [DOI: 10.1080/17435390.2020.1808252] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Shahin Ahmadi
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Alla P. Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Andrey A. Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
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Singh AV, Ansari MHD, Rosenkranz D, Maharjan RS, Kriegel FL, Gandhi K, Kanase A, Singh R, Laux P, Luch A. Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine. Adv Healthc Mater 2020; 9:e1901862. [PMID: 32627972 DOI: 10.1002/adhm.201901862] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 04/17/2020] [Indexed: 12/22/2022]
Abstract
Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial-based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)-based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure-activity relationships at nanoscale (nano-QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
| | - Mohammad Hasan Dad Ansari
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Via Rinaldo Piaggio 34, Pontedera, 56025, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Via Rinaldo Piaggio 34, Pontedera, 56025, Italy
| | - Daniel Rosenkranz
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
| | - Romi Singh Maharjan
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
| | - Fabian L Kriegel
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
| | - Kaustubh Gandhi
- Bosch Sensortec GmbH, Gerhard-Kindler-Straße 9, Reutlingen, 72770, Germany
| | - Anurag Kanase
- Department of Bioengineering, Northeastern University, Boston, MA, 02215, USA
| | - Rishabh Singh
- Rajarshi Shahu College of Engineering, Pune, Maharashtra, 411033, India
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, 10589, Germany
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Toropov AA, Toropova AP. The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR. Curr Comput Aided Drug Des 2020; 16:197-206. [DOI: 10.2174/1573409915666190328123112] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 02/15/2019] [Accepted: 03/19/2019] [Indexed: 11/22/2022]
Abstract
Background:
The Monte Carlo method has a wide application in various scientific researches.
For the development of predictive models in a form of the quantitative structure-property / activity relationships
(QSPRs/QSARs), the Monte Carlo approach also can be useful. The CORAL software provides the
Monte Carlo calculations aimed to build up QSPR/QSAR models for different endpoints.
Methods:
Molecular descriptors are a mathematical function of so-called correlation weights of various
molecular features. The numerical values of the correlation weights give the maximal value of a target
function. The target function leads to a correlation between endpoint and optimal descriptor for the visible
training set. The predictive potential of the model is estimated with the validation set, i.e. compounds that
are not involved in the process of building up the model.
Results:
The approach gave quite good models for a large number of various physicochemical, biochemical,
ecological, and medicinal endpoints. Bibliography and basic statistical characteristics of several CORAL
models are collected in the present review. In addition, the extended version of the approach for more
complex systems (nanomaterials and peptides), where behaviour of systems is defined by a group of conditions
besides the molecular structure is demonstrated.
Conclusion:
The Monte Carlo technique available via the CORAL software can be a useful and convenient
tool for the QSPR/QSAR analysis.
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Affiliation(s)
- Andrey A. Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milan, Italy
| | - Alla P. Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milan, Italy
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Qi R, Pan Y, Cao J, Jia Z, Jiang J. The cytotoxicity of nanomaterials: Modeling multiple human cells uptake of functionalized magneto-fluorescent nanoparticles via nano-QSAR. CHEMOSPHERE 2020; 249:126175. [PMID: 32078856 DOI: 10.1016/j.chemosphere.2020.126175] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 02/04/2020] [Accepted: 02/09/2020] [Indexed: 06/10/2023]
Abstract
The vast majority of nanomaterials have attracted an upsurge of interest since their discovery and considerable researches are being carried out about their adverse outcomes for human health and the environment. In this study, two regression-based quantitative structure-activity relationship models for nanoparticles (nano-QSAR) were established to predict the cellular uptakes of 109 functionalized magneto-fluorescent nanoparticles to pancreatic cancer cells (PaCa2) and human umbilical vein endothelial cells (HUVEC) lines, respectively. The improved SMILES-based optimal descriptors encoded with certain easily available physicochemical properties were proposed to describe the molecular structure characteristics of the involved nanoparticles, and the Monte Carlo method was used for calculating the improved SMILES-based optimal descriptors. Both developed nano-QSAR models for cellular uptake prediction provided satisfactory statistical results, with the squared correlation coefficient (R2) being 0.852 and 0.905 for training sets, and 0.822 and 0.885 for test sets, respectively. Both models were rigorously validated and further extensively compared to literature models. Predominant physicochemical features responsible for cellular uptake were identified by model interpretation. The proposed models could be reasonably expected to provide guidance for synthesizing or choosing safer, more suitable surface modifiers of desired properties prior to their biomedical applications.
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Affiliation(s)
- Ronghua Qi
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 210009, China
| | - Yong Pan
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 210009, China.
| | - Jiakai Cao
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 210009, China
| | - Zhenhua Jia
- Institute of Advanced Synthesis, School of Chemistry and Molecular Engineering, Nanjing Tech University, Nanjing, 210009, China
| | - Juncheng Jiang
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 210009, China
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Toropova AP, Toropov AA, Leszczynska D, Leszczynski J. The index of ideality of correlation: models of the flash points of ternary mixtures. NEW J CHEM 2020. [DOI: 10.1039/d0nj00121j] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Reliable information related to the flash point of ternary mixtures assists in the rational classification of different ternary mixtures of liquids.
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Affiliation(s)
- Alla P. Toropova
- Laboratory of Environmental Chemistry and Toxicology
- Department of Environmental Health Science
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS
- 20156 Milano
- Italy
| | - Andrey A. Toropov
- Laboratory of Environmental Chemistry and Toxicology
- Department of Environmental Health Science
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS
- 20156 Milano
- Italy
| | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center
- Department of Civil and Environmental Engineering
- Jackson State University
- Jackson
- USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center
- Department of Chemistry
- Physics and Atmospheric Sciences
- Jackson State University
- Jackson
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Buglak AA, Zherdev AV, Dzantiev BB. Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials. Molecules 2019; 24:molecules24244537. [PMID: 31835808 PMCID: PMC6943593 DOI: 10.3390/molecules24244537] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 11/24/2019] [Accepted: 12/10/2019] [Indexed: 12/12/2022] Open
Abstract
Although nanotechnology is a new and rapidly growing area of science, the impact of nanomaterials on living organisms is unknown in many aspects. In this regard, it is extremely important to perform toxicological tests, but complete characterization of all varying preparations is extremely laborious. The computational technique called quantitative structure–activity relationship, or QSAR, allows reducing the cost of time- and resource-consuming nanotoxicity tests. In this review, (Q)SAR cytotoxicity studies of the past decade are systematically considered. We regard here five classes of engineered nanomaterials (ENMs): Metal oxides, metal-containing nanoparticles, multi-walled carbon nanotubes, fullerenes, and silica nanoparticles. Some studies reveal that QSAR models are better than classification SAR models, while other reports conclude that SAR is more precise than QSAR. The quasi-QSAR method appears to be the most promising tool, as it allows accurately taking experimental conditions into account. However, experimental artifacts are a major concern in this case.
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Affiliation(s)
- Andrey A. Buglak
- A. N. Bach Institute of Biochemistry, Research Center of Biotechnology, Russian Academy of Sciences, Leninsky Prospect 33, 119071 Moscow, Russia; (A.V.Z.); (B.B.D.)
- Physical Faculty, St. Petersburg State University, 7/9 Universitetskaya Naberezhnaya, 199034 St. Petersburg, Russia
- Correspondence: ; Tel.: +7-(495)-954-27-32
| | - Anatoly V. Zherdev
- A. N. Bach Institute of Biochemistry, Research Center of Biotechnology, Russian Academy of Sciences, Leninsky Prospect 33, 119071 Moscow, Russia; (A.V.Z.); (B.B.D.)
- Institute of Physiologically Active Compounds, Russian Academy of Sciences, Severny Proezd 1, 142432 Chernogolovka, Moscow Region, Russia
| | - Boris B. Dzantiev
- A. N. Bach Institute of Biochemistry, Research Center of Biotechnology, Russian Academy of Sciences, Leninsky Prospect 33, 119071 Moscow, Russia; (A.V.Z.); (B.B.D.)
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Toropov AA, Toropova AP. The Correlation Contradictions Index (CCI): Building up reliable models of mutagenic potential of silver nanoparticles under different conditions using quasi-SMILES. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 681:102-109. [PMID: 31102811 DOI: 10.1016/j.scitotenv.2019.05.114] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 05/07/2019] [Accepted: 05/08/2019] [Indexed: 06/09/2023]
Abstract
The interpretation of the mutagenic potential of silver nanoparticles as a mathematical function of (i) dose; (ii) coating; and (iii) type of mutagenicity (TA98 and TA100) gives quantitative models with good statistical quality. So-called quasi-SMILES are used to represent examined objects (silver nanoparticles under different conditions) for building up models. Simplified molecular input-line entry systems (SMILES) is a well-known sequence of symbols for representation of the molecular structure. Quasi-SMILES is a similar sequence of symbols for representation of experimental conditions. The Correlation Contradiction Index (CCI) calculated with data on the calibration set gives possibility to predict quality of correlation of "experimental vs. calculated values of endpoint" for external validation set.
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Affiliation(s)
- Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy
| | - Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy.
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Toropova AP, Toropov AA, Carnesecchi E, Benfenati E, Dorne JL. The index of ideality of correlation: models for flammability of binary liquid mixtures. CHEMICAL PAPERS 2019. [DOI: 10.1007/s11696-019-00903-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Forest V, Hochepied JF, Pourchez J. Importance of Choosing Relevant Biological End Points To Predict Nanoparticle Toxicity with Computational Approaches for Human Health Risk Assessment. Chem Res Toxicol 2019; 32:1320-1326. [PMID: 31243983 DOI: 10.1021/acs.chemrestox.9b00022] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Because it is impossible to assess in vitro or in vivo the toxicity of all nanoparticles available on the market on a case-by-case basis, computational approaches have been proposed as useful alternatives to predict in silico the hazard potential of engineered nanoparticles. Despite promising results, a major issue associated with these mathematical models lies in the a priori choice of the physicochemical descriptors and the biological end points. We performed a thorough bibliographic survey on the biological end points used for nanotoxicology purposes and compared them between experimental and computational approaches. They were found to be disparate: while conventional in vitro nanotoxicology assays usually investigate a large array of biological effects using eukaryotic cells (cytotoxicity, pro-inflammatory response, oxidative stress, genotoxicity), computational studies mostly focus on cell viability and also include studies on prokaryotic cells. We may thus wonder the relevance of building complex mathematical models able to predict accurately a biological end point if this latter is not the most relevant to support human health risk assessment. The choice of biological end points clearly deserves to be more carefully discussed. This could bridge the gap between experimental and computational nanotoxicology studies and allow in silico predictive models to reach their full potential.
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Affiliation(s)
- Valérie Forest
- Mines Saint-Etienne, Univ Lyon, Univ Jean Monnet , INSERM, U 1059 Sainbiose, Centre CIS , F-42023 Saint-Etienne , France
| | - Jean-François Hochepied
- MINES ParisTech , PSL Research University , MAT - Centre des matériaux, CNRS UMR 7633 , BP 87 91003 Evry , France.,UCP, ENSTA ParisTech , Université Paris-Saclay , 828 bd des Maréchaux , 91762 Palaiseau cedex , France
| | - Jérémie Pourchez
- Mines Saint-Etienne, Univ Lyon, Univ Jean Monnet , INSERM, U 1059 Sainbiose, Centre CIS , F-42023 Saint-Etienne , France
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Yan X, Sedykh A, Wang W, Zhao X, Yan B, Zhu H. In silico profiling nanoparticles: predictive nanomodeling using universal nanodescriptors and various machine learning approaches. NANOSCALE 2019; 11:8352-8362. [PMID: 30984943 DOI: 10.1039/c9nr00844f] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Rational nanomaterial design is urgently demanded for new nanomaterial development with desired properties. However, computational nanomaterial modeling and virtual nanomaterial screening are not applicable for this purpose due to the complexity of nanomaterial structures. To address this challenge, a new computational workflow is established in this study to virtually profile nanoparticles by (1) constructing a structurally diverse virtual gold nanoparticle (GNP) library and (2) developing novel universal nanodescriptors. The emphasis of this study is the second task by developing geometrical nanodescriptors that are suitable for the quantitative modeling of GNPs and virtual screening purposes. The feasibility, rigor and applicability of this novel computational method are validated by testing seven GNP datasets consisting of 191 unique GNPs of various nano-bioactivities and physicochemical properties. The high predictability of the developed GNP models suggests that this workflow can be used as a universal tool for nanomaterial profiling and rational nanomaterial design.
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Affiliation(s)
- Xiliang Yan
- School of Chemistry and Chemical Engineering, Shandong University, Jinan 250100, China
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16
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17
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Toropova AP, Toropov AA. Does the Index of Ideality of Correlation Detect the Better Model Correctly? Mol Inform 2019; 38:e1800157. [DOI: 10.1002/minf.201800157] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 01/18/2019] [Indexed: 12/17/2022]
Affiliation(s)
- Alla P. Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS Via La Masa 19 20156 Milan Italy
| | - Andrey A. Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS Via La Masa 19 20156 Milan Italy
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18
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Choi JS, Trinh TX, Yoon TH, Kim J, Byun HG. Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials. CHEMOSPHERE 2019; 217:243-249. [PMID: 30419378 DOI: 10.1016/j.chemosphere.2018.11.014] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 10/21/2018] [Accepted: 11/02/2018] [Indexed: 05/14/2023]
Abstract
A quasi-QSAR model was developed to predict the cell viability of human lung (BEAS-2B) and skin (HaCaT) cells exposed to 21 types of metal oxide nanomaterials. A wide range of toxicity datasets obtained from the S2NANO (www.s2nano.org) database was used. The data of descriptors representing the physicochemical properties and experimental conditions were coded to quasi-SMILES. In particular, hierarchical cluster analysis (HCA) and min-max normalization method were respectively used in assigning alphanumeric codes for numerical descriptors (e.g., core size, hydrodynamic size, surface charge, and dose) and then quasi-QSAR model performances for both methods were compared. The quasi-QSAR models were developed using CORAL software (www.insilico.eu/coral). Quasi-QSAR model built using quasi-SMILES generated by means of HCA showed better performance than the min-max normalization method. The model showed satisfactory statistical results (Radj2 for the training dataset: 0.71-0.73; Radj2 for the calibration dataset: 0.74-0.82; and Radj2 for the validation dataset: 0.70-0.76).
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Affiliation(s)
- Jang-Sik Choi
- Division of Electronics, Information and Communication Engineering, Kangwon National University (Samcheok), Kangwon-do, 25913, Republic of Korea
| | - Tung X Trinh
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul, 04763, Republic of Korea
| | - Tae-Hyun Yoon
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul, 04763, Republic of Korea
| | - Jongwoon Kim
- Environmental Safety Group, Korea Institute of Science and Technology (KIST) Europe, Campus E 7.1, Saarbrueck-en, Germany.
| | - Hyung-Gi Byun
- Division of Electronics, Information and Communication Engineering, Kangwon National University (Samcheok), Kangwon-do, 25913, Republic of Korea.
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19
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Utembe W, Wepener V, Yu IJ, Gulumian M. An assessment of applicability of existing approaches to predicting the bioaccumulation of conventional substances in nanomaterials. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2018; 37:2972-2988. [PMID: 30117187 DOI: 10.1002/etc.4253] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 01/24/2018] [Accepted: 08/11/2018] [Indexed: 06/08/2023]
Abstract
The experimental determination of bioaccumulation is challenging, and a number of approaches have been developed for its prediction. It is important to assess the applicability of these predictive approaches to nanomaterials (NMs), which have been shown to bioaccumulate. The octanol/water partition coefficient (KOW ) may not be applicable to some NMs that are not found in either the octanol or water phases but rather are found at the interface. Thus the KOW values obtained for certain NMs are shown not to correlate well with the experimentally determined bioaccumulation. Implementation of quantitative structure-activity relationships (QSARs) for NMs is also challenging because the bioaccumulation of NMs depends on nano-specific properties such as shape, size, and surface area. Thus there is a need to develop new QSAR models based on these new nanodescriptors; current efforts appear to focus on digital processing of NM images as well as the conversion of surface chemistry parameters into adsorption indices. Water solubility can be used as a screening tool for the exclusion of NMs with short half-lives. Adaptation of fugacity/aquivalence models, which include physicochemical properties, may give some insights into the bioaccumulation potential of NMs, especially with the addition of a biota component. The use of kinetic models, including physiologically based pharmacokinetic models, appears to be the most suitable approach for predicting bioaccumulation of NMs. Furthermore, because bioaccumulation of NMs depends on a number of biotic and abiotic factors, it is important to take these factors into account when one is modeling bioaccumulation and interpreting bioaccumulation results. Environ Toxicol Chem 2018;37:2972-2988. © 2018 SETAC.
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Affiliation(s)
- Wells Utembe
- National Institute for Occupational Health, Johannesburg, South Africa
| | - Victor Wepener
- Unit for Environmental Sciences and Management, North West University, Potchefstroom, South Africa
| | | | - Mary Gulumian
- National Institute for Occupational Health, Johannesburg, South Africa
- Haematology and Molecular Medicine, University of the Witwatersrand, Parktown, Johannesburg, South Africa
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20
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Leone C, Bertuzzi EE, Toropova AP, Toropov AA, Benfenati E. CORAL: Predictive models for cytotoxicity of functionalized nanozeolites based on quasi-SMILES. CHEMOSPHERE 2018; 210:52-56. [PMID: 29986223 DOI: 10.1016/j.chemosphere.2018.06.161] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 06/25/2018] [Accepted: 06/26/2018] [Indexed: 06/08/2023]
Abstract
Unlike the well-known simplified molecular input-line entry system (SMILES), the so-called quasi-SMILES contains information related to physicochemical and biochemical conditions by a special additional symbols (codes), each standing for different conditions (time exposure, concentration, type of cell, etc.). Thus, quasi-SMILES can be used to build up models for cytotoxicity of functionalized nanozeolites using a mathematical function of eclectic information. These calculations were done with the Monte Carlo CORAL software. The statistical quality of models based on quasi-SMILES was usually considerably better than the statistical quality of models based on traditional SMILES.
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Affiliation(s)
- Caterina Leone
- Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156, Milan, Italy.
| | - Elia E Bertuzzi
- Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156, Milan, Italy
| | - Alla P Toropova
- Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156, Milan, Italy
| | - Andrey A Toropov
- Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156, Milan, Italy
| | - Emilio Benfenati
- Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156, Milan, Italy
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21
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The index of ideality of correlation: hierarchy of Monte Carlo models for glass transition temperatures of polymers. JOURNAL OF POLYMER RESEARCH 2018. [DOI: 10.1007/s10965-018-1618-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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22
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Chen M, Yang F, Kang J, Gan H, Yang X, Lai X, Gao Y. Identfication of Potent LXRβ-Selective Agonists without LXRα Activation by In Silico Approaches. Molecules 2018; 23:molecules23061349. [PMID: 29867043 PMCID: PMC6099648 DOI: 10.3390/molecules23061349] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2018] [Revised: 05/30/2018] [Accepted: 05/31/2018] [Indexed: 11/16/2022] Open
Abstract
Activating Liver X receptors (LXRs) represents a promising therapeutic option for dyslipidemia. However, activating LXRα may cause undesired lipogenic effects. Discovery of highly LXRβ-selective agonists without LXRα activation were indispensable for dyslipidemia. In this study, in silico approaches were applied to develop highly potent LXRβ-selective agonists based on a series of newly reported 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione-based LXRα/β dual agonists. Initially, Kohonen and stepwise multiple linear regression SW-MLR were performed to construct models for LXRβ agonists and LXRα agonists based on the structural characteristics of LXRα/β dual agonists, respectively. The obtained LXRβ agonist model gave a good predictive ability (R2train = 0.837, R2test = 0.843, Q2LOO = 0.715), and the LXRα agonist model produced even better predictive ability (R2train = 0.968, R2test = 0.914, Q2LOO = 0.895). Also, the two QSAR models were independent and can well distinguish LXRβ and LXRα activity. Then, compounds in the ZINC database met the lower limit of structural similarity of 0.7, compared to the 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione scaffold subjected to our QSAR models, which resulted in the discovery of ZINC55084484 with an LXRβ prediction value of pEC50 equal to 7.343 and LXRα prediction value of pEC50 equal to −1.901. Consequently, nine newly designed compounds were proposed as highly LXRβ-selective agonists based on ZINC55084484 and molecular docking, of which LXRβ prediction values almost exceeded 8 and LXRα prediction values were below 0.
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Affiliation(s)
- Meimei Chen
- College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
- College of Chemistry and Materials Science, Fujian Normal University, Fuzhou 350007, China.
| | - Fafu Yang
- College of Chemistry and Materials Science, Fujian Normal University, Fuzhou 350007, China.
| | - Jie Kang
- College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
| | - Huijuan Gan
- College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
| | - Xuemei Yang
- College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
| | - Xinmei Lai
- College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
| | - Yuxing Gao
- College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
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23
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Toropov AA, Sizochenko N, Toropova AP, Leszczynski J. Towards the Development of Global Nano-Quantitative Structure-Property Relationship Models: Zeta Potentials of Metal Oxide Nanoparticles. NANOMATERIALS 2018; 8:nano8040243. [PMID: 29662037 PMCID: PMC5923573 DOI: 10.3390/nano8040243] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 04/12/2018] [Accepted: 04/12/2018] [Indexed: 11/25/2022]
Abstract
Zeta potential indirectly reflects a charge of the surface of nanoparticles in solutions and could be used to represent the stability of the colloidal solution. As processes of synthesis, testing and evaluation of new nanomaterials are expensive and time-consuming, so it would be helpful to estimate an approximate range of properties for untested nanomaterials using computational modeling. We collected the largest dataset of zeta potential measurements of bare metal oxide nanoparticles in water (87 data points). The dataset was used to develop quantitative structure–property relationship (QSPR) models. Essential features of nanoparticles were represented using a modified simplified molecular input line entry system (SMILES). SMILES strings reflected the size-dependent behavior of zeta potentials, as the considered quasi-SMILES modification included information about both chemical composition and the size of the nanoparticles. Three mathematical models were generated using the Monte Carlo method, and their statistical quality was evaluated (R2 for the training set varied from 0.71 to 0.87; for the validation set, from 0.67 to 0.82; root mean square errors for both training and validation sets ranged from 11.3 to 17.2 mV). The developed models were analyzed and linked to aggregation effects in aqueous solutions.
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Affiliation(s)
- Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, 20156 Milano, Italy.
| | - Natalia Sizochenko
- Interdisciplinary Center for Nanotoxicity, Jackson State University, Jackson, MS 39217, USA.
- Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.
| | - Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, 20156 Milano, Italy.
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Jackson State University, Jackson, MS 39217, USA.
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24
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Trinh TX, Choi JS, Jeon H, Byun HG, Yoon TH, Kim J. Quasi-SMILES-Based Nano-Quantitative Structure-Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells. Chem Res Toxicol 2018; 31:183-190. [PMID: 29439565 DOI: 10.1021/acs.chemrestox.7b00303] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Quantitative structure-activity relationship (QSAR) models for nanomaterials (nano-QSAR) were developed to predict the cytotoxicity of 20 different types of multiwalled carbon nanotubes (MWCNTs) to human lung cells by using quasi-SMILES. The optimal descriptors, recorded as quasi-SMILES, were encoded to represent the physicochemical properties and experimental conditions for the MWCNTs from 276 data records collected from previously published studies. The quasi-SMILES used to build the optimal descriptors were (i) diameter, (ii) length, (iii) surface area, (iv) in vitro toxicity assay, (v) cell line, (vi) exposure time, and (vii) dose. The model calculations were performed by using the Monte Carlo method and computed with CORAL software ( www.insilico.eu/coral ). The quasi-SMILES-based nano-QSAR model provided satisfactory statistical results ( R2 for internal validation data sets: 0.60-0.80; R2pred for external validation data sets: 0.81-0.88). The model showed potential for use in the estimation of human lung cell viability after exposure to MWCNTs with the following properties: diameter, 12-74 nm; length, 0.19-20.25 μm; surface area, 11.3-380.0 m2/g; and dose, 0-200 ppm.
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Affiliation(s)
- Tung Xuan Trinh
- Department of Chemistry, College of Natural Sciences , Hanyang University , Seoul 04763 , Republic of Korea
| | - Jang-Sik Choi
- Division of Electronics, Information and Communication Engineering , Kangwon National University , Samcheok , Kangwon-do 24341 , Republic of Korea
| | - Hyunpyo Jeon
- Environmental Safety Group , Korea Institute of Science and Technology (KIST) Europe , Campus E 7.1 , D-66123 Saarbruecken , Germany
| | - Hyung-Gi Byun
- Division of Electronics, Information and Communication Engineering , Kangwon National University , Samcheok , Kangwon-do 24341 , Republic of Korea
| | - Tae-Hyun Yoon
- Department of Chemistry, College of Natural Sciences , Hanyang University , Seoul 04763 , Republic of Korea
| | - Jongwoon Kim
- Environmental Safety Group , Korea Institute of Science and Technology (KIST) Europe , Campus E 7.1 , D-66123 Saarbruecken , Germany
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25
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Varsou DD, Tsiliki G, Nymark P, Kohonen P, Grafström R, Sarimveis H. toxFlow: A Web-Based Application for Read-Across Toxicity Prediction Using Omics and Physicochemical Data. J Chem Inf Model 2017; 58:543-549. [DOI: 10.1021/acs.jcim.7b00160] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Dimitra-Danai Varsou
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Georgia Tsiliki
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Misvik Biology Oy, 20520 Turku, Finland
| | - Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Misvik Biology Oy, 20520 Turku, Finland
| | - Roland Grafström
- Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Misvik Biology Oy, 20520 Turku, Finland
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
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26
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Wang Y, Yan F, Jia Q, Wang Q. Assessment for multi-endpoint values of carbon nanotubes: Quantitative nanostructure-property relationship modeling with norm indexes. J Mol Liq 2017. [DOI: 10.1016/j.molliq.2017.10.082] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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27
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Environmental Risk Assessment Strategy for Nanomaterials. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2017; 14:ijerph14101251. [PMID: 29048395 PMCID: PMC5664752 DOI: 10.3390/ijerph14101251] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Revised: 10/01/2017] [Accepted: 10/09/2017] [Indexed: 11/16/2022]
Abstract
An Environmental Risk Assessment (ERA) for nanomaterials (NMs) is outlined in this paper. Contrary to other recent papers on the subject, the main data requirements, models and advancement within each of the four risk assessment domains are described, i.e., in the: (i) materials, (ii) release, fate and exposure, (iii) hazard and (iv) risk characterisation domains. The material, which is obviously the foundation for any risk assessment, should be described according to the legislatively required characterisation data. Characterisation data will also be used at various levels within the ERA, e.g., exposure modelling. The release, fate and exposure data and models cover the input for environmental distribution models in order to identify the potential (PES) and relevant exposure scenarios (RES) and, subsequently, the possible release routes, both with regard to which compartment(s) NMs are distributed in line with the factors determining the fate within environmental compartment. The initial outcome in the risk characterisation will be a generic Predicted Environmental Concentration (PEC), but a refined PEC can be obtained by applying specific exposure models for relevant media. The hazard information covers a variety of representative, relevant and reliable organisms and/or functions, relevant for the RES and enabling a hazard characterisation. The initial outcome will be hazard characterisation in test systems allowing estimating a Predicted No-Effect concentration (PNEC), either based on uncertainty factors or on a NM adapted version of the Species Sensitivity Distributions approach. The risk characterisation will either be based on a deterministic risk ratio approach (i.e., PEC/PNEC) or an overlay of probability distributions, i.e., exposure and hazard distributions, using the nano relevant models.
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Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology. Food Chem Toxicol 2017; 112:478-494. [PMID: 28943385 DOI: 10.1016/j.fct.2017.09.037] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 08/31/2017] [Accepted: 09/19/2017] [Indexed: 11/20/2022]
Abstract
Nanotechnology and the production of nanomaterials have been expanding rapidly in recent years. Since many types of engineered nanoparticles are suspected to be toxic to living organisms and to have a negative impact on the environment, the process of designing new nanoparticles and their applications must be accompanied by a thorough risk analysis. (Quantitative) Structure-Activity Relationship ([Q]SAR) modelling creates promising options among the available methods for the risk assessment. These in silico models can be used to predict a variety of properties, including the toxicity of newly designed nanoparticles. However, (Q)SAR models must be appropriately validated to ensure the clarity, consistency and reliability of predictions. This paper is a joint initiative from recently completed European research projects focused on developing (Q)SAR methodology for nanomaterials. The aim was to interpret and expand the guidance for the well-known "OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models", with reference to nano-(Q)SAR, and present our opinions on the criteria to be fulfilled for models developed for nanoparticles.
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29
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Gajewicz A. What if the number of nanotoxicity data is too small for developing predictive Nano-QSAR models? An alternative read-across based approach for filling data gaps. NANOSCALE 2017; 9:8435-8448. [PMID: 28604902 DOI: 10.1039/c7nr02211e] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Over the past decade, computational nanotoxicology, in particular Quantitative Structure-Activity Relationship models (Nano-QSAR) that help in assessing the biological effects of nanomaterials, have received much attention. In effect, a solid basis for uncovering the relationships between the structure and property/activity of nanoparticles has been created. Nonetheless, six years after the first pioneering computational studies focusing on the investigation of nanotoxicity were commenced, these computational methods still suffer from many limitations. These are mainly related to the paucity of widely available, systematically varied, libraries of experimental data necessary for the development and validation of such models. This results in the still-low acceptance of these methods as valuable research tools for nanosafety and raises the query as to whether these methods could gain wide acceptance of regulatory bodies as alternatives for traditional in vitro methods. This study aimed to give an answer to the following question: How to remedy the paucity of experimental nanotoxicity data and thereby, overcome key roadblock that hinders the development of approaches for data-driven modeling of nanoparticle properties and toxicities? Here, a simple and transparent read-across algorithm for a pre-screening hazard assessment of nanomaterials that provides reasonably accurate results by making the best use of existing limited set of observations will be introduced.
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Affiliation(s)
- Agnieszka Gajewicz
- University of Gdansk, Faculty of Chemistry, Laboratory of Environmental Chemometrics, Gdansk, Poland.
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30
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Toropov AA, Toropova AP. The index of ideality of correlation: A criterion of predictive potential of QSPR/QSAR models? MUTATION RESEARCH-GENETIC TOXICOLOGY AND ENVIRONMENTAL MUTAGENESIS 2017. [PMID: 28622828 DOI: 10.1016/j.mrgentox.2017.05.008] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
The index of ideality of correlation (IIC) is a new criterion of the predictive potential of quantitative structure-property/activity relationships (QSPRs/QSARs). This IIC is calculated with using of the correlation coefficient between experimental and calculated values of endpoint for the calibration set, with taking into account the positive and negative dispersions between experimental and calculated values. The mutagenicity is well-known important characteristic of substances from ecological point of view. Consequently, the estimation of the IIC for mutagenicity is well motivated. It is confirmed that the utilization of this criterion significantly improves the predictive potential of QSAR models of mutagenicity. The new criterion can be used for other endpoints.
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Affiliation(s)
- Andrey A Toropov
- IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, 20156, Italy
| | - Alla P Toropova
- IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, 20156, Italy.
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31
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Toropova AP, Toropov AA, Leszczynska D, Leszczynski J. CORAL and Nano-QFAR: Quantitative feature - Activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, Co 3O 4, and TiO 2). ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2017; 139:404-407. [PMID: 28192776 DOI: 10.1016/j.ecoenv.2017.01.054] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 01/25/2017] [Accepted: 01/31/2017] [Indexed: 06/06/2023]
Abstract
Quantitative feature - activity relationships (QFAR) approach was applied to prediction of bioavailability of metal oxide nanoparticles. ZnO, CuO, Co3O4, and TiO2 nanoxides were considered. The computational model for bioavailability of investigated species is asserted. The model was calculated using the Monte Carlo method. The CORAL free software (http://www.insilico.eu/coral) was used in this study. The developed model was tested by application of three different splits of data into the training and validation sets. So-called, quasi-SMILES are used to represent the conditions of action of metal oxide nanoparticles. A new paradigm of building up predictive models of endpoints related to nanomaterials is suggested. The paradigm is the following "An endpoint is a mathematical function of available eclectic data (conditions)". Recently, the paradigm has been checked up with endpoints related to metal oxide nanoparticles, fullerenes, and multi-walled carbon-nanotubes.
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Affiliation(s)
- Alla P Toropova
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milan, Italy.
| | - Andrey A Toropov
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milan, Italy
| | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch Street, Jackson, MS 39217-0510, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 J. R. Lynch Street, P.O. Box 17910, Jackson, MS 39217, USA
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González-Durruthy M, Alberici LC, Curti C, Naal Z, Atique-Sawazaki DT, Vázquez-Naya JM, González-Díaz H, Munteanu CR. Experimental-Computational Study of Carbon Nanotube Effects on Mitochondrial Respiration: In Silico Nano-QSPR Machine Learning Models Based on New Raman Spectra Transform with Markov-Shannon Entropy Invariants. J Chem Inf Model 2017; 57:1029-1044. [PMID: 28414908 DOI: 10.1021/acs.jcim.6b00458] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The study of selective toxicity of carbon nanotubes (CNTs) on mitochondria (CNT-mitotoxicity) is of major interest for future biomedical applications. In the current work, the mitochondrial oxygen consumption (E3) is measured under three experimental conditions by exposure to pristine and oxidized CNTs (hydroxylated and carboxylated). Respiratory functional assays showed that the information on the CNT Raman spectroscopy could be useful to predict structural parameters of mitotoxicity induced by CNTs. The in vitro functional assays show that the mitochondrial oxidative phosphorylation by ATP-synthase (or state V3 of respiration) was not perturbed in isolated rat-liver mitochondria. For the first time a star graph (SG) transform of the CNT Raman spectra is proposed in order to obtain the raw information for a nano-QSPR model. Box-Jenkins and perturbation theory operators are used for the SG Shannon entropies. A modified RRegrs methodology is employed to test four regression methods such as multiple linear regression (LM), partial least squares regression (PLS), neural networks regression (NN), and random forest (RF). RF provides the best models to predict the mitochondrial oxygen consumption in the presence of specific CNTs with R2 of 0.998-0.999 and RMSE of 0.0068-0.0133 (training and test subsets). This work is aimed at demonstrating that the SG transform of Raman spectra is useful to encode CNT information, similarly to the SG transform of the blood proteome spectra in cancer or electroencephalograms in epilepsy and also as a prospective chemoinformatics tool for nanorisk assessment. All data files and R object models are available at https://dx.doi.org/10.6084/m9.figshare.3472349 .
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Affiliation(s)
| | | | | | | | | | - José M Vázquez-Naya
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna , Campus de Elviña s/n, 15071 A Coruña, Spain
| | - Humberto González-Díaz
- Department of Organic Chemistry II, Faculty of Science and Technology, University of the Basque Country UPV/EHU , 48940, Leioa, Bizkaia, Spain.,IKERBASQUE, Basque Foundation for Science , 48011, Bilbao, Bizkaia, Spain
| | - Cristian R Munteanu
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna , Campus de Elviña s/n, 15071 A Coruña, Spain.,Instituto de Investigación Biomédica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC) , A Coruña, 15006, Spain
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Toropova AP, Toropov AA. Nano-QSAR in cell biology: Model of cell viability as a mathematical function of available eclectic data. J Theor Biol 2017; 416:113-118. [DOI: 10.1016/j.jtbi.2017.01.012] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Revised: 12/25/2016] [Accepted: 01/06/2017] [Indexed: 10/20/2022]
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Toropova AP, Achary PGR, Toropov AA. Quasi-SMILES for Nano-QSAR Prediction of Toxic Effect of Al2O3 Nanoparticles. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
The level of malondialdehyde (MDA) in wet tissue of different organs is utilized as a measure of toxic effect. The numerical data on the concentration of MDA in wet tissue of liver, kidneys, brain, and heart of rat is examined as the endpoint which are impacted by different dose (mg/kg), exposure time (3 and 14 days) and single oral treatment of aluminium nano-oxide (Al2O3) with 30 nm or 40 nm. An attempt to develop predictive model for this endpoint has been carried out in this work. SMILES is a traditional tool to represent molecular structure for QSPRs/QSARs. In contrast to traditional SMILES, so-called quasi-SMILES can be a tool to build up quantitative features – property / activity relationships (QFPRs/QFARs) for endpoints which are not defined by solely molecular structure, but by a group of physicochemical and/or biochemical conditions. The quasi-SMILES is the representation of the above eclectic conditions whereas the QFPR/QFAR are models of endpoints which are modified under impacts of these eclectic conditions.
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Affiliation(s)
| | - P. Ganga Raju Achary
- Institute of Technical Education and Research (ITER), Siksha ‘O'Anusandhan University, India
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Toropov AA, Toropova AP, Cappellini L, Benfenati E, Davoli E. Odor threshold prediction by means of the Monte Carlo method. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2016; 133:390-394. [PMID: 27500544 DOI: 10.1016/j.ecoenv.2016.07.039] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Revised: 07/25/2016] [Accepted: 07/27/2016] [Indexed: 06/06/2023]
Abstract
A large set of organic compounds (n=906) has been used as a basis to build up a model for the odor threshold (mg/m(3)). The statistical characteristics of the best model are the following: n=523, r(2)=0.647, RMSE=1.18 (training set); n=191, r(2)=0.610, RMSE=1.03, (calibration set); and n=192, r(2)=0.686, RMSE=1.06 (validation set). A mechanistic interpretation of the model is presented as the lists of statistical promoters of the increase and decrease in the odor threshold.
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Affiliation(s)
- Andrey A Toropov
- IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, Milan, Italy.
| | - Alla P Toropova
- IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, Milan, Italy
| | - Luigi Cappellini
- IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Department of Environmental Health Sciences, Laboratory of Mass Spectrometry, Milan, Italy
| | - Emilio Benfenati
- IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, Milan, Italy
| | - Enrico Davoli
- IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Department of Environmental Health Sciences, Laboratory of Mass Spectrometry, Milan, Italy
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Toropov AA, Achary PGR, Toropova AP. Quasi-SMILES and nano-QFPR: The predictive model for zeta potentials of metal oxide nanoparticles. Chem Phys Lett 2016. [DOI: 10.1016/j.cplett.2016.08.018] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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Assessment of nano-QSPR models of organic contaminant absorption by carbon nanotubes for ecological impact studies. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/j.md.2016.03.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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Gobbi M, Beeg M, Toropova MA, Toropov AA, Salmona M. Monte Carlo method for predicting of cardiac toxicity: hERG blocker compounds. Toxicol Lett 2016; 250-251:42-6. [DOI: 10.1016/j.toxlet.2016.04.010] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Revised: 04/05/2016] [Accepted: 04/07/2016] [Indexed: 11/27/2022]
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Winkler DA. Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials. Toxicol Appl Pharmacol 2016; 299:96-100. [DOI: 10.1016/j.taap.2015.12.016] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Revised: 12/10/2015] [Accepted: 12/21/2015] [Indexed: 12/26/2022]
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Toropov AA, Toropova AP, Begum S, Achary PGR. Towards predicting the solubility of CO2 and N2 in different polymers using a quasi-SMILES based QSPR approach. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:293-301. [PMID: 27097272 DOI: 10.1080/1062936x.2016.1172666] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The solubility of gases in various polymers plays an important role for the design of new polymeric materials. Quantitative structure-property relationship (QSPR) models were designed to predict the solubility of gases such as CO2 and N2 in polyethylene (PE), polypropylene (PP), polystyrene (PS), polyvinyl acetate (PVA) and poly (butylene succinate) (PBS) at different temperatures and pressures by using quasi-SMILES codes. The dataset of 315 systems was split randomly into training, calibration and validation sets; random split 1 led to 214 training (r(2) = 0.870 and RMSE = 0.019), 51 calibration (r(2) = 0.858 and RMSE = 0.020) and 50 validation (r(2) = 0.869 and RMSE = 0.017) sets. The suggested approach based on the quasi-SMILES, which are analogues of the traditional SMILES gives reasonable good predictions for solubility of CO2 and N2 in different polymers. The described methodology is universal for situations where the aim is to predict the response of an eclectic system upon a variety of physicochemical and/or biochemical conditions.
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Affiliation(s)
- A A Toropov
- a IRCCS, Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | - A P Toropova
- a IRCCS, Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | - S Begum
- b Department of Chemistry , Siksha 'O' Anusandhan University , Bhubaneswar , Odisha , India
| | - P G R Achary
- b Department of Chemistry , Siksha 'O' Anusandhan University , Bhubaneswar , Odisha , India
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Toropova AP, Schultz TW, Toropov AA. Building up a QSAR model for toxicity toward Tetrahymena pyriformis by the Monte Carlo method: A case of benzene derivatives. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2016; 42:135-145. [PMID: 26851376 DOI: 10.1016/j.etap.2016.01.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Revised: 01/12/2016] [Accepted: 01/14/2016] [Indexed: 06/05/2023]
Abstract
Data on toxicity toward Tetrahymena pyriformis is indicator of applicability of a substance in ecologic and pharmaceutical aspects. Quantitative structure-activity relationships (QSARs) between the molecular structure of benzene derivatives and toxicity toward T. pyriformis (expressed as the negative logarithms of the population growth inhibition dose, mmol/L) are established. The available data were randomly distributed three times into the visible training and calibration sets, and invisible validation sets. The statistical characteristics for the validation set are the following: r(2)=0.8179 and s=0.338 (first distribution); r(2)=0.8682 and s=0.341 (second distribution); r(2)=0.8435 and s=0.323 (third distribution). These models are built up using only information on the molecular structure: no data on physicochemical parameters, 3D features of the molecular structure and quantum mechanics descriptors are involved in the modeling process.
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Affiliation(s)
- Alla P Toropova
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, Italy.
| | - Terry W Schultz
- College of Veterinary Medicine, The University of Tennessee, 2407 River Drive, Knoxville, TN 37996-4543, United States
| | - Andrey A Toropov
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, Italy
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Manganelli S, Leone C, Toropov AA, Toropova AP, Benfenati E. QSAR model for predicting cell viability of human embryonic kidney cells exposed to SiO₂ nanoparticles. CHEMOSPHERE 2016; 144:995-1001. [PMID: 26439516 DOI: 10.1016/j.chemosphere.2015.09.086] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Revised: 09/19/2015] [Accepted: 09/22/2015] [Indexed: 06/05/2023]
Abstract
A predictive model for the viability (%) of cultured human embryonic kidney cells (HEK293) exposed to 20 and 50 nm silica nanoparticles was built using 'optimal descriptors' as mathematical functions of size, concentration and exposure time. The calculation was carried out with CORAL software (http://www.insilico.eu/coral/) on five random splits of combined systems (particle size-particle concentration-cell exposure time) into training, calibration, and validation sets. The R(2) values of the best models were above 0.68. The average statistical quality of the model for the viability (%) of HEK293 exposed to different concentrations of silica nanoparticles measured by MTT assay is satisfactory.
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Affiliation(s)
- Serena Manganelli
- IRCSS-Istituto di Ricerche Farmacologiche Mario Negri, Via Giuseppe La Masa, 19, 20156, Milan, Italy.
| | - Caterina Leone
- IRCSS-Istituto di Ricerche Farmacologiche Mario Negri, Via Giuseppe La Masa, 19, 20156, Milan, Italy
| | - Andrey A Toropov
- IRCSS-Istituto di Ricerche Farmacologiche Mario Negri, Via Giuseppe La Masa, 19, 20156, Milan, Italy
| | - Alla P Toropova
- IRCSS-Istituto di Ricerche Farmacologiche Mario Negri, Via Giuseppe La Masa, 19, 20156, Milan, Italy
| | - Emilio Benfenati
- IRCSS-Istituto di Ricerche Farmacologiche Mario Negri, Via Giuseppe La Masa, 19, 20156, Milan, Italy
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Manganelli S, Leone C, Toropov AA, Toropova AP, Benfenati E. QSAR Model for Cytotoxicity of Silica Nanoparticles on Human Embryonic Kidney Cells1. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/j.matpr.2016.02.018] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Jagiello K, Grzonkowska M, Swirog M, Ahmed L, Rasulev B, Avramopoulos A, Papadopoulos MG, Leszczynski J, Puzyn T. Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives. JOURNAL OF NANOPARTICLE RESEARCH : AN INTERDISCIPLINARY FORUM FOR NANOSCALE SCIENCE AND TECHNOLOGY 2016; 18:256. [PMID: 27642255 PMCID: PMC5003910 DOI: 10.1007/s11051-016-3564-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Accepted: 08/18/2016] [Indexed: 05/19/2023]
Abstract
In this contribution, the advantages and limitations of two computational techniques that can be used for the investigation of nanoparticles activity and toxicity: classic nano-QSAR (Quantitative Structure-Activity Relationships employed for nanomaterials) and 3D nano-QSAR (three-dimensional Quantitative Structure-Activity Relationships, such us Comparative Molecular Field Analysis, CoMFA/Comparative Molecular Similarity Indices Analysis, CoMSIA analysis employed for nanomaterials) have been briefly summarized. Both approaches were compared according to the selected criteria, including: efficiency, type of experimental data, class of nanomaterials, time required for calculations and computational cost, difficulties in the interpretation. Taking into account the advantages and limitations of each method, we provide the recommendations for nano-QSAR modellers and QSAR model users to be able to determine a proper and efficient methodology to investigate biological activity of nanoparticles in order to describe the underlying interactions in the most reliable and useful manner.
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Affiliation(s)
- Karolina Jagiello
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, Institute for Environmental and Human Health Protection, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Monika Grzonkowska
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, Institute for Environmental and Human Health Protection, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Marta Swirog
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, Institute for Environmental and Human Health Protection, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Lucky Ahmed
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 JR Lynch Street, Jackson, MS 39217-0510 USA
| | - Bakhtiyor Rasulev
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 JR Lynch Street, Jackson, MS 39217-0510 USA
- Center for Computationally Assisted Science and Technology, North Dakota State University, 1805 NDSU Research Park Drive, Post Office Box 6050, Fargo, ND 58108 USA
| | - Aggelos Avramopoulos
- Institute of Biology, Pharmaceutical Chemistry and Biotechnology, National Hellenic Research Foundation, 48 Vas. Constantinou Ave., 11635 Athens, Greece
| | - Manthos G. Papadopoulos
- Institute of Biology, Pharmaceutical Chemistry and Biotechnology, National Hellenic Research Foundation, 48 Vas. Constantinou Ave., 11635 Athens, Greece
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 JR Lynch Street, Jackson, MS 39217-0510 USA
| | - Tomasz Puzyn
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, Institute for Environmental and Human Health Protection, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
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Toropov AA, Toropova AP. Quasi-SMILES and nano-QFAR: united model for mutagenicity of fullerene and MWCNT under different conditions. CHEMOSPHERE 2015; 139:18-22. [PMID: 26026259 DOI: 10.1016/j.chemosphere.2015.05.042] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Revised: 05/11/2015] [Accepted: 05/14/2015] [Indexed: 06/04/2023]
Abstract
Simplified molecular input-line entry system (SMILES) are a tool to represent molecular features of various compounds. Quasi-SMILES is a tool to represent various eclectic features of interaction between complex substances and bio targets (cells, organs, organisms). The construction and the application of quasi-SMILES in order to build up a model for prediction of mutagenicity of fullerene and multi-walled carbon-nanotubes (MWCNTs) are described in this work: instead of paradigm "endpoint is a mathematical function of molecular structure", the paradigm "endpoint is a mathematical function of eclectic data (features)" is used.
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Affiliation(s)
- Andrey A Toropov
- IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milano, Italy
| | - Alla P Toropova
- IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milano, Italy.
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Metal Oxide Nanomaterial QNAR Models: Available Structural Descriptors and Understanding of Toxicity Mechanisms. NANOMATERIALS 2015; 5:1620-1637. [PMID: 28347085 PMCID: PMC5304772 DOI: 10.3390/nano5041620] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Revised: 10/03/2015] [Accepted: 10/03/2015] [Indexed: 11/17/2022]
Abstract
Metal oxide nanomaterials are widely used in various areas; however, the divergent published toxicology data makes it difficult to determine whether there is a risk associated with exposure to metal oxide nanomaterials. The application of quantitative structure activity relationship (QSAR) modeling in metal oxide nanomaterials toxicity studies can reduce the need for time-consuming and resource-intensive nanotoxicity tests. The nanostructure and inorganic composition of metal oxide nanomaterials makes this approach different from classical QSAR study; this review lists and classifies some structural descriptors, such as size, cation charge, and band gap energy, in recent metal oxide nanomaterials quantitative nanostructure activity relationship (QNAR) studies and discusses the mechanism of metal oxide nanomaterials toxicity based on these descriptors and traditional nanotoxicity tests.
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Prediction of retention characteristics of heterocyclic compounds. Anal Bioanal Chem 2015; 407:9185-9. [DOI: 10.1007/s00216-015-9067-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 09/18/2015] [Indexed: 01/30/2023]
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48
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Predicting the cytotoxicity of ionic liquids using QSAR model based on SMILES optimal descriptors. J Mol Liq 2015. [DOI: 10.1016/j.molliq.2015.04.049] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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49
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Marchese Robinson RL, Cronin MTD, Richarz AN, Rallo R. An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2015; 6:1978-99. [PMID: 26665069 PMCID: PMC4660926 DOI: 10.3762/bjnano.6.202] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 08/27/2015] [Indexed: 05/20/2023]
Abstract
Analysis of trends in nanotoxicology data and the development of data driven models for nanotoxicity is facilitated by the reporting of data using a standardised electronic format. ISA-TAB-Nano has been proposed as such a format. However, in order to build useful datasets according to this format, a variety of issues has to be addressed. These issues include questions regarding exactly which (meta)data to report and how to report them. The current article discusses some of the challenges associated with the use of ISA-TAB-Nano and presents a set of resources designed to facilitate the manual creation of ISA-TAB-Nano datasets from the nanotoxicology literature. These resources were developed within the context of the NanoPUZZLES EU project and include data collection templates, corresponding business rules that extend the generic ISA-TAB-Nano specification as well as Python code to facilitate parsing and integration of these datasets within other nanoinformatics resources. The use of these resources is illustrated by a "Toy Dataset" presented in the Supporting Information. The strengths and weaknesses of the resources are discussed along with possible future developments.
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Affiliation(s)
- Richard L Marchese Robinson
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool, L3 3AF, United Kingdom
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool, L3 3AF, United Kingdom
| | - Andrea-Nicole Richarz
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool, L3 3AF, United Kingdom
| | - Robert Rallo
- Departament d'Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili, Av. Paisos Catalans 26, 43007 Tarragona, Catalunya, Spain
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