1
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Roy J, Roy K. Understanding mechanism governing the inflammatory potential of metal oxide nanoparticles using periodic table-based descriptors: a nano-QSAR approach. SAR QSAR Environ Res 2023:1-16. [PMID: 37350771 DOI: 10.1080/1062936x.2023.2227557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
Metal oxide nanoparticles (MeOxNPs) can be made safer by understanding the interaction between the immune system and nanoparticles. A nano-quantitative structure-activity relationship (nano-QSAR) model can be used to evaluate nanoparticle risk quickly and conveniently. The present work attempts to develop nano-QSAR models to determine the inflammatory potential of MeOxNPs based on the THP-1 cell line. A comprehensive dataset comprising 32 MeOxNPs was used to develop a regression model with fold change (FC) of pro-inflammatory cytokine interleukin (IL)-1beta (IL-1b) release in the THP-1 cell line as the endpoint. Further, the same number of MeOx NPs with varying doses was modelled for the cell viability [-ln(p/(1-p))] endpoint. The results obtained from regression models were statistically significant. The descriptors obtained from the developed predictive models inferred that dose, electronegativity and the presence of metal ions and oxygen can be responsible for IL-1β leakage from the THP-1 cell line. Based on our results, we can conclude that periodic table-based descriptors, incorporated into the QSAR models, are reliable for modelling pro-inflammatory potential. Researchers can use these comprehensive results to design metal oxide nanoparticles with lower toxicity and determine the cause of pro-inflammatory conditions induced by MeOxNPs.
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Affiliation(s)
- J Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - K Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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2
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Toropova AP, Toropov AA, Fjodorova N. Quasi-SMILES for predicting toxicity of Nano-mixtures to Daphnia Magna. NanoImpact 2022; 28:100427. [PMID: 36113716 DOI: 10.1016/j.impact.2022.100427] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 09/05/2022] [Accepted: 09/07/2022] [Indexed: 06/15/2023]
Abstract
Quasi-SMILES is an extension of the traditional SMILES. The classic SMILES is a way to represent the molecular structure. The quasi-SMILES is a way to describe all eclectic conditions that are able to affect the activity of a substance or a mixture. Nano-QSAR for prediction of toxicity of Nano-mixtures built up using the database on the corresponding experimental data. Models built up for five random splits of available data in training and validation sets are suggested. The Monte Carlo method of optimization is applied to calculate so-called optimal descriptors. The optimization was carried out with two criteria of predictive potential. These are the so-called index of ideality of correlation (IIC) and correlation intensity index (CII). Applying CII gives the better statistical quality of models for toxicity Nano-mixtures towards Daphnia Magna. The statistical quality of the best model follows the determination coefficients 0.987 (training set) and 0.980 (validation set).
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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.
| | - 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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3
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Stoliński F, Rybińska-Fryca A, Gromelski M, Mikolajczyk A, Puzyn T. NanoMixHamster: a web-based tool for predicting cytotoxicity of TiO 2-based multicomponent nanomaterials toward Chinese hamster ovary (CHO-K1) cells. Nanotoxicology 2022; 16:276-289. [PMID: 35713578 DOI: 10.1080/17435390.2022.2080609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Nano-QSAR models can be effectively used for prediction of the biological activity of nanomaterials that have not been experimentally tested before. However, their use is associated with the need to have appropriate knowledge and skills in chemoinformatics. Thus, they are mainly aimed at specialists in the field. This significantly limits the potential group of recipients of the developed solutions. In this perspective, the purpose of the presented research was to develop an easily accessible and user-friendly web-based application that could enable the prediction of TiO2-based multicomponent nanomaterials cytotoxicity toward Chinese Hamster Ovary (CHO-K1) cells. The graphical user interface is clear and intuitive and the only information required from the user is the type and concentration of the metals which will be modifying TiO2-based nanomaterial. Thanks to this, the application will be easy to use not only by cheminformatics but also by specialists in the field of nanotechnology or toxicology, who will be able to quickly predict cytotoxicity of desired nanoclusters. We have performed case studies to demonstrate the features and utilities of developed application. The NanoMixHamster application is freely available at https://nanomixhamster.cloud.nanosolveit.eu/.
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Affiliation(s)
- Filip Stoliński
- QSAR Lab Ltd, Gdansk, Poland.,Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | | | - Alicja Mikolajczyk
- QSAR Lab Ltd, Gdansk, Poland.,Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Tomasz Puzyn
- QSAR Lab Ltd, Gdansk, Poland.,Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
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4
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Gromelski M, Stoliński F, Jagiello K, Rybińska-Fryca A, Williams A, Halappanavar S, Vogel U, Puzyn T. AOP173 key event associated pathway predictor - online application for the prediction of benchmark dose lower bound (BMDLs) of a transcriptomic pathway involved in MWCNTs-induced lung fibrosis. Nanotoxicology 2022; 16:183-194. [PMID: 35452346 DOI: 10.1080/17435390.2022.2064250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Nano-QSAR model allows for prediction of the toxicity of materials that have not been experimentally tested before by linking the nano-related structural properties with the biological responses induced by nanomaterials. Prediction of adverse effects caused by substances without having to perform time- and cost-consuming experiments makes QSAR models promising tools for supporting risk assessment. However, very often, newly developed nano-QSAR models are not used in practice due to the complexity of their algorithms, the necessity to have experience in chemoinformatics, and their poor accessibility. In this perspective, the aim of this paper is to encourage developers of the QSAR models to take the effort to prepare user-friendly applications based on predictive models. This would make the developed models accessible to a wider community, and, in effect, promote their further application by regulators and decision-makers. Here, we describe a web-based application that enables to predict the transcriptomic pathway-level response perturbated in the lungs of mice exposed to multiwalled carbon nanotubes. The developed application is freely available at http://aop173-event1.nanoqsar-aop.com/apps/aop_app. It requires only two types of input information related to analyzed nanotubes (their length and diameter) to assess the doses that initiate the inflammation process that may lead to lung fibrosis.
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Affiliation(s)
| | | | - Karolina Jagiello
- QSAR Lab, ul. Trzy Lipy 3, Gdańsk, Poland.,Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Andrew Williams
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Canada
| | - Sabina Halappanavar
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Canada
| | - Ulla Vogel
- National Research Centre for the Working Environment, Copenhagen, Denmark
| | - Tomasz Puzyn
- QSAR Lab, ul. Trzy Lipy 3, Gdańsk, Poland.,Faculty of Chemistry, University of Gdansk, Gdansk, Poland
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5
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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6
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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. Environ Toxicol Pharmacol 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] [What about the content of this article? (0)] [Affiliation(s)] [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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7
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Abstract
Quantitative structure-property/activity relationships (QSPRs/QSARs) are an important component of modern science. Validation of the QSPR/QSAR is the basis for applying. The system of self-consistent models is a new approach to validate QSPR/QSAR. The principle 'QSAR is a random event' means that an approach may be recognized as robust only if the statistical characteristics of models obtained by this approach for different splits (training/test) are reproduced. The above principle applies to the case of the nano-QSAR, also. Here, the cellular uptake of nanoparticles in pancreatic cancer cells examines as the endpoint. Groups of models for different splits (training/test) are compared. This comparison gives the possibility to formulate the system of self-consistent models as a way to assess the predictive potential for an arbitrary QSPR/QSAR and/or nano-QSPR/QSAR. The correlation intensity index (CII) has been tested as a tool to improve the quality of models for the cellular uptake of nanoparticles in pancreatic cancer cells (PaCa2). It has shown, that the CII can be useful, but only incorporating with the Index of ideality of correlation (IIC).
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Affiliation(s)
- Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology; Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology; Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
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8
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Gousiadou C, Marchese Robinson RL, Kotzabasaki M, Doganis P, Wilkins TA, Jia X, Sarimveis H, Harper SL. Machine learning predictions of concentration-specific aggregate hazard scores of inorganic nanomaterials in embryonic zebrafish. Nanotoxicology 2021; 15:446-476. [PMID: 33586589 DOI: 10.1080/17435390.2021.1872113] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
The possibility of employing computational approaches like nano-QSAR or nano-read-across to predict nanomaterial hazard is attractive from both a financial, and most importantly, where in vivo tests are required, ethical perspective. In the present work, we have employed advanced Machine Learning techniques, including stacked model ensembles, to create nano-QSAR tools for modeling the toxicity of metallic and metal oxide nanomaterials, both coated and uncoated and with a variety of different core compositions, tested at different dosage concentrations on embryonic zebrafish. Using both computed and experimental descriptors, we have identified a set of properties most relevant for the assessment of nanomaterial toxicity and successfully correlated these properties with the associated biological responses observed in zebrafish. Our findings suggest that for the group of metal and metal oxide nanomaterials, the core chemical composition, concentration and properties dependent upon nanomaterial surface and medium composition (such as zeta potential and agglomerate size) are significant factors influencing toxicity, albeit the ranking of different variables is sensitive to the exact analysis method and data modeled. Our generalized nano-QSAR ensemble models provide a promising framework for anticipating the toxicity potential of new nanomaterials and may contribute to the transition out of the animal testing paradigm. However, future experimental studies are required to generate comparable, similarly high quality data, using consistent protocols, for well characterized nanomaterials, as per the dataset modeled herein. This would enable the predictive power of our promising ensemble modeling approaches to be robustly assessed on large, diverse and truly external datasets.
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Affiliation(s)
- C Gousiadou
- School of Chemical Engineering, National Technical University of Athens, Athens, Greece
| | - R L Marchese Robinson
- School of Chemical and Process Engineering, University of Leeds, Leeds, United Kingdom
| | - M Kotzabasaki
- School of Chemical Engineering, National Technical University of Athens, Athens, Greece
| | - P Doganis
- School of Chemical Engineering, National Technical University of Athens, Athens, Greece
| | - T A Wilkins
- School of Chemical and Process Engineering, University of Leeds, Leeds, United Kingdom
| | - X Jia
- School of Chemical and Process Engineering, University of Leeds, Leeds, United Kingdom
| | - H Sarimveis
- School of Chemical Engineering, National Technical University of Athens, Athens, Greece
| | - S L Harper
- School of Chemical, Biological and Environmental Engineering, Oregon State University, Corvallis, OR, USA.,Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR, USA.,Safer Nanomaterials and Nanomanufacturing Initiative, Oregon Nanoscience and Microtechnologies Institute, Eugene, OR, USA
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9
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Jagiello K, Halappanavar S, Rybińska-Fryca A, Willliams A, Vogel U, Puzyn T. Transcriptomics-Based and AOP-Informed Structure-Activity Relationships to Predict Pulmonary Pathology Induced by Multiwalled Carbon Nanotubes. Small 2021; 17:e2003465. [PMID: 33502096 DOI: 10.1002/smll.202003465] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 08/31/2020] [Indexed: 06/12/2023]
Abstract
This study presents a novel strategy that employs quantitative structure-activity relationship models for nanomaterials (Nano-QSAR) for predicting transcriptomic pathway level response using lung tissue inflammation, an essential key event (KEs) in the existing adverse outcome pathway (AOP) for lung fibrosis, as a model response. Transcriptomic profiles of mouse lungs exposed to ten different multiwalled carbon nanotubes (MWCNTs) are analyzed using statistical and bioinformatics tools. Three pathways "agranulocyte adhesion and diapedesis," "granulocyte adhesion and diapedesis," and "acute phase signaling," that (1) are commonly perturbed across the MWCNTs panel, (2) show dose response (Benchmark dose, BMDs), and (3) are anchored to the KEs identified in the lung fibrosis AOP, are considered in modelling. The three pathways are associated with tissue inflammation. The results show that the aspect ratio (κ) of MWCNTs is directly correlated with the pathway BMDs. The study establishes a methodology for QSAR construction based on canonical pathways and proposes a MWCNTs grouping strategy based on the κ-values of the specific pathway associated genes. Finally, the study shows how the AOP framework can help guide QSAR modelling efforts; conversely, the outcome of the QSAR modelling can aid in refining certain aspects of the AOP in question (here, lung fibrosis).
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Affiliation(s)
- Karolina Jagiello
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, Gdansk, 80-266, Poland
- Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, Gdansk, 80-308, Poland
| | - Sabina Halappanavar
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, K1A 0K9, Canada
- Department of Biology, University of Ottawa, Ottawa, Ontario, K1N 9A7, Canada
| | | | - Andrew Willliams
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, K1A 0K9, Canada
| | - Ulla Vogel
- The National Research Centre for the Working Environment, Copenhagen, DK-2100, Denmark
| | - Tomasz Puzyn
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, Gdansk, 80-266, Poland
- Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, Gdansk, 80-308, Poland
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10
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Shin HK, Kim S, Yoon S. Use of size-dependent electron configuration fingerprint to develop general prediction models for nanomaterials. NanoImpact 2021; 21:100298. [PMID: 35559785 DOI: 10.1016/j.impact.2021.100298] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Revised: 01/18/2021] [Accepted: 01/18/2021] [Indexed: 06/15/2023]
Abstract
Due to the lack of nano descriptors that can appropriately represent the wide chemical space of engineered nanomaterials (ENMs), applicability domain of nano-quantitative structure-activity relationship models are limited to certain types of ENMs, such as metal oxides, metals, carbon-based nanomaterials, or quantum dots. In this study, a size-dependent electron configuration fingerprint (SDEC FP) was introduced to estimate the quantity of electrons based on the core, doping, and coating materials of ENMs in different sizes. SDEC FP was used in prediction model development and nanostructure similarity analysis on datasets including metal and carbon-based nanomaterials with and without surface modifications. Cytotoxicity and zeta potential prediction models developed with SDEC FP achieved good prediction accuracies on test set. Nanostructure similarity analysis was performed through principal component analysis which showed that structural similarity between ENMs measured by SDEC FP was highly correlated with their properties.
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Affiliation(s)
- Hyun Kil Shin
- Toxicoinformatics Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon 34114, Republic of Korea.
| | - Soojin Kim
- Molecular Toxicology Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon 34114, Republic of Korea
| | - Seokjoo Yoon
- Molecular Toxicology Group, Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon 34114, Republic of Korea
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11
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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12
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Sizochenko N, Syzochenko M, Fjodorova N, Rasulev B, Leszczynski J. Evaluating genotoxicity of metal oxide nanoparticles: Application of advanced supervised and unsupervised machine learning techniques. Ecotoxicol Environ Saf 2019; 185:109733. [PMID: 31580980 DOI: 10.1016/j.ecoenv.2019.109733] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 09/21/2019] [Accepted: 09/24/2019] [Indexed: 06/10/2023]
Abstract
Presence of missing data points in datasets is among main challenges in handling the toxicological data for nanomaterials. As the processing of missing data is an important part of data analysis, we have introduced a read-across approach that uses a combination of supervised and unsupervised machine learning techniques to fill the missing values. A series of classification models (supervised learning) was developed to predict class label, and self-organizing map approach (unsupervised learning) was used to estimate relative distances between nanoparticles and refine results obtained during supervised learning. In this study, genotoxicity of 49 silicon and metal oxide nanoparticles in Ames and Comet tests. Collected literature data did not demonstrate significant variations related to the change of size including selected bulk materials. Genotoxicity-related features of nanomaterials were represented by ionic characteristics. General tendencies found in the current study were convincingly linked to known theories of genotoxic action at nano-level. Mechanisms of primary and secondary genotoxic effects were discussed in the context of developed models.
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Affiliation(s)
- Natalia Sizochenko
- Interdisciplinary Center for Nanotoxicity, Jackson State University, Jackson, MS, USA; Department of Computer Science, Dartmouth College, Hanover, 03755, NH, USA.
| | - Michael Syzochenko
- Interdisciplinary Center for Nanotoxicity, Jackson State University, Jackson, MS, USA; Department of Computer Science, Dartmouth College, Hanover, 03755, NH, USA.
| | - Natalja Fjodorova
- Department of Chemoinformatics, National Institute of Chemistry, Ljubljana, 1000, Slovenia.
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, 58108, ND, USA.
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Jackson State University, Jackson, MS, USA.
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13
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Puzyn T, Jeliazkova N, Sarimveis H, Marchese Robinson RL, Lobaskin V, Rallo R, Richarz AN, Gajewicz A, Papadopulos MG, Hastings J, Cronin MTD, Benfenati E, Fernández A. Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology. Food Chem Toxicol 2018; 112:478-94. [PMID: 28943385 DOI: 10.1016/j.fct.2017.09.037] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 08/31/2017] [Accepted: 09/19/2017] [Indexed: 11/20/2022]
Abstract
Nanotechnology and the production of nanomaterials have been expanding rapidly in recent years. Since many types of engineered nanoparticles are suspected to be toxic to living organisms and to have a negative impact on the environment, the process of designing new nanoparticles and their applications must be accompanied by a thorough risk analysis. (Quantitative) Structure-Activity Relationship ([Q]SAR) modelling creates promising options among the available methods for the risk assessment. These in silico models can be used to predict a variety of properties, including the toxicity of newly designed nanoparticles. However, (Q)SAR models must be appropriately validated to ensure the clarity, consistency and reliability of predictions. This paper is a joint initiative from recently completed European research projects focused on developing (Q)SAR methodology for nanomaterials. The aim was to interpret and expand the guidance for the well-known "OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models", with reference to nano-(Q)SAR, and present our opinions on the criteria to be fulfilled for models developed for nanoparticles.
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Toropova AP, Toropov AA, Leszczynska D, Leszczynski J. CORAL and Nano-QFAR: Quantitative feature - Activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, Co 3O 4, and TiO 2). Ecotoxicol Environ Saf 2017; 139:404-407. [PMID: 28192776 DOI: 10.1016/j.ecoenv.2017.01.054] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 01/25/2017] [Accepted: 01/31/2017] [Indexed: 06/06/2023]
Abstract
Quantitative feature - activity relationships (QFAR) approach was applied to prediction of bioavailability of metal oxide nanoparticles. ZnO, CuO, Co3O4, and TiO2 nanoxides were considered. The computational model for bioavailability of investigated species is asserted. The model was calculated using the Monte Carlo method. The CORAL free software (http://www.insilico.eu/coral) was used in this study. The developed model was tested by application of three different splits of data into the training and validation sets. So-called, quasi-SMILES are used to represent the conditions of action of metal oxide nanoparticles. A new paradigm of building up predictive models of endpoints related to nanomaterials is suggested. The paradigm is the following "An endpoint is a mathematical function of available eclectic data (conditions)". Recently, the paradigm has been checked up with endpoints related to metal oxide nanoparticles, fullerenes, and multi-walled carbon-nanotubes.
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Affiliation(s)
- Alla P Toropova
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milan, Italy.
| | - Andrey A Toropov
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milan, Italy
| | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch Street, Jackson, MS 39217-0510, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 J. R. Lynch Street, P.O. Box 17910, Jackson, MS 39217, USA
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15
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Richarz AN, Avramopoulos A, Benfenati E, Gajewicz A, Golbamaki Bakhtyari N, Leonis G, Marchese Robinson RL, Papadopoulos MG, Cronin MT, Puzyn T. Compilation of Data and Modelling of Nanoparticle Interactions and Toxicity in the NanoPUZZLES Project. Adv Exp Med Biol 2017; 947:303-324. [PMID: 28168672 DOI: 10.1007/978-3-319-47754-1_10] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The particular properties of nanomaterials have led to their rapidly increasing use in diverse fields of application. However, safety assessment is not keeping pace and there are still gaps in the understanding of their hazards. Computational models predicting nanotoxicity, such as (quantitative) structure-activity relationships ((Q)SARs), can contribute to safety evaluation, in line with general efforts to apply alternative methods in chemical risk assessment. Their development is highly dependent on the availability of reliable and high quality experimental data, both regarding the compounds' properties as well as the measured toxic effects. In particular, "nano-QSARs" should take the nano-specific characteristics into account. The information compiled needs to be well organized, quality controlled and standardized. Integrating the data in an overarching, structured data collection aims to (a) organize the data in a way to support modelling, (b) make (meta)data necessary for modelling available, and (c) add value by making a comparison between data from different sources possible.Based on the available data, specific descriptors can be derived to parameterize the nanomaterial-specific structure and physico-chemical properties appropriately. Furthermore, the interactions between nanoparticles and biological systems as well as small molecules, which can lead to modifications of the structure of the active nanoparticles, need to be described and taken into account in the development of models to predict the biological activity and toxicity of nanoparticles. The EU NanoPUZZLES project was part of a global cooperative effort to advance data availability and modelling approaches supporting the characterization and evaluation of nanomaterials.
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Affiliation(s)
- Andrea-Nicole Richarz
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - Aggelos Avramopoulos
- Institute of Biology, Pharmaceutical Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
| | - Agnieszka Gajewicz
- Laboratory of Environmental Chemometrics, Institute for Environmental and Human Health Protection, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | - Nazanin Golbamaki Bakhtyari
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
| | - Georgios Leonis
- Institute of Biology, Pharmaceutical Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece
| | | | - Manthos G Papadopoulos
- Institute of Biology, Pharmaceutical Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece
| | - Mark Td Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - Tomasz Puzyn
- Laboratory of Environmental Chemometrics, Institute for Environmental and Human Health Protection, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
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Abstract
Traditional Quantitative Structure-Activity Relationships (QSAR) models based on molecular descriptors as translators of chemical information show some drawbacks in predicting toxicity of nanomaterials due to their unique properties and to their nonhomogeneous structure.This chapter provides instructions on how to use CORAL, freely available software for building nano-QSAR models. CORAL makes use of descriptors based on "quasi-SMILES" representing physicochemical features and/or experimental conditions as an alternative to traditional SMILES encoding chemical structure to build up predictive nano-QSAR models for cytotoxicity.
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Affiliation(s)
- Serena Manganelli
- Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche "Mario Negri", Via La Masa 19, Milan, 20156, Italy.
| | - Emilio Benfenati
- Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche "Mario Negri", Via La Masa 19, Milan, 20156, Italy
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17
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Kar S, Gajewicz A, Roy K, Leszczynski J, Puzyn T. Extrapolating between toxicity endpoints of metal oxide nanoparticles: Predicting toxicity to Escherichia coli and human keratinocyte cell line (HaCaT) with Nano-QTTR. Ecotoxicol Environ Saf 2016; 126:238-244. [PMID: 26773833 DOI: 10.1016/j.ecoenv.2015.12.033] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2015] [Revised: 10/14/2015] [Accepted: 12/25/2015] [Indexed: 05/29/2023]
Abstract
Synthesis of novel nanoparticles should always be accompanied by a comprehensive assessment of risk to human health and to ecosystem. Application of in silico models is encouraged by regulatory authorities to fill the data gaps related to the properties of nanoparticles affecting the environment and human health. Interspecies toxicity correlations provide a tool for estimation of contaminant's sensitivity with known levels of uncertainty for a diverse pool of species. We propose here first interspecies cytotoxicity correlation models between Escherichia coli (prokaryotic system) and human keratinocyte cell line (HaCaT) (eukaryotic system) to assess the discriminatory features for cytotoxicity of metal oxide nanoparticles. The nano-QTTR models can be employed for extrapolating cytotoxicity to E. coli and human keratinocyte cell line (HaCaT) for metal nanoparticles when the data for the other species are available. Informative illustrations of the contributing mechanisms of toxic action of the metal oxide nanoparticles to the HaCaT cell line as well as to the E. coli are identified from the developed nano quantitative toxicity-toxicity relationship (nano-QTTR) models.
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Affiliation(s)
- Supratik Kar
- Laboratory of Environmental Chemometrics, Institute for Environmental and Human Health Protection, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland; Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India; Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS, USA
| | - Agnieszka Gajewicz
- Laboratory of Environmental Chemometrics, Institute for Environmental and Human Health Protection, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS, USA
| | - Tomasz Puzyn
- Laboratory of Environmental Chemometrics, Institute for Environmental and Human Health Protection, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland.
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18
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Toropova AP, Toropov AA, Veselinović AM, Veselinović JB, Benfenati E, Leszczynska D, Leszczynski J. Nano-QSAR: Model of mutagenicity of fullerene as a mathematical function of different conditions. Ecotoxicol Environ Saf 2016; 124:32-36. [PMID: 26452192 DOI: 10.1016/j.ecoenv.2015.09.038] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 09/18/2015] [Accepted: 09/25/2015] [Indexed: 06/05/2023]
Abstract
The experimental data on the bacterial reverse mutation test (under various conditions) on C60 nanoparticles for the cases (i) TA100, and (ii) WP2uvrA/pkM101 are examined as endpoints. By means of the optimal descriptors calculated with the Monte Carlo method a mathematical model of these endpoints has been built up. The models are a mathematical function of eclectic data such as (i) dose (g/plate); (ii) metabolic activation (i.e. with mix S9 or without mix S9); and (iii) illumination (i.e. darkness or irradiation). The eclectic data on different conditions were represented by so-called quasi-SMILES. In contrast to the traditional SMILES which are representation of molecular structure, the quasi-SMILES are representation of conditions by sequence of symbols. The calculations were carried out with the CORAL software, available on the Internet at http://www.insilico.eu/coral. The main idea of the suggested descriptors is the accumulation of all available eclectic information in the role of logical and digital basis for building up a model. The computational experiments have shown that the described approach can be a tool to build up models of mutagenicity of fullerene under different conditions.
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Affiliation(s)
- Alla P Toropova
- IRCCS-I stituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, Italy
| | - Andrey A Toropov
- IRCCS-I stituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, Italy.
| | | | | | - Emilio Benfenati
- IRCCS-I stituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, Italy
| | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch St, Jackson, MS 39217-0510, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 J. R. Lynch Street, P.O. Box 17910, Jackson, MS 39217, USA
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Jagiello K, Grzonkowska M, Swirog M, Ahmed L, Rasulev B, Avramopoulos A, Papadopoulos MG, Leszczynski J, Puzyn T. Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives. J Nanopart Res 2016; 18:256. [PMID: 27642255 PMCID: PMC5003910 DOI: 10.1007/s11051-016-3564-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Accepted: 08/18/2016] [Indexed: 05/19/2023]
Abstract
In this contribution, the advantages and limitations of two computational techniques that can be used for the investigation of nanoparticles activity and toxicity: classic nano-QSAR (Quantitative Structure-Activity Relationships employed for nanomaterials) and 3D nano-QSAR (three-dimensional Quantitative Structure-Activity Relationships, such us Comparative Molecular Field Analysis, CoMFA/Comparative Molecular Similarity Indices Analysis, CoMSIA analysis employed for nanomaterials) have been briefly summarized. Both approaches were compared according to the selected criteria, including: efficiency, type of experimental data, class of nanomaterials, time required for calculations and computational cost, difficulties in the interpretation. Taking into account the advantages and limitations of each method, we provide the recommendations for nano-QSAR modellers and QSAR model users to be able to determine a proper and efficient methodology to investigate biological activity of nanoparticles in order to describe the underlying interactions in the most reliable and useful manner.
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Affiliation(s)
- Karolina Jagiello
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, Institute for Environmental and Human Health Protection, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Monika Grzonkowska
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, Institute for Environmental and Human Health Protection, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Marta Swirog
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, Institute for Environmental and Human Health Protection, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Lucky Ahmed
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 JR Lynch Street, Jackson, MS 39217-0510 USA
| | - Bakhtiyor Rasulev
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 JR Lynch Street, Jackson, MS 39217-0510 USA
- Center for Computationally Assisted Science and Technology, North Dakota State University, 1805 NDSU Research Park Drive, Post Office Box 6050, Fargo, ND 58108 USA
| | - Aggelos Avramopoulos
- Institute of Biology, Pharmaceutical Chemistry and Biotechnology, National Hellenic Research Foundation, 48 Vas. Constantinou Ave., 11635 Athens, Greece
| | - Manthos G. Papadopoulos
- Institute of Biology, Pharmaceutical Chemistry and Biotechnology, National Hellenic Research Foundation, 48 Vas. Constantinou Ave., 11635 Athens, Greece
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 JR Lynch Street, Jackson, MS 39217-0510 USA
| | - Tomasz Puzyn
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, Institute for Environmental and Human Health Protection, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
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20
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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21
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Kar S, Gajewicz A, Puzyn T, Roy K. Nano-quantitative structure-activity relationship modeling using easily computable and interpretable descriptors for uptake of magnetofluorescent engineered nanoparticles in pancreatic cancer cells. Toxicol In Vitro 2014; 28:600-6. [PMID: 24412539 DOI: 10.1016/j.tiv.2013.12.018] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2013] [Revised: 12/23/2013] [Accepted: 12/27/2013] [Indexed: 01/28/2023]
Abstract
As experimental evaluation of the safety of nanoparticles (NPs) is expensive and time-consuming, computational approaches have been found to be an efficient alternative for predicting the potential toxicity of new NPs before mass production. In this background, we have developed here a regression-based nano quantitative structure-activity relationship (nano-QSAR) model to establish statistically significant relationships between the measured cellular uptakes of 109 magnetofluorescent NPs in pancreatic cancer cells with their physical, chemical, and structural properties encoded within easily computable, interpretable and reproducible descriptors. The developed model was rigorously validated internally as well as externally with the application of the principles of Organization for Economic Cooperation and Development (OECD). The test for domain of applicability was also carried out for checking reliability of the predictions. Important fragments contributing to higher/lower cellular uptake of NPs were identified through critical analysis and interpretation of the developed model. Considering all these identified structural attributes, one can choose or design safe, economical and suitable surface modifiers for NPs. The presented approach provides rich information in the context of virtual screening of relevant NP libraries.
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Affiliation(s)
- Supratik Kar
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Agnieszka Gajewicz
- Laboratory of Environmental Chemometrics, Institute for Environmental and Human Health Protection, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | - Tomasz Puzyn
- Laboratory of Environmental Chemometrics, Institute for Environmental and Human Health Protection, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
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