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Xiao X, Trinh TX, Gerelkhuu Z, Ha E, Yoon TH. Automated machine learning in nanotoxicity assessment: A comparative study of predictive model performance. Comput Struct Biotechnol J 2024; 25:9-19. [PMID: 38414794 PMCID: PMC10899003 DOI: 10.1016/j.csbj.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/24/2024] [Accepted: 02/05/2024] [Indexed: 02/29/2024] Open
Abstract
Computational modeling has earned significant interest as an alternative to animal testing of toxicity assessment. However, the process of selecting an appropriate algorithm and fine-tuning hyperparameters for the developing of optimized models takes considerable time, expertise, and an intensive search. The recent emergence of automated machine learning (autoML) approaches, available as user-friendly platforms, has proven beneficial for individuals with limited knowledge in ML-based predictive model development. These autoML platforms automate crucial steps in model development, including data preprocessing, algorithm selection, and hyperparameter tuning. In this study, we used seven previously published and publicly available datasets for oxides and metals to develop nanotoxicity prediction models. AutoML platforms, namely Vertex AI, Azure, and Dataiku, were employed and performance measures such as accuracy, F1 score, precision, and recall for these autoML-based models were then compared with those of conventional ML-based models. The results demonstrated clearly that the autoML platforms produced more reliable nanotoxicity prediction models, outperforming those built with conventional ML algorithms. While none of the three autoML platforms significantly outperformed the others, distinctions exist among them in terms of the available options for choosing technical features throughout the model development steps. This allows users to select an autoML platform that aligns with their knowledge of predictive model development and its technical features. Additionally, prediction models constructed from datasets with better data quality displayed, enhanced performance than those built from datasets with lower data quality, indicating that future studies with high-quality datasets can further improve the performance of those autoML-based prediction models.
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Affiliation(s)
- Xiao Xiao
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, the Republic of Korea
| | - Tung X Trinh
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, the Republic of Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, the Republic of Korea
| | - Zayakhuu Gerelkhuu
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, the Republic of Korea
- Yoon Idea Lab. Co. Ltd, Seoul 04763, the Republic of Korea
| | - Eunyong Ha
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, the Republic of Korea
| | - Tae Hyun Yoon
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, the Republic of Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, the Republic of Korea
- Yoon Idea Lab. Co. Ltd, Seoul 04763, the Republic of Korea
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2
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Cheng K, Pan Y, Yuan B. Cytotoxicity prediction of nano metal oxides on different lung cells via Nano-QSAR. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 344:123405. [PMID: 38244905 DOI: 10.1016/j.envpol.2024.123405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 12/27/2023] [Accepted: 01/17/2024] [Indexed: 01/22/2024]
Abstract
In recent years, nanomaterials have found extensive applications across diverse domains owing to their distinctive physical and chemical characteristics. It is of great importance in theoretical and practical terms to carry out the relationship between structural characteristics of nanomaterials and different cytotoxicity and to achieve practical assessment and prediction of cytotoxicity. This study investigated the intrinsic quantitative constitutive relationships between the cytotoxicity of nano-metal oxides on human normal lung epithelial cells and human lung adenocarcinoma cells. We first employed quasi-SMILES-based nanostructural descriptors by selecting the five physicochemical properties that are most closely related to the cytotoxicity of nanometal oxides, then established SMILES-based descriptors that can effectively describe and characterize the molecular structure of nanometal oxides, and then built the corresponding Nano-Quantitative Structure-Activity Relationship (Nano-QSAR) prediction models, finally, combined with the theory of reactive oxygen species (ROS) biotoxicity, to reveal the mechanism of toxicity and differences between the two cell types. The established model can efficiently and accurately predict the properties of targets, reveal the corresponding toxicity mechanisms, and guide the safe design, synthesis, and application of nanometal oxides.
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Affiliation(s)
- Kaixiao Cheng
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 211816, Jiangsu, PR China.
| | - Yong Pan
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 211816, Jiangsu, PR China.
| | - Beilei Yuan
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 211816, Jiangsu, PR China
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3
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Gakis GP, Aviziotis IG, Charitidis CA. A structure-activity approach towards the toxicity assessment of multicomponent metal oxide nanomaterials. NANOSCALE 2023; 15:16432-16446. [PMID: 37791566 DOI: 10.1039/d3nr03174h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
The increase of human and environmental exposure to engineered nanomaterials (ENMs) due to the emergence of nanotechnology has raised concerns over their safety. The challenging nature of in vivo and in vitro toxicity assessment methods for ENMs, has led to emerging in silico techniques for ENM toxicity assessment, such as structure-activity relationship (SAR) models. Although such approaches have been extensively developed for the case of single-component nanomaterials, the case of multicomponent nanomaterials (MCNMs) has not been thoroughly addressed. In this paper, we present a SAR approach for the case metal and metal oxide MCNMs. The developed SAR framework is built using a dataset of 796 individual toxicity measurements for 340 different MCNMs, towards human cells, mammalian cells, and bacteria. The novelty of the approach lies in the multicomponent nature of the nanomaterials, as well as the size, diversity and heterogeneous nature of the dataset used. Furthermore, the approach used to calculate descriptors for surface loaded MCNMs, and the mechanistic insight provided by the model results can assist the understanding of MCNM toxicity. The developed models are able to correctly predict the toxic class of the MCNMs in the heterogeneous dataset, towards a wide range of human cells, mammalian cells and bacteria. Using the abovementioned approach, the principal toxicity pathways and mechanisms are identified, allowing a more holistic understanding of metal oxide MCNM toxicity.
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Affiliation(s)
- G P Gakis
- Research Lab of Advanced, Composite, Nano-Materials and Nanotechnology, Materials Science and Engineering Department, School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Street, Zografos, Athens 15780, Greece.
| | - I G Aviziotis
- Research Lab of Advanced, Composite, Nano-Materials and Nanotechnology, Materials Science and Engineering Department, School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Street, Zografos, Athens 15780, Greece.
| | - C A Charitidis
- Research Lab of Advanced, Composite, Nano-Materials and Nanotechnology, Materials Science and Engineering Department, School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Street, Zografos, Athens 15780, Greece.
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4
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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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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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6
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Chen C, Yaari Z, Apfelbaum E, Grodzinski P, Shamay Y, Heller DA. Merging data curation and machine learning to improve nanomedicines. Adv Drug Deliv Rev 2022; 183:114172. [PMID: 35189266 PMCID: PMC9233944 DOI: 10.1016/j.addr.2022.114172] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/28/2022] [Accepted: 02/16/2022] [Indexed: 12/12/2022]
Abstract
Nanomedicine design is often a trial-and-error process, and the optimization of formulations and in vivo properties requires tremendous benchwork. To expedite the nanomedicine research progress, data science is steadily gaining importance in the field of nanomedicine. Recently, efforts have explored the potential to predict nanomaterials synthesis and biological behaviors via advanced data analytics. Machine learning algorithms process large datasets to understand and predict various material properties in nanomedicine synthesis, pharmacologic parameters, and efficacy. "Big data" approaches may enable even larger advances, especially if researchers capitalize on data curation methods. However, the concomitant use of data curation processes needed to facilitate the acquisition and standardization of large, heterogeneous data sets, to support advanced data analytics methods such as machine learning has yet to be leveraged. Currently, data curation and data analytics areas of nanotechnology-focused data science, or 'nanoinformatics', have been proceeding largely independently. This review highlights the current efforts in both areas and the potential opportunities for coordination to advance the capabilities of data analytics in nanomedicine.
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Affiliation(s)
- Chen Chen
- Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Tri-institutional Ph.D. Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Zvi Yaari
- Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Elana Apfelbaum
- Department of Pharmacology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA
| | - Piotr Grodzinski
- Nanodelivery Systems and Devices Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yosi Shamay
- Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Daniel A Heller
- Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Tri-institutional Ph.D. Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Pharmacology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA.
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7
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Ji Z, Guo W, Wood EL, Liu J, Sakkiah S, Xu X, Patterson TA, Hong H. Machine Learning Models for Predicting Cytotoxicity of Nanomaterials. Chem Res Toxicol 2022; 35:125-139. [PMID: 35029374 DOI: 10.1021/acs.chemrestox.1c00310] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.
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Affiliation(s)
- Zuowei Ji
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Wenjing Guo
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Erin L Wood
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Jie Liu
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Sugunadevi Sakkiah
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Xiaoming Xu
- Office of Testing and Research, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Tucker A Patterson
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Huixiao Hong
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
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8
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Furxhi I. Health and environmental safety of nanomaterials: O Data, Where Art Thou? NANOIMPACT 2022; 25:100378. [PMID: 35559884 DOI: 10.1016/j.impact.2021.100378] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/15/2021] [Accepted: 12/17/2021] [Indexed: 06/15/2023]
Abstract
Nanotechnology keeps drawing attention due to the great tunable properties of nanomaterials in comparison to their bulk conventional materials. The growth of nanotechnology in combination with the digitization era has led to an increased need of safety related data. In addition to safety, new data-driven paradigms on safe and sustainable by design materials are stressing the necessity of data even more. Data is a fundamental asset to the scientific community in studying and analysing the entire life-cycle of nanomaterials. Unfortunately, data exist in a scattered fashion, in different sources and formats. To our knowledge, there is no study focusing on aspects of actual data-structure knowledge that exists in literature and databases. The purpose of this review research is to transparently and comprehensively, display to the nanoscience community the datasets readily available for machine learning purposes making it convenient and more efficient for the next users such as modellers or data curators to retrieve information. We systematically recorded the features and descriptors available in the datasets and provide synopsised information on their ranges, forms and metrics in the supplementary material.
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Affiliation(s)
- Irini Furxhi
- Transgero Limited, Cullinagh, Newcastle West, Co. Limerick, Ireland; Dept. of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93, Ireland.
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9
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Jeliazkova N, Bleeker E, Cross R, Haase A, Janer G, Peijnenburg W, Pink M, Rauscher H, Svendsen C, Tsiliki G, Zabeo A, Hristozov D, Stone V, Wohlleben W. How can we justify grouping of nanoforms for hazard assessment? Concepts and tools to quantify similarity. NANOIMPACT 2022; 25:100366. [PMID: 35559874 DOI: 10.1016/j.impact.2021.100366] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/15/2021] [Accepted: 11/12/2021] [Indexed: 06/15/2023]
Abstract
The risk of each nanoform (NF) of the same substance cannot be assumed to be the same, as they may vary in their physicochemical characteristics, exposure and hazard. However, neither can we justify a need for more animal testing and resources to test every NF individually. To reduce the need to test all NFs, (regulatory) information requirements may be fulfilled by grouping approaches. For such grouping to be acceptable, it is important to demonstrate similarities in physicochemical properties, toxicokinetic behaviour, and (eco)toxicological behaviour. The GRACIOUS Framework supports the grouping of NFs, by identifying suitable grouping hypotheses that describe the key similarities between different NFs. The Framework then supports the user to gather the evidence required to test these hypotheses and to subsequently assess the similarity of the NFs within the proposed group. The evidence needed to support a hypothesis is gathered by an Integrated Approach to Testing and Assessment (IATA), designed as decision trees constructed of decision nodes. Each decision node asks the questions and provides the methods needed to obtain the most relevant information. This White paper outlines existing and novel methods to assess similarity of the data generated for each decision node, either via a pairwise analysis conducted property-by-property, or by assessing multiple decision nodes simultaneously via a multidimensional analysis. For the pairwise comparison conducted property-by-property we included in this White paper: The x-fold, Bayesian and Arsinh-OWA distance algorithms performed comparably in the scoring of similarity between NF pairs. The Euclidean distance was also useful, but only with proper data transformation. The x-fold method does not standardize data, and thus produces skewed histograms, but has the advantage that it can be implemented without programming knowhow. A range of multidimensional evaluations, using for example dendrogram clustering approaches, were also investigated. Multidimensional distance metrics were demonstrated to be difficult to use in a regulatory context, but from a scientific perspective were found to offer unexpected insights into the overall similarity of very different materials. In conclusion, for regulatory purposes, a property-by-property evaluation of the data matrix is recommended to substantiate grouping, while the multidimensional approaches are considered to be tools of discovery rather than regulatory methods.
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Affiliation(s)
| | - Eric Bleeker
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Richard Cross
- UKRI Centre for Ecology and Hydrology, MacLean Building, Benson Lane, Wallingford OX10 8BB, UK
| | - Andrea Haase
- German Federal Institute for Risk Assessment (BfR), Department of Chemical and Product Safety, Berlin, Germany
| | - Gemma Janer
- LEITAT Technological Center, Barcelona, Spain
| | - Willie Peijnenburg
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands; Institute of Environmental Sciences (CML), Leiden University, Leiden, the Netherlands
| | - Mario Pink
- German Federal Institute for Risk Assessment (BfR), Department of Chemical and Product Safety, Berlin, Germany
| | - Hubert Rauscher
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Claus Svendsen
- UKRI Centre for Ecology and Hydrology, MacLean Building, Benson Lane, Wallingford OX10 8BB, UK
| | - Georgia Tsiliki
- Athena-Research and Innovation Center in Information, Communication and Knowledge Technologies, Marousi, Greece
| | | | | | - Vicki Stone
- NanoSafety Research Group, Heriot-Watt University, Riccarton, Edinburgh EH14 4AS, UK
| | - Wendel Wohlleben
- BASF SE, Dept. Material Physics and Dept Experimental Toxicology & Ecology, Ludwigshafen, Germany.
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Adeel M, Shakoor N, Shafiq M, Pavlicek A, Part F, Zafiu C, Raza A, Ahmad MA, Jilani G, White JC, Ehmoser EK, Lynch I, Ming X, Rui Y. A critical review of the environmental impacts of manufactured nano-objects on earthworm species. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 290:118041. [PMID: 34523513 DOI: 10.1016/j.envpol.2021.118041] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 08/07/2021] [Accepted: 08/23/2021] [Indexed: 05/27/2023]
Abstract
The presence of manufactured nano-objects (MNOs) in various consumer or their (future large-scale) use as nanoagrochemical have increased with the rapid development of nanotechnology and therefore, concerns associated with its possible ecotoxicological effects are also arising. MNOs are releasing along the product life cycle, consequently accumulating in soils and other environmental matrices, and potentially leading to adverse effects on soil biota and their associated processes. Earthworms, of the group of Oligochaetes, are an ecologically significant group of organisms and play an important role in soil remediation, as well as acting as a potential vector for trophic transfer of MNOs through the food chain. This review presents a comprehensive and critical overview of toxic effects of MNOs on earthworms in soil system. We reviewed pathways of MNOs in agriculture soil environment with its expected production, release, and bioaccumulation. Furthermore, we thoroughly examined scientific literature from last ten years and critically evaluated the potential ecotoxicity of 16 different metal oxide or carbon-based MNO types. Various adverse effects on the different earthworm life stages have been reported, including reduction in growth rate, changes in biochemical and molecular markers, reproduction and survival rate. Importantly, this literature review reveals the scarcity of long-term toxicological data needed to actually characterize MNOs risks, as well as an understanding of mechanisms causing toxicity to earthworm species. This review sheds light on this knowledge gap as investigating bio-nano interplay in soil environment improves our major understanding for safer applications of MNOs in the agriculture environment.
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Affiliation(s)
- Muhammad Adeel
- BNU-HKUST Laboratory of Green Innovation, Advanced Institute of Natural Sciences, Beijing Normal University Zhuhai Subcampus, 18 Jinfeng Road, Tangjiawan, Zhuhai, Guangdong, PR China; Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation and College of Resources and Environmental Sciences, China Agricultural University, Beijing, 100193, PR China
| | - Noman Shakoor
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation and College of Resources and Environmental Sciences, China Agricultural University, Beijing, 100193, PR China
| | - Muhammad Shafiq
- University of Guadalajara-University Center for Biological and Agricultural Sciences, Camino Ing. Ramón Padilla Sánchez núm. 2100, La Venta del Astillero, Zapopan, Jalisco, CP. 45110, Mexico
| | - Anna Pavlicek
- Department of Water-Atmosphere-Environment, Institute of Waste Management, University of Natural Resources and Life Sciences, Muthgasse 107, 1190, Vienna, Austria; Department of Nanobiotechnology, Institute for Synthetic Bioarchitectures, University of Natural Resources and Life Sciences, Muthgasse 11/II, 1190, Vienna, Austria
| | - Florian Part
- Department of Water-Atmosphere-Environment, Institute of Waste Management, University of Natural Resources and Life Sciences, Muthgasse 107, 1190, Vienna, Austria; Department of Nanobiotechnology, Institute for Synthetic Bioarchitectures, University of Natural Resources and Life Sciences, Muthgasse 11/II, 1190, Vienna, Austria
| | - Christian Zafiu
- Department of Water-Atmosphere-Environment, Institute of Waste Management, University of Natural Resources and Life Sciences, Muthgasse 107, 1190, Vienna, Austria
| | - Ali Raza
- Institute of Soil and Environmental Sciences, University of Agriculture Faisalabad, 38000, Pakistan
| | - Muhammad Arslan Ahmad
- Shenzhen Key Laboratory of Marine Bioresource and Eco-Environmental Science, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, 518060, China
| | - Ghulam Jilani
- Institute of Soil Science, PMAS Arid Agriculture University Rawalpindi, Pakistan
| | - Jason C White
- The Connecticut Agricultural Experiment Station, New Haven, CT, 06504, USA
| | - Eva-Kathrin Ehmoser
- Department of Water-Atmosphere-Environment, Institute of Waste Management, University of Natural Resources and Life Sciences, Muthgasse 107, 1190, Vienna, Austria
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, B15 2TT, Birmingham, UK
| | - Xu Ming
- BNU-HKUST Laboratory of Green Innovation, Advanced Institute of Natural Sciences, Beijing Normal University Zhuhai Subcampus, 18 Jinfeng Road, Tangjiawan, Zhuhai, Guangdong, PR China
| | - Yukui Rui
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation and College of Resources and Environmental Sciences, China Agricultural University, Beijing, 100193, PR China.
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11
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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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12
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Subramanian N, Palaniappan A. NanoTox: Development of a Parsimonious In Silico Model for Toxicity Assessment of Metal-Oxide Nanoparticles Using Physicochemical Features. ACS OMEGA 2021; 6:11729-11739. [PMID: 34056326 PMCID: PMC8154018 DOI: 10.1021/acsomega.1c01076] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 04/14/2021] [Indexed: 05/30/2023]
Abstract
Metal-oxide nanoparticles find widespread applications in mundane life today, and cost-effective evaluation of their cytotoxicity and ecotoxicity is essential for sustainable progress. Machine learning models use existing experimental data and learn quantitative feature-toxicity relationships to yield predictive models. In this work, we adopted a principled approach to this problem by formulating a novel feature space based on intrinsic and extrinsic physicochemical properties, including periodic table properties but exclusive of in vitro characteristics such as cell line, cell type, and assay method. An optimal hypothesis space was developed by applying variance inflation analysis to the correlation structure of the features. Consequent to a stratified train-test split, the training dataset was balanced for the toxic outcomes and a mapping was then achieved from the normalized feature space to the toxicity class using various hyperparameter-tuned machine learning models, namely, logistic regression, random forest, support vector machines, and neural networks. Evaluation on an unseen test set yielded >96% balanced accuracy for the random forest, and neural network with one-hidden-layer models. The obtained cytotoxicity models are parsimonious, with intelligible inputs, and an embedded applicability check. Interpretability investigations of the models identified the key predictor variables of metal-oxide nanoparticle cytotoxicity. Our models could be applied on new, untested oxides, using a majority-voting ensemble classifier, NanoTox, that incorporates the best of the above models. NanoTox is the first open-source nanotoxicology pipeline, freely available under the GNU General Public License (https://github.com/NanoTox).
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Affiliation(s)
- Nilesh
Anantha Subramanian
- Department
of Medical Nanotechnology and Department of Bioinformatics, School of Chemical and BioTechnology, SASTRA Deemed
University, Thanjavur 613401, India
| | - Ashok Palaniappan
- Department
of Medical Nanotechnology and Department of Bioinformatics, School of Chemical and BioTechnology, SASTRA Deemed
University, Thanjavur 613401, India
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13
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Huang HJ, Lee YH, Hsu YH, Liao CT, Lin YF, Chiu HW. Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing. Int J Mol Sci 2021; 22:4216. [PMID: 33921715 PMCID: PMC8073679 DOI: 10.3390/ijms22084216] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/01/2021] [Accepted: 04/16/2021] [Indexed: 12/12/2022] Open
Abstract
Millions of experimental animals are widely used in the assessment of toxicological or biological effects of manufactured nanomaterials in medical technology. However, the animal consciousness has increased and become an issue for debate in recent years. Currently, the principle of the 3Rs (i.e., reduction, refinement, and replacement) is applied to ensure the more ethical application of humane animal research. In order to avoid unethical procedures, the strategy of alternatives to animal testing has been employed to overcome the drawbacks of animal experiments. This article provides current alternative strategies to replace or reduce the use of experimental animals in the assessment of nanotoxicity. The currently available alternative methods include in vitro and in silico approaches, which can be used as cost-effective approaches to meet the principle of the 3Rs. These methods are regarded as non-animal approaches and have been implemented in many countries for scientific purposes. The in vitro experiments related to nanotoxicity assays involve cell culture testing and tissue engineering, while the in silico methods refer to prediction using molecular docking, molecular dynamics simulations, and quantitative structure-activity relationship (QSAR) modeling. The commonly used novel cell-based methods and computational approaches have the potential to help minimize the use of experimental animals for nanomaterial toxicity assessments.
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Affiliation(s)
- Hung-Jin Huang
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
| | - Yu-Hsuan Lee
- Department of Cosmeceutics, China Medical University, Taichung 406040, Taiwan;
| | - Yung-Ho Hsu
- Division of Nephrology, Department of Internal Medicine, Hsin Kuo Min Hospital, Taipei Medical University, Taoyuan City 320001, Taiwan;
- Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei 11031, Taiwan
| | - Chia-Te Liao
- Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei 11031, Taiwan
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
| | - Yuh-Feng Lin
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei 11031, Taiwan
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
| | - Hui-Wen Chiu
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei 11031, Taiwan
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
- Department of Medical Research, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
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14
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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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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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Papadiamantis AG, Jänes J, Voyiatzis E, Sikk L, Burk J, Burk P, Tsoumanis A, Ha MK, Yoon TH, Valsami-Jones E, Lynch I, Melagraki G, Tämm K, Afantitis A. Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E2017. [PMID: 33066094 PMCID: PMC7601995 DOI: 10.3390/nano10102017] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 10/03/2020] [Accepted: 10/07/2020] [Indexed: 02/07/2023]
Abstract
A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MexOy NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⟂ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project's Integrated Approach to Testing and Assessment (IATA).
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Affiliation(s)
- Anastasios G. Papadiamantis
- NovaMechanics Ltd., Nicosia 1065, Cyprus; (A.G.P.); (E.V.); (A.T.)
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK; (E.V.-J.); (I.L.)
| | - Jaak Jänes
- Institute of Chemistry, University of Tartu, 50411 Tartu, Estonia; (J.J.); (L.S.); (J.B.); (P.B.)
| | | | - Lauri Sikk
- Institute of Chemistry, University of Tartu, 50411 Tartu, Estonia; (J.J.); (L.S.); (J.B.); (P.B.)
| | - Jaanus Burk
- Institute of Chemistry, University of Tartu, 50411 Tartu, Estonia; (J.J.); (L.S.); (J.B.); (P.B.)
| | - Peeter Burk
- Institute of Chemistry, University of Tartu, 50411 Tartu, Estonia; (J.J.); (L.S.); (J.B.); (P.B.)
| | | | - My Kieu Ha
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea; (M.K.H.); (T.H.Y.)
| | - Tae Hyun Yoon
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea; (M.K.H.); (T.H.Y.)
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
| | - Eugenia Valsami-Jones
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK; (E.V.-J.); (I.L.)
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK; (E.V.-J.); (I.L.)
| | - Georgia Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, 16672 Vari, Greece;
| | - Kaido Tämm
- Institute of Chemistry, University of Tartu, 50411 Tartu, Estonia; (J.J.); (L.S.); (J.B.); (P.B.)
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17
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Abstract
Nanoparticles from natural and anthropogenic sources are abundant in the environment, thus human exposure to nanoparticles is inevitable. Due to this constant exposure, it is critically important to understand the potential acute and chronic adverse effects that nanoparticles may cause to humans. In this review, we explore and highlight the current state of nanotoxicology research with a focus on mechanistic understanding of nanoparticle toxicity at organ, tissue, cell, and biomolecular levels. We discuss nanotoxicity mechanisms, including generation of reactive oxygen species, nanoparticle disintegration, modulation of cell signaling pathways, protein corona formation, and poly(ethylene glycol)-mediated immunogenicity. We conclude with a perspective on potential approaches to advance current understanding of nanoparticle toxicity. Such improved understanding may lead to mitigation strategies that could enable safe application of nanoparticles in humans. Advances in nanotoxicity research will ultimately inform efforts to establish standardized regulatory frameworks with the goal of fully exploiting the potential of nanotechnology while minimizing harm to humans.
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Affiliation(s)
- Wen Yang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, USA;
| | - Lin Wang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, USA;
| | - Evan M Mettenbrink
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, USA;
| | - Paul L DeAngelis
- Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma 73104, USA
| | - Stefan Wilhelm
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, USA; .,Institute for Biomedical Engineering, Science, and Technology (IBEST), Norman, Oklahoma 73019, USA.,Stephenson Cancer Center, Oklahoma City, Oklahoma 73104, USA
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18
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Furxhi I, Murphy F. Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning. Int J Mol Sci 2020; 21:E5280. [PMID: 32722414 PMCID: PMC7432486 DOI: 10.3390/ijms21155280] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 07/21/2020] [Accepted: 07/23/2020] [Indexed: 12/31/2022] Open
Abstract
The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models.
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Affiliation(s)
- Irini Furxhi
- Transgero Limited, V42V384 Newcastle, Limerick, Ireland;
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19
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Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, Oprea TI, Baskin II, Varnek A, Roitberg A, Isayev O, Curtarolo S, Fourches D, Cohen Y, Aspuru-Guzik A, Winkler DA, Agrafiotis D, Cherkasov A, Tropsha A. QSAR without borders. Chem Soc Rev 2020; 49:3525-3564. [PMID: 32356548 PMCID: PMC8008490 DOI: 10.1039/d0cs00098a] [Citation(s) in RCA: 327] [Impact Index Per Article: 81.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
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Affiliation(s)
- Eugene N Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
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20
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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: 52] [Impact Index Per Article: 13.0] [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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Furxhi I, Murphy F, Mullins M, Arvanitis A, Poland CA. Nanotoxicology data for in silico tools: a literature review. Nanotoxicology 2020; 14:612-637. [PMID: 32100604 DOI: 10.1080/17435390.2020.1729439] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies.
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Affiliation(s)
- Irini Furxhi
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Finbarr Murphy
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Martin Mullins
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Athanasios Arvanitis
- Department of Mechanical Engineering, Environmental Informatics Research Group, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Craig A Poland
- ELEGI/Colt Laboratory, Queen's Medical Research Institute, 47 Little France Crescent, University of Edinburgh, Edinburgh, Scotland
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22
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Kotzabasaki MI, Sotiropoulos I, Sarimveis H. QSAR modeling of the toxicity classification of superparamagnetic iron oxide nanoparticles (SPIONs) in stem-cell monitoring applications: an integrated study from data curation to model development. RSC Adv 2020; 10:5385-5391. [PMID: 35498319 PMCID: PMC9049038 DOI: 10.1039/c9ra09475j] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 01/21/2020] [Indexed: 11/23/2022] Open
Abstract
The use of in silico approaches for the prediction of biomedical properties of nano-biomaterials (NBMs) can play a significant role in guiding and reducing wetlab experiments. Computational methods, such as data mining and machine learning techniques, can increase the efficiency and reduce the time and cost required for hazard and risk assesment and for designing new safer NBMs. A major obstacle in developing accurate and well-validated in silico models such as Nano Quantitative Structure-Activity Relationships (Nano-QSARs) is that although the volume of data published in the literature is increasing, the data are fragmented in many different publications and are not sufficiently curated for modelling purposes. Moreover, NBMs exhibit high complexity and heterogeneity in their structures, making data collection and curation and QSAR model development more challenging compared to traditional small molecules. The aim of this study was to construct and fully validate a Nano-QSAR model for the prediction of toxicological properties of superparamagnetic iron oxide nanoparticles (SPIONs), focusing on their application as Magnetic Resonance Imaging (MRI) contrast agents for non-invasive stem cell labelling and tracking. To achieve this goal, we first performed an extensive search through the literature for collecting and curating relevant data and we developed a dataset containing both physicochemical and toxicological properties of SPIONs. The data were analysed next, using Automated machine learning (Auto-ML) approaches for optimising the development and validation of nanotoxicity classification QSAR models of SPIONs. Further analysis of relative attribute importances revealed that physicochemical properties such as the size and the magnetic core are the dominant attributes correlated to the toxicity of SPIONs. Our results suggest that as more systematic information from NBM experimental tests becomes available, computational tools could play an important role in supporting the safety-by-design (SbD) concept in regenerative medicine and disease therapeutics.
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Affiliation(s)
- Marianna I Kotzabasaki
- School of Chemical Engineering, National Technical University of Athens 9 Heroon Polytechneiou Street, Zografou Campus 15780 Athens Greece +302107723138 +302107723236 +306936396688 +302107723237
| | - Iason Sotiropoulos
- School of Chemical Engineering, National Technical University of Athens 9 Heroon Polytechneiou Street, Zografou Campus 15780 Athens Greece +302107723138 +302107723236 +306936396688 +302107723237
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens 9 Heroon Polytechneiou Street, Zografou Campus 15780 Athens Greece +302107723138 +302107723236 +306936396688 +302107723237
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23
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Furxhi I, Murphy F, Mullins M, Arvanitis A, Poland CA. Practices and Trends of Machine Learning Application in Nanotoxicology. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E116. [PMID: 31936210 PMCID: PMC7023261 DOI: 10.3390/nano10010116] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 12/31/2019] [Accepted: 01/06/2020] [Indexed: 02/07/2023]
Abstract
Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.
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Affiliation(s)
- Irini Furxhi
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Finbarr Murphy
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Martin Mullins
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (F.M.); (M.M.)
- Transgero Limited, Newcastle, V42V384 Limerick, Ireland
| | - Athanasios Arvanitis
- Department of Mechanical Engineering, Environmental Informatics Research Group, Aristotle University of Thessaloniki, 54124 Thessaloniki Box 483, Greece;
| | - Craig A. Poland
- ELEGI/Colt Laboratory, Queen’s Medical Research Institute, 47 Little France Crescent, University of Edinburgh, Edinburgh EH16 4TJ, Scotland, UK;
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24
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Bilal M, Oh E, Liu R, Breger JC, Medintz IL, Cohen Y. Bayesian Network Resource for Meta-Analysis: Cellular Toxicity of Quantum Dots. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2019; 15:e1900510. [PMID: 31207082 DOI: 10.1002/smll.201900510] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Indexed: 05/14/2023]
Abstract
A web-based resource for meta-analysis of nanomaterials toxicity is developed whereby the utility of Bayesian networks (BNs) is illustrated for exploring the cellular toxicity of Cd-containing quantum dots (QDs). BN models are developed based on a dataset compiled from 517 publications comprising 3028 cell viability data samples and 837 IC50 values. BN QD toxicity (BN-QDTox) models are developed using both continuous (i.e., numerical) and categorical attributes. Using these models, the most relevant attributes identified for correlating IC50 are: QD diameter, exposure time, surface ligand, shell, assay type, surface modification, and surface charge, with the addition of QD concentration for the cell viability analysis. Data exploration via BN models further enables identification of possible association rules for QDs cellular toxicity. The BN models as web-based applications can be used for rapid intelligent query of the available body of evidence for a given nanomaterial and can be readily updated as the body of knowledge expands.
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Affiliation(s)
- Muhammad Bilal
- Center for Environmental Implications of Nanotechnology, University of California, Los Angeles, Los Angeles, CA, 90095-7227, USA
- Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA, 90095-1496, USA
| | - Eunkeu Oh
- Optical Sciences Division, Code 5611, U.S. Naval Research Laboratory, Washington, DC, 20375, USA
- KeyW Corporation, Hanover, MD, 21076, USA
| | - Rong Liu
- Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA, 90095-1496, USA
| | - Joyce C Breger
- Center for Biomolecular Science and Engineering, Code 6900, U.S. Naval Research Laboratory, Washington, DC, 20375, USA
| | - Igor L Medintz
- Center for Biomolecular Science and Engineering, Code 6900, U.S. Naval Research Laboratory, Washington, DC, 20375, USA
| | - Yoram Cohen
- Center for Environmental Implications of Nanotechnology, University of California, Los Angeles, Los Angeles, CA, 90095-7227, USA
- Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA, 90095-1496, USA
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA
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Furxhi I, Murphy F, Poland CA, Sheehan B, Mullins M, Mantecca P. Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics. Nanotoxicology 2019; 13:827-848. [PMID: 31140895 DOI: 10.1080/17435390.2019.1595206] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Inroads have been made in our understanding of the risks posed to human health and the environment by nanoparticles (NPs) but this area requires continuous research and monitoring. Machine learning techniques have been applied to nanotoxicology with very encouraging results. This study deals with bridging physicochemical properties of NPs, experimental exposure conditions and in vitro characteristics with biological effects of NPs on a molecular cellular level from transcriptomics studies. The bridging is done by developing and implementing Bayesian Networks (BNs) with or without data preprocessing. The BN structures are derived either automatically or methodologically and compared. Early stage nanotoxicity measurements represent a challenge, not least when attempting to predict adverse outcomes and modeling is critical to understanding the biological effects of exposure to NPs. The preprocessed data-driven BN showed improved performance over automatically structured BN and the BN with unprocessed datasets. The prestructured BN captures inter relationships between NP properties, exposure condition and in vitro characteristics and links those with cellular effects based on statistic correlation findings. Information gain analysis showed that exposure dose, NP and cell line variables were the most influential attributes in predicting the biological effects. The BN methodology proposed in this study successfully predicts a number of toxicologically relevant cellular disrupted biological processes such as cell cycle and proliferation pathways, cell adhesion and extracellular matrix responses, DNA damage and repair mechanisms etc., with a success rate >80%. The model validation from independent data shows a robust and promising methodology for incorporating transcriptomics outcomes in a hazard and, by extension, risk assessment modeling framework by predicting affected cellular functions from experimental conditions.
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Affiliation(s)
- Irini Furxhi
- a Department of Accounting and Finance , Kemmy Business School University of Limerick , Limerick , Ireland
| | - Finbarr Murphy
- a Department of Accounting and Finance , Kemmy Business School University of Limerick , Limerick , Ireland
| | - Craig A Poland
- b ELEGI/Colt Laboratory , Queen's Medical Research Institute, University of Edinburgh , Edinburgh , Scotland
| | - Barry Sheehan
- a Department of Accounting and Finance , Kemmy Business School University of Limerick , Limerick , Ireland
| | - Martin Mullins
- a Department of Accounting and Finance , Kemmy Business School University of Limerick , Limerick , Ireland
| | - Paride Mantecca
- c Department of Earth and Environmental Sciences , Particulate Matter and Health Risk (POLARIS) Research Centre University of Milano Bicocca , Milano , Italy
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26
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Furxhi I, Murphy F, Mullins M, Poland CA. Machine learning prediction of nanoparticle in vitro toxicity: A comparative study of classifiers and ensemble-classifiers using the Copeland Index. Toxicol Lett 2019; 312:157-166. [PMID: 31102714 DOI: 10.1016/j.toxlet.2019.05.016] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 04/12/2019] [Accepted: 05/13/2019] [Indexed: 01/22/2023]
Abstract
Nano-Particles (NPs) are well established as important components across a broad range of products from cosmetics to electronics. Their utilization is increasing with their significant economic and societal potential yet to be fully realized. Inroads have been made in our understanding of the risks posed to human health and the environment by NPs but this area will require continuous research and monitoring. In recent years Machine Learning (ML) techniques have exploited large datasets and computation power to create breakthroughs in diverse fields from facial recognition to genomics. More recently, ML techniques have been applied to nanotoxicology with very encouraging results. In this study, categories of ML classifiers (rules, trees, lazy, functions and bayes) were compared using datasets from the Safe and Sustainable Nanotechnology (S2NANO) database to investigate their performance in predicting NPs in vitro toxicity. Physicochemical properties, toxicological and quantum-mechanical attributes and in vitro experimental conditions were used as input variables to predict the toxicity of NPs based on cell viability. Voting, an ensemble meta-classifier, was used to combine base models to optimize the classification prediction of toxicity. To facilitate inter-comparison, a Copeland Index was applied that ranks the classifiers according to their performance and suggested the optimal classifier. Neural Network (NN) and Random forest (RF) showed the best performance in the majority of the datasets used in this study. However, the combination of classifiers demonstrated an improved prediction resulting meta-classifier to have higher indices. This proposed Copeland Index can now be used by researchers to identify and clearly prioritize classifiers in order to achieve more accurate classification predictions for NP toxicity for a given dataset.
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Affiliation(s)
- Irini Furxhi
- Dept. of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93, Ireland.
| | - Finbarr Murphy
- Dept. of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93, Ireland.
| | - Martin Mullins
- Dept. of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93, Ireland.
| | - Craig A Poland
- ELEGI/Colt Laboratory, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, Scotland, United Kingdom.
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27
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Lewis RW, Bertsch PM, McNear DH. Nanotoxicity of engineered nanomaterials (ENMs) to environmentally relevant beneficial soil bacteria - a critical review. Nanotoxicology 2019; 13:392-428. [PMID: 30760121 DOI: 10.1080/17435390.2018.1530391] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Deposition of engineered nanomaterials (ENMs) in various environmental compartments is projected to continue rising exponentially. Terrestrial environments are expected to be the largest repository for environmentally released ENMs. Because ENMs are enriched in biosolids during wastewater treatment, agriculturally applied biosolids facilitate ENM exposure of key soil micro-organisms, such as plant growth-promoting rhizobacteria (PGPR). The ecological ramifications of increasing levels of ENM exposure of terrestrial micro-organisms are not clearly understood, but a growing body of research has investigated the toxicity of ENMs to various soil bacteria using a myriad of toxicity end-points and experimental procedures. This review explores what is known regarding ENM toxicity to important soil bacteria, with a focus on ENMs which are expected to accumulate in terrestrial ecosystems at the highest concentrations and pose the greatest potential threat to soil micro-organisms having potential indirect detrimental effects on plant growth. Knowledge gaps in the fundamental understanding of nanotoxicity to bacteria are identified, including the role of physicochemical properties of ENMs in toxicity responses, particularly in agriculturally relevant micro-organisms. Strategies for improving the impact of future research through the implementation of in-depth ENM characterization and use of necessary experimental controls are proposed. The future of nanotoxicological research employing microbial ecoreceptors is also explored, highlighting the need for continued research utilizing bacterial isolates while concurrently expanding efforts to study ENM-bacteria interactions in more complex environmentally relevant media, e.g. soil. Additionally, the particular importance of future work to extensively examine nanotoxicity in the context of bacterial ecosystem function, especially of plant growth-promoting agents, is proposed.
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Affiliation(s)
- Ricky W Lewis
- a Rhizosphere Science Laboratory, Department of Plant and Soil Sciences , University of Kentucky , Lexington , KY , USA
| | - Paul M Bertsch
- a Rhizosphere Science Laboratory, Department of Plant and Soil Sciences , University of Kentucky , Lexington , KY , USA.,b CSIRO Land and Water , Ecosciences Precinct , Brisbane , Australia.,c Center for the Environmental Implications of Nanotechnology (CEINT) , Duke University , Durham , NC , USA
| | - David H McNear
- a Rhizosphere Science Laboratory, Department of Plant and Soil Sciences , University of Kentucky , Lexington , KY , USA
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28
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Choi JS, Trinh TX, Yoon TH, Kim J, Byun HG. Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials. CHEMOSPHERE 2019; 217:243-249. [PMID: 30419378 DOI: 10.1016/j.chemosphere.2018.11.014] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 10/21/2018] [Accepted: 11/02/2018] [Indexed: 05/14/2023]
Abstract
A quasi-QSAR model was developed to predict the cell viability of human lung (BEAS-2B) and skin (HaCaT) cells exposed to 21 types of metal oxide nanomaterials. A wide range of toxicity datasets obtained from the S2NANO (www.s2nano.org) database was used. The data of descriptors representing the physicochemical properties and experimental conditions were coded to quasi-SMILES. In particular, hierarchical cluster analysis (HCA) and min-max normalization method were respectively used in assigning alphanumeric codes for numerical descriptors (e.g., core size, hydrodynamic size, surface charge, and dose) and then quasi-QSAR model performances for both methods were compared. The quasi-QSAR models were developed using CORAL software (www.insilico.eu/coral). Quasi-QSAR model built using quasi-SMILES generated by means of HCA showed better performance than the min-max normalization method. The model showed satisfactory statistical results (Radj2 for the training dataset: 0.71-0.73; Radj2 for the calibration dataset: 0.74-0.82; and Radj2 for the validation dataset: 0.70-0.76).
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Affiliation(s)
- Jang-Sik Choi
- Division of Electronics, Information and Communication Engineering, Kangwon National University (Samcheok), Kangwon-do, 25913, Republic of Korea
| | - Tung X Trinh
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul, 04763, Republic of Korea
| | - Tae-Hyun Yoon
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul, 04763, Republic of Korea
| | - Jongwoon Kim
- Environmental Safety Group, Korea Institute of Science and Technology (KIST) Europe, Campus E 7.1, Saarbrueck-en, Germany.
| | - Hyung-Gi Byun
- Division of Electronics, Information and Communication Engineering, Kangwon National University (Samcheok), Kangwon-do, 25913, Republic of Korea.
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29
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Fröhlich E. Comparison of conventional and advanced in vitro models in the toxicity testing of nanoparticles. ARTIFICIAL CELLS, NANOMEDICINE, AND BIOTECHNOLOGY 2018; 46:1091-1107. [PMID: 29956556 PMCID: PMC6214528 DOI: 10.1080/21691401.2018.1479709] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 05/12/2018] [Accepted: 05/15/2018] [Indexed: 01/02/2023]
Abstract
Humans are exposed to a wide variety of nanoparticles (NPs) present in the environment, in consumer, health and medical products, and in food. Conventional cytotoxicity testing compared to animal testing is less expensive, faster and avoids ethical problems at the expense of a lower predictive value. New cellular models and exposure conditions have been developed to overcome the limitations of conventional cell culture and obtain more predictive data. The use of three-dimensional culture, co-culture and inclusion of mechanical stimulation can provide physiologically more relevant culture conditions. These systems are particularly relevant for oral, respiratory and intravenous exposure to NPs and it may be assumed that physiologically relevant application of the NPs can improve the predictive value of in vitro testing. Various groups have used advanced culture and exposure systems, but few direct comparisons between data from conventional cultures and from advanced systems exist. In silico models may present another option to predict human health risk by NPs without using animal studies. In the absence of validation, the question whether these alternative models provide more predictive data than conventional testing remains elusive.
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Affiliation(s)
- Eleonore Fröhlich
- Center for Medical Research, Medical University of Graz, Graz, Austria
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30
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Multivariate statistical analysis for selecting optimal descriptors in the toxicity modeling of nanomaterials. Comput Biol Med 2018; 99:161-172. [PMID: 29933127 DOI: 10.1016/j.compbiomed.2018.06.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 06/13/2018] [Accepted: 06/13/2018] [Indexed: 01/05/2023]
Abstract
The present study is based on the application of a multivariate statistical analysis approach for the selection of optimal descriptors of nanomaterials with the objective of robust qualitative modeling of their toxicity. A novel data mining protocol has been developed for the selection of an optimal subset of descriptors of nanomaterials by using the well-known multivariate method principal component analysis (PCA). The selected subsets of descriptors were validated for qualitative modeling of the toxicity of nanomaterials in the PC space. The analysis and validation of the proposed schemes were based on five decisive nanomaterial toxicity data sets available in the published literature. Optimal descriptors were selected on the basis of the maximum loading criteria and using a threshold value of cumulative variance ≤90% on PC directions. A maximum inter-class separation(B) and the minimum intra-classes separation(A) were obtained for toxic vs. nontoxic nanomaterials in the PC space with the selected subsets of optimal descriptors compared to their other combinations for each of the datasets.
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31
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Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 2018; 8:6110. [PMID: 29666463 PMCID: PMC5904177 DOI: 10.1038/s41598-018-24483-z] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 04/04/2018] [Indexed: 12/18/2022] Open
Abstract
A generalized toxicity classification model for 7 different oxide nanomaterials is presented in this study. A data set extracted from multiple literature sources and screened by physicochemical property based quality scores were used for model development. Moreover, a few more preprocessing techniques, such as synthetic minority over-sampling technique, were applied to address the imbalanced class problem in the data set. Then, classification models using four different algorithms, such as generalized linear model, support vector machine, random forest, and neural network, were developed and their performances were compared to find the best performing preprocessing methods as well as algorithms. The neural network model built using the balanced data set was identified as the model with best predictive performance, while applicability domain was defined using k-nearest neighbours algorithm. The analysis of relative attribute importance for the built neural network model identified dose, formation enthalpy, exposure time, and hydrodynamic size as the four most important attributes. As the presented model can predict the toxicity of the nanomaterials in consideration of various experimental conditions, it has the advantage of having a broader and more general applicability domain than the existing quantitative structure-activity relationship model.
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