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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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2
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Cheimarios N, Pem B, Tsoumanis A, Ilić K, Vrček IV, Melagraki G, Bitounis D, Isigonis P, Dusinska M, Lynch I, Demokritou P, Afantitis A. An In Vitro Dosimetry Tool for the Numerical Transport Modeling of Engineered Nanomaterials Powered by the Enalos RiskGONE Cloud Platform. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:3935. [PMID: 36432221 PMCID: PMC9697428 DOI: 10.3390/nano12223935] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/29/2022] [Accepted: 11/01/2022] [Indexed: 06/16/2023]
Abstract
A freely available "in vitro dosimetry" web application is presented enabling users to predict the concentration of nanomaterials reaching the cell surface, and therefore available for attachment and internalization, from initial dispersion concentrations. The web application is based on the distorted grid (DG) model for the dispersion of engineered nanoparticles (NPs) in culture medium used for in vitro cellular experiments, in accordance with previously published protocols for cellular dosimetry determination. A series of in vitro experiments for six different NPs, with Ag and Au cores, are performed to demonstrate the convenience of the web application for calculation of exposure concentrations of NPs. Our results show that the exposure concentrations at the cell surface can be more than 30 times higher compared to the nominal or dispersed concentrations, depending on the NPs' properties and their behavior in the cell culture medium. Therefore, the importance of calculating the exposure concentration at the bottom of the cell culture wells used for in vitro arrays, i.e., the particle concentration at the cell surface, is clearly presented, and the tool introduced here allows users easy access to such calculations. Widespread application of this web tool will increase the reliability of subsequent toxicity data, allowing improved correlation of the real exposure concentration with the observed toxicity, enabling the hazard potentials of different NPs to be compared on a more robust basis.
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Affiliation(s)
| | - Barbara Pem
- Institute for Medical Research and Occupational Health, 10 000 Zagreb, Croatia
| | | | - Krunoslav Ilić
- Institute for Medical Research and Occupational Health, 10 000 Zagreb, Croatia
| | | | | | - Dimitrios Bitounis
- Center for Nanotechnology and Nanotoxicology, HSPH-NIEHS Nanosafety Research Center, Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115, USA
| | - Panagiotis Isigonis
- Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, 30172 Venice, Italy
| | - Maria Dusinska
- Department of Environmental Chemistry, Health Effects Laboratory, NILU—Norwegian Institute for Air Research, 2007 Kjeller, Norway
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK
| | - Philip Demokritou
- Center for Nanotechnology and Nanotoxicology, HSPH-NIEHS Nanosafety Research Center, Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115, USA
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3
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Wyrzykowska E, Mikolajczyk A, Lynch I, Jeliazkova N, Kochev N, Sarimveis H, Doganis P, Karatzas P, Afantitis A, Melagraki G, Serra A, Greco D, Subbotina J, Lobaskin V, Bañares MA, Valsami-Jones E, Jagiello K, Puzyn T. Representing and describing nanomaterials in predictive nanoinformatics. NATURE NANOTECHNOLOGY 2022; 17:924-932. [PMID: 35982314 DOI: 10.1038/s41565-022-01173-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 06/08/2022] [Indexed: 06/15/2023]
Abstract
Engineered nanomaterials (ENMs) enable new and enhanced products and devices in which matter can be controlled at a near-atomic scale (in the range of 1 to 100 nm). However, the unique nanoscale properties that make ENMs attractive may result in as yet poorly known risks to human health and the environment. Thus, new ENMs should be designed in line with the idea of safe-and-sustainable-by-design (SSbD). The biological activity of ENMs is closely related to their physicochemical characteristics, changes in these characteristics may therefore cause changes in the ENMs activity. In this sense, a set of physicochemical characteristics (for example, chemical composition, crystal structure, size, shape, surface structure) creates a unique 'representation' of a given ENM. The usability of these characteristics or nanomaterial descriptors (nanodescriptors) in nanoinformatics methods such as quantitative structure-activity/property relationship (QSAR/QSPR) models, provides exciting opportunities to optimize ENMs at the design stage by improving their functionality and minimizing unforeseen health/environmental hazards. A computational screening of possible versions of novel ENMs would return optimal nanostructures and manage ('design out') hazardous features at the earliest possible manufacturing step. Safe adoption of ENMs on a vast scale will depend on the successful integration of the entire bulk of nanodescriptors extracted experimentally with data from theoretical and computational models. This Review discusses directions for developing appropriate nanomaterial representations and related nanodescriptors to enhance the reliability of computational modelling utilized in designing safer and more sustainable ENMs.
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Affiliation(s)
| | - Alicja Mikolajczyk
- QSAR Lab Ltd, Gdańsk, Poland
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | | | - Nikolay Kochev
- Ideaconsult Ltd, Sofia, Bulgaria
- Department of Analytical Chemistry and Computer Chemistry, University of Plovdiv, Plovdiv, Bulgaria
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, Zografou, Athens, Greece
| | - Philip Doganis
- School of Chemical Engineering, National Technical University of Athens, Zografou, Athens, Greece
| | - Pantelis Karatzas
- School of Chemical Engineering, National Technical University of Athens, Zografou, Athens, Greece
| | | | - Georgia Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, Vari, Greece
| | - Angela Serra
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
| | - Dario Greco
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Julia Subbotina
- School of Physics, University College Dublin, Belfield, Dublin, Ireland
| | - Vladimir Lobaskin
- School of Physics, University College Dublin, Belfield, Dublin, Ireland
| | - Miguel A Bañares
- Instituto de Catálisis y Petroleoquimica, ICP CSIC, Madrid, Spain
| | - Eugenia Valsami-Jones
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Karolina Jagiello
- QSAR Lab Ltd, Gdańsk, Poland
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | - Tomasz Puzyn
- QSAR Lab Ltd, Gdańsk, Poland.
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland.
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Diéguez-Santana K, Casañola-Martin GM, Torres R, Rasulev B, Green JR, González-Díaz H. Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds. Mol Pharm 2022; 19:2151-2163. [PMID: 35671399 PMCID: PMC9986951 DOI: 10.1021/acs.molpharmaceut.2c00029] [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: 11/29/2022]
Abstract
Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.
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Affiliation(s)
- Karel Diéguez-Santana
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain.,Universidad Regional Amazónica IKIAM, Tena, Napo 150150, Ecuador
| | - Gerardo M Casañola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States.,Department of Systems and Computer Engineering, Carleton University, K1S5B6 Ottawa, Ontario, Canada
| | - Roldan Torres
- Universidad Regional Amazónica IKIAM, Tena, Napo 150150, Ecuador
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - James R Green
- Department of Systems and Computer Engineering, Carleton University, K1S5B6 Ottawa, Ontario, Canada
| | - Humbert González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain.,BIOFISIKA, Basque Center for Biophysics CSIC-UPVEH, 48940 Leioa, Spain.,IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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5
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Forest V. Experimental and Computational Nanotoxicology-Complementary Approaches for Nanomaterial Hazard Assessment. NANOMATERIALS 2022; 12:nano12081346. [PMID: 35458054 PMCID: PMC9031966 DOI: 10.3390/nano12081346] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/07/2022] [Accepted: 04/12/2022] [Indexed: 12/25/2022]
Abstract
The growing development and applications of nanomaterials lead to an increasing release of these materials in the environment. The adverse effects they may elicit on ecosystems or human health are not always fully characterized. Such potential toxicity must be carefully assessed with the underlying mechanisms elucidated. To that purpose, different approaches can be used. First, experimental toxicology consisting of conducting in vitro or in vivo experiments (including clinical studies) can be used to evaluate the nanomaterial hazard. It can rely on variable models (more or less complex), allowing the investigation of different biological endpoints. The respective advantages and limitations of in vitro and in vivo models are discussed as well as some issues associated with experimental nanotoxicology. Perspectives of future developments in the field are also proposed. Second, computational nanotoxicology, i.e., in silico approaches, can be used to predict nanomaterial toxicity. In this context, we describe the general principles, advantages, and limitations especially of quantitative structure–activity relationship (QSAR) models and grouping/read-across approaches. The aim of this review is to provide an overview of these different approaches based on examples and highlight their complementarity.
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Affiliation(s)
- Valérie Forest
- Mines Saint-Etienne, Univ Lyon, Univ Jean Monnet, Etablissement Français du Sang, INSERM, U1059 Sainbiose, Centre CIS, F-42023 Saint-Etienne, France
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Hofer S, Hofstätter N, Punz B, Hasenkopf I, Johnson L, Himly M. Immunotoxicity of nanomaterials in health and disease: Current challenges and emerging approaches for identifying immune modifiers in susceptible populations. WILEY INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY 2022; 14:e1804. [PMID: 36416020 PMCID: PMC9787548 DOI: 10.1002/wnan.1804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 03/24/2022] [Accepted: 03/30/2022] [Indexed: 11/24/2022]
Abstract
Nanosafety assessment has experienced an intense era of research during the past decades driven by a vivid interest of regulators, industry, and society. Toxicological assays based on in vitro cellular models have undergone an evolution from experimentation using nanoparticulate systems on singular epithelial cell models to employing advanced complex models more realistically mimicking the respective body barriers for analyzing their capacity to alter the immune state of exposed individuals. During this phase, a number of lessons were learned. We have thus arrived at a state where the next chapters have to be opened, pursuing the following objectives: (1) to elucidate underlying mechanisms, (2) to address effects on vulnerable groups, (3) to test material mixtures, and (4) to use realistic doses on (5) sophisticated models. Moreover, data reproducibility has become a significant demand. In this context, we studied the emerging concept of adverse outcome pathways (AOPs) from the perspective of immune activation and modulation resulting in pro-inflammatory versus tolerogenic responses. When considering the interaction of nanomaterials with biological systems, protein corona formation represents the relevant molecular initiating event (e.g., by potential alterations of nanomaterial-adsorbed proteins). Using this as an example, we illustrate how integrated experimental-computational workflows combining in vitro assays with in silico models aid in data enrichment and upon comprehensive ontology-annotated (meta)data upload to online repositories assure FAIRness (Findability, Accessibility, Interoperability, Reusability). Such digital twinning may, in future, assist in early-stage decision-making during therapeutic development, and hence, promote safe-by-design innovation in nanomedicine. Moreover, it may, in combination with in silico-based exposure-relevant dose-finding, serve for risk monitoring in particularly loaded areas, for example, workplaces, taking into account pre-existing health conditions. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials.
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Affiliation(s)
- Sabine Hofer
- Division of Allergy & Immunology, Department of Biosciences & Medical BiologyParis Lodron University of SalzburgSalzburgAustria
| | - Norbert Hofstätter
- Division of Allergy & Immunology, Department of Biosciences & Medical BiologyParis Lodron University of SalzburgSalzburgAustria
| | - Benjamin Punz
- Division of Allergy & Immunology, Department of Biosciences & Medical BiologyParis Lodron University of SalzburgSalzburgAustria
| | - Ingrid Hasenkopf
- Division of Allergy & Immunology, Department of Biosciences & Medical BiologyParis Lodron University of SalzburgSalzburgAustria
| | - Litty Johnson
- Division of Allergy & Immunology, Department of Biosciences & Medical BiologyParis Lodron University of SalzburgSalzburgAustria
| | - Martin Himly
- Division of Allergy & Immunology, Department of Biosciences & Medical BiologyParis Lodron University of SalzburgSalzburgAustria
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Sánchez Jiménez A, Puelles R, Perez-Fernandez M, Barruetabeña L, Jacobsen NR, Suarez-Merino B, Micheletti C, Manier N, Salieri B, Hischier R, Tsekovska R, Handzhiyski Y, Bouillard J, Oudart Y, Galea KS, Kelly S, Shandilya N, Goede H, Gomez-Cordon J, Jensen KA, van Tongeren M, Apostolova MD, Llopis IR. Safe(r) by design guidelines for the nanotechnology industry. NANOIMPACT 2022; 25:100385. [PMID: 35559891 DOI: 10.1016/j.impact.2022.100385] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 01/24/2022] [Accepted: 01/24/2022] [Indexed: 06/15/2023]
Abstract
Expectations for safer and sustainable chemicals and products are growing to comply with the United Nations and European strategies for sustainability. The application of Safe(r) by Design (SbD) in nanotechnology implies an iterative process where functionality, human health and safety, environmental and economic impact and cost are assessed and balanced as early as possible in the innovation process and updated at each step. The EU H2020 NanoReg2 project was the first European project to implement SbD in six companies handling and/or manufacturing nanomaterials (NMs) and nano-enabled products (NEP). The results from this experience have been used to develop these guidelines on the practical application of SbD. The SbD approach foresees the identification, estimation, and reduction of human and environmental risks as early as possible in the development of a NM or NEP, and it is based on three pillars: (i) safer NMs and NEP; (ii) safer use and end of life and (iii) safer industrial production. The presented guidelines include a set of information and tools that will help deciding at each step of the innovation process whether to continue, apply SbD measures or carry out further tests to reduce uncertainty. It does not intend to be a prescriptive protocol where all suggested steps have to be followed to achieve a SbD NM/NEP or process. Rather, the guidelines are designed to identify risks at an early state and information to be considered to identify those risks. Each company adapts the approach to its specific needs and circumstances as company decisions influence the way forward.
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Affiliation(s)
| | - Raquel Puelles
- Avanzare Innovación Tecnológica S.L., Av. Lentiscares, 4-6, 26370 Navarrete, La Rioja, Spain
| | - Marta Perez-Fernandez
- Avanzare Innovación Tecnológica S.L., Av. Lentiscares, 4-6, 26370 Navarrete, La Rioja, Spain
| | - Leire Barruetabeña
- GAIKER Technology Centre, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, E-48170 Zamudio, Spain
| | - Nicklas Raun Jacobsen
- National Research Centre for the Working Environment (NRCWE), Lersoe Park Alle 105, 2100 Copenhagen, Denmark
| | | | | | - Nicolas Manier
- Institut national de l'environnement industriel et des risques (INERIS), Verneuil-en-Halatte 60550, France
| | - Beatrice Salieri
- TEMAS AG, 8048 Zurich, Switzerland; Swiss Federal Laboratories for Materials Science and Technology (Empa), Technology and Society Lab (TSL), Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland
| | - Roland Hischier
- Swiss Federal Laboratories for Materials Science and Technology (Empa), Technology and Society Lab (TSL), Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland
| | - Rositsa Tsekovska
- Roumen Tsanev Institute of Molecular Biology, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., bl. 21, 1113 Sofia, Bulgaria
| | - Yordan Handzhiyski
- Roumen Tsanev Institute of Molecular Biology, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., bl. 21, 1113 Sofia, Bulgaria
| | - Jacques Bouillard
- Institut national de l'environnement industriel et des risques (INERIS), Verneuil-en-Halatte 60550, France
| | - Yohan Oudart
- Nanomakers, 1 Rue de Clairefontaine, 78 120 Rambouillet, France
| | - Karen S Galea
- Institute of Occupational Medicine (IOM), Research Avenue North, Edinburgh, UK
| | - Sean Kelly
- Nanotechnology Industries Association (NIA), Avenue Tervueren 143, 1150 Brussels, Belgium
| | | | - Henk Goede
- TNO, Princetonlaan 6, 3584 CB Utrecht, Netherlands
| | - Julio Gomez-Cordon
- Avanzare Innovación Tecnológica S.L., Av. Lentiscares, 4-6, 26370 Navarrete, La Rioja, Spain
| | - Keld Alstrup Jensen
- National Research Centre for the Working Environment (NRCWE), Lersoe Park Alle 105, 2100 Copenhagen, Denmark
| | - Martie van Tongeren
- School of Health Sciences, The University of Manchester, Oxford Rd., Manchester M13 9PL,UK
| | - Margarita D Apostolova
- Roumen Tsanev Institute of Molecular Biology, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., bl. 21, 1113 Sofia, Bulgaria
| | - Isabel Rodríguez Llopis
- GAIKER Technology Centre, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, E-48170 Zamudio, Spain
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Thwala MM, Afantitis A, Papadiamantis AG, Tsoumanis A, Melagraki G, Dlamini LN, Ouma CNM, Ramasami P, Harris R, Puzyn T, Sanabria N, Lynch I, Gulumian M. Using the Isalos platform to develop a (Q)SAR model that predicts metal oxide toxicity utilizing facet-based electronic, image analysis-based, and periodic table derived properties as descriptors. Struct Chem 2021. [DOI: 10.1007/s11224-021-01869-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
AbstractEngineered nanoparticles (NPs) are being studied for their potential to harm humans and the environment. Biological activity, toxicity, physicochemical properties, fate, and transport of NPs must all be evaluated and/or predicted. In this work, we explored the influence of metal oxide nanoparticle facets on their toxicity towards bronchial epithelial (BEAS-2B), Murine myeloid (RAW 264.7), and E. coli cell lines. To estimate the toxicity of metal oxide nanoparticles grown to a low facet index, a quantitative structure–activity relationship ((Q)SAR) approach was used. The novel model employs theoretical (density functional theory calculations) and experimental studies (transmission electron microscopy images from which several particle descriptors are extracted and toxicity data extracted from the literature) to investigate the properties of faceted metal oxides, which are then utilized to construct a toxicity model. The classification mode of the k-nearest neighbour algorithm (EnaloskNN, Enalos Chem/Nanoinformatics) was used to create the presented model for metal oxide cytotoxicity. Four descriptors were identified as significant: core size, chemical potential, enthalpy of formation, and electronegativity count of metal oxides. The relationship between these descriptors and metal oxide facets is discussed to provide insights into the relative toxicities of the nanoparticle. The model and the underpinning dataset are freely available on the NanoSolveIT project cloud platform and the NanoPharos database, respectively.
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9
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Bondarenko O, Mortimer M, Kahru A, Feliu N, Javed I, Kakinen A, Lin S, Xia T, Song Y, Davis TP, Lynch I, Parak WJ, Leong DT, Ke PC, Chen C, Zhao Y. Nanotoxicology and Nanomedicine: The Yin and Yang of Nano-Bio Interactions for the New Decade. NANO TODAY 2021; 39:101184. [PMID: 36937379 PMCID: PMC10018814 DOI: 10.1016/j.nantod.2021.101184] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Nanotoxicology and nanomedicine are two sub-disciplines of nanotechnology focusing on the phenomena, mechanisms, and engineering at the nano-bio interface. For the better part of the past three decades, these two disciplines have been largely developing independently of each other. Yet recent breakthroughs in microbiome research and the current COVID-19 pandemic demonstrate that holistic approaches are crucial for solving grand challenges in global health. Here we show the Yin and Yang relationship between the two fields by highlighting their shared goals of making safer nanomaterials, improved cellular and organism models, as well as advanced methodologies. We focus on the transferable knowledge between the two fields as nanotoxicological research is moving from pristine to functional nanomaterials, while inorganic nanomaterials - the main subjects of nanotoxicology - have become an emerging source for the development of nanomedicines. We call for a close partnership between the two fields in the new decade, to harness the full potential of nanotechnology for benefiting human health and environmental safety.
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Affiliation(s)
- Olesja Bondarenko
- Laboratory of Environmental Toxicology, National Institute of Chemical Physics and Biophysics, Akadeemia tee 23, 12618 Tallinn, Estonia
- Institute of Biotechnology, HiLIFE, University of Helsinki, Viikinkaari 5d, 00790 Helsinki, Finland
| | - Monika Mortimer
- Institute of Environmental and Health Sciences, College of Quality and Safety Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
| | - Anne Kahru
- Laboratory of Environmental Toxicology, National Institute of Chemical Physics and Biophysics, Akadeemia tee 23, 12618 Tallinn, Estonia
- Estonian Academy of Sciences, Kohtu 6, 10130 Tallinn, Estonia
| | - Neus Feliu
- Fachbereich Physik und Chemie, Universität Hamburg, 22607 Hamburg, Germany
| | - Ibrahim Javed
- Australian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane, QLD 4072, Australia
| | - Aleksandr Kakinen
- Australian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane, QLD 4072, Australia
| | - Sijie Lin
- College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai East Hospital, Tongji University, 1239 Siping Road, Shanghai 200092, China
| | - Tian Xia
- Key Laboratory of Environmental Nanotechnology and Health Effects, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- Division of NanoMedicine, Department of Medicine, University of California Los Angeles (UCLA), 570 Westwood Plaza, CNSI 6511, Los Angeles, CA 90095, United States
| | - Yang Song
- Key Laboratory of Environmental Nanotechnology and Health Effects, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
- Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education, College of Pharmaceutical Sciences, Southwest University, 2 Tiansheng Rd, Beibei District, Chongqing, 400715, China
| | - Thomas P. Davis
- Australian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane, QLD 4072, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC 3052, Australia
| | - Iseult Lynch
- School of Geography Earth and Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | - Wolfgang J. Parak
- Fachbereich Physik und Chemie, Universität Hamburg, 22607 Hamburg, Germany
- Department of Instrument Science and Engineering, Institute of Nano Biomedicine and Engineering, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - David Tai Leong
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore
| | - Pu Chun Ke
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC 3052, Australia
- Zhongshan Hospital, Fudan University, 111 Yixueyuan Rd, Xuhui District, Shanghai, 200032, China
- Address correspondence to: Pu Chun Ke, ; Chunying Chen, ; Yuliang Zhao,
| | - Chunying Chen
- CAS Center for Excellence in Nanoscience and CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology of China, Beijing 100190, China
- Address correspondence to: Pu Chun Ke, ; Chunying Chen, ; Yuliang Zhao,
| | - Yuliang Zhao
- CAS Center for Excellence in Nanoscience and CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology of China, Beijing 100190, China
- Address correspondence to: Pu Chun Ke, ; Chunying Chen, ; Yuliang Zhao,
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10
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Zhang P, Guo Z, Ullah S, Melagraki G, Afantitis A, Lynch I. Nanotechnology and artificial intelligence to enable sustainable and precision agriculture. NATURE PLANTS 2021; 7:864-876. [PMID: 34168318 DOI: 10.1038/s41477-021-00946-6] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 05/17/2021] [Indexed: 06/13/2023]
Abstract
Climate change, increasing populations, competing demands on land for production of biofuels and declining soil quality are challenging global food security. Finding sustainable solutions requires bold new approaches and integration of knowledge from diverse fields, such as materials science and informatics. The convergence of precision agriculture, in which farmers respond in real time to changes in crop growth with nanotechnology and artificial intelligence, offers exciting opportunities for sustainable food production. Coupling existing models for nutrient cycling and crop productivity with nanoinformatics approaches to optimize targeting, uptake, delivery, nutrient capture and long-term impacts on soil microbial communities will enable design of nanoscale agrochemicals that combine optimal safety and functionality profiles.
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Affiliation(s)
- Peng Zhang
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK.
| | - Zhiling Guo
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
| | - Sami Ullah
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
| | - Georgia Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, Vari, Greece
| | | | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
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11
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Marcoulaki E, López de Ipiña JM, Vercauteren S, Bouillard J, Himly M, Lynch I, Witters H, Shandilya N, van Duuren-Stuurman B, Kunz V, Unger WES, Hodoroaba VD, Bard D, Evans G, Jensen KA, Pilou M, Viitanen AK, Bochon A, Duschl A, Geppert M, Persson K, Cotgreave I, Niga P, Gini M, Eleftheriadis K, Scalbi S, Caillard B, Arevalillo A, Frejafon E, Aguerre-Chariol O, Dulio V. Blueprint for a self-sustained European Centre for service provision in safe and sustainable innovation for nanotechnology. NANOIMPACT 2021; 23:100337. [PMID: 35559838 DOI: 10.1016/j.impact.2021.100337] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/05/2021] [Accepted: 06/17/2021] [Indexed: 06/15/2023]
Abstract
The coming years are expected to bring rapid changes in the nanotechnology regulatory landscape, with the establishment of a new framework for nano-risk governance, in silico approaches for characterisation and risk assessment of nanomaterials, and novel procedures for the early identification and management of nanomaterial risks. In this context, Safe(r)-by-Design (SbD) emerges as a powerful preventive approach to support the development of safe and sustainable (SSbD) nanotechnology-based products and processes throughout the life cycle. This paper summarises the work undertaken to develop a blueprint for the deployment and operation of a permanent European Centre of collaborating laboratories and research organisations supporting safe innovation in nanotechnologies. The proposed entity, referred to as "the Centre", will establish a 'one-stop shop' for nanosafety-related services and a central contact point for addressing stakeholder questions about nanosafety. Its operation will rely on significant business, legal and market knowledge, as well as other tools developed and acquired through the EU-funded EC4SafeNano project and subsequent ongoing activities. The proposed blueprint adopts a demand-driven service update scheme to allow the necessary vigilance and flexibility to identify opportunities and adjust its activities and services in the rapidly evolving regulatory and nano risk governance landscape. The proposed Centre will play a major role as a conduit to transfer scientific knowledge between the research and commercial laboratories or consultants able to provide high quality nanosafety services, and the end-users of such services (e.g., industry, SMEs, consultancy firms, and regulatory authorities). The Centre will harmonise service provision, and bring novel risk assessment and management approaches, e.g. in silico methodologies, closer to practice, notably through SbD/SSbD, and decisively support safe and sustainable innovation of industrial production in the nanotechnology industry according to the European Chemicals Strategy for Sustainability.
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Affiliation(s)
- Effie Marcoulaki
- National Centre for Scientific Research "Demokritos", PO Box 60037, 15310 Agia Paraskevi, Greece.
| | - Jesús M López de Ipiña
- TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Alava, 01510 Miñano, Spain.
| | | | - Jacques Bouillard
- Institut national de l'environnement industriel et des risques (INERIS), Rue Jacques Taffanel, Parc technologique ALATA, Verneuil-en-Halatte, 60550, France.
| | - Martin Himly
- Paris Lodron University of Salzburg, Kapitelgasse 4/6, 5020 Salzburg, Austria.
| | - Iseult Lynch
- School of Geography, Earth & Environmental Sciences, University of Birmingham, Edgbaston, B15 2TT Birmingham, UK.
| | - Hilda Witters
- VITO NV, Health Unit, Boeretang 200, 2400 Mol, Belgium.
| | - Neeraj Shandilya
- TNO, Research group Risk Analysis for Products in Development (RAPID), Princetonlaan 6, 3584 CB Utrecht, Netherlands.
| | - Birgit van Duuren-Stuurman
- TNO, Research group Risk Analysis for Products in Development (RAPID), Princetonlaan 6, 3584 CB Utrecht, Netherlands.
| | - Valentin Kunz
- Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 44-46, 12203 Berlin, Germany
| | - Wolfgang E S Unger
- Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 44-46, 12203 Berlin, Germany
| | - Vasile-Dan Hodoroaba
- Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 44-46, 12203 Berlin, Germany.
| | - Delphine Bard
- Health & Safety Executive Science and Research Centre, Harpur Hill, Buxton, Derbyshire SK17 9JN, UK.
| | - Gareth Evans
- Health & Safety Executive Science and Research Centre, Harpur Hill, Buxton, Derbyshire SK17 9JN, UK.
| | - Keld Alstrup Jensen
- National Research Center for the Work Environment (NRCWE), Lersø Parkallé 105, 2100 København, Denmark.
| | - Marika Pilou
- National Centre for Scientific Research "Demokritos", PO Box 60037, 15310 Agia Paraskevi, Greece.
| | - Anna-Kaisa Viitanen
- Finnish Institute of Occupational Health (FIOH), P.O. Box 40, FI-00032 Työterveyslaitos, Finland.
| | - Anthony Bochon
- JurisLab, Centre de droit privé, Université Libre de Bruxelles, Avenue F. Roosevelt 50, CP 137, 1050 Bruxelles, Belgium.
| | - Albert Duschl
- Paris Lodron University of Salzburg, Kapitelgasse 4/6, 5020 Salzburg, Austria.
| | - Mark Geppert
- Paris Lodron University of Salzburg, Kapitelgasse 4/6, 5020 Salzburg, Austria.
| | - Karin Persson
- RISE Surface, Process and Formulation, Box 5607, SE-114 86 Stockholm, Sweden.
| | - Ian Cotgreave
- RISE Surface, Process and Formulation, Box 5607, SE-114 86 Stockholm, Sweden.
| | - Petru Niga
- RISE Surface, Process and Formulation, Box 5607, SE-114 86 Stockholm, Sweden.
| | - Maria Gini
- National Centre for Scientific Research "Demokritos", PO Box 60037, 15310 Agia Paraskevi, Greece.
| | | | - Simona Scalbi
- ENEA, Agenzia Nazionale per le nuove tecnologie, l'energia e lo sviluppo sostenibile, SSPT-USER-RISE, Via martiri di monte sole 4, 40129 Bologna, Italy.
| | - Bastien Caillard
- European Risk Management Institute (EU-VRi), Fangelsbachstr. 14, 70178 Stuttgart, Germany.
| | - Alfonso Arevalillo
- TECNALIA, Basque Research and Technology Alliance (BRTA), Area Anardi 5, 20730 Azpeitia, Spain.
| | - Emeric Frejafon
- BRGM, 3 av. Claude-Guillemin, BP 36009, 45100 Orléans Cedex 2, France.
| | - Olivier Aguerre-Chariol
- Institut national de l'environnement industriel et des risques (INERIS), Rue Jacques Taffanel, Parc technologique ALATA, Verneuil-en-Halatte, 60550, France.
| | - Valeria Dulio
- Institut national de l'environnement industriel et des risques (INERIS), Rue Jacques Taffanel, Parc technologique ALATA, Verneuil-en-Halatte, 60550, France.
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12
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Papadiamantis AG, Afantitis A, Tsoumanis A, Valsami-Jones E, Lynch I, Melagraki G. Computational enrichment of physicochemical data for the development of a ζ-potential read-across predictive model with Isalos Analytics Platform. NANOIMPACT 2021; 22:100308. [PMID: 35559965 DOI: 10.1016/j.impact.2021.100308] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/01/2021] [Accepted: 03/01/2021] [Indexed: 06/15/2023]
Abstract
The physicochemical characterisation data from a library of 69 engineered nanomaterials (ENMs) has been exploited in silico following enrichment with a set of molecular descriptors that can be easily acquired or calculated using atomic periodicity and other fundamental atomic parameters. Based on the extended set of twenty descriptors, a robust and validated nanoinformatics model has been proposed to predict the ENM ζ-potential. The five critical parameters selected as the most significant for the model development included the ENM size and coating as well as three molecular descriptors, metal ionic radius (rion), the sum of metal electronegativity divided by the number of oxygen atoms present in a particular metal oxide (Σχ/nO) and the absolute electronegativity (χabs), each of which is thoroughly discussed to interpret their influence on ζ-potential values. The model was developed using the Isalos Analytics Platform and is available to the community as a web service through the Horizon 2020 (H2020) NanoCommons Transnational Access services and the H2020 NanoSoveIT Integrated Approach to Testing and Assessment (IATA).
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Affiliation(s)
- Anastasios G Papadiamantis
- NovaMechanics Ltd, 1065 Nicosia, Cyprus; School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, United Kingdom
| | | | | | - Eugenia Valsami-Jones
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, United Kingdom
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, United Kingdom.
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13
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Afantitis A. Nanoinformatics: Artificial Intelligence and Nanotechnology in the New Decade. Comb Chem High Throughput Screen 2021; 23:4-5. [PMID: 32189589 DOI: 10.2174/138620732301200316112000] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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14
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Saeedimasine M, Brandt EG, Lyubartsev AP. Atomistic Perspective on Biomolecular Adsorption on Functionalized Carbon Nanomaterials under Ambient Conditions. J Phys Chem B 2021; 125:416-430. [PMID: 33373230 PMCID: PMC7871326 DOI: 10.1021/acs.jpcb.0c08622] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 12/03/2020] [Indexed: 01/23/2023]
Abstract
The use of carbon-based nanomaterials is tremendously increasing in various areas of technological, bioengineering, and biomedical applications. The functionality of carbon-based nanomaterials can be further broadened via chemical functionalization of carbon nanomaterial surfaces. On the other hand, concern is rising on possible adverse effects when nanomaterials are taken up by biological organisms. In order to contribute into understanding of interactions of carbon-based nanomaterials with biological matter, we have investigated adsorption of small biomolecules on nanomaterials using enhanced sampling molecular dynamics. The biomolecules included amino acid side chain analogues, fragments of lipids, and sugar monomers. The adsorption behavior on unstructured amorphous carbon, pristine graphene and its derivatives (such as few-layer graphene, graphene oxide, and reduced graphene oxide) as well as pristine carbon nanotubes, and those functionalized with OH-, COOH-, COO-, NH2-, and NH3+ groups was investigated with respect to surface concentration. An adsorption profile, that is, the free energy as a function of distance from the nanomaterial surfaces, was determined for each molecule and surface using the Metadynamics approach. The results were analyzed in terms of chemical specificity, surface charge, and surface concentration. It was shown that although morphology of the nanomaterial has a limited effect on the adsorption properties, functionalization of the surface by various molecular groups can drastically change the adsorption behavior that can be used in the design of nanosurfaces with highly selective adsorption properties and safe for human health and environment.
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Affiliation(s)
- Marzieh Saeedimasine
- Department of Materials and Environmental
Chemistry, Stockholm University, Stockholm 10691, Sweden
| | - Erik G. Brandt
- Department of Materials and Environmental
Chemistry, Stockholm University, Stockholm 10691, Sweden
| | - Alexander P. Lyubartsev
- Department of Materials and Environmental
Chemistry, Stockholm University, Stockholm 10691, Sweden
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15
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Papadiamantis AG, Klaessig FC, Exner TE, Hofer S, Hofstaetter N, Himly M, Williams MA, Doganis P, Hoover MD, Afantitis A, Melagraki G, Nolan TS, Rumble J, Maier D, Lynch I. Metadata Stewardship in Nanosafety Research: Community-Driven Organisation of Metadata Schemas to Support FAIR Nanoscience Data. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E2033. [PMID: 33076428 PMCID: PMC7602672 DOI: 10.3390/nano10102033] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 10/08/2020] [Accepted: 10/11/2020] [Indexed: 12/15/2022]
Abstract
The emergence of nanoinformatics as a key component of nanotechnology and nanosafety assessment for the prediction of engineered nanomaterials (NMs) properties, interactions, and hazards, and for grouping and read-across to reduce reliance on animal testing, has put the spotlight firmly on the need for access to high-quality, curated datasets. To date, the focus has been around what constitutes data quality and completeness, on the development of minimum reporting standards, and on the FAIR (findable, accessible, interoperable, and reusable) data principles. However, moving from the theoretical realm to practical implementation requires human intervention, which will be facilitated by the definition of clear roles and responsibilities across the complete data lifecycle and a deeper appreciation of what metadata is, and how to capture and index it. Here, we demonstrate, using specific worked case studies, how to organise the nano-community efforts to define metadata schemas, by organising the data management cycle as a joint effort of all players (data creators, analysts, curators, managers, and customers) supervised by the newly defined role of data shepherd. We propose that once researchers understand their tasks and responsibilities, they will naturally apply the available tools. Two case studies are presented (modelling of particle agglomeration for dose metrics, and consensus for NM dissolution), along with a survey of the currently implemented metadata schema in existing nanosafety databases. We conclude by offering recommendations on the steps forward and the needed workflows for metadata capture to ensure FAIR nanosafety data.
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Affiliation(s)
- Anastasios G. Papadiamantis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK
- Novamechanics Ltd., 1065 Nicosia, Cyprus; (A.A.); (G.M.)
| | | | | | - Sabine Hofer
- Department of Biosciences, Paris Lodron University of Salzburg, 5020 Salzburg, Austria; (S.H.); (N.H.); (M.H.)
| | - Norbert Hofstaetter
- Department of Biosciences, Paris Lodron University of Salzburg, 5020 Salzburg, Austria; (S.H.); (N.H.); (M.H.)
| | - Martin Himly
- Department of Biosciences, Paris Lodron University of Salzburg, 5020 Salzburg, Austria; (S.H.); (N.H.); (M.H.)
| | - Marc A. Williams
- U.S. Army Public Health Center (APHC), Aberdeen Proving Ground—South, Aberdeen, MD 21010, USA;
| | - Philip Doganis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece;
| | | | | | | | - Tracy S. Nolan
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - John Rumble
- R&R Data Services, Gaithersburg, MD 20877, USA;
- CODATA-VAMAS Working Group on Nanomaterials, 75016 Paris, France
| | | | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK
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16
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Winkler DA. Role of Artificial Intelligence and Machine Learning in Nanosafety. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e2001883. [PMID: 32537842 DOI: 10.1002/smll.202001883] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 05/07/2020] [Indexed: 06/11/2023]
Abstract
Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and analysis by modern machine learning (ML) methods. Nanomaterials have not yet fully captured the benefits of automation, so lag behind in the application of ML methods of data analysis. Here, some key developments in, and roadblocks to the application of ML methods are reviewed to model and predict potentially adverse biological and environmental effects of nanomaterials. This work focuses on the diverse ways a range of ML algorithms are applied to understand and predict nanomaterials properties, provides examples of the application of traditional ML and deep learning methods to nanosafety, and provides context and future perspectives on developments that are likely to occur, or need to occur in the near future that allow artificial intelligence to make a deeper contribution to nanosafety.
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Affiliation(s)
- David A Winkler
- La Trobe Institute for Molecular Science, La Trobe University, Kingsbury Drive, Bundoora, 3042, Australia
- CSIRO Data61, 1 Technology Court, Pullenvale, 4069, Australia
- School of Pharmacy, University of Nottingham, Nottingham, NG7 2QL, UK
- Monash Institute of Pharmaceutical Sciences, Monash University, 392 Royal Parade, Parkville, 3052, Australia
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17
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Karatzas P, Melagraki G, Ellis LJA, Lynch I, Varsou DD, Afantitis A, Tsoumanis A, Doganis P, Sarimveis H. Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e2001080. [PMID: 32548897 DOI: 10.1002/smll.202001080] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 04/27/2020] [Accepted: 04/28/2020] [Indexed: 06/11/2023]
Abstract
This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (ENMs). A workflow applying two different deep learning architectures on microscopic images of Daphnia magna is proposed that can automatically detect possible malformations, such as effects on the length of the tail, and the overall size, and uncommon lipid concentrations and lipid deposit shapes, which are due to direct or parental exposure to ENMs. Next, classification models assign specific objects (heart, abdomen/claw) to classes that depend on lipid densities and compare the results with controls. The models are statistically validated in terms of their prediction accuracy on external D. magna images and illustrate that deep learning technologies can be useful in the nanoinformatics field, because they can automate time-consuming manual procedures, accelerate the investigation of adverse effects of ENMs, and facilitate the process of designing safer nanostructures. It may even be possible in the future to predict impacts on subsequent generations from images of parental exposure, reducing the time and cost involved in long-term reproductive toxicity assays over multiple generations.
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Affiliation(s)
- Pantelis Karatzas
- School of Chemical Engineering, National Technical University of Athens, Athens, 15780, Greece
| | - Georgia Melagraki
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia, 1065, Cyprus
| | - Laura-Jayne A Ellis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, UK
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, UK
| | - Dimitra-Danai Varsou
- School of Chemical Engineering, National Technical University of Athens, Athens, 15780, Greece
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia, 1065, Cyprus
| | - Antreas Afantitis
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia, 1065, Cyprus
| | - Andreas Tsoumanis
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia, 1065, Cyprus
| | - Philip Doganis
- School of Chemical Engineering, National Technical University of Athens, Athens, 15780, Greece
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, Athens, 15780, Greece
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18
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Daly CA, Hernandez R. Optimizing bags of artificial neural networks for the prediction of viability from sparse data. J Chem Phys 2020; 153:054112. [DOI: 10.1063/5.0017229] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Affiliation(s)
- Clyde A. Daly
- Department of Chemistry, The Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Rigoberto Hernandez
- Department of Chemistry, The Johns Hopkins University, Baltimore, Maryland 21218, USA
- Departments of Chemical and Biomolecular Engineering, and Materials Science and Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
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19
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Wang Y, Chen X. A joint optimization QSAR model of fathead minnow acute toxicity based on a radial basis function neural network and its consensus modeling. RSC Adv 2020; 10:21292-21308. [PMID: 35518745 PMCID: PMC9054390 DOI: 10.1039/d0ra02701d] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 05/24/2020] [Indexed: 01/07/2023] Open
Abstract
Acute toxicity of the fathead minnow (Pimephales promelas) is an important indicator to evaluate the hazards and risks of compounds in aquatic environments. The aim of our study is to explore the predictive power of the quantitative structure-activity relationship (QSAR) model based on a radial basis function (RBF) neural network with the joint optimization method to study the acute toxicity mechanism, and to develop a potential acute toxicity prediction model, for fathead minnow. To ensure the symmetry and fairness of the data splitting and to generate multiple chemically diverse training and validation sets, we used a self-organizing mapping (SOM) neural network to split the modeling dataset (containing 955 compounds) characterized by PaDEL-descriptors. After preliminary selection of descriptors via the mean decrease impurity method, a hybrid quantum particle swarm optimization (HQPSO) algorithm was used to jointly optimize the parameters of RBF and select the key descriptors. We established 20 RBF-based QSAR models, and the statistical results showed that the 10-fold cross-validation results (R cv10 2) and the adjusted coefficients of determination (R adj 2) were all great than 0.7 and 0.8, respectively. The Q ext 2 of these models was between 0.6480 and 0.7317, and the R ext 2 was between 0.6563 and 0.7318. Combined with the frequency and importance of the descriptors used in RBF-based models, and the correlation between the descriptors and acute toxicity, we concluded that the water distribution coefficient, molar refractivity, and first ionization potential are important factors affecting the acute toxicity of fathead minnow. A consensus QSAR model with RBF-based models was established; this model showed good performance with R 2 = 0.9118, R cv10 2 = 0.7632, and Q ext 2 = 0.7430. A frequency weighted and distance (FWD)-based application domain (AD) definition method was proposed, and the outliers were analyzed carefully. Compared with previous studies the method proposed in this paper has obvious advantages and its robustness and external predictive power are also better than Xgboost-based model. It is an effective QSAR modeling method.
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Affiliation(s)
- Yukun Wang
- School of Chemical Engineering, University of Science and Technology Liaoning No. 185, Qianshan Anshan 114051 Liaoning China
- School of Electronic and Information Engineering, University of Science and Technology Liaoning No. 185, Qianshan Anshan 114051 Liaoning China +864125928367
| | - Xuebo Chen
- School of Electronic and Information Engineering, University of Science and Technology Liaoning No. 185, Qianshan Anshan 114051 Liaoning China +864125928367
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20
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Varsou DD, Afantitis A, Tsoumanis A, Papadiamantis A, Valsami-Jones E, Lynch I, Melagraki G. Zeta-Potential Read-Across Model Utilizing Nanodescriptors Extracted via the NanoXtract Image Analysis Tool Available on the Enalos Nanoinformatics Cloud Platform. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e1906588. [PMID: 32174008 DOI: 10.1002/smll.201906588] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 02/07/2020] [Indexed: 06/10/2023]
Abstract
Zeta potential is one of the most critical properties of nanomaterials (NMs) which provides an estimation of the surface charge, and therefore electrostatic stability in medium and, in practical terms, influences the NM's tendency to form agglomerates and to interact with cellular membranes. This paper describes a robust and accurate read-across model to predict NM zeta potential utilizing as the input data a set of image descriptors derived from transmission electron microscopy (TEM) images of the NMs. The image descriptors are calculated using NanoXtract (http://enaloscloud.novamechanics.com/EnalosWebApps/NanoXtract/), a unique online tool that generates 18 image descriptors from the TEM images, which can then be explored by modeling to identify those most predictive of NM behavior and biological effects. NM TEM images are used to develop a model for prediction of zeta potential based on grouping of the NMs according to their nearest neighbors. The model provides interesting insights regarding the most important similarity features between NMs-in addition to core composition the main elongation emerged, which links to key drivers of NM toxicity such as aspect ratio. Both the NanoXtract image analysis tool and the validated model for zeta potential (http://enaloscloud.novamechanics.com/EnalosWebApps/ZetaPotential/) are freely available online through the Enalos Nanoinformatics platform.
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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, Athens, 15780, Greece
| | - Antreas Afantitis
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia, 1065, Cyprus
| | - Andreas Tsoumanis
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia, 1065, Cyprus
| | - Anastasios Papadiamantis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B152TT, Birmingham, UK
| | - Eugenia Valsami-Jones
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B152TT, Birmingham, UK
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B152TT, Birmingham, UK
| | - Georgia Melagraki
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia, 1065, Cyprus
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Serra A, Fratello M, Cattelani L, Liampa I, Melagraki G, Kohonen P, Nymark P, Federico A, Kinaret PAS, Jagiello K, Ha MK, Choi JS, Sanabria N, Gulumian M, Puzyn T, Yoon TH, Sarimveis H, Grafström R, Afantitis A, Greco D. Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E708. [PMID: 32276469 PMCID: PMC7221955 DOI: 10.3390/nano10040708] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 03/25/2020] [Accepted: 03/26/2020] [Indexed: 12/30/2022]
Abstract
Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.
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Affiliation(s)
- Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Michele Fratello
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Luca Cattelani
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Irene Liampa
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; (I.L.); (H.S.)
| | - Georgia Melagraki
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia 1065, Cyprus; (G.M.); (A.A.)
| | - Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; (P.K.); (P.N.); (R.G.)
- Division of Toxicology, Misvik Biology, 20520 Turku, Finland
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; (P.K.); (P.N.); (R.G.)
- Division of Toxicology, Misvik Biology, 20520 Turku, Finland
| | - Antonio Federico
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Pia Anneli Sofia Kinaret
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
- Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
| | - Karolina Jagiello
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; (K.J.); (T.P.)
- University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - My Kieu Ha
- Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea; (M.K.H.); (J.-S.C.); (T.-H.Y.)
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
| | - Jang-Sik Choi
- Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea; (M.K.H.); (J.-S.C.); (T.-H.Y.)
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
| | - Natasha Sanabria
- National Institute for Occupational Health, Johannesburg 30333, South Africa; (N.S.); (M.G.)
| | - Mary Gulumian
- National Institute for Occupational Health, Johannesburg 30333, South Africa; (N.S.); (M.G.)
- Haematology and Molecular Medicine Department, School of Pathology, University of the Witwatersrand, Johannesburg 2050, South Africa
| | - Tomasz Puzyn
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; (K.J.); (T.P.)
- University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Tae-Hyun Yoon
- Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea; (M.K.H.); (J.-S.C.); (T.-H.Y.)
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; (I.L.); (H.S.)
| | - Roland Grafström
- Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; (P.K.); (P.N.); (R.G.)
- Division of Toxicology, Misvik Biology, 20520 Turku, Finland
| | - Antreas Afantitis
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia 1065, Cyprus; (G.M.); (A.A.)
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
- Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
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22
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Afantitis A, Melagraki G, Isigonis P, Tsoumanis A, Varsou DD, Valsami-Jones E, Papadiamantis A, Ellis LJA, Sarimveis H, Doganis P, Karatzas P, Tsiros P, Liampa I, Lobaskin V, Greco D, Serra A, Kinaret PAS, Saarimäki LA, Grafström R, Kohonen P, Nymark P, Willighagen E, Puzyn T, Rybinska-Fryca A, Lyubartsev A, Alstrup Jensen K, Brandenburg JG, Lofts S, Svendsen C, Harrison S, Maier D, Tamm K, Jänes J, Sikk L, Dusinska M, Longhin E, Rundén-Pran E, Mariussen E, El Yamani N, Unger W, Radnik J, Tropsha A, Cohen Y, Leszczynski J, Ogilvie Hendren C, Wiesner M, Winkler D, Suzuki N, Yoon TH, Choi JS, Sanabria N, Gulumian M, Lynch I. NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment. Comput Struct Biotechnol J 2020; 18:583-602. [PMID: 32226594 PMCID: PMC7090366 DOI: 10.1016/j.csbj.2020.02.023] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 02/18/2020] [Accepted: 02/29/2020] [Indexed: 01/26/2023] Open
Abstract
Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.
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Key Words
- (quantitative) Structure–activity relationships
- AI, Artificial Intelligence
- AOPs, Adverse Outcome Pathways
- API, Application Programming interface
- CG, coarse-grained (model)
- CNTs, carbon nanotubes
- Computational toxicology
- Engineered nanomaterials
- FAIR, Findable Accessible Inter-operable and Re-usable
- GUI, Graphical Processing Unit
- HOMO-LUMO, Highest Occupied Molecular Orbital Lowest Unoccupied Molecular Orbital
- Hazard assessment
- IATA, Integrated Approaches to Testing and Assessment
- Integrated approach for testing and assessment
- KE, key events
- MIE, molecular initiating events
- ML, machine learning
- MOA, mechanism (mode) of action
- MWCNT, multi-walled carbon nanotubes
- Machine learning
- NMs, nanomaterials
- Nanoinformatics
- OECD, Organisation for Economic Co-operation and Development
- PBPK, Physiologically Based PharmacoKinetics
- PC, Protein Corona
- PChem, Physicochemical
- PTGS, Predictive Toxicogenomics Space
- Predictive modelling
- QC, quantum-chemical
- QM, quantum-mechanical
- QSAR, quantitative structure-activity relationship
- QSPR, quantitative structure-property relationship
- RA, risk assessment
- REST, Representational State Transfer
- ROS, reactive oxygen species
- Read across
- SAR, structure-activity relationship
- SMILES, Simplified Molecular Input Line Entry System
- SOPs, standard operating procedures
- Safe-by-design
- Toxicogenomics
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Affiliation(s)
| | | | | | | | | | - Eugenia Valsami-Jones
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Anastasios Papadiamantis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Laura-Jayne A. Ellis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Philip Doganis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Pantelis Karatzas
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Periklis Tsiros
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Irene Liampa
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Vladimir Lobaskin
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland
| | - Dario Greco
- Faculty of Medicine and Health Technology, University of Tampere, FI-33014, Finland
| | - Angela Serra
- Faculty of Medicine and Health Technology, University of Tampere, FI-33014, Finland
| | | | | | - Roland Grafström
- Misvik Biology OY, Itäinen Pitkäkatu 4, 20520 Turku, Finland
- Karolinska Institute, Institute of Environmental Medicine, Nobels väg 13, 17177 Stockholm, Sweden
| | - Pekka Kohonen
- Misvik Biology OY, Itäinen Pitkäkatu 4, 20520 Turku, Finland
- Karolinska Institute, Institute of Environmental Medicine, Nobels väg 13, 17177 Stockholm, Sweden
| | - Penny Nymark
- Misvik Biology OY, Itäinen Pitkäkatu 4, 20520 Turku, Finland
- Karolinska Institute, Institute of Environmental Medicine, Nobels väg 13, 17177 Stockholm, Sweden
| | - Egon Willighagen
- Department of Bioinformatics – BiGCaT, School of Nutrition and Translational Research in Metabolism, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, the Netherlands
| | - Tomasz Puzyn
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland
- University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
| | | | - Alexander Lyubartsev
- Institutionen för material- och miljökemi, Stockholms Universitet, 106 91 Stockholm, Sweden
| | - Keld Alstrup Jensen
- The National Research Center for the Work Environment, Lersø Parkallé 105, 2100 Copenhagen, Denmark
| | - Jan Gerit Brandenburg
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Germany
- Chief Digital Organization, Merck KGaA, Frankfurter Str. 250, 64293 Darmstadt, Germany
| | - Stephen Lofts
- UK Centre for Ecology and Hydrology, Library Ave, Bailrigg, Lancaster LA1 4AP, UK
| | - Claus Svendsen
- UK Centre for Ecology and Hydrology, MacLean Bldg, Benson Ln, Crowmarsh Gifford, Wallingford OX10 8BB, UK
| | - Samuel Harrison
- UK Centre for Ecology and Hydrology, Library Ave, Bailrigg, Lancaster LA1 4AP, UK
| | - Dieter Maier
- Biomax Informatics AG, Robert-Koch-Str. 2, 82152 Planegg, Germany
| | - Kaido Tamm
- Department of Chemistry, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia
| | - Jaak Jänes
- Department of Chemistry, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia
| | - Lauri Sikk
- Department of Chemistry, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia
| | - Maria Dusinska
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Eleonora Longhin
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Elise Rundén-Pran
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Espen Mariussen
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Naouale El Yamani
- NILU-Norwegian Institute for Air Research, Instituttveien 18, 2002 Kjeller, Norway
| | - Wolfgang Unger
- Federal Institute for Material Testing and Research (BAM), Unter den Eichen 44-46, 12203 Berlin, Germany
| | - Jörg Radnik
- Federal Institute for Material Testing and Research (BAM), Unter den Eichen 44-46, 12203 Berlin, Germany
| | - Alexander Tropsha
- Eschelman School of Pharmacy, University of North Carolina at Chapel Hill, 100K Beard Hall, CB# 7568, Chapel Hill, NC 27955-7568, USA
| | - Yoram Cohen
- Samueli School Of Engineering, University of California, Los Angeles, 5531 Boelter Hall, Los Angeles, CA 90095, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 J. R. Lynch Street, Jackson, MS 39217, USA
| | - Christine Ogilvie Hendren
- Center for Environmental Implications of Nanotechnologies, Duke University, 121 Hudson Hall, Durham, NC 27708-0287, USA
| | - Mark Wiesner
- Center for Environmental Implications of Nanotechnologies, Duke University, 121 Hudson Hall, Durham, NC 27708-0287, USA
| | - David Winkler
- La Trobe Institute of Molecular Sciences, La Trobe University, Plenty Rd & Kingsbury Dr, Bundoora, VIC 3086, Australia
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville 3052, Australia
- CSIRO Data61, Clayton 3168, Australia
- School of Pharmacy, University of Nottingham, Nottingham, UK
| | - Noriyuki Suzuki
- National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-0053, Japan
| | - Tae Hyun Yoon
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Republic of Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Republic of Korea
| | - Jang-Sik Choi
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Republic of Korea
| | - Natasha Sanabria
- National Health Laboratory Services, 1 Modderfontein Rd, Sandringham, Johannesburg 2192, South Africa
| | - Mary Gulumian
- National Health Laboratory Services, 1 Modderfontein Rd, Sandringham, Johannesburg 2192, South Africa
- Haematology and Molecular Medicine, University of the Witwatersrand, Johannesburg, South Africa
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
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Wang Y, Chen X. QSPR model for Caco-2 cell permeability prediction using a combination of HQPSO and dual-RBF neural network. RSC Adv 2020; 10:42938-42952. [PMID: 35514900 PMCID: PMC9058322 DOI: 10.1039/d0ra08209k] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 11/06/2020] [Indexed: 12/23/2022] Open
Abstract
The aim of this study is to establish a promising QSPR model for the Caco-2 permeability prediction.
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Affiliation(s)
- Yukun Wang
- School of Chemical Engineering
- University of Science and Technology Liaoning
- Anshan 114051
- China
- School of Electronic and Information Engineering
| | - Xuebo Chen
- School of Electronic and Information Engineering
- University of Science and Technology Liaoning
- Anshan 114051
- China
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Nasser F, Constantinou J, Lynch I. Nanomaterials in the Environment Acquire an "Eco-Corona" Impacting their Toxicity to Daphnia Magna-a Call for Updating Toxicity Testing Policies. Proteomics 2019; 20:e1800412. [PMID: 31750982 DOI: 10.1002/pmic.201800412] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 10/06/2019] [Indexed: 01/07/2023]
Abstract
Nanomaterials (NMs) are particles with at least one dimension between 1 and 100 nm and a large surface area to volume ratio, providing them with exceptional qualities that are exploited in a variety of industrial fields. Deposition of NMs into environmental waters during or after use leads to the adsorption of an ecological (eco-) corona, whereby a layer of natural biomolecules coats the NM changing its stability, identity and ultimately toxicity. The eco-corona is not currently incorporated into ecotoxicity tests, although it has been shown to alter the interactions of NMs with organisms such as Daphnia magna (D. magna). Here, the literature on environmental biomolecule interactions with NMs is synthesized and a framework for understanding the eco-corona composition and its role in modulating NMs ecotoxicity is presented, utilizing D. magna as a model. The importance of including biomolecules as part of the current international efforts to update the standard testing protocols for NMs, is highlighted. Facilitating the formation of an eco-corona prior to NMs ecotoxicity testing will ensure that signaling pathways perturbed by the NMs are real rather than being associated with the damage arising from reactive NM surfaces "acquiring" a corona by pulling biomolecules from the organism's surface.
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Affiliation(s)
- Fatima Nasser
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, B15 2TT, Birmingham, UK
| | - Julia Constantinou
- School of Biosciences, University of Birmingham, Edgbaston, B15 2TT, Birmingham, UK
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, B15 2TT, Birmingham, UK
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Toropov AA, Toropova AP. The Correlation Contradictions Index (CCI): Building up reliable models of mutagenic potential of silver nanoparticles under different conditions using quasi-SMILES. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 681:102-109. [PMID: 31102811 DOI: 10.1016/j.scitotenv.2019.05.114] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 05/07/2019] [Accepted: 05/08/2019] [Indexed: 06/09/2023]
Abstract
The interpretation of the mutagenic potential of silver nanoparticles as a mathematical function of (i) dose; (ii) coating; and (iii) type of mutagenicity (TA98 and TA100) gives quantitative models with good statistical quality. So-called quasi-SMILES are used to represent examined objects (silver nanoparticles under different conditions) for building up models. Simplified molecular input-line entry systems (SMILES) is a well-known sequence of symbols for representation of the molecular structure. Quasi-SMILES is a similar sequence of symbols for representation of experimental conditions. The Correlation Contradiction Index (CCI) calculated with data on the calibration set gives possibility to predict quality of correlation of "experimental vs. calculated values of endpoint" for external validation set.
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Affiliation(s)
- Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy
| | - Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy.
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26
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Zhu P, Kang X, Zhao Y, Latif U, Zhang H. Predicting the Toxicity of Ionic Liquids toward Acetylcholinesterase Enzymes Using Novel QSAR Models. Int J Mol Sci 2019; 20:ijms20092186. [PMID: 31052561 PMCID: PMC6539465 DOI: 10.3390/ijms20092186] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Revised: 04/26/2019] [Accepted: 04/26/2019] [Indexed: 01/28/2023] Open
Abstract
Limited information on the potential toxicity of ionic liquids (ILs) becomes the bottleneck that creates a barrier in their large-scale application. In this work, two quantitative structure-activity relationships (QSAR) models were used to evaluate the toxicity of ILs toward the acetylcholinesterase enzyme using multiple linear regression (MLR) and extreme learning machine (ELM) algorithms. The structures of 57 cations and 21 anions were optimized using quantum chemistry calculations. The electrostatic potential surface area (SEP) and the screening charge density distribution area (Sσ) descriptors were calculated and used for prediction of IL toxicity. Performance and predictive aptitude between MLR and ELM models were analyzed. Highest squared correlation coefficient (R2), and also lowest average absolute relative deviation (AARD%) and root-mean-square error (RMSE) were observed for training set, test set, and total set for the ELM model. These findings validated the superior performance of ELM over the MLR toxicity prediction model.
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Affiliation(s)
- Peng Zhu
- School of Materials Science and Energy Engineering, Foshan University, Foshan 528000, China.
| | - Xuejing Kang
- School of Material and Chemical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China.
| | - Yongsheng Zhao
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106-5080, USA.
| | - Ullah Latif
- Department Beijing Key Laboratory of Ionic Liquids Clean Process, Key Laboratory of Green Process and Engineering, State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China.
| | - Hongzhong Zhang
- School of Material and Chemical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China.
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27
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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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