1
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Toropova AP, Toropov AA, Fjodorova N. In Silico Simulation of Impacts of Metal Nano-Oxides on Cell Viability in THP-1 Cells Based on the Correlation Weights of the Fragments of Molecular Structures and Codes of Experimental Conditions Represented by Means of Quasi-SMILES. Int J Mol Sci 2023; 24:ijms24032058. [PMID: 36768396 PMCID: PMC9917241 DOI: 10.3390/ijms24032058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/10/2023] [Accepted: 01/18/2023] [Indexed: 01/21/2023] Open
Abstract
A simulation of the effect of metal nano-oxides at various concentrations (25, 50, 100, and 200 milligrams per millilitre) on cell viability in THP-1 cells (%) based on data on the molecular structure of the oxide and its concentration is proposed. We used a simplified molecular input-line entry system (SMILES) to represent the molecular structure. So-called quasi-SMILES extends usual SMILES with special codes for experimental conditions (concentration). The approach based on building up models using quasi-SMILES is self-consistent, i.e., the predictive potential of the model group obtained by random splits into training and validation sets is stable. The Monte Carlo method was used as a basis for building up the above groups of models. The CORAL software was applied to building the Monte Carlo calculations. The average determination coefficient for the five different validation sets was R2 = 0.806 ± 0.061.
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Affiliation(s)
- Alla P. Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156 Milano, Italy
- Correspondence: ; Tel.: +39-02-3901-4595
| | - Andrey A. Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156 Milano, Italy
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Li J, Wang C, Yue L, Chen F, Cao X, Wang Z. Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 243:113955. [PMID: 35961199 DOI: 10.1016/j.ecoenv.2022.113955] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/11/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Given the rapid development of nanotechnology, it is crucial to understand the effects of nanoparticles on living organisms. However, it is laborious to perform toxicological tests on a case-by-case basis. Quantitative structure-activity relationship (QSAR) is an effective computational technique because it saves time, costs, and animal sacrifice. Therefore, this review presents general procedures for the construction and application of nano-QSAR models of metal-based and metal-oxide nanoparticles (MBNPs and MONPs). We also provide an overview of available databases and common algorithms. The molecular descriptors and their roles in the toxicological interpretation of MBNPs and MONPs are systematically reviewed and the future of nano-QSAR is discussed. Finally, we address the growing demand for novel nano-specific descriptors, new computational strategies to address the data shortage, in situ data for regulatory concerns, a better understanding of the physicochemical properties of NPs with bioactivity, and, most importantly, the design of nano-QSAR for real-life environmental predictions rather than laboratory simulations.
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Affiliation(s)
- Jing Li
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Chuanxi Wang
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Le Yue
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Feiran Chen
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Xuesong Cao
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Zhenyu Wang
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China.
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3
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Toropov AA, Kjeldsen F, Toropova AP. Use of quasi-SMILES to build models based on quantitative results from experiments with nanomaterials. CHEMOSPHERE 2022; 303:135086. [PMID: 35618064 DOI: 10.1016/j.chemosphere.2022.135086] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 05/20/2022] [Accepted: 05/21/2022] [Indexed: 06/15/2023]
Abstract
Quasi-SMILES deviate from traditional SMILES (simplified molecular input-line entry system) by the extension of additional symbols that encode for conditions of an experiment. Descriptors calculated with SMILES are useful for the development of quantitative structure-property/activity relationships (QSPRs/QSARs), while descriptors calculated with quasi-SMILES can be useful for the development of quantitative models of experimental results obtained under different conditions. Here, this approach has been applied for the development of generalized models using aquatic nanotoxicity data (i.e., related to fish and daphnia). The statistical quality of the above models (pLC50) is quite good with a determination coefficient for the external validation set ranging from 0.62 to 0.71 and RMSE ranging from 0.58 to 0.60. The principle of the approach includes splitting the experimental data into three random distributions defining training, calibration, and validation sets.
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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
| | - Frank Kjeldsen
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, DK-5230, Odense, Denmark.
| | - 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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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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Ahmadi S, Ketabi S, Qomi M. CO 2 uptake prediction of metal–organic frameworks using quasi-SMILES and Monte Carlo optimization. NEW J CHEM 2022. [DOI: 10.1039/d2nj00596d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The first report of quasi-SMILES-based QSPR models for CO2 capture of MOFs based on experimental data.
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Affiliation(s)
- Shahin Ahmadi
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Sepideh Ketabi
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Mahnaz Qomi
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
- Active Pharmaceutical Ingredients Research (APIRC), Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
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6
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Toropov AA, Toropova AP, Benfenati E. The QSAR-search of effective agents towards coronaviruses applying the Monte Carlo method. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:689-698. [PMID: 34293992 DOI: 10.1080/1062936x.2021.1952649] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 07/04/2021] [Indexed: 06/13/2023]
Abstract
Perhaps there is some similarity between the coronavirus of 2017 and the COVID-19. Consequently, a predictive model for the antiviral activity for the Middle East respiratory syndrome coronavirus (MERS-CoV, 2017) could be useful for designing the strategy and tactics in the struggle with coronaviruses in general and with COVID 19 in particular. Quantitative structure-activity relationships (QSARs) of inhibitory activity to MERS-CoV were developed. The index of ideality of correlation was applied to build up these models for the antiviral activity. The statistical quality of the best model is quite good (r2 = 0.84). A mechanistic interpretation of these models based on the molecular features with strong positive (i.e. promoters for endpoint increase) and strong negative (i.e. promoters for endpoint decrease) influence on the inhibitory activity is suggested. A collection of possible biologically active compounds, constructed using data on the above molecular features which are statistically reliable promoters of increase or decrease of the activity, is presented.
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Affiliation(s)
- A A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - A P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - E Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
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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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Toropova AP, Toropov AA, Leszczynski J, Sizochenko N. Using quasi-SMILES for the predictive modeling of the safety of 574 metal oxide nanoparticles measured in different experimental conditions. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2021; 86:103665. [PMID: 33895354 DOI: 10.1016/j.etap.2021.103665] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/31/2021] [Accepted: 04/20/2021] [Indexed: 06/12/2023]
Abstract
The production of nanomaterials continues its rapid growth; however, newly manufactured nanomaterials' environmental and health safety are among the most significant concerns. A safety assessment is usually a lengthy and costly process, so computational studies are often used to complement experimental testing. One of the most time-efficient techniques is structure-activity relationships (SAR) modeling. In this project, we analyzed the Sustainable Nanotechnology (S2NANO) dataset that contains 574 experimental cell viability and toxicity datapoints for Al2O3, CuO, Fe2O3, Fe3O4, SiO2, TiO2, and ZnO measured in different conditions. We aimed to develop classification- and regression-based structure-activity relationship models using quasi-SMILES molecular representation. Introduced quasi-SMILES took into consideration all available information, including structural features of nanoparticles (molecular structure, core size, etc.) and related experimental parameters (cell line, dose, exposure time, assay, hydrodynamic size, surface charge, etc.). Resultant regression models demonstrated sufficient predictive power, while classification models demonstrated higher accuracy.
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Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics, and Atmospheric Sciences, Jackson State University, Jackson, MS, USA
| | - Natalia Sizochenko
- Department of Informatics, Postdoctoral Institute for Computational Studies, Enfield, NH, USA; The Ronin Institute of Independent Scholarship, Montclair, NJ, USA.
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Toropov AA, Toropova AP. Quasi-SMILES as a basis for the development of models for the toxicity of ZnO nanoparticles. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 772:145532. [PMID: 33578164 DOI: 10.1016/j.scitotenv.2021.145532] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 01/07/2021] [Accepted: 01/26/2021] [Indexed: 06/12/2023]
Abstract
The application of nanomaterials is expanding. Therefore, it is necessary to investigate the relationship between the structure and toxicity of different nanomaterials. Quasi-SMILES is a line of symbols which are codes of corresponding conditions of experiments aimed to estimate the toxicity of ZnO nanoparticles towards the rat via intraperitoneal injections. By means of the Monte Carlo method, the so-called correlation weights for fragments of quasi-SMILES can be calculated. Having the numerical data on the correlation weights one can build up a one-variable model for the toxicity. The checking up of the approach with five random splits of all available data on results of thirty-six experiments into a sub-system of training and sub-system of validation has confirmed the significance of the statistical quality of models obtained with the above approach. The average determination coefficient equal to 0.957 (dispersion 0.010) and average root mean square error equal to 7.25 [mg/kg] (dispersion 0.59 [mg/kg]).
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Affiliation(s)
- Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
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Cappelli CI, Toropov AA, Toropova AP, Benfenati E. Ecosystem ecology: Models for acute toxicity of pesticides towards Daphnia magna. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2020; 80:103459. [PMID: 32721590 DOI: 10.1016/j.etap.2020.103459] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 06/22/2020] [Accepted: 07/23/2020] [Indexed: 06/11/2023]
Abstract
Quantitative structure - activity relationships (QSARs) which are obtained with a representation of the molecular architecture via simplified molecular input-line entry system (SMILES) are applied to build up predictive models of acute toxicity of pesticides towards Daphnia magna. The acute toxicity towards Daphnia magna is an adequate measure of the ecological impact of various substances. The Monte Carlo technique is the basis to build up the above QSAR models. The statistical quality of suggested models is good: the best model is characterized by n = 103, R2 = 0.76, RMSE = 0.91 (training set); n = 53, R2 = 0.82, RMSE = 0.87 (validation set). The approach provides the mechanistic interpretation (e.g. aromaticity and branching of carbon skeleton are promoters of increase for toxicity towards Daphnia magna in the case of the examined set of pesticides). The approach is attractive to build up predictive models since instead of a large number of different molecular descriptors the corresponding model is based on solely one optimal descriptor calculated with SMILES and all necessary calculations can be done using the CORAL software available on the Internet (http://ww.insilico.eu/coral).
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Affiliation(s)
- Claudia Ileana Cappelli
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
| | - Andrey A Toropov
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
| | - Alla P Toropova
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy.
| | - Emilio Benfenati
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
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Toropov AA, Sizochenko N, Toropova AP, Leszczynska D, Leszczynski J. Advancement of predictive modeling of zeta potentials (ζ) in metal oxide nanoparticles with correlation intensity index (CII). J Mol Liq 2020. [DOI: 10.1016/j.molliq.2020.113929] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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12
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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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Achary PGR, Toropova AP, Toropov AA. Prediction of the self‐accelerating decomposition temperature of organic peroxides. PROCESS SAFETY PROGRESS 2020. [DOI: 10.1002/prs.12189] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Patnala Ganga Raju Achary
- Department of Chemistry Institute of Technical Education and Research (ITER), Siksha 'O' Anusandhan deemed to be University Bhubaneswar Odisha India
| | - Alla P. Toropova
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology Istituto di Ricerche Farmacologiche Mario Negri IRCCS Milan Italy
| | - Andrey A. Toropov
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology Istituto di Ricerche Farmacologiche Mario Negri IRCCS Milan Italy
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Jafari K, Fatemi MH. A new approach to model isobaric heat capacity and density of some nitride-based nanofluids using Monte Carlo method. ADV POWDER TECHNOL 2020. [DOI: 10.1016/j.apt.2020.05.023] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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15
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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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Toropov AA, Toropova AP, Marzo M, Carnesecchi E, Selvestrel G, Benfenati E. Pesticides, cosmetics, drugs: identical and opposite influences of various molecular features as measures of endpoints similarity and dissimilarity. Mol Divers 2020; 25:1137-1144. [PMID: 32323128 DOI: 10.1007/s11030-020-10085-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 04/06/2020] [Indexed: 11/26/2022]
Abstract
The similarity is an important category in natural sciences. A measure of similarity for a group of various biochemical endpoints is suggested. The list of examined endpoints contains (1) toxicity of pesticides towards rainbow trout; (2) human skin sensitization; (3) mutagenicity; (4) toxicity of psychotropic drugs; and (5) anti HIV activity. Further applying and evolution of the suggested approach is discussed. In particular, the conception of the similarity (dissimilarity) of endpoints can play the role of a "useful bridge" between quantitative structure property/activity relationships (QSPRs/QSARs) and read-across technique.
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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 Mario Negri 2, 20156, Milan, Italy
| | - 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, Milan, Italy.
| | - Marco Marzo
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy
| | - Edoardo Carnesecchi
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, P.O. Box 80177, 3508 TD, Utrecht, The Netherlands
| | - Gianluca Selvestrel
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy
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17
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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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18
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Ahmadi S. Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria. CHEMOSPHERE 2020; 242:125192. [PMID: 31677509 DOI: 10.1016/j.chemosphere.2019.125192] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 10/17/2019] [Accepted: 10/21/2019] [Indexed: 06/10/2023]
Abstract
Several types of metal oxide nanoparticles (MO-NPs) are often utilized as one of the novel class of materials in the pharmaceutical industry and human health. The wide use of MO-NPs forces an enhanced understanding of their potential impact on human health and the environment. The research aims to investigate and develop a nano-QFAR (nano-quantitative feature activity relationship) model applying the quasi-SMILES such as cell line, assay, time exposition, concentration, nanoparticles size and metal oxide type for prediction of cell viability (%) of MO-NPs. The total set of 83 quasi-SMILES of MO-NPs divided into training, validation and test sets randomly three times. The statistical model results based on the balance of correlation target function (TF1) and index of ideality correlation target function (TF2) and the Monte Carlo optimization were compared. The comparison of two target function results indicated that TF2 improves the predictability of models. The significance of various eclectic features of both increase and decrease of cell viability (%) is provided. Mechanistic interpretation of significant factors for the model are proposed as well. The sufficient statistical quality of three nano-QFAR models based on TF2 reveals that the developed models can be efficiency for predictions of the cell viability (%) of MO-NPs.
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Affiliation(s)
- Shahin Ahmadi
- Department of Chemistry, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
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Nymark P, Bakker M, Dekkers S, Franken R, Fransman W, García-Bilbao A, Greco D, Gulumian M, Hadrup N, Halappanavar S, Hongisto V, Hougaard KS, Jensen KA, Kohonen P, Koivisto AJ, Dal Maso M, Oosterwijk T, Poikkimäki M, Rodriguez-Llopis I, Stierum R, Sørli JB, Grafström R. Toward Rigorous Materials Production: New Approach Methodologies Have Extensive Potential to Improve Current Safety Assessment Practices. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e1904749. [PMID: 31913582 DOI: 10.1002/smll.201904749] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 12/09/2019] [Indexed: 06/10/2023]
Abstract
Advanced material development, including at the nanoscale, comprises costly and complex challenges coupled to ensuring human and environmental safety. Governmental agencies regulating safety have announced interest toward acceptance of safety data generated under the collective term New Approach Methodologies (NAMs), as such technologies/approaches offer marked potential to progress the integration of safety testing measures during innovation from idea to product launch of nanomaterials. Divided in overall eight main categories, searchable databases for grouping and read across purposes, exposure assessment and modeling, in silico modeling of physicochemical structure and hazard data, in vitro high-throughput and high-content screening assays, dose-response assessments and modeling, analyses of biological processes and toxicity pathways, kinetics and dose extrapolation, consideration of relevant exposure levels and biomarker endpoints typify such useful NAMs. Their application generally agrees with articulated stakeholder needs for improvement of safety testing procedures. They further fit for inclusion and add value in nanomaterials risk assessment tools. Overall 37 of 50 evaluated NAMs and tiered workflows applying NAMs are recommended for considering safer-by-design innovation, including guidance to the selection of specific NAMs in the eight categories. An innovation funnel enriched with safety methods is ultimately proposed under the central aim of promoting rigorous nanomaterials innovation.
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Affiliation(s)
- Penny Nymark
- Karolinska Institutet, Institute of Environmental Medicine, Nobels väg 13, 171 77, Stockholm, Sweden
- Department of Toxicology, Misvik Biology, Karjakatu 35 B, 20520, Turku, Finland
| | - Martine Bakker
- National Institute for Public Health and the Environment, RIVM, P.O. Box 1, 3720 BA, Bilthoven, The Netherlands
| | - Susan Dekkers
- National Institute for Public Health and the Environment, RIVM, P.O. Box 1, 3720 BA, Bilthoven, The Netherlands
| | - Remy Franken
- Netherlands Organisation for Applied Scientific Research, TNO, P.O. Box 96800, NL-2509 JE, The Hague, The Netherlands
| | - Wouter Fransman
- Netherlands Organisation for Applied Scientific Research, TNO, P.O. Box 96800, NL-2509 JE, The Hague, The Netherlands
| | - Amaia García-Bilbao
- GAIKER Technology Centre, Parque Tecnológico, Ed. 202, 48170, Zamudio, Bizkaia, Spain
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Korkeakoulunkatu 6, 33720, Tampere, Finland
- Institute of Biotechnology, University of Helsinki, P.O. Box 56, FI-00014, Helsinki, Finland
| | - Mary Gulumian
- National Institute for Occupational Health, 25 Hospital St, Constitution Hill, 2000, Johannesburg, South Africa
- Haematology and Molecular Medicine Department, University of the Witwatersrand, 7 York Road, Parktown, 2193, Johannesburg, South Africa
| | - Niels Hadrup
- National Research Center for the Work Environment, Lersø Parkallé 105, 2100, Copenhagen, Denmark
| | - Sabina Halappanavar
- Environmental Health Science and Research Bureau, Health Canada, 50 Colombine Driveway, Ottawa, ON, K1A 0K9, Canada
| | - Vesa Hongisto
- Department of Toxicology, Misvik Biology, Karjakatu 35 B, 20520, Turku, Finland
| | - Karin Sørig Hougaard
- National Research Center for the Work Environment, Lersø Parkallé 105, 2100, Copenhagen, Denmark
| | - Keld Alstrup Jensen
- National Research Center for the Work Environment, Lersø Parkallé 105, 2100, Copenhagen, Denmark
| | - Pekka Kohonen
- Karolinska Institutet, Institute of Environmental Medicine, Nobels väg 13, 171 77, Stockholm, Sweden
- Department of Toxicology, Misvik Biology, Karjakatu 35 B, 20520, Turku, Finland
| | - Antti Joonas Koivisto
- National Research Center for the Work Environment, Lersø Parkallé 105, 2100, Copenhagen, Denmark
| | - Miikka Dal Maso
- Aerosol Physics Laboratory, Physics Unit, Tampere University, Korkeakoulunkatu 6, 33720, Tampere, Finland
| | - Thies Oosterwijk
- Netherlands Organisation for Applied Scientific Research, TNO, P.O. Box 96800, NL-2509 JE, The Hague, The Netherlands
| | - Mikko Poikkimäki
- Aerosol Physics Laboratory, Physics Unit, Tampere University, Korkeakoulunkatu 6, 33720, Tampere, Finland
| | | | - Rob Stierum
- Netherlands Organisation for Applied Scientific Research, TNO, P.O. Box 96800, NL-2509 JE, The Hague, The Netherlands
| | - Jorid Birkelund Sørli
- National Research Center for the Work Environment, Lersø Parkallé 105, 2100, Copenhagen, Denmark
| | - Roland Grafström
- Karolinska Institutet, Institute of Environmental Medicine, Nobels väg 13, 171 77, Stockholm, Sweden
- Department of Toxicology, Misvik Biology, Karjakatu 35 B, 20520, Turku, Finland
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20
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Kumar P, Kumar A, Sindhu J. In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2019; 30:525-541. [PMID: 31331203 DOI: 10.1080/1062936x.2019.1629998] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 06/06/2019] [Indexed: 06/10/2023]
Abstract
Diabetes, obesity and other diseases related to metabolism are worldwide health problems. These syndromes can be well treated when a particular enzyme-based therapy is developed. Diacylglycerol acyltransferase (DGAT; EC 2.3.1.20) is a microsomal enzyme which is responsible for the synthesis of triglycerides from 1,2-diacylglycerol by catalyzing the acyl-CoA-dependent acylation. The obesity and type-II diabetes can be checked by the inhibition of DGAT1 enzyme. Quantitative structure-activity relationship (QSAR) modelling is an essential technique in drug design and development. To study the aspect of DGAT1 inhibitors, Monte-Carlo method-based QSAR was developed for 197 DGAT1 inhibitors. QSAR models were derived by using the optimal descriptor based on SMILES notation. Different statistical parameters including the novel index of ideality of correlation were applied to validate the generated QSAR models. Four random splits were prepared from the data set. The statistical criteria r2 = 0.8129, CCC = 0.8979 and Q2 = 0.7962 of the validation set of split 1 were the best; therefore, the developed QSAR model of split 1 was decided to be the leading model. The molecular fragments, which were promoter of endpoint increase or decrease were also determined. Thirteen new DGAT1 inhibitors were designed from the lead compound DGAT011.
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Affiliation(s)
- P Kumar
- Department of Chemistry, Kurukshetra University , Kurukshetra , India
| | - A Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology , Hisar , India
| | - J Sindhu
- Department of Chemsitry, COBS&H CCS Haryana Agriculture University , Hisar , India
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21
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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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22
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23
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González-Durruthy M, Monserrat JM, Viera de Oliveira P, Fagan SB, Werhli AV, Machado K, Melo A, González-Díaz H, Concu R, D. S. Cordeiro MN. Computational MitoTarget Scanning Based on Topological Vacancies of Single-Walled Carbon Nanotubes with the Human Mitochondrial Voltage-Dependent Anion Channel (hVDAC1). Chem Res Toxicol 2019; 32:566-577. [DOI: 10.1021/acs.chemrestox.8b00266] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Michael González-Durruthy
- Department of Chemistry and Biochemistry, Faculty of Science, University of Porto, 4169-007 Porto, Portugal
| | - José M. Monserrat
- Institute of Biological Sciences (ICB), Universidade Federal do Rio Grande (FURG), 96270-900 Rio Grande, RS, Brazil
- ICB-FURG Post-Graduate Program in Physiological Sciences, 96270-900 Rio Grande, RS, Brazil
| | | | - Solange Binotto Fagan
- Post-Graduate Program in Nanoscience, Franciscana University (UFN), 97010-032 Santa Maria, RS, Brazil
| | - Adriano V. Werhli
- Center of Computational Sciences (C3), Federal University of Rio Grande (FURG), Cx. P. 474, CEP, 96200-970 Rio Grande, RS, Brazil
| | - Karina Machado
- Center of Computational Sciences (C3), Federal University of Rio Grande (FURG), Cx. P. 474, CEP, 96200-970 Rio Grande, RS, Brazil
| | - André Melo
- Department of Chemistry and Biochemistry, Faculty of Science, University of Porto, 4169-007 Porto, Portugal
| | - Humberto González-Díaz
- Department of Organic Chemistry II, University of the Basque Country (UPV/EHU), 48940 Leioa, Biscay, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
| | - Riccardo Concu
- Department of Chemistry and Biochemistry, Faculty of Science, University of Porto, 4169-007 Porto, Portugal
| | - M. Natália D. S. Cordeiro
- Department of Chemistry and Biochemistry, Faculty of Science, University of Porto, 4169-007 Porto, Portugal
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24
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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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25
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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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26
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González-Durruthy M, Manske Nunes S, Ventura-Lima J, Gelesky MA, González-Díaz H, Monserrat JM, Concu R, Cordeiro MND. MitoTarget Modeling Using ANN-Classification Models Based on Fractal SEM Nano-Descriptors: Carbon Nanotubes as Mitochondrial F0F1-ATPase Inhibitors. J Chem Inf Model 2018; 59:86-97. [DOI: 10.1021/acs.jcim.8b00631] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Michael González-Durruthy
- LAQV-REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, 4169-007, University of Porto, Porto, Portugal
| | - Silvana Manske Nunes
- Institute of Biological Sciences (ICB), Universidade Federal do Rio Grande -FURG, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
- ICB-FURG Post-Graduate Program in Physiological Sciences, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
| | - Juliane Ventura-Lima
- Institute of Biological Sciences (ICB), Universidade Federal do Rio Grande -FURG, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
- ICB-FURG Post-Graduate Program in Physiological Sciences, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
- National Institute of Carbon Nanomaterial Science and Technology, 30123970, Belo Horizonte, Minas Gerais, Brazil
- Nanotoxicology Network (MCTI/CNPq), 96270-900, Rio Grande, Rio Grande do Sul, Brazil
| | - Marcos A. Gelesky
- Post-Graduate Program in Technological and Environmental Chemistry, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
| | - Humberto González-Díaz
- Department of Organic Chemistry II, College of Science and Technology, University of the Basque Country UPV/EHU, 48940, Leioa, Bizkaia, Spain
- IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Bizkaia, Spain
| | - José M. Monserrat
- Institute of Biological Sciences (ICB), Universidade Federal do Rio Grande -FURG, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
- ICB-FURG Post-Graduate Program in Physiological Sciences, 96270-900, Rio Grande, Rio Grande do Sul, Brazil
- National Institute of Carbon Nanomaterial Science and Technology, 30123970, Belo Horizonte, Minas Gerais, Brazil
- Nanotoxicology Network (MCTI/CNPq), 96270-900, Rio Grande, Rio Grande do Sul, Brazil
| | - Riccardo Concu
- LAQV-REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, 4169-007, University of Porto, Porto, Portugal
| | - M. Natália D.S. Cordeiro
- LAQV-REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, 4169-007, University of Porto, Porto, Portugal
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27
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Toropova AP, Toropov AA, Benfenati E, Leszczynska D, Leszczynski J. Prediction of antimicrobial activity of large pool of peptides using quasi-SMILES. Biosystems 2018; 169-170:5-12. [DOI: 10.1016/j.biosystems.2018.05.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 05/10/2018] [Accepted: 05/14/2018] [Indexed: 11/24/2022]
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Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 2018; 8:6110. [PMID: 29666463 PMCID: PMC5904177 DOI: 10.1038/s41598-018-24483-z] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 04/04/2018] [Indexed: 12/18/2022] Open
Abstract
A generalized toxicity classification model for 7 different oxide nanomaterials is presented in this study. A data set extracted from multiple literature sources and screened by physicochemical property based quality scores were used for model development. Moreover, a few more preprocessing techniques, such as synthetic minority over-sampling technique, were applied to address the imbalanced class problem in the data set. Then, classification models using four different algorithms, such as generalized linear model, support vector machine, random forest, and neural network, were developed and their performances were compared to find the best performing preprocessing methods as well as algorithms. The neural network model built using the balanced data set was identified as the model with best predictive performance, while applicability domain was defined using k-nearest neighbours algorithm. The analysis of relative attribute importance for the built neural network model identified dose, formation enthalpy, exposure time, and hydrodynamic size as the four most important attributes. As the presented model can predict the toxicity of the nanomaterials in consideration of various experimental conditions, it has the advantage of having a broader and more general applicability domain than the existing quantitative structure-activity relationship model.
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29
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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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30
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González-Durruthy M, Werhli AV, Seus V, Machado KS, Pazos A, Munteanu CR, González-Díaz H, Monserrat JM. Decrypting Strong and Weak Single-Walled Carbon Nanotubes Interactions with Mitochondrial Voltage-Dependent Anion Channels Using Molecular Docking and Perturbation Theory. Sci Rep 2017; 7:13271. [PMID: 29038520 PMCID: PMC5643473 DOI: 10.1038/s41598-017-13691-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 09/25/2017] [Indexed: 01/30/2023] Open
Abstract
The current molecular docking study provided the Free Energy of Binding (FEB) for the interaction (nanotoxicity) between VDAC mitochondrial channels of three species (VDAC1-Mus musculus, VDAC1-Homo sapiens, VDAC2-Danio rerio) with SWCNT-H, SWCNT-OH, SWCNT-COOH carbon nanotubes. The general results showed that the FEB values were statistically more negative (p < 0.05) in the following order: (SWCNT-VDAC2-Danio rerio) > (SWCNT-VDAC1-Mus musculus) > (SWCNT-VDAC1-Homo sapiens) > (ATP-VDAC). More negative FEB values for SWCNT-COOH and OH were found in VDAC2-Danio rerio when compared with VDAC1-Mus musculus and VDAC1-Homo sapiens (p < 0.05). In addition, a significant correlation (0.66 > r2 > 0.97) was observed between n-Hamada index and VDAC nanotoxicity (or FEB) for the zigzag topologies of SWCNT-COOH and SWCNT-OH. Predictive Nanoparticles-Quantitative-Structure Binding-Relationship models (nano-QSBR) for strong and weak SWCNT-VDAC docking interactions were performed using Perturbation Theory, regression and classification models. Thus, 405 SWCNT-VDAC interactions were predicted using a nano-PT-QSBR classifications model with high accuracy, specificity, and sensitivity (73–98%) in training and validation series, and a maximum AUROC value of 0.978. In addition, the best regression model was obtained with Random Forest (R2 of 0.833, RMSE of 0.0844), suggesting an excellent potential to predict SWCNT-VDAC channel nanotoxicity. All study data are available at https://doi.org/10.6084/m9.figshare.4802320.v2.
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Affiliation(s)
- Michael González-Durruthy
- Institute of Biological Sciences (ICB)- Federal University of Rio Grande - FURG, Postgraduate Program in Physiological Sciences, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil.
| | - Adriano V Werhli
- Center of Computational Sciences (C3)- Federal University of Rio Grande - FURG, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil
| | - Vinicius Seus
- Center of Computational Sciences (C3)- Federal University of Rio Grande - FURG, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil
| | - Karina S Machado
- Center of Computational Sciences (C3)- Federal University of Rio Grande - FURG, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil
| | - Alejandro Pazos
- Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), A Coruña, 15006, Spain.,RNASA-IMEDIR, Computer Science Faculty, University of A Coruña, Campus de Elviña s/n, 15071, A Coruña, Spain
| | - Cristian R Munteanu
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruña, Campus de Elviña s/n, 15071, A Coruña, Spain
| | - Humberto González-Díaz
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Spain.,IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain
| | - José M Monserrat
- Institute of Biological Sciences (ICB)- Federal University of Rio Grande - FURG, Postgraduate Program in Physiological Sciences, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil
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Zhou Z, Tang X, Dai W, Shi J, Chen H. Nano-QSAR models for predicting cytotoxicity of metal oxide nanoparticles (MONPs) to E. coli. CAN J CHEM 2017. [DOI: 10.1139/cjc-2017-0172] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Nanotechnology has been applied to many aspects of human life. Meanwhile, concerns regarding the toxicity of engineered nanomaterials to the environment have also been growing. Herein, an economic and convenient approach based on quantitative structure–activity relationship for nanomaterials (nano-QSAR) was proposed to evaluate the cytotoxicity of metal oxide nanoparticles (MONPs) to E. coli. Six molecular descriptors of 17 MONPs were selected and calculated using Gaussian98 software and DFT-B3LYP method on the LANL2DZ basis set. Two multivariable models, linear and nonlinear, were built based on the calculated molecular descriptors using multiple linear regression (MLR) and support vector machine (SVM) methods, respectively. Results demonstrated that both models presented high reliability, good predictive performance, and fine generalization ability, with all R2 values greater than 0.84. It was also revealed that the lowest unoccupied molecular orbital (LUMO) and molar heat capacity (Cp) were the two key descriptors influencing the cytotoxicity of MONPs.
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Affiliation(s)
- Zhengwei Zhou
- School of Environmental & Safety Engineering, Changzhou University, Changzhou 213164, Jiangsu, China
- School of Environmental & Safety Engineering, Changzhou University, Changzhou 213164, Jiangsu, China
| | - Xinwen Tang
- School of Environmental & Safety Engineering, Changzhou University, Changzhou 213164, Jiangsu, China
- School of Environmental & Safety Engineering, Changzhou University, Changzhou 213164, Jiangsu, China
| | - Wen Dai
- School of Environmental & Safety Engineering, Changzhou University, Changzhou 213164, Jiangsu, China
- School of Environmental & Safety Engineering, Changzhou University, Changzhou 213164, Jiangsu, China
| | - Jingjie Shi
- School of Environmental & Safety Engineering, Changzhou University, Changzhou 213164, Jiangsu, China
- School of Environmental & Safety Engineering, Changzhou University, Changzhou 213164, Jiangsu, China
| | - Haiqun Chen
- School of Environmental & Safety Engineering, Changzhou University, Changzhou 213164, Jiangsu, China
- School of Environmental & Safety Engineering, Changzhou University, Changzhou 213164, Jiangsu, China
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