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Roy J, Roy K. Exploring the relationships between physiochemical properties of nanoparticles and cell damage to combat cancer growth using simple periodic table-based descriptors. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2024; 15:297-309. [PMID: 38505811 PMCID: PMC10949013 DOI: 10.3762/bjnano.15.27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 02/23/2024] [Indexed: 03/21/2024]
Abstract
A comprehensive knowledge of the physical and chemical properties of nanomaterials (NMs) is necessary to design them effectively for regulated use. Although NMs are utilized in therapeutics, their cytotoxicity has attracted great attention. Nanoscale quantitative structure-property relationship (nano-QSPR) models can help in understanding the relationship between NMs and the biological environment and provide new ways for modeling the structural properties and bio-toxic effects of NMs. The goal of the study is to construct fully validated property-based models to extract relevant features for estimating and influencing the zeta potential and obtaining the toxicity profile regarding cell damage in the treatment of cancer cells. To achieve this, QSPR modeling was first performed with 18 metal oxide (MeOx) NMs to measure their materials properties using periodic table-based descriptors. The features obtained were later applied for zeta potential calculation (imputation for sparse data) for MeOx NMs that lack such information. To further clarify the influence of the zeta potential on cell damage, a QSPR model was developed with 132 MeOx NMs to understand the possible mechanisms of cell damage. The results showed that zeta potential, along with seven other descriptors, had the potential to influence oxidative damage through free radical accumulation, which could lead to changes in the survival rate of cancerous cells. The developed QSPR and quantitative structure-activity relationship models also give hints regarding safer design and toxicity assessment of MeOx NMs.
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Affiliation(s)
- Joyita Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
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2
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Varon E, Blumrosen G, Shefi O. A predictive model for personalization of nanotechnology-based phototherapy in cancer treatment. Front Oncol 2022; 12:1037419. [PMID: 36911792 PMCID: PMC9999042 DOI: 10.3389/fonc.2022.1037419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 11/21/2022] [Indexed: 01/06/2023] Open
Abstract
A major challenge in radiation oncology is the prediction and optimization of clinical responses in a personalized manner. Recently, nanotechnology-based cancer treatments are being combined with photodynamic therapy (PDT) and photothermal therapy (PTT). Predictive models based on machine learning techniques can be used to optimize the clinical setup configuration, including such parameters as laser radiation intensity, treatment duration, and nanoparticle features. In this article we demonstrate a methodology that can be used to identify the optimal treatment parameters for PDT and PTT by collecting data from in vitro cytotoxicity assay of PDT/PTT-induced cell death using a single nanocomplex. We construct three machine learning prediction models, employing regression, interpolation, and low- degree analytical function fitting, to predict the laser radiation intensity and duration settings that maximize the treatment efficiency. To examine the accuracy of these prediction models, we construct a dedicated dataset for PDT, PTT, and a combined treatment; this dataset is based on cell death measurements after light radiation treatment and is divided into training and test sets. The preliminary results show that the performance of all three models is sufficient, with death rate errors of 0.09, 0.15, and 0.12 for the regression, interpolation, and analytical function fitting approaches, respectively. Nevertheless, due to its simple form, the analytical function method has an advantage in clinical application and can be used for further analysis of the sensitivity of performance to the treatment parameters. Overall, the results of this study form a baseline for a future personalized prediction model based on machine learning in the domain of combined nanotechnology- and phototherapy-based cancer treatment.
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Affiliation(s)
- Eli Varon
- Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel.,Bar-Ilan Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan, Israel
| | - Gaddi Blumrosen
- Department of Digital Medical Technologies, Holon Institute of Technology, Holon, Israel.,Department of Computer Science, Holon Institute of Technology, Holon, Israel
| | - Orit Shefi
- Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel.,Bar-Ilan Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan, Israel.,Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
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3
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Thwala MM, Afantitis A, Papadiamantis AG, Tsoumanis A, Melagraki G, Dlamini LN, Ouma CNM, Ramasami P, Harris R, Puzyn T, Sanabria N, Lynch I, Gulumian M. Using the Isalos platform to develop a (Q)SAR model that predicts metal oxide toxicity utilizing facet-based electronic, image analysis-based, and periodic table derived properties as descriptors. Struct Chem 2021. [DOI: 10.1007/s11224-021-01869-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
AbstractEngineered nanoparticles (NPs) are being studied for their potential to harm humans and the environment. Biological activity, toxicity, physicochemical properties, fate, and transport of NPs must all be evaluated and/or predicted. In this work, we explored the influence of metal oxide nanoparticle facets on their toxicity towards bronchial epithelial (BEAS-2B), Murine myeloid (RAW 264.7), and E. coli cell lines. To estimate the toxicity of metal oxide nanoparticles grown to a low facet index, a quantitative structure–activity relationship ((Q)SAR) approach was used. The novel model employs theoretical (density functional theory calculations) and experimental studies (transmission electron microscopy images from which several particle descriptors are extracted and toxicity data extracted from the literature) to investigate the properties of faceted metal oxides, which are then utilized to construct a toxicity model. The classification mode of the k-nearest neighbour algorithm (EnaloskNN, Enalos Chem/Nanoinformatics) was used to create the presented model for metal oxide cytotoxicity. Four descriptors were identified as significant: core size, chemical potential, enthalpy of formation, and electronegativity count of metal oxides. The relationship between these descriptors and metal oxide facets is discussed to provide insights into the relative toxicities of the nanoparticle. The model and the underpinning dataset are freely available on the NanoSolveIT project cloud platform and the NanoPharos database, respectively.
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Santana R, Onieva E, Zuluaga R, Duardo-Sánchez A, Gañán P. The Role of Machine Learning in Centralized Authorization Process of Nanomedicines in European Union. Curr Top Med Chem 2021; 21:828-838. [PMID: 33745436 DOI: 10.2174/1568026621666210319101847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 11/12/2020] [Accepted: 12/31/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Machine Learning (ML) has experienced an increasing use, given the possibilities to expand the scientific knowledge of different disciplines, such as nanotechnology. This has allowed the creation of Cheminformatic models capable of predicting biological activity and physicochemical characteristics of new components with high success rates in training and test partitions. Given the current gaps of scientific knowledge and the need for efficient application of medicines products law, this paper analyzes the position of regulators for marketing medicinal nanoproducts in the European Union and the role of ML in the authorization process. METHODS In terms of methodology, a dogmatic study of the European regulation and the guidance of the European Medicine Agency on the use of predictive models for nanomaterials was carried out. The study has, as the framework of reference, the European Regulation 726/2004 and has focused on the analysis of how ML processes are contemplated in the regulations. RESULTS As a result, we present a discussion of the information that must be provided for every case for simulation methods. The results show a favorable and flexible position for the development of the use of predictive models to complement the applicant's information. CONCLUSION It is concluded that Machine Learning has the capacity to help improve the application of nanotechnology medicine products regulation. Future regulations should promote this kind of information given the advanced state of the art in terms of algorithms that are able to build accurate predictive models. This especially applies to methods, such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles promoted by the standards of Organization for Economic Co-operation and Development (OECD), European Union regulations, and European Authority Medicine. To our best knowledge, this is the first study focused on nanotechnology medicine products and machine learning used to support technical European public assessment reports (EPAR) for complementary information.
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Affiliation(s)
- Ricardo Santana
- DeustoTech-Fundacion Deusto, Avda. Universidades, 24,48007 Bilbao, Spain
| | - Enrique Onieva
- DeustoTech-Fundacion Deusto, Avda. Universidades, 24,48007 Bilbao, Spain
| | - Robin Zuluaga
- Facultad de Ingeniería Agroindustrial, Universidad Pontificia Bolivariana UPB050031, Medellin, Colombia
| | - Aliuska Duardo-Sánchez
- Department of Public Law, Law and the Human Genome Research Group, University of the Basque Country UPV/EHU 48940, Leioa, Biscay, Spain
| | - Piedad Gañán
- Facultad de Ingenieria Quimica, Universidad Pontificia Bolivariana UPB050031, Medellin, Colombia
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New Mechanistic Insights on Carbon Nanotubes' Nanotoxicity Using Isolated Submitochondrial Particles, Molecular Docking, and Nano-QSTR Approaches. BIOLOGY 2021; 10:biology10030171. [PMID: 33668702 PMCID: PMC7996163 DOI: 10.3390/biology10030171] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 02/17/2021] [Accepted: 02/19/2021] [Indexed: 01/08/2023]
Abstract
Single-walled carbon nanotubes can induce mitochondrial F0F1-ATPase nanotoxicity through inhibition. To completely characterize the mechanistic effect triggering the toxicity, we have developed a new approach based on the combination of experimental and computational study, since the use of only one or few techniques may not fully describe the phenomena. To this end, the in vitro inhibition responses in submitochondrial particles (SMP) was combined with docking, elastic network models, fractal surface analysis, and Nano-QSTR models. In vitro studies suggest that inhibition responses in SMP of F0F1-ATPase enzyme were strongly dependent on the concentration assay (from 3 to 5 µg/mL) for both pristine and COOH single-walled carbon nanotubes types (SWCNT). Besides, both SWCNTs show an interaction inhibition pattern mimicking the oligomycin A (the specific mitochondria F0F1-ATPase inhibitor blocking the c-ring F0 subunit). Performed docking studies denote the best crystallography binding pose obtained for the docking complexes based on the free energy of binding (FEB) fit well with the in vitro evidence from the thermodynamics point of view, following an affinity order such as: FEB (oligomycin A/F0-ATPase complex) = -9.8 kcal/mol > FEB (SWCNT-COOH/F0-ATPase complex) = -6.8 kcal/mol ~ FEB (SWCNT-pristine complex) = -5.9 kcal/mol, with predominance of van der Waals hydrophobic nano-interactions with key F0-ATPase binding site residues (Phe 55 and Phe 64). Elastic network models and fractal surface analysis were performed to study conformational perturbations induced by SWCNT. Our results suggest that interaction may be triggering abnormal allosteric responses and signals propagation in the inter-residue network, which could affect the substrate recognition ligand geometrical specificity of the F0F1-ATPase enzyme in order (SWCNT-pristine > SWCNT-COOH). In addition, Nano-QSTR models have been developed to predict toxicity induced by both SWCNTs, using results of in vitro and docking studies. Results show that this method may be used for the fast prediction of the nanotoxicity induced by SWCNT, avoiding time- and money-consuming techniques. Overall, the obtained results may open new avenues toward to the better understanding and prediction of new nanotoxicity mechanisms, rational drug design-based nanotechnology, and potential biomedical application in precision nanomedicine.
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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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Toropov AA, Toropova AP. QSPR/QSAR: State-of-Art, Weirdness, the Future. Molecules 2020; 25:E1292. [PMID: 32178379 PMCID: PMC7143984 DOI: 10.3390/molecules25061292] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 03/06/2020] [Accepted: 03/10/2020] [Indexed: 12/15/2022] Open
Abstract
Ability of quantitative structure-property/activity relationships (QSPRs/QSARs) to serve for epistemological processes in natural sciences is discussed. Some weirdness of QSPR/QSAR state-of-art is listed. There are some contradictions in the research results in this area. Sometimes, these should be classified as paradoxes or weirdness. These points are often ignored. Here, these are listed and briefly commented. In addition, hypotheses on the future evolution of the QSPR/QSAR theory and practice are suggested. In particular, the possibility of extending of the QSPR/QSAR problematic by searching for the "statistical similarity" of different endpoints is suggested and illustrated by an example for relatively "distanced each from other" endpoints, namely (i) mutagenicity, (ii) anticancer activity, and (iii) blood-brain barrier.
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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;
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Furxhi I, Murphy F, Mullins M, Arvanitis A, Poland CA. Nanotoxicology data for in silico tools: a literature review. Nanotoxicology 2020; 14:612-637. [PMID: 32100604 DOI: 10.1080/17435390.2020.1729439] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies.
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Affiliation(s)
- Irini Furxhi
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Finbarr Murphy
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Martin Mullins
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Athanasios Arvanitis
- Department of Mechanical Engineering, Environmental Informatics Research Group, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Craig A Poland
- ELEGI/Colt Laboratory, Queen's Medical Research Institute, 47 Little France Crescent, University of Edinburgh, Edinburgh, Scotland
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Furxhi I, Murphy F, Mullins M, Arvanitis A, Poland CA. Practices and Trends of Machine Learning Application in Nanotoxicology. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E116. [PMID: 31936210 PMCID: PMC7023261 DOI: 10.3390/nano10010116] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 12/31/2019] [Accepted: 01/06/2020] [Indexed: 02/07/2023]
Abstract
Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.
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Affiliation(s)
- Irini Furxhi
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Finbarr Murphy
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Martin Mullins
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Athanasios Arvanitis
- Department of Mechanical Engineering, Environmental Informatics Research Group, Aristotle University of Thessaloniki, 54124 Thessaloniki Box 483, Greece;
| | - Craig A. Poland
- ELEGI/Colt Laboratory, Queen’s Medical Research Institute, 47 Little France Crescent, University of Edinburgh, Edinburgh EH16 4TJ, Scotland, UK;
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10
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Nanomaterials in the Environment: Research Hotspots and Trends. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16245138. [PMID: 31888212 PMCID: PMC6950608 DOI: 10.3390/ijerph16245138] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 12/08/2019] [Accepted: 12/09/2019] [Indexed: 01/29/2023]
Abstract
Research on the field of nanomaterials in environment has continued to be a major area of interest in recent years. To present the up-to-date progress in this field, a bibliometric study is conducted to analyze 7087 related publications in the Science Citation Index (SCI) core collection of Web of Science based on the expanded SCI. These publications are identified through using representative keywords in the research directions environment of the Web of Science. This study finds that China and the United States dominate the field; one difference between them is that China issued more independent publications and the United States issued more cooperative publications. In addition, the number of the related publications in Asian countries has exceeded that of European and American ones. A Chinese institution, the Chinese Academy of Sciences, has an absolute dominance in this field. Traditional high-impact environmental journals have ruled this field. The number of publications in the Energy and Environmental Science field has gradually decreased. In addition, a co-citation analysis shows that previous studies in this field can be divided into four major branches, and that graphene oxide and nano-inorganic particles are increasingly becoming research hotspots.
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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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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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13
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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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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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15
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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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16
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Gozalbes R, Vicente de Julián-Ortiz J. Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation. ACTA ACUST UNITED AC 2018. [DOI: 10.4018/ijqspr.2018010101] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Chemoinformatics methodologies such as QSAR/QSPR have been used for decades in drug discovery projects, especially for the finding of new compounds with therapeutic properties and the optimization of ADME properties on chemical series. The application of computational techniques in predictive toxicology is much more recent, and they are experiencing an increasingly interest because of the new legal requirements imposed by national and international regulations. In the pharmaceutical field, the US Food and Drug Administration (FDA) support the use of predictive models for regulatory decision-making when assessing the genotoxic and carcinogenic potential of drug impurities. In Europe, the REACH legislation promotes the use of QSAR in order to reduce the huge amount of animal testing needed to demonstrate the safety of new chemical entities subjected to registration, provided they meet specific conditions to ensure their quality and predictive power. In this review, the authors summarize the state of art of in silico methods for regulatory purposes, with especial emphasis on QSAR models.
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Gajewicz A, Puzyn T, Odziomek K, Urbaszek P, Haase A, Riebeling C, Luch A, Irfan MA, Landsiedel R, van der Zande M, Bouwmeester H. Decision tree models to classify nanomaterials according to the DF4nanoGrouping scheme. Nanotoxicology 2017; 12:1-17. [DOI: 10.1080/17435390.2017.1415388] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Agnieszka Gajewicz
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Tomasz Puzyn
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Katarzyna Odziomek
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Piotr Urbaszek
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Andrea Haase
- German Federal Institute for Risk Assessment (BfR), Department of Chemical and Product Safety, Berlin, Germany
| | - Christian Riebeling
- German Federal Institute for Risk Assessment (BfR), Department of Chemical and Product Safety, Berlin, Germany
| | - Andreas Luch
- German Federal Institute for Risk Assessment (BfR), Department of Chemical and Product Safety, Berlin, Germany
| | - Muhammad A. Irfan
- Department of Experimental Toxicology and Ecology, BASF SE, Ludwigshafen, Germany
| | - Robert Landsiedel
- Department of Experimental Toxicology and Ecology, BASF SE, Ludwigshafen, Germany
| | | | - Hans Bouwmeester
- RIKILT – Wageningen University and Research, Wageningen, The Netherlands
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18
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Shin HK, Kim KY, Park JW, No KT. Use of metal/metal oxide spherical cluster and hydroxyl metal coordination complex for descriptor calculation in development of nanoparticle cytotoxicity classification model. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2017; 28:875-888. [PMID: 29189078 DOI: 10.1080/1062936x.2017.1400998] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Accepted: 11/01/2017] [Indexed: 06/07/2023]
Abstract
Computational approaches have been suggested as an informative tool for risk assessment of nanomaterials. Nano (quantitative) structure-activity relationship, nano-(Q)SAR, models have been developed to predict toxicity of metal oxide (MOx) nanoparticles (NPs); however, the packing structure and cluster of nanoparticle have been included for the descriptor calculation in only two studies. This study proposed spherical cluster and hydroxyl metal coordination complex to calculate descriptors for development of nanoparticle cytotoxicity classification model. The model cluster was generated from metal (M) or MOx crystal structure to calculate physicochemical properties of M/MOx NPs and the hydroxyl metal coordination complex was used to calculate the properties of the metal cation in an aqueous environment. Data were collected for 2 M and 19 MOx NPs in human bronchial epithelial cell lines and murine myeloid cell lines at 100 μg/ml after 24 hours exposure. The model was developed with scaled HOMO energy of the model cluster and polarizability of the hydroxyl metal coordination complex, as reactivity of the particles and the cations explained cause of cytotoxic action by M/MOx NPs. As the developed model achieved 90.31% accuracy, the classification model in this work can be used for virtual screening of toxic action of M/MOx NPs.
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Affiliation(s)
- H K Shin
- a Department of Biotechnology, College of Life Science and Biotechnology , Yonsei University , Seoul , Republic of Korea
| | - K Y Kim
- b Bioinformatics and Molecular Design Research Center , Yonsei Engineering Research Park , Seoul , Republic of Korea
| | - J W Park
- c Gyeongnam Department of Environmental Toxicology and Chemistry , Korea Institute of Toxicology , Jinju-si , Gyeongsangnam-do , Republic of Korea
| | - K T No
- a Department of Biotechnology, College of Life Science and Biotechnology , Yonsei University , Seoul , Republic of Korea
- b Bioinformatics and Molecular Design Research Center , Yonsei Engineering Research Park , Seoul , Republic of Korea
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19
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Deng CH, Gong JL, Zeng GM, Zhang P, Song B, Zhang XG, Liu HY, Huan SY. Graphene sponge decorated with copper nanoparticles as a novel bactericidal filter for inactivation of Escherichia coli. CHEMOSPHERE 2017; 184:347-357. [PMID: 28605705 DOI: 10.1016/j.chemosphere.2017.05.118] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Revised: 04/20/2017] [Accepted: 05/20/2017] [Indexed: 06/07/2023]
Abstract
Nanotechnology has great potential in water purification. However, the limitations such as aggregation and toxicity of nanomaterials have blocked their practical application. In this work, a novel copper nanoparticles-decorated graphene sponge (Cu-GS) was synthesized using a facile hydrothermal method. Cu-GS consisting of three-dimensional (3D) porous graphene network and well-dispersed Cu nanoparticles exhibited high antibacterial efficiency against Esherichia coli when used as a bactericidal filter. The morphological changes determined by scanning electron microscope and fluorescence images measured by flow cytometry confirmed the involvement of membrane damage induced by Cu-GS in their antibacterial process. The oxidative ability of Cu-GS and intercellular reactive oxygen species (ROS) were also determined to elucidate the possible antibacterial mechanism of Cu-GS. Moreover, the concentration of released copper ions from Cu-GS was far below the drinking water standard, and the copper ions also have an effect on the antibacterial activity of Cu-GS. Results suggested that Cu-GS as a novel bactericidal filter possessed a potential application of water disinfection.
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Affiliation(s)
- Can-Hui Deng
- Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China
| | - Ji-Lai Gong
- Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China.
| | - Guang-Ming Zeng
- Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China.
| | - Peng Zhang
- Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China
| | - Biao Song
- Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China
| | - Xue-Gang Zhang
- Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China
| | - Hong-Yu Liu
- Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China
| | - Shuang-Yan Huan
- State Key Laboratory for Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, PR China
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20
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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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21
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Fjodorova N, Novic M, Gajewicz A, Rasulev B. The way to cover prediction for cytotoxicity for all existing nano-sized metal oxides by using neural network method. Nanotoxicology 2017; 11:475-483. [PMID: 28330416 DOI: 10.1080/17435390.2017.1310949] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The regulatory agencies should fulfil the data gap in toxicity for new chemicals including nano-sized compounds, like metal oxides nanoparticles (MeOx NPs) according to the registration, evaluation, authorisation and restriction of chemicals (REACH) legislation policy. This study demonstrates the perspective capability of neural network models for prediction of cytotoxicity of MeOx NPs to bacteria Escherichia coli (E. coli) for the widest range of metal oxides extracted from Periodic table. The counter propagation artificial neural network (CP ANN) models for prediction of cytotoxicity of MeOx NPs for data sets of 17, 36 and 72 metal oxides were employed in the study. The cytotoxicity of studied metal oxide NPs was correlated with (i) χ-metal electronegativity (EN) by Pauling scale and composition of metal oxides characterised by (ii) number of metal atoms in oxide, (iii) number of oxygen atoms in oxide and (iv) charge of metal cation in oxide. The paper describes the models in context of five OECD principles of validation models accepted for regulatory use. The recommendations were done for the minimal number of cytotoxicity tests needs for evaluation of the large set of MeOx with different oxidation states. The methodology is expected to be useful for potential hazard assessment of MeOx NPs and prioritisation for further testing and risk assessment.
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Affiliation(s)
- Natalja Fjodorova
- a Department of Chemoinformatics , National Institute of Chemistry , Ljubljana , Slovenia
| | - Marjana Novic
- a Department of Chemoinformatics , National Institute of Chemistry , Ljubljana , Slovenia
| | - Agnieszka Gajewicz
- b Laboratory of Environmental Chemometrics, Faculty of Chemistry , University of Gdansk , Gdańsk , Poland
| | - Bakhtiyor Rasulev
- c Department of Coatings and Polymeric Materials , North Dakota State University , Fargo , ND , USA
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22
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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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23
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Riebeling C, Jungnickel H, Luch A, Haase A. Systems Biology to Support Nanomaterial Grouping. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 947:143-171. [PMID: 28168668 DOI: 10.1007/978-3-319-47754-1_6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The assessment of potential health risks of engineered nanomaterials (ENMs) is a challenging task due to the high number and great variety of already existing and newly emerging ENMs. Reliable grouping or categorization of ENMs with respect to hazards could help to facilitate prioritization and decision making for regulatory purposes. The development of grouping criteria, however, requires a broad and comprehensive data basis. A promising platform addressing this challenge is the systems biology approach. The different areas of systems biology, most prominently transcriptomics, proteomics and metabolomics, each of which provide a wealth of data that can be used to reveal novel biomarkers and biological pathways involved in the mode-of-action of ENMs. Combining such data with classical toxicological data would enable a more comprehensive understanding and hence might lead to more powerful and reliable prediction models. Physico-chemical data provide crucial information on the ENMs and need to be integrated, too. Overall statistical analysis should reveal robust grouping and categorization criteria and may ultimately help to identify meaningful biomarkers and biological pathways that sufficiently characterize the corresponding ENM subgroups. This chapter aims to give an overview on the different systems biology technologies and their current applications in the field of nanotoxicology, as well as to identify the existing challenges.
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Affiliation(s)
- Christian Riebeling
- German Federal Institute for Risk Assessment, Department of Chemical and Product Safety, Berlin, Germany
| | - Harald Jungnickel
- German Federal Institute for Risk Assessment, Department of Chemical and Product Safety, Berlin, Germany
| | - Andreas Luch
- German Federal Institute for Risk Assessment, Department of Chemical and Product Safety, Berlin, Germany
| | - Andrea Haase
- German Federal Institute for Risk Assessment, Department of Chemical and Product Safety, Berlin, Germany.
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24
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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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25
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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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26
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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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27
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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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28
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González-Durruthy M, Monserrat JM, Alberici LC, Naal Z, Curti C, González-Díaz H. Mitoprotective activity of oxidized carbon nanotubes against mitochondrial swelling induced in multiple experimental conditions and predictions with new expected-value perturbation theory. RSC Adv 2015. [DOI: 10.1039/c5ra14435c] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Mitochondrial Permeability Transition Pore (MPTP) is involved in neurodegeneration, hepatotoxicity, cardiac necrosis, nervous and muscular dystrophies.
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Affiliation(s)
- Michael González-Durruthy
- Institute of Biological Science (ICB)
- Universidade Federal do Rio Grande (FURG)
- Porto Alegre
- Brazil
- ICB-FURG Post-graduate Program Physiological Sciences – Comparative Animal Physiology, Brazil
| | - Jose Maria Monserrat
- Institute of Biological Science (ICB)
- Universidade Federal do Rio Grande (FURG)
- Porto Alegre
- Brazil
- ICB-FURG Post-graduate Program Physiological Sciences – Comparative Animal Physiology, Brazil
| | - Luciane C. Alberici
- Department of Physic-Chemistry
- Faculty of Pharmacy of Ribeirão Preto
- University of São Paulo (USP)
- 14040-903 Ribeirão Preto
- Brazil
| | - Zeki Naal
- Department of Physic-Chemistry
- Faculty of Pharmacy of Ribeirão Preto
- University of São Paulo (USP)
- 14040-903 Ribeirão Preto
- Brazil
| | - Carlos Curti
- Department of Physic-Chemistry
- Faculty of Pharmacy of Ribeirão Preto
- University of São Paulo (USP)
- 14040-903 Ribeirão Preto
- Brazil
| | - Humberto González-Díaz
- Department of Organic Chemistry II
- Faculty of Science and Technology
- University of the Basque Country UPV/EHU
- Leioa
- Spain
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