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Zhou Y, Wang Y, Peijnenburg W, Vijver MG, Balraadjsing S, Fan W. Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17786-17795. [PMID: 36730792 DOI: 10.1021/acs.est.2c07039] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The wide production and use of metallic nanomaterials (MNMs) leads to increased emissions into the aquatic environments and induces high potential risks. Experimentally evaluating the (eco)toxicity of MNMs is time-consuming and expensive due to the multiple environmental factors, the complexity of material properties, and the species diversity. Machine learning (ML) models provide an option to deal with heterogeneous data sets and complex relationships. The present study established an in silico model based on a machine learning properties-environmental conditions-multi species-toxicity prediction model (ML-PEMST) that can be applied to predict the toxicity of different MNMs toward multiple aquatic species. Feature importance and interaction analysis based on the random forest method indicated that exposure duration, illumination, primary size, and hydrodynamic diameter were the main factors affecting the ecotoxicity of MNMs to a variety of aquatic organisms. Illumination was demonstrated to have the most interaction with the other features. Moreover, incorporating additional detailed information on the ecological traits of the test species will allow us to further optimize and improve the predictive performance of the model. This study provides a new approach for ecotoxicity predictions for organisms in the aquatic environment and will help us to further explore exposure pathways and the risk assessment of MNMs.
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Affiliation(s)
- Yunchi Zhou
- School of Space and Environment, Beihang University, Beijing100191, China
| | - Ying Wang
- School of Space and Environment, Beihang University, Beijing100191, China
| | - Willie Peijnenburg
- Institute of Environmental Science (CML), Leiden University, Leiden2300, RA, The Netherlands
- Center for Safety of Substances and Products, National Institute of Public Health and the Environment (RIVM), Bilthoven3720, BA, The Netherlands
| | - Martina G Vijver
- Institute of Environmental Science (CML), Leiden University, Leiden2300, RA, The Netherlands
| | - Surendra Balraadjsing
- Institute of Environmental Science (CML), Leiden University, Leiden2300, RA, The Netherlands
| | - Wenhong Fan
- School of Space and Environment, Beihang University, Beijing100191, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing100191, China
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2
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Yan X, Yue T, Winkler DA, Yin Y, Zhu H, Jiang G, Yan B. Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation. Chem Rev 2023. [PMID: 37262026 DOI: 10.1021/acs.chemrev.3c00070] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.
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Affiliation(s)
- Xiliang Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Tongtao Yue
- Key Laboratory of Marine Environment and Ecology, Ministry of Education, Institute of Coastal Environmental Pollution Control, Ocean University of China, Qingdao 266100, China
| | - David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia
- School of Pharmacy, University of Nottingham, Nottingham NG7 2QL, U.K
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, Australia
| | - Yongguang Yin
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Hao Zhu
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Bing Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
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3
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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: 12] [Impact Index Per Article: 6.0] [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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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] [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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Montiel Schneider MG, Martín MJ, Otarola J, Vakarelska E, Simeonov V, Lassalle V, Nedyalkova M. Biomedical Applications of Iron Oxide Nanoparticles: Current Insights Progress and Perspectives. Pharmaceutics 2022; 14:204. [PMID: 35057099 PMCID: PMC8780449 DOI: 10.3390/pharmaceutics14010204] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/01/2022] [Accepted: 01/14/2022] [Indexed: 01/08/2023] Open
Abstract
The enormous development of nanomaterials technology and the immediate response of many areas of science, research, and practice to their possible application has led to the publication of thousands of scientific papers, books, and reports. This vast amount of information requires careful classification and order, especially for specifically targeted practical needs. Therefore, the present review aims to summarize to some extent the role of iron oxide nanoparticles in biomedical research. Summarizing the fundamental properties of the magnetic iron oxide nanoparticles, the review's next focus was to classify research studies related to applying these particles for cancer diagnostics and therapy (similar to photothermal therapy, hyperthermia), in nano theranostics, multimodal therapy. Special attention is paid to research studies dealing with the opportunities of combining different nanomaterials to achieve optimal systems for biomedical application. In this regard, original data about the synthesis and characterization of nanolipidic magnetic hybrid systems are included as an example. The last section of the review is dedicated to the capacities of magnetite-based magnetic nanoparticles for the management of oncological diseases.
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Affiliation(s)
- María Gabriela Montiel Schneider
- INQUISUR, Departamento de Química, Universidad Nacional del Sur (UNS)-CONICET, Bahía Blanca 8000, Argentina; (M.G.M.S.); (M.J.M.); (J.O.); (V.L.)
| | - María Julia Martín
- INQUISUR, Departamento de Química, Universidad Nacional del Sur (UNS)-CONICET, Bahía Blanca 8000, Argentina; (M.G.M.S.); (M.J.M.); (J.O.); (V.L.)
| | - Jessica Otarola
- INQUISUR, Departamento de Química, Universidad Nacional del Sur (UNS)-CONICET, Bahía Blanca 8000, Argentina; (M.G.M.S.); (M.J.M.); (J.O.); (V.L.)
| | - Ekaterina Vakarelska
- Faculty of Chemistry and Pharmacy, University of Sofia, 1 James Bourchier Blvd., 1164 Sofia, Bulgaria;
| | - Vasil Simeonov
- Faculty of Chemistry and Pharmacy, University of Sofia, 1 James Bourchier Blvd., 1164 Sofia, Bulgaria;
| | - Verónica Lassalle
- INQUISUR, Departamento de Química, Universidad Nacional del Sur (UNS)-CONICET, Bahía Blanca 8000, Argentina; (M.G.M.S.); (M.J.M.); (J.O.); (V.L.)
| | - Miroslava Nedyalkova
- Faculty of Chemistry and Pharmacy, University of Sofia, 1 James Bourchier Blvd., 1164 Sofia, Bulgaria;
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6
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Gupta R, Chen Y, Xie H. In vitro dissolution considerations associated with nano drug delivery systems. WILEY INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY 2021; 13:e1732. [PMID: 34132050 PMCID: PMC8526385 DOI: 10.1002/wnan.1732] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 12/17/2022]
Abstract
Nano drug delivery systems (NDDS) offer promising solution for the translation of future nanomedicines. As bioavailability and therapeutic outcomes can be improved by altering the drug release from these NDDS, it becomes essential to thoroughly understand their drug release kinetics. Moreover, U.S. Food and Drug Administration requires critical evaluation of potential safety, efficacy, and public health impacts of nanomaterials. Spiraling up market share of NDDS has also stimulated the pharmaceutical industry to develop their cost-effective generic versions after the expiry of patent and associated exclusivity. However, unlike the conventional dosage forms, the in vivo disposition of NDDS is highly intricate and different from their in vitro behavior. Significant challenges exist in the establishment of in vitro-in vivo correlation (IVIVC) due to incomplete understanding of nanoparticles' in vivo biofate and its impact on in vitro experimental protocols. A rational design of dissolution may serve as quality and quantity control tool and help develop a meaningful IVIVC for favorable economic implications. Clinically relevant drug product specifications (critical quality attributes) can be identified by establishing a link between in vitro performance and in vivo exposure. In vitro dissolution may also play a pivotal role to understand the dissolution-mediated clearance and safety of NDDS. Prevalent in vitro dissolution methods for NDDS and their limitations are discussed in this review, among which USP 4 is gaining more interest recently. Researchers are working diligently to develop biorelevant in vitro release assays to ensure optimal therapeutic performance of generic versions of these NDDS. This article focuses on these studies and presents important considerations for the future development of clinically relevant in vitro release methods. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Regulatory and Policy Issues in Nanomedicine.
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Affiliation(s)
- Ritu Gupta
- Department of Pharmaceutical Science, College of Pharmacy and Health Sciences, Texas Southern University, Houston, TX, USA 77004
| | - Yuan Chen
- Department of Pharmaceutical Science, College of Pharmacy and Health Sciences, Texas Southern University, Houston, TX, USA 77004
| | - Huan Xie
- Department of Pharmaceutical Science, College of Pharmacy and Health Sciences, Texas Southern University, Houston, TX, USA 77004
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7
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Shi H, Pan Y, Yang F, Cao J, Tan X, Yuan B, Jiang J. Nano-SAR Modeling for Predicting the Cytotoxicity of Metal Oxide Nanoparticles to PaCa2. Molecules 2021; 26:molecules26082188. [PMID: 33920258 PMCID: PMC8069170 DOI: 10.3390/molecules26082188] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/03/2021] [Accepted: 04/06/2021] [Indexed: 11/16/2022] Open
Abstract
Nowadays, the impact of engineered nanoparticles (NPs) on human health and environment has aroused widespread attention. It is essential to assess and predict the biological activity, toxicity, and physicochemical properties of NPs. Computation-based methods have been developed to be efficient alternatives for understanding the negative effects of nanoparticles on the environment and human health. Here, a classification-based structure-activity relationship model for nanoparticles (nano-SAR) was developed to predict the cellular uptake of 109 functionalized magneto-fluorescent nanoparticles to pancreatic cancer cells (PaCa2). The norm index descriptors were employed for describing the structure characteristics of the involved nanoparticles. The Random forest algorithm (RF), combining with the Recursive Feature Elimination (RFE) was employed to develop the nano-SAR model. The resulted model showed satisfactory statistical performance, with the accuracy (ACC) of the test set and the training set of 0.950 and 0.966, respectively, demonstrating that the model had satisfactory classification effect. The model was rigorously verified and further extensively compared with models in the literature. The proposed model could be reasonably expected to predict the cellular uptakes of nanoparticles and provide some guidance for the design and manufacture of safer nanomaterials.
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Affiliation(s)
- Haihua Shi
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China; (H.S.); (F.Y.); (J.C.); (X.T.); (B.Y.); (J.J.)
| | - Yong Pan
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China; (H.S.); (F.Y.); (J.C.); (X.T.); (B.Y.); (J.J.)
- Correspondence: ; Tel.: +86-25-581-398-73
| | - Fan Yang
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China; (H.S.); (F.Y.); (J.C.); (X.T.); (B.Y.); (J.J.)
| | - Jiakai Cao
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China; (H.S.); (F.Y.); (J.C.); (X.T.); (B.Y.); (J.J.)
| | - Xinlong Tan
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China; (H.S.); (F.Y.); (J.C.); (X.T.); (B.Y.); (J.J.)
| | - Beilei Yuan
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China; (H.S.); (F.Y.); (J.C.); (X.T.); (B.Y.); (J.J.)
| | - Juncheng Jiang
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China; (H.S.); (F.Y.); (J.C.); (X.T.); (B.Y.); (J.J.)
- School of Environment & Safety Engineering, Changzhou University, Changzhou 213164, China
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8
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Jaidev LR, Chede LS, Kandikattu HK. Theranostic Nanoparticles for Pancreatic Cancer Treatment. Endocr Metab Immune Disord Drug Targets 2021; 21:203-214. [PMID: 32416712 DOI: 10.2174/1871530320666200516164911] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 03/30/2020] [Accepted: 04/06/2020] [Indexed: 11/22/2022]
Abstract
Pancreatic cancer is one of the low vascular permeable tumors with a high mortality rate. The five-year survival period is ~5%. The field of drug delivery is at its pace in developing unique drug delivery carriers to treat high mortality rate cancers such as pancreatic cancer. Theranostic nanoparticles are the new novel delivery carriers where the carrier is loaded with both diagnostic and therapeutic agents. The present review discusses various therapeutic and theranostic nanocarriers for pancreatic cancer.
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Affiliation(s)
- Leela R Jaidev
- College of Pharmacy, University of Iowa, 52246, Iowa, United States
| | - Laxmi S Chede
- College of Pharmacy, University of Iowa, 52246, Iowa, United States
| | - Hemanth K Kandikattu
- Department of Medicine, Tulane Eosinophilic Disorders Centre (TEDC), Section of Pulmonary Diseases, Tulane University School of Medicine, New Orleans, LA 70112, United States
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9
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Kar S, Pathakoti K, Tchounwou PB, Leszczynska D, Leszczynski J. Evaluating the cytotoxicity of a large pool of metal oxide nanoparticles to Escherichia coli: Mechanistic understanding through In Vitro and In Silico studies. CHEMOSPHERE 2021; 264:128428. [PMID: 33022504 PMCID: PMC7919734 DOI: 10.1016/j.chemosphere.2020.128428] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/23/2020] [Accepted: 09/21/2020] [Indexed: 05/25/2023]
Abstract
The toxic effect of eight metal oxide nanoparticles (MONPs) on Escherichia coli was experimentally evaluated following standard bioassay protocols. The obtained cytotoxicity ranking of these studied MONPs is Er2O3, Gd2O3, CeO2, Co2O3, Mn2O3, Co3O4, Fe3O4/WO3 (in descending order). The computed EC50 values from experimental data suggested that Er2O3 and Gd2O3 were the most acutely toxic MONPs to E. coli. To identify the mechanism of toxicity of these 8 MONPs along with 17 other MONPs from our previous study, we employed seven classifications and machine learning (ML) algorithms including linear discriminant analysis (LDA), naïve bayes (NB), multinomial logistic regression (MLogitR), sequential minimal optimization (SMO), AdaBoost, J48, and random forest (RF). We also employed 1st and 2nd generation periodic table descriptors developed by us (without any sophisticated computing facilities) along with experimentally analyzed Zeta-potential, to model the cytotoxicity of these MONPs. Based on qualitative validation metrics, the LDA model appeared to be the best among the 7 tested models. The core environment of metal defined by the ratio of the number of core electrons to the number of valence electrons and the electronegativity count of oxygen showed a positive impact on toxicity. The identified properties were important for understanding the mechanisms of nanotoxicity and for predicting the potential environmental risk associated with MONPs exposure. The developed models can be utilized for environmental risk assessment of any untested MONP to E. coli, thereby providing a scientific basis for the design and preparation of safe nanomaterials.
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Affiliation(s)
- Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA
| | - Kavitha Pathakoti
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA; RCMI Center for Environmental Health, Department of Biology, Jackson State University, Jackson, MS, 39217, USA
| | - Paul B Tchounwou
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA; RCMI Center for Environmental Health, Department of Biology, Jackson State University, Jackson, MS, 39217, USA
| | - Danuta Leszczynska
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA; Department of Civil and Environmental Engineering, Jackson State University, Jackson, MS, 39217, USA
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA.
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10
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Cottura N, Howarth A, Rajoli RKR, Siccardi M. The Current Landscape of Novel Formulations and the Role of Mathematical Modeling in Their Development. J Clin Pharmacol 2020; 60 Suppl 1:S77-S97. [PMID: 33205431 DOI: 10.1002/jcph.1715] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 07/25/2020] [Indexed: 12/15/2022]
Abstract
Drug delivery is an integral part of the drug development process, influencing safety and efficacy of active pharmaceutical ingredients. The application of nanotechnology has enabled the discovery of novel formulations for numerous therapeutic purposes across multiple disease areas. However, evaluation of novel formulations in clinical scenarios is slow and hampered due to various ethical and logistical barriers. Computational models have the ability to integrate existing domain knowledge and mathematical correlations, to rationalize the feasibility of using novel formulations for safely enhancing drug delivery, identifying suitable candidates, and reducing the burden on preclinical and clinical studies. In this review, types of novel formulations and their application through several routes of administration and the use of modeling approaches that can find application in different stages of the novel formulation development process are discussed.
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Affiliation(s)
- Nicolas Cottura
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Alice Howarth
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Rajith K R Rajoli
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Marco Siccardi
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
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11
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Rybińska-Fryca A, Mikolajczyk A, Puzyn T. Structure-activity prediction networks (SAPNets): a step beyond Nano-QSAR for effective implementation of the safe-by-design concept. NANOSCALE 2020; 12:20669-20676. [PMID: 33048104 DOI: 10.1039/d0nr05220e] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
A significant number of experimental studies are supported by computational methods such as quantitative structure-activity relationship modeling of nanoparticles (Nano-QSAR). This is especially so in research focused on design and synthesis of new, safer nanomaterials using safe-by-design concepts. However, Nano-QSAR has a number of important limitations. For example, it is not clear which descriptors that describe the nanoparticle physicochemical and structural properties are essential and can be adjusted to alter the target properties. This limitation can be overcome with the use of the Structure-Activity Prediction Network (SAPNet) presented in this paper. There are three main phases of building the SAPNet. First, information about the structural characterization of a nanomaterial, its physical and chemical properties and toxicity is compiled. Then, the most relevant properties (intrinsic/extrinsic) likely to influence the ENM toxicity are identified by developing "meta-models". Finally, these "meta-models" describing the dependencies between the most relevant properties of the ENMs and their adverse biological properties are developed. In this way, the network is built layer by layer from the endpoint (e.g. toxicity or other properties of interest) to descriptors that describe the particle structure. Therefore, SAPNets go beyond the current standards and provide sufficient information on what structural features should be altered to obtain a material with desired properties.
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Affiliation(s)
| | - Alicja Mikolajczyk
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland. and University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Tomasz Puzyn
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland. and University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
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12
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Zhu X, Kong Q, Niu X, Chen L, Ge C. Mapping Intellectual Structure and Research Performance for the Nanoparticles in Pancreatic Cancer Field. Int J Nanomedicine 2020; 15:5503-5516. [PMID: 32801702 PMCID: PMC7415461 DOI: 10.2147/ijn.s253599] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 07/13/2020] [Indexed: 01/15/2023] Open
Abstract
Objective To comprehensively analyze the global scientific outputs of nanoparticles in pancreatic cancer research. Methods Publications regarding the nanoparticles in pancreatic cancer research published from 1986 to 2019 were retrieved from the Web of Science Core Collection (WoSCC). Highly frequent keywords, publication years, journals, cited papers, cited journals and cited authors were identified using BICOMB software, and then a binary matrix and a co-word matrix were constructed. gCLUTO was used for double clustering of highly frequent journals. Co-citation analysis was performed using CiteSpace V software, including keywords, references, journals author or institution cooperation network. Results A total of 1171 publications were included in this study. Publications mainly came from 10 countries, led by the US (n=470) and China (n=349). Among the top 20 journals ranked by the number of citations, nanoscience nanotechnology was the leader with 300. Cluster analysis of citation network identified 12 co-citation clusters, headed by “stromal barrier” and “emerging inorganic nanomaterial”. Conclusion Our findings reveal the research performance and intellectual structure of the nanoparticles in pancreatic cancer research, which may help researchers understand the research trends and hotspots in this field.
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Affiliation(s)
- Xuan Zhu
- Department of General Surgery, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, People's Republic of China.,Institute of Translational Medicine, China Medical University, Shenyang, Liaoning 110122, People's Republic of China.,Anshan Hospital, The First Affiliated Hospital of China Medical University, Anshan, Liaoning 114011, People's Republic of China
| | - Qingquan Kong
- Institute of Translational Medicine, China Medical University, Shenyang, Liaoning 110122, People's Republic of China
| | - Xing Niu
- Department of Second Clinical College, Shengjing Hospital Affiliated to China Medical University, Shenyang 110004, Liaoning, People's Republic of China
| | - Lijie Chen
- Department of Second Clinical College, Shengjing Hospital Affiliated to China Medical University, Shenyang 110004, Liaoning, People's Republic of China
| | - Chunlin Ge
- Department of General Surgery, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, People's Republic of China
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13
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Furxhi I, Murphy F, Mullins M, Arvanitis A, Poland CA. Practices and Trends of Machine Learning Application in Nanotoxicology. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E116. [PMID: 31936210 PMCID: PMC7023261 DOI: 10.3390/nano10010116] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 12/31/2019] [Accepted: 01/06/2020] [Indexed: 02/07/2023]
Abstract
Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.
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Affiliation(s)
- Irini Furxhi
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Finbarr Murphy
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Martin Mullins
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Athanasios Arvanitis
- Department of Mechanical Engineering, Environmental Informatics Research Group, Aristotle University of Thessaloniki, 54124 Thessaloniki Box 483, Greece;
| | - Craig A. Poland
- ELEGI/Colt Laboratory, Queen’s Medical Research Institute, 47 Little France Crescent, University of Edinburgh, Edinburgh EH16 4TJ, Scotland, UK;
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14
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Santana R, Onieva E, Zuluaga R, Duardo-Sánchez A, Gañán P. Machine Learning as a Proposal for a Better Application of Food Nanotechnology Regulation in the European Union. Curr Top Med Chem 2019; 20:324-332. [PMID: 31804168 DOI: 10.2174/1568026619666191205152538] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 09/07/2019] [Accepted: 09/10/2019] [Indexed: 11/22/2022]
Abstract
AIMS Given the current gaps of scientific knowledge and the need of efficient application of food law, this paper makes an analysis of principles of European food law for the appropriateness of applying biological activity Machine Learning prediction models to guarantee public safety. BACKGROUND Cheminformatic methods are able to design and create predictive models with high rate of accuracy saving time, costs and animal sacrifice. It has been applied on different disciplines including nanotechnology. OBJECTIVE Given the current gaps of scientific knowledge and the need of efficient application of food law, this paper makes an analysis of principles of European food law for the appropriateness of applying biological activity Machine Learning prediction models to guarantee public safety. METHODS A systematic study of the regulation and the incorporation of predictive models of biological activity of nanomaterials was carried out through the analysis of the express nanotechnology regulation on foods, applicable in European Union. RESULTS It is concluded Machine Learning could improve the application of nanotechnology food regulation, especially methods such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles promoted by the standards of Organization for Economic Co-operation and Development, European Union regulations and European Food Safety Authority. CONCLUSION To our best knowledge this is the first study focused on nanotechnology food regulation and it can help to support technical European Food Safety Authority Opinions for complementary information.
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Affiliation(s)
- Ricardo Santana
- DeustoTech-Fundacion Deusto, Avda. Universidades, 24, 48007, Bilbao, Spain.,Faculty of Engineering, University of Deusto, Avda. Universidades, 24, 48007, Bilbao, Spain.,Grupo de Investigación Sobre Nuevos Materiales, Universidad Pontificia Bolivariana, Circular 1° N° 70-01, Medellín, Colombia
| | - Enrique Onieva
- DeustoTech-Fundacion Deusto, Avda. Universidades, 24, 48007, Bilbao, Spain
| | - Robin Zuluaga
- Facultad de Ingenieria Agroindustrial, Universidad Pontificia Bolivariana UPB, 050031, Medellin, Colombia
| | - Aliuska Duardo-Sánchez
- Department of Public Law, Law and the Human Genome Research Group, University of the Basque Country UPV/EHU, 48940, Leioa, Biscay, Spain
| | - Piedad Gañán
- Facultad de Ingeniería Quimica, Universidad Pontificia Bolivariana UPB, 050031, Medellin, Colombia
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15
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Yan L, Zhao F, Wang J, Zu Y, Gu Z, Zhao Y. A Safe-by-Design Strategy towards Safer Nanomaterials in Nanomedicines. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2019; 31:e1805391. [PMID: 30701603 DOI: 10.1002/adma.201805391] [Citation(s) in RCA: 82] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 09/13/2018] [Indexed: 05/25/2023]
Abstract
The marriage of nanotechnology and medicine offers new opportunities to fight against human diseases. Benefiting from their unique optical, thermal, magnetic, or redox properties, a wide range of nanomaterials have shown potential in applications such as diagnosis, drug delivery, or tissue repair and regeneration. Despite the considerable success achieved over the past decades, the newly emerging nanomedicines still suffer from an incomplete understanding of their safety risks, and of the relationships between their physicochemical characteristics and safety profiles. Herein, the most important categories of nanomaterials with clinical potential and their toxicological mechanisms are summarized, and then, based on this available information, an overview of the principles in developing safe-by-design nanomaterials for medical applications and of the recent progress in this field is provided. These principles may serve as a starting point to guide the development of more effective safe-by-design strategies and to help identify the major knowledge and skill gaps.
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Affiliation(s)
- Liang Yan
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Nanoscience National Center for Nanoscience and Technology of China, Beijing, 100190, China
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Feng Zhao
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Nanoscience National Center for Nanoscience and Technology of China, Beijing, 100190, China
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Jing Wang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Nanoscience National Center for Nanoscience and Technology of China, Beijing, 100190, China
| | - Yan Zu
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Nanoscience National Center for Nanoscience and Technology of China, Beijing, 100190, China
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhanjun Gu
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Nanoscience National Center for Nanoscience and Technology of China, Beijing, 100190, China
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
- College of Materials Science and Optoelectronic Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuliang Zhao
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Nanoscience National Center for Nanoscience and Technology of China, Beijing, 100190, China
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
- College of Materials Science and Optoelectronic Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology of China, Beijing, 100190, China
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16
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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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17
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Varsou DD, Afantitis A, Tsoumanis A, Melagraki G, Sarimveis H, Valsami-Jones E, Lynch I. A safe-by-design tool for functionalised nanomaterials through the Enalos Nanoinformatics Cloud platform. NANOSCALE ADVANCES 2019; 1:706-718. [PMID: 36132268 PMCID: PMC9473200 DOI: 10.1039/c8na00142a] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Accepted: 10/30/2018] [Indexed: 05/16/2023]
Abstract
Multi-walled carbon nanotubes are currently used in numerous industrial applications and products, therefore fast and accurate evaluation of their biological and toxicological effects is of utmost importance. Computational methods and techniques, previously applied in the area of cheminformatics for the prediction of adverse effects of chemicals, can also be applied in the case of nanomaterials (NMs), in an effort to reduce expensive and time consuming experimental procedures. In this context, a validated and predictive nanoinformatics model has been developed for the accurate prediction of the biological and toxicological profile of decorated multi-walled carbon nanotubes. The nanoinformatics workflow was fully validated according to the OECD principles before it was released online via the Enalos Cloud platform. The web-service is a ready-to-use, user-friendly application whose purpose is to facilitate decision making, as part of a safe-by-design framework for novel carbon nanotubes.
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Affiliation(s)
- Dimitra-Danai Varsou
- Nanoinformatics Department, Novamechanics Ltd Nicosia 1065 Cyprus
- School of Chemical Engineering, National Technical University of Athens 157 80 Athens Greece
| | | | | | | | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens 157 80 Athens Greece
| | - Eugenia Valsami-Jones
- School of Geography, Earth and Environmental Sciences, University of Birmingham B15 2TT Birmingham UK
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham B15 2TT Birmingham UK
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18
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Wang Y, Chen J, Tang W, Xia D, Liang Y, Li X. Modeling adsorption of organic pollutants onto single-walled carbon nanotubes with theoretical molecular descriptors using MLR and SVM algorithms. CHEMOSPHERE 2019; 214:79-84. [PMID: 30261420 DOI: 10.1016/j.chemosphere.2018.09.074] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 09/11/2018] [Accepted: 09/13/2018] [Indexed: 06/08/2023]
Abstract
Prediction of adsorption equilibrium coefficients (K) of organic compounds onto single walled carbon nanotubes (SWNTs) from in silico molecular descriptors is of importance for probing potential applications of SWNTs as well as for evaluating environmental behavior and ecological risks of organic pollutants and SWNTs. In this study, two models for predicting logK were developed with multiple linear regression (MLR) and support vector machine (SVM) algorithms. The two models have satisfactory goodness-of-fit, robustness and predictive ability, and the SVM model performs slightly better than the MLR model. The two models are based on the up-to-date experimental dataset consisting of 61 logK values, and the applicability domains cover diverse organic compounds with functional groups > CC<, CC, C6H5, >CO, COOH, C(O)O, OH, O, F, Cl, Br, NH2, NH, >N, >NN<, NO2, >NC(O)NH2, >NC(O)NH, S and S(O)(O). The adsorption of organic compounds toward SWNTs is mainly determined by van der Waals forces and hydrophobic interactions. Since only in silico molecular descriptors were employed for the modeling, the developed models are beneficial for prediction purposes.
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Affiliation(s)
- Ya Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Linggong Road 2, Dalian 116024, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Linggong Road 2, Dalian 116024, China.
| | - Weihao Tang
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Linggong Road 2, Dalian 116024, China
| | - Deming Xia
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Linggong Road 2, Dalian 116024, China
| | - Yuzhen Liang
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China
| | - Xuehua Li
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Linggong Road 2, Dalian 116024, China
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19
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Wang J, Wang Y, Huang Y, Peijnenburg WJG, Chen J, Li X. Development of a nano-QSPR model to predict band gaps of spherical metal oxide nanoparticles. RSC Adv 2019; 9:8426-8434. [PMID: 35518709 PMCID: PMC9061875 DOI: 10.1039/c8ra10226k] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 03/06/2019] [Indexed: 01/11/2023] Open
Abstract
Antibacterial activities and cytotoxicity of metal oxide nanoparticles are determined by their special band structures, which also influence their potential ecological risks. Traditional experimental determination of the band gap is time-consuming, while the accuracy of theoretical computation depends on the selected algorithm, for which higher precision algorithms, being more expensive, can give a more accurate band gap. Therefore, in this study, a quantitative structure–property relationship (QSPR) model, highlighting the influence of crystalline type and material size, was developed to predict the band gap of metal oxide nanoparticles rapidly and accurately. The structural descriptors for metal oxide nanoparticles were generated via quantum chemistry computations, among which heat of formation and beta angle of the unit cell were the most important parameters influencing band gaps. The developed model shows great robustness and predictive ability (R2 = 0.848, RMSE = 0.378 eV, RMSECV = 0.478 eV, QEXT2 = 0.814, RMSEP = 0.408 eV). The current study can assist in screening the ecological risks of existing metal oxide nanoparticles and may act as a reference for newly designed materials. Antibacterial activities and cytotoxicity of metal oxide nanoparticles are determined by their special band structures, which also influence their potential ecological risks.![]()
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Affiliation(s)
- Jiaxing Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE)
- School of Environmental Science and Technology
- Dalian University of Technology
- Dalian 116024
- China
| | - Ya Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE)
- School of Environmental Science and Technology
- Dalian University of Technology
- Dalian 116024
- China
| | - Yang Huang
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE)
- School of Environmental Science and Technology
- Dalian University of Technology
- Dalian 116024
- China
| | - Willie J. G. M. Peijnenburg
- Institute of Environmental Sciences
- Leiden University
- 2300 RA Leiden
- The Netherlands
- National Institute of Public Health and the Environment
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE)
- School of Environmental Science and Technology
- Dalian University of Technology
- Dalian 116024
- China
| | - Xuehua Li
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE)
- School of Environmental Science and Technology
- Dalian University of Technology
- Dalian 116024
- China
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20
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Burk J, Sikk L, Burk P, Manshian BB, Soenen SJ, Scott-Fordsmand JJ, Tamm T, Tämm K. Fe-Doped ZnO nanoparticle toxicity: assessment by a new generation of nanodescriptors. NANOSCALE 2018; 10:21985-21993. [PMID: 30452031 DOI: 10.1039/c8nr05220d] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In the search for novel tools to combat cancer, nanoparticles (NPs) have attracted a lot of attention. Recently, the controlled release of cancer-cell-killing metal ions from doped NPs has shown promise, but fine tuning of dissolution kinetics is required to ensure specificity and minimize undesirable toxic side-effects. Theoretical tools to help in reaching a proper understanding and finally be able to control the dissolution kinetics by NP design have not been available until now. Here, we present a novel set of true nanodescriptors to analyze the charge distribution, the effect of doping and surface coating of whole metal oxide NP structures. The polarizable model of oxygen atoms enables light to be shed on the charge distribution on the NP surface, allowing the in detail study of the factors influencing the release of metal ions from NPs. The descriptors and their capabilities are demonstrated on a Fe-doped ZnO nanoparticle system, a system with practical outlook and available experimental data.
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Affiliation(s)
- Jaanus Burk
- Institute of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia.
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21
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Ojha PK, Kar S, Roy K, Leszczynski J. Toward comprehension of multiple human cells uptake of engineered nano metal oxides: quantitative inter cell line uptake specificity (QICLUS) modeling. Nanotoxicology 2018; 13:14-34. [PMID: 30354872 DOI: 10.1080/17435390.2018.1529836] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
To address the nanomaterial exposure threat, it is imperative to understand how nanomaterials are recognized, internalized, and distributed within diverse cell systems. Targeting of nanomaterials to a specific cell type is generally attained through the modification of the nanoparticle (NP) surface leading to required cellular uptake. The enhanced cellular uptake to normal cells can direct to the higher interaction of NPs with subcellular organelles resulting the provocation of various signaling pathways. The successes of NPs rely on the prospect for the synthesis of functionalized NPs with necessary properties and their enhanced potential for cellular uptake for specific targeting. In the present study, we have modeled the cellular uptake of 109 surface modifiers of metal oxide nanoparticles (MNPs) for three different cell lines: HUVEC (Human endothelial cells), U937 (human macrophage cells), and PaCa2 (cancer cell lines). Along with the quantitative structure-activity relationship (QSAR) models, for the very first time we have developed and performed quantitative inter cell line uptake specificity (QICLUS) modeling to identify the physicochemical properties, as well as majorly structural fragments responsible for cellular uptake differences between two specific cell lines. The present work provides a comprehensive understanding of the cellular uptake of MNPs and the underlying structural parameters controlling the nano-cellular interactions. This phenomenon has also been analyzed from the QSAR and QICLUS models that concluded the functional groups of surface modifiers like amine, anhydride, halogen atoms, nitro group, acids have the dominating roles for the uptake of MNPs into the cell lines. Thus, the developed models may be used for designing of novel surface modifiers of MNPs of desired characteristics for proper cell-NPs interactions, as well as in the context of virtual screening aspect. Moreover, the MNP-cell interactions can give some idea about the toxicity for target-specific drug delivery treatment as higher cellular uptake is required for specific cells to treat the disease and lower uptake to the neighboring cells for lower toxicity.
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Affiliation(s)
- Probir Kumar Ojha
- a Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology , Jadavpur University , Kolkata , India
| | - Supratik Kar
- b Interdisciplinary Nanotoxicity Center, Department of Chemistry, Physics and Atmospheric Sciences , Jackson State University , Jackson , MS , USA
| | - Kunal Roy
- a Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology , Jadavpur University , Kolkata , India
| | - Jerzy Leszczynski
- b Interdisciplinary Nanotoxicity Center, Department of Chemistry, Physics and Atmospheric Sciences , Jackson State University , Jackson , MS , USA
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22
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Luan F, Tang L, Zhang L, Zhang S, Monteagudo MC, Cordeiro MD. A further development of the QNAR model to predict the cellular uptake of nanoparticles by pancreatic cancer cells. Food Chem Toxicol 2018; 112:571-580. [DOI: 10.1016/j.fct.2017.04.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2016] [Revised: 03/14/2017] [Accepted: 04/11/2017] [Indexed: 02/06/2023]
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Concu R, Kleandrova VV, Speck-Planche A, Cordeiro MNDS. Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory. Nanotoxicology 2017; 11:891-906. [PMID: 28937298 DOI: 10.1080/17435390.2017.1379567] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Nanoparticles (NPs) are part of our daily life, having a wide range of applications in engineering, physics, chemistry, and biomedicine. However, there are serious concerns regarding the harmful effects that NPs can cause to the different biological systems and their ecosystems. Toxicity testing is an essential step for assessing the potential risks of the NPs, but the experimental assays are often very expensive and usually too slow to flag the number of NPs that may cause adverse effects. In silico models centered on quantitative structure-activity/toxicity relationships (QSAR/QSTR) are alternative tools that have become valuable supports to risk assessment, rationalizing the search for safer NPs. In this work, we develop a unified QSTR-perturbation model based on artificial neural networks, aimed at simultaneously predicting general toxicity profiles of NPs under diverse experimental conditions. The model is derived from 54,371 NP-NP pair cases generated by applying the perturbation theory to a set of 260 unique NPs, and showed an accuracy higher than 97% in both training and validation sets. Physicochemical interpretation of the different descriptors in the model are additionally provided. The QSTR-perturbation model is then employed to predict the toxic effects of several NPs not included in the original dataset. The theoretical results obtained for this independent set are strongly consistent with the experimental evidence found in the literature, suggesting that the present QSTR-perturbation model can be viewed as a promising and reliable computational tool for probing the toxicity of NPs.
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Affiliation(s)
- Riccardo Concu
- a LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences , University of Porto , Porto , Portugal
| | - Valeria V Kleandrova
- b Faculty of Technology and Production Management , Moscow State University of Food Production , Moscow , Russia
| | - Alejandro Speck-Planche
- a LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences , University of Porto , Porto , Portugal
| | - M Natália D S Cordeiro
- a LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences , University of Porto , Porto , Portugal
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Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology. Food Chem Toxicol 2017; 112:478-494. [PMID: 28943385 DOI: 10.1016/j.fct.2017.09.037] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 08/31/2017] [Accepted: 09/19/2017] [Indexed: 11/20/2022]
Abstract
Nanotechnology and the production of nanomaterials have been expanding rapidly in recent years. Since many types of engineered nanoparticles are suspected to be toxic to living organisms and to have a negative impact on the environment, the process of designing new nanoparticles and their applications must be accompanied by a thorough risk analysis. (Quantitative) Structure-Activity Relationship ([Q]SAR) modelling creates promising options among the available methods for the risk assessment. These in silico models can be used to predict a variety of properties, including the toxicity of newly designed nanoparticles. However, (Q)SAR models must be appropriately validated to ensure the clarity, consistency and reliability of predictions. This paper is a joint initiative from recently completed European research projects focused on developing (Q)SAR methodology for nanomaterials. The aim was to interpret and expand the guidance for the well-known "OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models", with reference to nano-(Q)SAR, and present our opinions on the criteria to be fulfilled for models developed for nanoparticles.
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Chen G, Vijver MG, Xiao Y, Peijnenburg WJGM. A Review of Recent Advances towards the Development of (Quantitative) Structure-Activity Relationships for Metallic Nanomaterials. MATERIALS 2017; 10:ma10091013. [PMID: 28858269 PMCID: PMC5615668 DOI: 10.3390/ma10091013] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2017] [Revised: 08/08/2017] [Accepted: 08/28/2017] [Indexed: 11/16/2022]
Abstract
Gathering required information in a fast and inexpensive way is essential for assessing the risks of engineered nanomaterials (ENMs). The extension of conventional (quantitative) structure-activity relationships ((Q)SARs) approach to nanotoxicology, i.e., nano-(Q)SARs, is a possible solution. The preliminary attempts of correlating ENMs' characteristics to the biological effects elicited by ENMs highlighted the potential applicability of (Q)SARs in the nanotoxicity field. This review discusses the current knowledge on the development of nano-(Q)SARs for metallic ENMs, on the aspects of data sources, reported nano-(Q)SARs, and mechanistic interpretation. An outlook is given on the further development of this frontier. As concluded, the used experimental data mainly concern the uptake of ENMs by different cell lines and the toxicity of ENMs to cells lines and Escherichia coli. The widely applied techniques of deriving models are linear and non-linear regressions, support vector machine, artificial neural network, k-nearest neighbors, etc. Concluded from the descriptors, surface properties of ENMs are seen as vital for the cellular uptake of ENMs; the capability of releasing ions and surface redox properties of ENMs are of importance for evaluating nanotoxicity. This review aims to present key advances in relevant nano-modeling studies and stimulate future research efforts in this quickly developing field of research.
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Affiliation(s)
- Guangchao Chen
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
| | - Martina G Vijver
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
| | - Yinlong Xiao
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
| | - Willie J G M Peijnenburg
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
- Centre for Safety of Substances and Products, National Institute of Public Health and the Environment (RIVM), 3720 BA Bilthoven, The Netherlands.
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Gajewicz A. What if the number of nanotoxicity data is too small for developing predictive Nano-QSAR models? An alternative read-across based approach for filling data gaps. NANOSCALE 2017; 9:8435-8448. [PMID: 28604902 DOI: 10.1039/c7nr02211e] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Over the past decade, computational nanotoxicology, in particular Quantitative Structure-Activity Relationship models (Nano-QSAR) that help in assessing the biological effects of nanomaterials, have received much attention. In effect, a solid basis for uncovering the relationships between the structure and property/activity of nanoparticles has been created. Nonetheless, six years after the first pioneering computational studies focusing on the investigation of nanotoxicity were commenced, these computational methods still suffer from many limitations. These are mainly related to the paucity of widely available, systematically varied, libraries of experimental data necessary for the development and validation of such models. This results in the still-low acceptance of these methods as valuable research tools for nanosafety and raises the query as to whether these methods could gain wide acceptance of regulatory bodies as alternatives for traditional in vitro methods. This study aimed to give an answer to the following question: How to remedy the paucity of experimental nanotoxicity data and thereby, overcome key roadblock that hinders the development of approaches for data-driven modeling of nanoparticle properties and toxicities? Here, a simple and transparent read-across algorithm for a pre-screening hazard assessment of nanomaterials that provides reasonably accurate results by making the best use of existing limited set of observations will be introduced.
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Affiliation(s)
- Agnieszka Gajewicz
- University of Gdansk, Faculty of Chemistry, Laboratory of Environmental Chemometrics, Gdansk, Poland.
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27
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González-Durruthy M, Alberici LC, Curti C, Naal Z, Atique-Sawazaki DT, Vázquez-Naya JM, González-Díaz H, Munteanu CR. Experimental-Computational Study of Carbon Nanotube Effects on Mitochondrial Respiration: In Silico Nano-QSPR Machine Learning Models Based on New Raman Spectra Transform with Markov-Shannon Entropy Invariants. J Chem Inf Model 2017; 57:1029-1044. [PMID: 28414908 DOI: 10.1021/acs.jcim.6b00458] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The study of selective toxicity of carbon nanotubes (CNTs) on mitochondria (CNT-mitotoxicity) is of major interest for future biomedical applications. In the current work, the mitochondrial oxygen consumption (E3) is measured under three experimental conditions by exposure to pristine and oxidized CNTs (hydroxylated and carboxylated). Respiratory functional assays showed that the information on the CNT Raman spectroscopy could be useful to predict structural parameters of mitotoxicity induced by CNTs. The in vitro functional assays show that the mitochondrial oxidative phosphorylation by ATP-synthase (or state V3 of respiration) was not perturbed in isolated rat-liver mitochondria. For the first time a star graph (SG) transform of the CNT Raman spectra is proposed in order to obtain the raw information for a nano-QSPR model. Box-Jenkins and perturbation theory operators are used for the SG Shannon entropies. A modified RRegrs methodology is employed to test four regression methods such as multiple linear regression (LM), partial least squares regression (PLS), neural networks regression (NN), and random forest (RF). RF provides the best models to predict the mitochondrial oxygen consumption in the presence of specific CNTs with R2 of 0.998-0.999 and RMSE of 0.0068-0.0133 (training and test subsets). This work is aimed at demonstrating that the SG transform of Raman spectra is useful to encode CNT information, similarly to the SG transform of the blood proteome spectra in cancer or electroencephalograms in epilepsy and also as a prospective chemoinformatics tool for nanorisk assessment. All data files and R object models are available at https://dx.doi.org/10.6084/m9.figshare.3472349 .
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Affiliation(s)
| | | | | | | | | | - José M Vázquez-Naya
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna , Campus de Elviña s/n, 15071 A Coruña, Spain
| | - Humberto González-Díaz
- Department of Organic Chemistry II, Faculty of Science and Technology, University of the Basque Country UPV/EHU , 48940, Leioa, Bizkaia, Spain.,IKERBASQUE, Basque Foundation for Science , 48011, Bilbao, Bizkaia, Spain
| | - Cristian R Munteanu
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna , Campus de Elviña s/n, 15071 A Coruña, Spain.,Instituto de Investigación Biomédica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC) , A Coruña, 15006, Spain
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28
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Brehm M, Kafka A, Bamler M, Kühne R, Schüürmann G, Sikk L, Burk J, Burk P, Tamm T, Tämm K, Pokhrel S, Mädler L, Kahru A, Aruoja V, Sihtmäe M, Scott-Fordsmand J, Sorensen PB, Escorihuela L, Roca CP, Fernández A, Giralt F, Rallo R. An Integrated Data-Driven Strategy for Safe-by-Design Nanoparticles: The FP7 MODERN Project. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 947:257-301. [PMID: 28168671 DOI: 10.1007/978-3-319-47754-1_9] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The development and implementation of safe-by-design strategies is key for the safe development of future generations of nanotechnology enabled products. The safety testing of the huge variety of nanomaterials that can be synthetized is unfeasible due to time and cost constraints. Computational modeling facilitates the implementation of alternative testing strategies in a time and cost effective way. The development of predictive nanotoxicology models requires the use of high quality experimental data on the structure, physicochemical properties and bioactivity of nanomaterials. The FP7 Project MODERN has developed and evaluated the main components of a computational framework for the evaluation of the environmental and health impacts of nanoparticles. This chapter describes each of the elements of the framework including aspects related to data generation, management and integration; development of nanodescriptors; establishment of nanostructure-activity relationships; identification of nanoparticle categories; hazard ranking and risk assessment.
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Affiliation(s)
- Martin Brehm
- UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstrasse 15, 04318, Leipzig, Germany
| | - Alexander Kafka
- UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstrasse 15, 04318, Leipzig, Germany
- Faculty for Chemistry and Mineralogy, University of Leipzig, Johannisallee 29, 04103, Leipzig, Germany
| | - Markus Bamler
- UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstrasse 15, 04318, Leipzig, Germany
- Institute for Organic Chemistry, Technical University Bergakademie Freiberg, Leipziger Strasse 29, 09596, Freiberg, Germany
| | - Ralph Kühne
- UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstrasse 15, 04318, Leipzig, Germany
| | - Gerrit Schüürmann
- UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstrasse 15, 04318, Leipzig, Germany
- Institute for Organic Chemistry, Technical University Bergakademie Freiberg, Leipziger Strasse 29, 09596, Freiberg, Germany
| | - Lauri Sikk
- Institute of Chemistry, University of Tartu, Ravila 14a, Tartu, 50411, Estonia
- Institut de Chimie de Nice (UMR CNRS 7272), Université Nice Sophia Antipolis, 06108, Nice, France
| | - Jaanus Burk
- Institute of Chemistry, University of Tartu, Ravila 14a, Tartu, 50411, Estonia
| | - Peeter Burk
- Institute of Chemistry, University of Tartu, Ravila 14a, Tartu, 50411, Estonia
| | - Tarmo Tamm
- Institute of Technology, University of Tartu, Nooruse 1, Tartu, 50411, Estonia
| | - Kaido Tämm
- Institute of Chemistry, University of Tartu, Ravila 14a, Tartu, 50411, Estonia
| | - Suman Pokhrel
- Foundation Institute of Materials Science (IWT), Department of Production Engineering, University of Bremen, Bremen, Germany
| | - Lutz Mädler
- Foundation Institute of Materials Science (IWT), Department of Production Engineering, University of Bremen, Bremen, Germany
| | - Anne Kahru
- Laboratory of Environmental Toxicology, National Institute of Chemical Physics and Biophysics, Akadeemia tee 23, Tallinn, 12618, Estonia
| | - Villem Aruoja
- Laboratory of Environmental Toxicology, National Institute of Chemical Physics and Biophysics, Akadeemia tee 23, Tallinn, 12618, Estonia
| | - Mariliis Sihtmäe
- Laboratory of Environmental Toxicology, National Institute of Chemical Physics and Biophysics, Akadeemia tee 23, Tallinn, 12618, Estonia
| | - Janeck Scott-Fordsmand
- Department of Bioscience, Aarhus Universit, Vejlsovej 25, PO BOX 314, DK, 8600, Silkeborg, Denmark
| | - Peter B Sorensen
- Department of Bioscience, Aarhus Universit, Vejlsovej 25, PO BOX 314, DK, 8600, Silkeborg, Denmark
| | - Laura Escorihuela
- Departament d'Enginyeria Química, Universitat Rovira i Virgili, Av. Paisos Catalans, 26, 43007, Tarragona, Spain
| | - Carlos P Roca
- UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstrasse 15, 04318, Leipzig, Germany
| | - Alberto Fernández
- Departament d'Enginyeria Química, Universitat Rovira i Virgili, Av. Paisos Catalans, 26, 43007, Tarragona, Spain
| | - Francesc Giralt
- Departament d'Enginyeria Química, Universitat Rovira i Virgili, Av. Paisos Catalans, 26, 43007, Tarragona, Spain
| | - Robert Rallo
- Departament d'Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili, Av. Paisos Catalans, 26, 43007, Tarragona, Spain.
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Oksel C, Ma CY, Liu JJ, Wilkins T, Wang XZ. Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 947:103-142. [PMID: 28168667 DOI: 10.1007/978-3-319-47754-1_5] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Despite the clear benefits that nanotechnology can bring to various sectors of industry, there are serious concerns about the potential health risks associated with engineered nanomaterials (ENMs), intensified by the limited understanding of what makes ENMs toxic and how to make them safe. As the use of ENMs for commercial purposes and the number of workers/end-users being exposed to these materials on a daily basis increases, the need for assessing the potential adverse effects of multifarious ENMs in a time- and cost-effective manner becomes more apparent. One strategy to alleviate the problem of testing a large number and variety of ENMs in terms of their toxicological properties is through the development of computational models that decode the relationships between the physicochemical features of ENMs and their toxicity. Such data-driven models can be used for hazard screening, early identification of potentially harmful ENMs and the toxicity-governing physicochemical properties, and accelerating the decision-making process by maximising the use of existing data. Moreover, these models can also support industrial, regulatory and public needs for designing inherently safer ENMs. This chapter is mainly concerned with the investigation of the applicability of (quantitative) structure-activity relationship ((Q)SAR) methods to modelling of ENMs' toxicity. It summarizes the key components required for successful application of data-driven toxicity prediction techniques to ENMs, the published studies in this field and the current limitations of this approach.
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Affiliation(s)
- Ceyda Oksel
- Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Cai Y Ma
- Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Jing J Liu
- Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
- School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, 510641, China
| | - Terry Wilkins
- Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Xue Z Wang
- Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK.
- School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, 510641, China.
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Kleandrova VV, Luan F, Speck-Planche A, Cordeiro MNDS. QSAR-Based Studies of Nanomaterials in the Environment. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Nanotechnology is a newly emerging field, posing substantial impacts on society, economy, and the environment. In recent years, the development of nanotechnology has led to the design and large-scale production of many new materials and devices with a vast range of applications. However, along with the benefits, the use of nanomaterials raises many questions and generates concerns due to the possible health-risks and environmental impacts. This chapter provides an overview of the Quantitative Structure-Activity Relationships (QSAR) studies performed so far towards predicting nanoparticles' environmental toxicity. Recent progresses on the application of these modeling studies are additionally pointed out. Special emphasis is given to the setup of a QSAR perturbation-based model for the assessment of ecotoxic effects of nanoparticles in diverse conditions. Finally, ongoing challenges that may lead to new and exciting directions for QSAR modeling are discussed.
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Affiliation(s)
| | - Feng Luan
- Yantai University, China & University of Porto, Portugal
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31
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Basant N, Gupta S. Modeling uptake of nanoparticles in multiple human cells using structure–activity relationships and intercellular uptake correlations. Nanotoxicology 2016; 11:20-30. [DOI: 10.1080/17435390.2016.1257075] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Nikita Basant
- Environmental and Technical Research Centre, Gomtinagar, Lucknow, India
| | - Shikha Gupta
- CSIR-Indian Institute of Toxicology Research, Mahatma Gandhi Marg, Lucknow, India
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32
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Hristozov D, Gottardo S, Semenzin E, Oomen A, Bos P, Peijnenburg W, van Tongeren M, Nowack B, Hunt N, Brunelli A, Scott-Fordsmand JJ, Tran L, Marcomini A. Frameworks and tools for risk assessment of manufactured nanomaterials. ENVIRONMENT INTERNATIONAL 2016; 95:36-53. [PMID: 27523267 DOI: 10.1016/j.envint.2016.07.016] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2016] [Revised: 07/20/2016] [Accepted: 07/28/2016] [Indexed: 06/06/2023]
Abstract
Commercialization of nanotechnologies entails a regulatory requirement for understanding their environmental, health and safety (EHS) risks. Today we face challenges to assess these risks, which emerge from uncertainties around the interactions of manufactured nanomaterials (MNs) with humans and the environment. In order to reduce these uncertainties, it is necessary to generate sound scientific data on hazard and exposure by means of relevant frameworks and tools. The development of such approaches to facilitate the risk assessment (RA) of MNs has become a dynamic area of research. The aim of this paper was to review and critically analyse these approaches against a set of relevant criteria. The analysis concluded that none of the reviewed frameworks were able to fulfill all evaluation criteria. Many of the existing modelling tools are designed to provide screening-level assessments rather than to support regulatory RA and risk management. Nevertheless, there is a tendency towards developing more quantitative, higher-tier models, capable of incorporating uncertainty into their analyses. There is also a trend towards developing validated experimental protocols for material identification and hazard testing, reproducible across laboratories. These tools could enable a shift from a costly case-by-case RA of MNs towards a targeted, flexible and efficient process, based on grouping and read-across strategies and compliant with the 3R (Replacement, Reduction, Refinement) principles. In order to facilitate this process, it is important to transform the current efforts on developing databases and computational models into creating an integrated data and tools infrastructure to support the risk assessment and management of MNs.
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Affiliation(s)
- Danail Hristozov
- Department of Environmental Sciences, Informatics and Statistics, University Ca' Foscari of Venice, c/o PST Vega di Venezia - Via della Libertà 12, 30175 Marghera (VE), Italy.
| | - Stefania Gottardo
- European Commission's Joint Research Centre, Via E. Fermi 2749, 21027 Ispra, Italy.
| | - Elena Semenzin
- Department of Environmental Sciences, Informatics and Statistics, University Ca' Foscari of Venice, c/o PST Vega di Venezia - Via della Libertà 12, 30175 Marghera (VE), Italy.
| | - Agnes Oomen
- National Institute of Public Health & the Environment (RIVM), P.O. Box 1, 3720, BA, Bilthoven, The Netherlands.
| | - Peter Bos
- National Institute of Public Health & the Environment (RIVM), P.O. Box 1, 3720, BA, Bilthoven, The Netherlands.
| | - Willie Peijnenburg
- National Institute of Public Health & the Environment (RIVM), P.O. Box 1, 3720, BA, Bilthoven, The Netherlands.
| | - Martie van Tongeren
- Centre for Human Exposure Science, Institute of Occupational Medicine, Research Avenue, North, Riccarton, Edinburgh, EH14 4AP, Scotland.
| | - Bernd Nowack
- EMPA-Swiss Federal Laboratories for Materials Science and Technology, Technology and Society Laboratory, CH-9014 St. Gallen, Switzerland.
| | - Neil Hunt
- The REACH Centre, Lancaster Environment Centre, Lancaster University, Lancaster, Lancashire, LA1 4YQ, United Kingdom.
| | - Andrea Brunelli
- Department of Environmental Sciences, Informatics and Statistics, University Ca' Foscari of Venice, c/o PST Vega di Venezia - Via della Libertà 12, 30175 Marghera (VE), Italy.
| | - Janeck J Scott-Fordsmand
- Department of Bioscience-Terrestrial Ecology, Aarhus University, Vejlsøvej 25, 8600 Silkeborg, Denmark.
| | - Lang Tran
- Centre for Human Exposure Science, Institute of Occupational Medicine, Research Avenue, North, Riccarton, Edinburgh, EH14 4AP, Scotland.
| | - Antonio Marcomini
- Department of Environmental Sciences, Informatics and Statistics, University Ca' Foscari of Venice, c/o PST Vega di Venezia - Via della Libertà 12, 30175 Marghera (VE), Italy.
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Tämm K, Sikk L, Burk J, Rallo R, Pokhrel S, Mädler L, Scott-Fordsmand JJ, Burk P, Tamm T. Parametrization of nanoparticles: development of full-particle nanodescriptors. NANOSCALE 2016; 8:16243-16250. [PMID: 27714136 DOI: 10.1039/c6nr04376c] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
While metal oxide nanoparticles (NPs) are one of the most commonly used nanomaterials, the theoretical models used to analyze and predict their behavior have been mostly based on just the chemical composition or the extrapolation from small metal oxide clusters' calculations. In this study, a set of novel, theoretical full-particle descriptors for modeling, grouping or read-across of metal oxide NP properties and biological activity was developed based on the force-field calculation of the potential energies of whole NPs. The capability of these nanodescriptors to group the nanomaterials acoording to their biological activity was demonstrated by Principal Component Analysis (PCA). The grouping provided by the PCA approach was found to be in good accordance with the algal growth inhibition data of well characterized nanoparticles, synthesized and measured inside the consortia of the EU 7FP framework MODERN project.
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Affiliation(s)
- K Tämm
- Institute of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia.
| | - L Sikk
- Institute of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia.
| | - J Burk
- Institute of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia.
| | - R Rallo
- Departament d'Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili, Av. Paisos Catalans 26, Tarragona 43007, Spain
| | - S Pokhrel
- Foundation Institute of Materials Science (IWT), Department of Production Engineering, University of Bremen, Germany
| | - L Mädler
- Foundation Institute of Materials Science (IWT), Department of Production Engineering, University of Bremen, Germany
| | - J J Scott-Fordsmand
- Aarhus University, Dept Bioscience, Vejlsøvej 25, PO Box 314, 8600 Silkeborg, Denmark
| | - P Burk
- Institute of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia.
| | - T Tamm
- Institute of Technology, University of Tartu, Nooruse 1, Tartu 50411, Estonia
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ODZIOMEK KATARZYNA, USHIZIMA DANIELA, OBERBEK PRZEMYSLAW, KURZYDŁOWSKI KRZYSZTOFJAN, PUZYN TOMASZ, HARANCZYK MACIEJ. Scanning electron microscopy image representativeness: morphological data on nanoparticles. J Microsc 2016; 265:34-50. [DOI: 10.1111/jmi.12461] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Revised: 04/25/2016] [Accepted: 07/28/2016] [Indexed: 01/18/2023]
Affiliation(s)
- KATARZYNA ODZIOMEK
- Laboratory of Environmental Chemometrics, Faculty of Chemistry University of Gdansk Gdansk Poland
- Computational Research Division Lawrence Berkeley National Laboratory Berkeley California U.S.A
| | - DANIELA USHIZIMA
- Computational Research Division Lawrence Berkeley National Laboratory Berkeley California U.S.A
| | - PRZEMYSLAW OBERBEK
- Materials Design Division Faculty of Materials Science and Engineering Warsaw University of Technology Warsaw Poland
| | - KRZYSZTOF JAN KURZYDŁOWSKI
- Materials Design Division Faculty of Materials Science and Engineering Warsaw University of Technology Warsaw Poland
| | - TOMASZ PUZYN
- Laboratory of Environmental Chemometrics, Faculty of Chemistry University of Gdansk Gdansk Poland
| | - MACIEJ HARANCZYK
- Computational Research Division Lawrence Berkeley National Laboratory Berkeley California U.S.A
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Winkler DA. Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials. Toxicol Appl Pharmacol 2016; 299:96-100. [DOI: 10.1016/j.taap.2015.12.016] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Revised: 12/10/2015] [Accepted: 12/21/2015] [Indexed: 12/26/2022]
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36
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Oksel C, Winkler DA, Ma CY, Wilkins T, Wang XZ. Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches. Nanotoxicology 2016; 10:1001-12. [DOI: 10.3109/17435390.2016.1161857] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Ceyda Oksel
- School of Chemical and Process Engineering, University of Leeds, Leeds, UK,
| | - David A. Winkler
- CSIRO Manufacturing Flagship, Clayton South, MDC, Melbourne, Australia,
- Monash Institute of Pharmaceutical Sciences, Parkville, Melbourne, Australia,
- Latrobe Institute for Molecular Science, Bundoora, Melbourne, Australia, and
- School of Chemical and Physical Sciences, Flinders University, Bedford Park, Adelaide, Australia
| | - Cai Y. Ma
- School of Chemical and Process Engineering, University of Leeds, Leeds, UK,
| | - Terry Wilkins
- School of Chemical and Process Engineering, University of Leeds, Leeds, UK,
| | - Xue Z. Wang
- School of Chemical and Process Engineering, University of Leeds, Leeds, UK,
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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. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 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] [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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38
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González-Durruthy M, Werhli AV, Cornetet L, Machado KS, González-Díaz H, Wasiliesky W, Ruas CP, Gelesky MA, Monserrat JM. Predicting the binding properties of single walled carbon nanotubes (SWCNT) with an ADP/ATP mitochondrial carrier using molecular docking, chemoinformatics, and nano-QSBR perturbation theory. RSC Adv 2016. [DOI: 10.1039/c6ra08883j] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Interactions between single walled carbon nanotubes (SWCNT) family with mitochondrial ADP/ATP carrier (ANT-1) were evaluated using constitutional and electronic nanodescriptors defined by (n, m)-Hamada indexes (armchair, zig-zag and chiral).
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Affiliation(s)
- Michael González-Durruthy
- Instituto de Ciências Biológicas (ICB) – Universidade Federal do Rio Grande-FURG
- Rio Grande
- Brazil
- Programa de Pós-Graduação em Ciências Fisiológicas-Fisiologia Animal Comparada-ICB-FURG
- Brazil
| | - Adriano V. Werhli
- Centro de Ciências Computacionais (C3) – Universidade Federal do Rio Grande-FURG
- Brazil
- Programa de Pós-Graduação em Computação-C3-FURG
- Brazil
| | - Luisa Cornetet
- Centro de Ciências Computacionais (C3) – Universidade Federal do Rio Grande-FURG
- Brazil
- Programa de Pós-Graduação em Computação-C3-FURG
- Brazil
| | - Karina S. Machado
- Centro de Ciências Computacionais (C3) – Universidade Federal do Rio Grande-FURG
- Brazil
- Programa de Pós-Graduação em Computação-C3-FURG
- Brazil
| | - Humberto González-Díaz
- Department of Organic Chemistry II
- Faculty of Science and Technology
- University of the Basque Country UPV/EHU
- Leioa
- Spain
| | | | | | - Marcos A. Gelesky
- Programa de Pós-graduação em Química Tecnológica e Ambiental
- FURG
- Brazil
| | - José M. Monserrat
- Instituto de Ciências Biológicas (ICB) – Universidade Federal do Rio Grande-FURG
- Rio Grande
- Brazil
- Programa de Pós-Graduação em Ciências Fisiológicas-Fisiologia Animal Comparada-ICB-FURG
- Brazil
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39
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Papa E, Doucet JP, Doucet-Panaye A. Computational approaches for the prediction of the selective uptake of magnetofluorescent nanoparticles into human cells. RSC Adv 2016. [DOI: 10.1039/c6ra07898b] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Modelling and screening the selective uptake of magnetofluorescent nanoparticles into human cells by combining QSAR and multivariate analysis.
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Affiliation(s)
- E. Papa
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology
- DiSTA
- University of Insubria
- Varese
- Italy
| | - J. P. Doucet
- Universitè Paris Diderot
- Laboratoire ITODYS
- Paris
- France
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40
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Pan Y, Li T, Cheng J, Telesca D, Zink JI, Jiang J. Nano-QSAR modeling for predicting the cytotoxicity of metal oxide nanoparticles using novel descriptors. RSC Adv 2016. [DOI: 10.1039/c6ra01298a] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Computational approaches have evolved as efficient alternatives to understand the adverse effects of nanoparticles on human health and the environment.
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Affiliation(s)
- Yong Pan
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control
- Nanjing Tech University
- Nanjing
- China
- Department of Chemistry & Biochemistry
| | - Ting Li
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control
- Nanjing Tech University
- Nanjing
- China
| | - Jie Cheng
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control
- Nanjing Tech University
- Nanjing
- China
| | | | - Jeffrey I. Zink
- Department of Chemistry & Biochemistry
- University of California
- Los Angeles
- USA
| | - Juncheng Jiang
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control
- Nanjing Tech University
- Nanjing
- China
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41
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Fourches D, Pu D, Li L, Zhou H, Mu Q, Su G, Yan B, Tropsha A. Computer-aided design of carbon nanotubes with the desired bioactivity and safety profiles. Nanotoxicology 2015; 10:374-83. [PMID: 26525350 DOI: 10.3109/17435390.2015.1073397] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Growing experimental evidences suggest the existence of direct relationships between the surface chemistry of nanomaterials and their biological effects. Herein, we have employed computational approaches to design a set of biologically active carbon nanotubes (CNTs) with controlled protein binding and cytotoxicity. Quantitative structure-activity relationship (QSAR) models were built and validated using a dataset of 83 surface-modified CNTs. A subset of a combinatorial virtual library of 240 000 ligands potentially attachable to CNTs was selected to include molecules that were within the chemical similarity threshold with respect to the modeling set compounds. QSAR models were then employed to virtually screen this subset and prioritize CNTs for chemical synthesis and biological evaluation. Ten putatively active and 10 putatively inactive CNTs decorated with the ligands prioritized by virtual screening for either protein-binding or cytotoxicity assay were synthesized and tested. We found that all 10 putatively inactive and 7 of 10 putatively active CNTs were confirmed in the protein-binding assay, whereas all 10 putatively inactive and 6 of 10 putatively active CNTs were confirmed in the cytotoxicity assay. This proof-of-concept study shows that computational models can be employed to guide the design of surface-modified nanomaterials with the desired biological and safety profiles.
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Affiliation(s)
- Denis Fourches
- a Department of Chemistry , Bioinformatics Research Center, North Carolina State University , Raleigh , NC , USA
| | - Dongqiuye Pu
- b Laboratory for Molecular Modeling , Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Chapel Hill , NC , USA , and
| | - Liwen Li
- c School of Chemistry and Chemical Engineering, Shandong University , Jinan , P.R. China
| | - Hongyu Zhou
- c School of Chemistry and Chemical Engineering, Shandong University , Jinan , P.R. China
| | - Qingxin Mu
- c School of Chemistry and Chemical Engineering, Shandong University , Jinan , P.R. China
| | - Gaoxing Su
- c School of Chemistry and Chemical Engineering, Shandong University , Jinan , P.R. China
| | - Bing Yan
- c School of Chemistry and Chemical Engineering, Shandong University , Jinan , P.R. China
| | - Alexander Tropsha
- b Laboratory for Molecular Modeling , Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Chapel Hill , NC , USA , and
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42
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Summary and Analysis of the Currently Existing Literature Data on Metal-based Nanoparticles Published for Selected Aquatic Organisms: Applicability for Toxicity Prediction by (Q)SARs. Altern Lab Anim 2015; 43:221-40. [DOI: 10.1177/026119291504300404] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This review establishes an inventory of existing toxicity data on nanoparticles (NPs) with the purpose of developing (Quantitative) Structure–Activity Relationships for NPs (nano-[Q]SARs), and also of maximising the use of scientific sources for NP risk assessment. From a data search carried out on 27 February 2014, a total of 910 publications were retrieved from the Web of Science™ Core Collection, and a database comprising 886 records of toxicity endpoints, based on these publications, was built. The test organisms mainly comprised bacteria, algae, yeast, protozoa, nematoda, crustacea and fish. The NPs consisted mostly of metals, metal oxides, nanocomposites and quantum dots. The data were analysed further, in order to: a) categorise each toxicity endpoint and the biological effects triggered by the NPs; b) survey the characterisation of the NPs used; and c) assess whether the data were suitable for nano-(Q)SAR development. Despite the efforts of numerous scientific programmes on nanomaterial safety and design, our study concluded that lack of data consistency prevents the use of experimental data in developing and validating nano-(Q)SARs. Finally, an outlook on the future of nano-(Q)SAR development is provided.
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43
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Liu R, Jiang W, Walkey CD, Chan WCW, Cohen Y. Prediction of nanoparticles-cell association based on corona proteins and physicochemical properties. NANOSCALE 2015; 7:9664-75. [PMID: 25959034 DOI: 10.1039/c5nr01537e] [Citation(s) in RCA: 96] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Cellular association of nanoparticles (NPs) in biological fluids is affected by proteins adsorbed onto the NP surface, forming a "protein corona", thereby impacting cellular bioactivity. Here we investigate, based on an extensive gold NPs protein corona dataset, the relationships between NP-cell association and protein corona fingerprints (PCFs) as well as NP physicochemical properties. Accordingly, quantitative structure-activity relationships (QSARs) were developed based on both linear and non-linear support vector regression (SVR) models making use of a sequential forward floating selection of descriptors. The SVR model with only 6 serum proteins and zeta potential had higher accuracy (R(2) = 0.895) relative to the linear model (R(2) = 0.850) with 11 PCFs. Considering the initial pool of 148 descriptors, the APOB, A1AT, ANT3, and PLMN serum proteins along with NP zeta potential were identified as most significant to correlating NP-cell association. The present study suggests that QSARs exploration of NP-cell association data, considering the role of both NP protein corona and physicochemical properties, can support the planning and interpretation of toxicity studies and guide the design of NPs for biomedical applications.
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Affiliation(s)
- Rong Liu
- Center for Environmental Implications of Nanotechnology, University of California, Los Angeles, CA90095, USA.
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44
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Toropov AA, Toropova AP. Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes. CHEMOSPHERE 2015; 124:40-46. [PMID: 25465947 DOI: 10.1016/j.chemosphere.2014.10.067] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Revised: 10/15/2014] [Accepted: 10/18/2014] [Indexed: 06/04/2023]
Abstract
Available on the Internet, the CORAL software (http://www.insilico.eu/coral) has been used to build up quasi-quantitative structure-activity relationships (quasi-QSAR) for prediction of mutagenic potential of multi-walled carbon-nanotubes (MWCNTs). In contrast with the previous models built up by CORAL which were based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) the quasi-QSARs based on the representation of conditions (not on the molecular structure) such as concentration, presence (absence) S9 mix, the using (or without the using) of preincubation were encoded by so-called quasi-SMILES. The statistical characteristics of these models (quasi-QSARs) for three random splits into the visible training set and test set and invisible validation set are the following: (i) split 1: n=13, r(2)=0.8037, q(2)=0.7260, s=0.033, F=45 (training set); n=5, r(2)=0.9102, s=0.071 (test set); n=6, r(2)=0.7627, s=0.044 (validation set); (ii) split 2: n=13, r(2)=0.6446, q(2)=0.4733, s=0.045, F=20 (training set); n=5, r(2)=0.6785, s=0.054 (test set); n=6, r(2)=0.9593, s=0.032 (validation set); and (iii) n=14, r(2)=0.8087, q(2)=0.6975, s=0.026, F=51 (training set); n=5, r(2)=0.9453, s=0.074 (test set); n=5, r(2)=0.8951, s=0.052 (validation set).
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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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45
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Oksel C, Ma CY, Wang XZ. Current situation on the availability of nanostructure-biological activity data. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2015; 26:79-94. [PMID: 25608859 DOI: 10.1080/1062936x.2014.993702] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Recent developments in nanotechnology have not only increased the number of nanoproducts on the market, but also raised concerns about the safety of engineered nanomaterials (ENMs) for human health and the environment. As the production and use of ENMs increase, we are approaching the point at which it is impossible to individually assess the toxicity of a vast number of ENMs. Therefore, it is desirable to use time-effective computational methods, such as the quantitative structure-activity relationship (QSAR) models, to predict the toxicity of ENMs. However, the accuracy of the nano-(Q)SARs is directly tied to the quality of the data from which the model is estimated. Although the amount of available nanotoxicity data is insufficient for generating robust nano-(Q)SAR models in most cases, there are a handful of studies that provide appropriate experimental data for (Q)SAR-like modelling investigations. The aim of this study is to review the available literature data that are particularly suitable for nano-(Q)SAR modelling. We hope that this paper can serve as a starting point for those who would like to know more about the current availability of experimental data on the health effects of ENMs for future modelling purposes.
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Affiliation(s)
- C Oksel
- a Institute of Particle Science and Engineering, School of Chemical and Process Engineering , University of Leeds , Leeds , LS2 9JT , UK
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46
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González-Durruthy M, Monserrat JM, Alberici LC, Naal Z, Curti C, González-Díaz H. Mitoprotective activity of oxidized carbon nanotubes against mitochondrial swelling induced in multiple experimental conditions and predictions with new expected-value perturbation theory. RSC Adv 2015. [DOI: 10.1039/c5ra14435c] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Mitochondrial Permeability Transition Pore (MPTP) is involved in neurodegeneration, hepatotoxicity, cardiac necrosis, nervous and muscular dystrophies.
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Affiliation(s)
- Michael González-Durruthy
- Institute of Biological Science (ICB)
- Universidade Federal do Rio Grande (FURG)
- Porto Alegre
- Brazil
- ICB-FURG Post-graduate Program Physiological Sciences – Comparative Animal Physiology, Brazil
| | - Jose Maria Monserrat
- Institute of Biological Science (ICB)
- Universidade Federal do Rio Grande (FURG)
- Porto Alegre
- Brazil
- ICB-FURG Post-graduate Program Physiological Sciences – Comparative Animal Physiology, Brazil
| | - Luciane C. Alberici
- Department of Physic-Chemistry
- Faculty of Pharmacy of Ribeirão Preto
- University of São Paulo (USP)
- 14040-903 Ribeirão Preto
- Brazil
| | - Zeki Naal
- Department of Physic-Chemistry
- Faculty of Pharmacy of Ribeirão Preto
- University of São Paulo (USP)
- 14040-903 Ribeirão Preto
- Brazil
| | - Carlos Curti
- Department of Physic-Chemistry
- Faculty of Pharmacy of Ribeirão Preto
- University of São Paulo (USP)
- 14040-903 Ribeirão Preto
- Brazil
| | - Humberto González-Díaz
- Department of Organic Chemistry II
- Faculty of Science and Technology
- University of the Basque Country UPV/EHU
- Leioa
- Spain
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47
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Kleandrova VV, Luan F, González-Díaz H, Ruso JM, Speck-Planche A, Cordeiro MNDS. Computational tool for risk assessment of nanomaterials: novel QSTR-perturbation model for simultaneous prediction of ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under multiple experimental conditions. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2014; 48:14686-14694. [PMID: 25384130 DOI: 10.1021/es503861x] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Nanomaterials have revolutionized modern science and technology due to their multiple applications in engineering, physics, chemistry, and biomedicine. Nevertheless, the use and manipulation of nanoparticles (NPs) can bring serious damages to living organisms and their ecosystems. For this reason, ecotoxicity and cytotoxicity assays are of special interest in order to determine the potential harmful effects of NPs. Processes based on ecotoxicity and cytotoxicity tests can significantly consume time and financial resources. In this sense, alternative approaches such as quantitative structure-activity/toxicity relationships (QSAR/QSTR) modeling have provided important insights for the better understanding of the biological behavior of NPs that may be responsible for causing toxicity. Until now, QSAR/QSTR models have predicted ecotoxicity or cytotoxicity separately against only one organism (bioindicator species or cell line) and have not reported information regarding the quantitative influence of characteristics other than composition or size. In this work, we developed a unified QSTR-perturbation model to simultaneously probe ecotoxicity and cytotoxicity of NPs under different experimental conditions, including diverse measures of toxicities, multiple biological targets, compositions, sizes and conditions to measure those sizes, shapes, times during which the biological targets were exposed to NPs, and coating agents. The model was created from 36488 cases (NP-NP pairs) and exhibited accuracies higher than 98% in both training and prediction sets. The model was used to predict toxicities of several NPs that were not included in the original data set. The results of the predictions suggest that the present QSTR-perturbation model can be employed as a highly promising tool for the fast and efficient assessment of ecotoxicity and cytotoxicity of NPs.
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Affiliation(s)
- Valeria V Kleandrova
- REQUIMTE/Department of Chemistry and Biochemistry, University of Porto , 4169-007 Porto, Portugal
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48
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Tantra R, Oksel C, Puzyn T, Wang J, Robinson KN, Wang XZ, Ma CY, Wilkins T. Nano(Q)SAR: Challenges, pitfalls and perspectives. Nanotoxicology 2014; 9:636-42. [DOI: 10.3109/17435390.2014.952698] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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49
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Kar S, Gajewicz A, Puzyn T, Roy K, Leszczynski J. Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: a mechanistic QSTR approach. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2014; 107:162-9. [PMID: 24949897 DOI: 10.1016/j.ecoenv.2014.05.026] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2014] [Revised: 05/20/2014] [Accepted: 05/22/2014] [Indexed: 05/03/2023]
Abstract
Nanotechnology has evolved as a frontrunner in the development of modern science. Current studies have established toxicity of some nanoparticles to human and environment. Lack of sufficient data and low adequacy of experimental protocols hinder comprehensive risk assessment of nanoparticles (NPs). In the present work, metal electronegativity (χ), the charge of the metal cation corresponding to a given oxide (χox), atomic number and valence electron number of the metal have been used as simple molecular descriptors to build up quantitative structure-toxicity relationship (QSTR) models for prediction of cytotoxicity of metal oxide NPs to bacteria Escherichia coli. These descriptors can be easily obtained from molecular formula and information acquired from periodic table in no time. It has been shown that a simple molecular descriptor χox can efficiently encode cytotoxicity of metal oxides leading to models with high statistical quality as well as interpretability. Based on this model and previously published experimental results, we have hypothesized the most probable mechanism of the cytotoxicity of metal oxide nanoparticles to E. coli. Moreover, the required information for descriptor calculation is independent of size range of NPs, nullifying a significant problem that various physical properties of NPs change for different size ranges.
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Affiliation(s)
- Supratik Kar
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India; Laboratory of Environmental Chemometrics, Institute for Environmental and Human Health Protection, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | - 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.
| | - Jerzy Leszczynski
- Department of Chemistry and Biochemistry, Jackson State University Jackson, MS 39217-0510, USA
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50
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Melagraki G, Afantitis A. Enalos InSilicoNano platform: an online decision support tool for the design and virtual screening of nanoparticles. RSC Adv 2014. [DOI: 10.1039/c4ra07756c] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
A QNAR model, available online through Enalos InSilicoNano platform, has been developed and validated for the risk assessment of nanoparticles (NPs).
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