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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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Moncho S, Serrano-Candelas E, de Julián-Ortiz JV, Gozalbes R. A review on the structural characterization of nanomaterials for nano-QSAR models. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2024; 15:854-866. [PMID: 39015425 PMCID: PMC11250003 DOI: 10.3762/bjnano.15.71] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 06/28/2024] [Indexed: 07/18/2024]
Abstract
Quantitative structure-activity relationship (QSAR) models are routinely used to predict the properties and biological activity of chemicals to direct synthetic advances, perform massive screenings, and even to register new substances according to international regulations. Currently, nanoscale QSAR (nano-QSAR) models, adapting this methodology to predict the intrinsic features of nanomaterials (NMs) and quantitatively assess their risks, are blooming. One of the challenges is the characterization of the NMs. This cannot be done with a simple SMILES representation, as for organic molecules, because their chemical structure is complex, including several layers and many inorganic materials, and their size and geometry are key features. In this review, we survey the literature for existing predictive models for NMs and discuss the variety of calculated and experimental features used to define and describe NMs. In the light of this research, we propose a classification of the descriptors including those that directly describe a component of the nanoform (core, surface, or structure) and also experimental features (related to the nanomaterial's behavior, preparation, or test conditions) that indirectly reflect its structure.
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Affiliation(s)
- Salvador Moncho
- ProtoQSAR S.L., CEEI Valencia, Avda. Benjamin Franklin 12, 46980 Paterna, Spain
| | | | - Jesús Vicente de Julián-Ortiz
- Universitat de València, Facultad de Farmacia, Departamento de Química Física, Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Avda. Vicent Andrés Estellés 0, 46100 Burjassot, Spain
| | - Rafael Gozalbes
- ProtoQSAR S.L., CEEI Valencia, Avda. Benjamin Franklin 12, 46980 Paterna, Spain
- MolDrug AI Systems S.L., Olimpia Arozena Torres 45, 46108 Valencia, Spain
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3
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Roy J, Roy K. Exploring the relationships between physiochemical properties of nanoparticles and cell damage to combat cancer growth using simple periodic table-based descriptors. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2024; 15:297-309. [PMID: 38505811 PMCID: PMC10949013 DOI: 10.3762/bjnano.15.27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 02/23/2024] [Indexed: 03/21/2024]
Abstract
A comprehensive knowledge of the physical and chemical properties of nanomaterials (NMs) is necessary to design them effectively for regulated use. Although NMs are utilized in therapeutics, their cytotoxicity has attracted great attention. Nanoscale quantitative structure-property relationship (nano-QSPR) models can help in understanding the relationship between NMs and the biological environment and provide new ways for modeling the structural properties and bio-toxic effects of NMs. The goal of the study is to construct fully validated property-based models to extract relevant features for estimating and influencing the zeta potential and obtaining the toxicity profile regarding cell damage in the treatment of cancer cells. To achieve this, QSPR modeling was first performed with 18 metal oxide (MeOx) NMs to measure their materials properties using periodic table-based descriptors. The features obtained were later applied for zeta potential calculation (imputation for sparse data) for MeOx NMs that lack such information. To further clarify the influence of the zeta potential on cell damage, a QSPR model was developed with 132 MeOx NMs to understand the possible mechanisms of cell damage. The results showed that zeta potential, along with seven other descriptors, had the potential to influence oxidative damage through free radical accumulation, which could lead to changes in the survival rate of cancerous cells. The developed QSPR and quantitative structure-activity relationship models also give hints regarding safer design and toxicity assessment of MeOx NMs.
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Affiliation(s)
- Joyita Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
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4
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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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Singh AV, Shelar A, Rai M, Laux P, Thakur M, Dosnkyi I, Santomauro G, Singh AK, Luch A, Patil R, Bill J. Harmonization Risks and Rewards: Nano-QSAR for Agricultural Nanomaterials. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:2835-2852. [PMID: 38315814 DOI: 10.1021/acs.jafc.3c06466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
This comprehensive review explores the emerging landscape of Nano-QSAR (quantitative structure-activity relationship) for assessing the risk and potency of nanomaterials in agricultural settings. The paper begins with an introduction to Nano-QSAR, providing background and rationale, and explicitly states the hypotheses guiding the review. The study navigates through various dimensions of nanomaterial applications in agriculture, encompassing their diverse properties, types, and associated challenges. Delving into the principles of QSAR in nanotoxicology, this article elucidates its application in evaluating the safety of nanomaterials, while addressing the unique limitations posed by these materials. The narrative then transitions to the progression of Nano-QSAR in the context of agricultural nanomaterials, exemplified by insightful case studies that highlight both the strengths and the limitations inherent in this methodology. Emerging prospects and hurdles tied to Nano-QSAR in agriculture are rigorously examined, casting light on important pathways forward, existing constraints, and avenues for research enhancement. Culminating in a synthesis of key insights, the review underscores the significance of Nano-QSAR in shaping the future of nanoenabled agriculture. It provides strategic guidance to steer forthcoming research endeavors in this dynamic field.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute of Risk Assessment (BfR), Maxdohrnstrasse 8-10, 10589 Berlin, Germany
| | - Amruta Shelar
- Department of Technology, Savitribai Phule Pune University, Pune 411007, India
| | - Mansi Rai
- Department of Microbiology, Central University of Rajasthan NH-8, Bandar Sindri, Dist-Ajmer-305817, Rajasthan, India
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute of Risk Assessment (BfR), Maxdohrnstrasse 8-10, 10589 Berlin, Germany
| | - Manali Thakur
- Uniklinik Köln, Kerpener Strasse 62, 50937 Köln Germany
| | - Ievgen Dosnkyi
- Institute of Chemistry and Biochemistry Department of Organic ChemistryFreie Universität Berlin Takustr. 3 14195 Berlin, Germany
| | - Giulia Santomauro
- Institute for Materials Science, Department of Bioinspired Materials, University of Stuttgart, 70569, Stuttgart, Germany
| | - Alok Kumar Singh
- Department of Plant Molecular Biology & Genetic Engineering, ANDUA&T, Ayodhya 224229, Uttar Pradesh, India
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute of Risk Assessment (BfR), Maxdohrnstrasse 8-10, 10589 Berlin, Germany
| | - Rajendra Patil
- Department of Technology, Savitribai Phule Pune University, Pune 411007, India
| | - Joachim Bill
- Institute for Materials Science, Department of Bioinspired Materials, University of Stuttgart, 70569, Stuttgart, Germany
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6
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Toropova AP, Toropov AA. Quasi-SMILES as a basis to build up models of endpoints for nanomaterials. ENVIRONMENTAL TECHNOLOGY 2023; 44:4460-4467. [PMID: 35748421 DOI: 10.1080/09593330.2022.2093655] [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: 05/09/2022] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Simplified molecular input-line entry system (SMILES) is a format for representing of the molecular structure. Quasi-SMILES is an extended format for representing molecular structure data and some eclectic data, which in principle could be applied to improve a model's predictive potential. Nano-quantitative structure-property relationships (nano-QSPRs) for energy gap (Eg, eV) of the metals oxide nanoparticles based on the quasi-SMILES give a predictive model for Eg, characterized by the following statistical quality for external validation set n = 22, R2 = 0.83, RMSE = 0.267.
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Affiliation(s)
- Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
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7
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Zhou Y, Wang Y, Peijnenburg W, Vijver MG, Balraadjsing S, Fan W. Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17786-17795. [PMID: 36730792 DOI: 10.1021/acs.est.2c07039] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The wide production and use of metallic nanomaterials (MNMs) leads to increased emissions into the aquatic environments and induces high potential risks. Experimentally evaluating the (eco)toxicity of MNMs is time-consuming and expensive due to the multiple environmental factors, the complexity of material properties, and the species diversity. Machine learning (ML) models provide an option to deal with heterogeneous data sets and complex relationships. The present study established an in silico model based on a machine learning properties-environmental conditions-multi species-toxicity prediction model (ML-PEMST) that can be applied to predict the toxicity of different MNMs toward multiple aquatic species. Feature importance and interaction analysis based on the random forest method indicated that exposure duration, illumination, primary size, and hydrodynamic diameter were the main factors affecting the ecotoxicity of MNMs to a variety of aquatic organisms. Illumination was demonstrated to have the most interaction with the other features. Moreover, incorporating additional detailed information on the ecological traits of the test species will allow us to further optimize and improve the predictive performance of the model. This study provides a new approach for ecotoxicity predictions for organisms in the aquatic environment and will help us to further explore exposure pathways and the risk assessment of MNMs.
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Affiliation(s)
- Yunchi Zhou
- School of Space and Environment, Beihang University, Beijing100191, China
| | - Ying Wang
- School of Space and Environment, Beihang University, Beijing100191, China
| | - Willie Peijnenburg
- Institute of Environmental Science (CML), Leiden University, Leiden2300, RA, The Netherlands
- Center for Safety of Substances and Products, National Institute of Public Health and the Environment (RIVM), Bilthoven3720, BA, The Netherlands
| | - Martina G Vijver
- Institute of Environmental Science (CML), Leiden University, Leiden2300, RA, The Netherlands
| | - Surendra Balraadjsing
- Institute of Environmental Science (CML), Leiden University, Leiden2300, RA, The Netherlands
| | - Wenhong Fan
- School of Space and Environment, Beihang University, Beijing100191, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing100191, China
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8
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Valeriano A, Bondaug F, Ebardo I, Almonte P, Sabugaa MA, Bagnol JR, Latayada MJ, Macalalag JM, Paradero BD, Mayes M, Balanay M, Alguno A, Capangpangan R. Predicting cytotoxicity of engineered nanoparticles using regularized regression models: an in silico approach. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023; 34:591-604. [PMID: 37551411 DOI: 10.1080/1062936x.2023.2242785] [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: 05/17/2023] [Accepted: 07/23/2023] [Indexed: 08/09/2023]
Abstract
The widespread application of engineered nanoparticles (NPs) in various industries has demonstrated their effectiveness over the years. However, modifications to NPs' physicochemical properties can lead to toxicological effects. Therefore, understanding the toxicity behaviour of NPs is crucial. In this paper, regularized regression models, such as ridge, LASSO, and elastic net, were constructed to predict the cytotoxicity of various engineered NPs. The dataset utilized in this study was compiled from several journals published between 2010 and 2022. Data exploration revealed missing values, which were addressed through listwise deletion and kNN imputation, resulting in two complete datasets. The ridge, LASSO, and elastic net models achieved F1 scores ranging from 91.81% to 92.65% during internal validation and 92.89% to 93.63% during external validation on Dataset 1. On Dataset 2, the models attained F1 scores between 92.16% and 92.43% during internal validation and 92% and 92.6% during external validation. These results indicate that the developed models effectively generalize to unseen data and demonstrate high accuracy in classifying cytotoxicity levels. Furthermore, the cell type, material, cell source, cell tissue, synthesis method, and coat or functional group were identified as the most important descriptors by the three models across both datasets.
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Affiliation(s)
- A Valeriano
- Research on Environment and Nanotechnology Laboratories, Research Division, Mindanao State University at Naawan, Naawan, Philippines
| | - F Bondaug
- Research on Environment and Nanotechnology Laboratories, Research Division, Mindanao State University at Naawan, Naawan, Philippines
- Department of Science and Technology, Science Education Institute, Taguig City, Philippines
| | - I Ebardo
- Research on Environment and Nanotechnology Laboratories, Research Division, Mindanao State University at Naawan, Naawan, Philippines
- Department of Science and Technology, Science Education Institute, Taguig City, Philippines
| | - P Almonte
- Research on Environment and Nanotechnology Laboratories, Research Division, Mindanao State University at Naawan, Naawan, Philippines
| | - M A Sabugaa
- Research on Environment and Nanotechnology Laboratories, Research Division, Mindanao State University at Naawan, Naawan, Philippines
| | - J R Bagnol
- Department of Mathematics and Statistics, University of Southeastern Philippines, Davao City, Philippines
| | - M J Latayada
- Department of Mathematics, Caraga State University, Butuan City, Philippines
| | - J M Macalalag
- Department of Mathematics, Caraga State University, Butuan City, Philippines
| | - B D Paradero
- Information, Communication and Technology Center, Mindanao State University at Naawan, Naawan, Philippines
| | - M Mayes
- Department of Chemistry and Biochemistry, University of Massachusetts, Dartmouth, NH, USA
| | - M Balanay
- Department of Chemistry, Nazarbayev University, Nur-Sultan, Kazakhstan
| | - A Alguno
- Department of Physics, Mindanao State University-Iligan Institute of Technology, Iligan City, Philippines
| | - R Capangpangan
- Research on Environment and Nanotechnology Laboratories, Research Division, Mindanao State University at Naawan, Naawan, Philippines
- College of Marine and Allied Sciences, Mindanao State University at Naawan, Naawan, Philippines
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9
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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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Wang N, Zhang Y, Wang W, Ye Z, Chen H, Hu G, Ouyang D. How can machine learning and multiscale modeling benefit ocular drug development? Adv Drug Deliv Rev 2023; 196:114772. [PMID: 36906232 DOI: 10.1016/j.addr.2023.114772] [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: 12/16/2022] [Revised: 02/06/2023] [Accepted: 03/05/2023] [Indexed: 03/12/2023]
Abstract
The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.
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Affiliation(s)
- Nannan Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Yunsen Zhang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Wei Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Zhuyifan Ye
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Hongyu Chen
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China; Faculty of Science and Technology (FST), University of Macau, Macau, China
| | - Guanghui Hu
- Faculty of Science and Technology (FST), University of Macau, Macau, China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China; Department of Public Health and Medicinal Administration, Faculty of Health Sciences (FHS), University of Macau, Macau, China.
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11
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Li J, Yue L, Zhao Q, Cao X, Tang W, Chen F, Wang C, Wang Z. Prediction models on biomass and yield of rice affected by metal (oxide) nanoparticles using nano-specific descriptors. NANOIMPACT 2022; 28:100429. [PMID: 36130713 DOI: 10.1016/j.impact.2022.100429] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/12/2022] [Accepted: 09/14/2022] [Indexed: 06/15/2023]
Abstract
The use of in silico tools to investigate the interactions between metal (oxide) nanoparticles (NPs) and plant biological responses is preferred because it allows us to understand molecular mechanisms and improve prediction efficiency by saving time, labor, and cost. In this study, four models (C5.0 decision tree, discriminant function analysis, random forest, and stepwise multiple linear regression analysis) were applied to predict the effect of NPs on rice biomass and yield. Nano-specific descriptors (size-dependent molecular descriptors and image-based descriptors) were introduced to estimate the behavior of NPs in plants to appropriately represent the wide space of NPs. The results showed that size-dependent molecular descriptors (e.g., E-state and connectivity indices) and image-based descriptors (e.g., extension, area, and minimum ferret diameter) were associated with the behavior of NPs in rice. The performance of the constructed models was within acceptable ranges (correlation coefficient ranged from 0.752 to 0.847 for biomass and from 0.803 to 0.905 for yield, while the accuracy ranged from 64% to 77% for biomass and 81% to 89% for yield). The developed model can be used to quickly and efficiently evaluate the impact of NPs under a wide range of experimental conditions and sufficient training data.
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Affiliation(s)
- Jing Li
- Institute of Environmental Processotes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Le Yue
- Institute of Environmental Processotes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Qing Zhao
- Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China; National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangzhou 510650, China
| | - Xuesong Cao
- Institute of Environmental Processotes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Weihao Tang
- Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China; National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangzhou 510650, China
| | - Feiran Chen
- Institute of Environmental Processotes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Chuanxi Wang
- Institute of Environmental Processotes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China.
| | - Zhenyu Wang
- Institute of Environmental Processotes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
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12
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Regonia PR, Olorocisimo JP, De Los Reyes F, Ikeda K, Pelicano CM. Machine learning-enabled nanosafety assessment of multi-metallic alloy nanoparticles modified TiO 2 system. NANOIMPACT 2022; 28:100442. [PMID: 36436823 DOI: 10.1016/j.impact.2022.100442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/04/2022] [Accepted: 11/20/2022] [Indexed: 06/16/2023]
Abstract
Establishing toxicological predictive modeling frameworks for heterogeneous nanomaterials is crucial for rapid environmental and health risk assessment. However, existing structure-toxicity correlation models for such nanomaterials are only based on simple linear regression algorithms that are prone to underfitting the training data. These models rely heavily on experimental and expensive computational quantum mechanical descriptors, which significantly limit their practical use. Herein, we present the application of empirical descriptors and complex machine learning algorithms to the development of high-performance quantitative structure-toxicity relationship (QSTR) models of TiO2 hybridized with multi-metallic (Ag, Au, Pt) alloy nanoparticles (multi-metallic NPs/TiO2). To confirm the viability of empirical descriptors as model input, we selected five distinct machine learning algorithms for predicting the toxicity of multi-metallic alloy NPs/TiO2 system in Chinese hamster ovary cell line. Notably, an empirical descriptor-based QSTR model (kernel ridge regression) revealed a predictive performance that is on par with density functional theory (DFT) descriptor-based counterparts. More specifically, the results indicated that model selection is influenced by descriptor choice, such that complex DFT descriptors worked best with a complex algorithm (random forest regression; RMSET = 0.0954, MAET = 0.0811, RT2 = 0.9411), whereas more straightforward empirical descriptors were most suitable with a simpler algorithm (kernel ridge regression; RMSET = 0.1244, MAET = 0.1106, RT2 = 0.8999). Moreover, our model outperforms existing QSAR models built on the same data set. This study offers a new perspective on using empirical features to develop accurate predictive computational models for the rapid discovery and profiling of safe-by-design nanomaterials.
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Affiliation(s)
- Paul Rossener Regonia
- Division of Information Science, Nara Institute of Science and Technology, Japan; College of Engineering, University of the Philippines Diliman, Philippines.
| | - Joshua Philippe Olorocisimo
- Division of Biological Science, Nara Institute of Science and Technology, Japan; Division of Materials Science, Nara Institute of Science and Technology, Japan
| | | | - Kazushi Ikeda
- Division of Information Science, Nara Institute of Science and Technology, Japan
| | - Christian Mark Pelicano
- Institute for Chemical Research, Kyoto University, Japan; Department of Colloid Chemistry, Max Planck Institute of Colloids and Interfaces, Germany.
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13
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Bunmahotama W, Vijver MG, Peijnenburg W. Development of a Quasi-Quantitative Structure-Activity Relationship Model for Prediction of the Immobilization Response of Daphnia magna Exposed to Metal-Based Nanomaterials. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2022; 41:1439-1450. [PMID: 35234298 PMCID: PMC9325417 DOI: 10.1002/etc.5322] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 12/17/2021] [Accepted: 02/24/2022] [Indexed: 06/14/2023]
Abstract
The conventional Hill equation model is suitable to fit dose-response data obtained from performing (eco)toxicity assays. Models based on quasi-quantitative structure-activity relationships (QSARs) to estimate the Hill coefficient ( n H ) ${n}_{{\rm{H}}})$ were developed with the aim of predicting the response of the invertebrate species Daphnia magna to exposure to metal-based nanomaterials. Descriptors representing the pristine properties of nanoparticles and media conditions were coded to a quasi-simplified molecular input line entry system and correlated to experimentally derived values of n H ${n}_{{\rm{H}}}$ . Monte Carlo optimization was used to model the set of n H ${n}_{{\rm{H}}}$ values, and the model was trained on the basis of reported dose-response relationships of 60 data sets (n = 367 individual response observations) of 11 metal-based nanomaterials as obtained from 20 literature reports. The model simulates the training data well, with only 2.3% deviation between experimental and modeled response data. The technique was employed to predict the dose-response relationships of 15 additional data sets (n = 72 individual observations) not included in model development of seven metal-based nanomaterials from 10 literature reports, with an average error of 3.5%. Combining the model output with either the median effective concentration value or any other known effect level as obtained from experimental data allows the prediction of full dose-response curves of D. magna immobilization. This model is an accurate screening tool that allows the determination of the shape and slope of dose-response curves, thereby greatly reducing experimental effort in case of novel advanced metal-based nanomaterials or the prediction of responses in altered exposure media. This screening model is compliant with the 3Rs (replacement, reduction, and refinement) principle, which is embraced by the scientific and regulatory communities dealing with nano-safety. Environ Toxicol Chem 2022;41:1439-1450. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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Affiliation(s)
- Warisa Bunmahotama
- Institute of Environmental SciencesLeiden UniversityLeidenThe Netherlands
| | - Martina G. Vijver
- Institute of Environmental SciencesLeiden UniversityLeidenThe Netherlands
| | - Willie Peijnenburg
- Institute of Environmental SciencesLeiden UniversityLeidenThe Netherlands
- Center for Safety of Substances and ProductsNational Institute of Public Health and the EnvironmentBilthovenThe Netherlands
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14
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Toropova AP, Toropov AA. Nanomaterials: Quasi-SMILES as a flexible basis for regulation and environmental risk assessment. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 823:153747. [PMID: 35149067 DOI: 10.1016/j.scitotenv.2022.153747] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 02/04/2022] [Accepted: 02/04/2022] [Indexed: 06/14/2023]
Abstract
Basic principles and problems of the systematization of data on nanomaterials are discussed. The eclectic character of nanomaterials is defined as the key difference between nanomaterials and traditional substances. The quasi-SMILES technique is described and discussed. The possible role of the approach is bridging between experimentalists and developers of models for endpoints related to nanomaterials. The use of models on the possible impact of nanomaterials on the environment and human health has been collected and compared. The new criteria of the predictive potential for the above models are discussed. The advantage of the statistical criteria sensitive simultaneously to both the correlation coefficient and the root mean square error noted. The rejection of the border between the effect of the biochemical reality of substances at a molecular level and the effect of experiment conditions at the macro level gives the possibility to develop models that are epistemologically more reliable in the comparison with traditional models based exclusively on the molecular structure-biological activity interdependence (without taking into account experimental conditions). Models of the physicochemical and biochemical behaviour of nanomaterials are necessary in order to develop and apply new industrial achievements, everyday comfort species, medicine, cosmetics, and foods without negative effects on ecology and human health. The CORAL (abbreviation CORrelation And Logic) software provides the user with the possibility to build up nano-QSAR models as a mathematical function of so-called correlation weights of fragments of quasi-SMILES. These models are built up via the Monte Carlo method. Apparently, the quasi-SMILES is a universal representation of nano-reality since there is no limitation to choose the list of eclectic data able to have an impact on nano-phenomena. This paradigm is a convenient language to the conversation of experimentalists and developers of models for nano-phenomena.
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Affiliation(s)
- Alla P Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
| | - Andrey A Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
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15
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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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16
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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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17
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Diéguez-Santana K, González-Díaz H. Towards machine learning discovery of dual antibacterial drug-nanoparticle systems. NANOSCALE 2021; 13:17854-17870. [PMID: 34671801 DOI: 10.1039/d1nr04178a] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Artificial Intelligence/Machine Learning (AI/ML) algorithms may speed up the design of DADNP systems formed by Antibacterial Drugs (AD) and Nanoparticles (NP). In this work, we used IFPTML = Information Fusion (IF) + Perturbation-Theory (PT) + Machine Learning (ML) algorithm for the first time to study of a large dataset of putative DADNP systems composed by >165 000 ChEMBL AD assays and 300 NP assays vs. multiple bacteria species. We trained alternative models with Linear Discriminant Analysis (LDA), Artificial Neural Networks (ANN), Bayesian Networks (BNN), K-Nearest Neighbour (KNN) and other algorithms. IFPTML-LDA model was simpler with values of Sp ≈ 90% and Sn ≈ 74% in both training (>124 K cases) and validation (>41 K cases) series. IFPTML-ANN and KNN models are notably more complicated even when they are more balanced Sn ≈ Sp ≈ 88.5%-99.0% and AUROC ≈ 0.94-0.99 in both series. We also carried out a simulation (>1900 calculations) of the expected behavior for putative DADNPs in 72 different biological assays. The putative DADNPs studied are formed by 27 different drugs with multiple classes of NP and types of coats. In addition, we tested the validity of our additive model with 80 DADNP complexes experimentally synthetized and biologically tested (reported in >45 papers). All these DADNPs show values of MIC < 50 μg mL-1 (cutoff used) better that MIC of AD and NP alone (synergistic or additive effect). The assays involve DADNP complexes with 10 types of NP, 6 coating materials, NP size range 5-100 nm vs. 15 different antibiotics, and 12 bacteria species. The IFPTML-LDA model classified correctly 100% (80 out of 80) DADNP complexes as biologically active. IFPMTL additive strategy may become a useful tool to assist the design of DADNP systems for antibacterial therapy taking into consideration only information about AD and NP components by separate.
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Affiliation(s)
- Karel Diéguez-Santana
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- Basque Center for Biophysics CSIC-UPVEH, University of Basque Country UPV/EHU, 48940 Leioa, Spain.
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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18
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Jung U, Lee B, Kim G, Shin HK, Kim KT. Nano-QTTR development for interspecies aquatic toxicity of silver nanoparticles between daphnia and fish. CHEMOSPHERE 2021; 283:131164. [PMID: 34144291 DOI: 10.1016/j.chemosphere.2021.131164] [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: 04/22/2021] [Revised: 06/07/2021] [Accepted: 06/08/2021] [Indexed: 06/12/2023]
Abstract
Limited studies of quantitative toxicity-toxicity relationship (QTTR) modeling have been conducted to predict interspecies toxicity of engineered nanomaterials (ENMs) between aquatic test species. A meta-analysis of 66 publications providing acute toxicity data of silver nanoparticles (AgNPs) to daphnia and fish was performed, and the toxicity data, physicochemical properties, and experimental conditions were collected and curated. Based on Euclidean distance (ED) grouping, a meaningful correlation of logarithmic lethal concentrations between daphnia and fish was derived for bare (R2bare = 0.47) and coated AgNPs (R2coated = 0.48) when a distance of 10 was applied. The correlation of coated AgNPs was improved (R2coated = 0.55) by the inclusion of descriptors of the coating materials. The correlations were further improved by R2bare = 0.57 and R2coated = 0.81 after additionally considering particle size only, and by R2bare = 0.59 and R2coated = 0.92 after considering particle size and zeta potential simultaneously. The developed ED-based nano-QTTR model demonstrated that inclusion of the coating material descriptors and physicochemical properties improved the goodness-of-fit to predict interspecies aquatic toxicity of AgNPs between daphnia and fish. This study provides insight for future in silico research on QTTR model development in ENM toxicology.
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Affiliation(s)
- Ukhyun Jung
- Department of Environmental Engineering, Seoul National University of Sciences and Technology, Seoul, 01811, Republic of Korea
| | - Byongcheun Lee
- Risk Assessment Division, National Institute of Environmental Research, Incheon, 22689, Republic of Korea
| | - Geunbae Kim
- Risk Assessment Division, National Institute of Environmental Research, Incheon, 22689, Republic of Korea
| | - Hyun Kil Shin
- Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, 34114, Republic of Korea
| | - Ki-Tae Kim
- Department of Environmental Engineering, Seoul National University of Sciences and Technology, Seoul, 01811, Republic of Korea.
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19
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Kerner J, Dogan A, von Recum H. Machine learning and big data provide crucial insight for future biomaterials discovery and research. Acta Biomater 2021; 130:54-65. [PMID: 34087445 DOI: 10.1016/j.actbio.2021.05.053] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 05/24/2021] [Accepted: 05/25/2021] [Indexed: 02/06/2023]
Abstract
Machine learning have been widely adopted in a variety of fields including engineering, science, and medicine revolutionizing how data is collected, used, and stored. Their implementation has led to a drastic increase in the number of computational models for the prediction of various numerical, categorical, or association events given input variables. We aim to examine recent advances in the use of machine learning when applied to the biomaterial field. Specifically, quantitative structure properties relationships offer the unique ability to correlate microscale molecular descriptors to larger macroscale material properties. These new models can be broken down further into four categories: regression, classification, association, and clustering. We examine recent approaches and new uses of machine learning in the three major categories of biomaterials: metals, polymers, and ceramics for rapid property prediction and trend identification. While current research is promising, limitations in the form of lack of standardized reporting and available databases complicates the implementation of described models. Herein, we hope to provide a snapshot of the current state of the field and a beginner's guide to navigating the intersection of biomaterials research and machine learning. STATEMENT OF SIGNIFICANCE: Machine learning and its methods have found a variety of uses beyond the field of computer science but have largely been neglected by those in realm of biomaterials. Through the use of more computational methods, biomaterials development can be expediated while reducing the need for standard trial and error methods. Within, we introduce four basic models that readers can potentially apply to their current research as well as current applications within the field. Furthermore, we hope that this article may act as a "call to action" for readers to realize and address the current lack of implementation within the biomaterials field.
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Affiliation(s)
- Jacob Kerner
- Case Western Reserve University; 10900 Euclid Ave., Cleveland Ohio 44106.
| | - Alan Dogan
- Case Western Reserve University; 10900 Euclid Ave., Cleveland Ohio 44106.
| | - Horst von Recum
- Case Western Reserve University; 10900 Euclid Ave., Cleveland Ohio 44106.
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20
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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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21
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Xin X, Huang G, Zhang B. Review of aquatic toxicity of pharmaceuticals and personal care products to algae. JOURNAL OF HAZARDOUS MATERIALS 2021; 410:124619. [PMID: 33248823 DOI: 10.1016/j.jhazmat.2020.124619] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 11/03/2020] [Accepted: 11/05/2020] [Indexed: 06/12/2023]
Abstract
Pharmaceuticals and Personal Care Products (PPCPs) have been frequently detected in the environment around the world. Algae play a significant role in aquatic ecosystem, thus the influence on algae may affect the life of higher trophic organisms. This review provides a state-of-the-art overview of current research on the toxicity of PPCPs to algae. Nanoparticles, contained in personal care products, also have been considered as the ingredients of PPCPs. PPCPs could cause unexpected effects on algae and their communities. Chlorophyta and diatoms are more accessible and sensitive to PPCPs. Multiple algal endpoints should be considered to provide a complete evaluation on PPCPs toxicity. The toxicity of organic ingredients in PPCPs could be predicted through quantitative structure-activity relationship model, whereas the toxicity of nanoparticles could be predicted with limitations. Light irradiation can change the toxicity through affecting algae and PPCPs. pH and natural organic matter can affect the toxicity through changing the existence of PPCPs. For joint and tertiary toxicity, experiments could be conducted to reveal the toxic mechanism. For multiple compound mixture toxicity, concentration addition and independent addition models are preferred. However, there has no empirical models to study nanoparticle-contained mixture toxicity. Algae-based remediation is an emerging technology to prevent the release of PPCPs from water treatment plants. Although many individual algal species are identified for removing a few compounds from PPCPs, algal-bacterial photobioreactor is a preferable alternative, with higher chances for industrial applications.
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Affiliation(s)
- Xiaying Xin
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Civil Engineering, Memorial University, NL A1B 3X5, St. John's Canada; Institute for Energy, Environment and Sustainable Communities, University of Regina, SK S4S 0A2 Regina, Canada
| | - Gordon Huang
- Institute for Energy, Environment and Sustainable Communities, University of Regina, SK S4S 0A2 Regina, Canada.
| | - Baiyu Zhang
- Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Civil Engineering, Memorial University, NL A1B 3X5, St. John's Canada.
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22
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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: 5] [Impact Index Per Article: 1.7] [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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23
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Shi H, Pan Y, Yang F, Cao J, Tan X, Yuan B, Jiang J. Nano-SAR Modeling for Predicting the Cytotoxicity of Metal Oxide Nanoparticles to PaCa2. Molecules 2021; 26:molecules26082188. [PMID: 33920258 PMCID: PMC8069170 DOI: 10.3390/molecules26082188] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/03/2021] [Accepted: 04/06/2021] [Indexed: 11/16/2022] Open
Abstract
Nowadays, the impact of engineered nanoparticles (NPs) on human health and environment has aroused widespread attention. It is essential to assess and predict the biological activity, toxicity, and physicochemical properties of NPs. Computation-based methods have been developed to be efficient alternatives for understanding the negative effects of nanoparticles on the environment and human health. Here, a classification-based structure-activity relationship model for nanoparticles (nano-SAR) was developed to predict the cellular uptake of 109 functionalized magneto-fluorescent nanoparticles to pancreatic cancer cells (PaCa2). The norm index descriptors were employed for describing the structure characteristics of the involved nanoparticles. The Random forest algorithm (RF), combining with the Recursive Feature Elimination (RFE) was employed to develop the nano-SAR model. The resulted model showed satisfactory statistical performance, with the accuracy (ACC) of the test set and the training set of 0.950 and 0.966, respectively, demonstrating that the model had satisfactory classification effect. The model was rigorously verified and further extensively compared with models in the literature. The proposed model could be reasonably expected to predict the cellular uptakes of nanoparticles and provide some guidance for the design and manufacture of safer nanomaterials.
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Affiliation(s)
- Haihua Shi
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China; (H.S.); (F.Y.); (J.C.); (X.T.); (B.Y.); (J.J.)
| | - Yong Pan
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China; (H.S.); (F.Y.); (J.C.); (X.T.); (B.Y.); (J.J.)
- Correspondence: ; Tel.: +86-25-581-398-73
| | - Fan Yang
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China; (H.S.); (F.Y.); (J.C.); (X.T.); (B.Y.); (J.J.)
| | - Jiakai Cao
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China; (H.S.); (F.Y.); (J.C.); (X.T.); (B.Y.); (J.J.)
| | - Xinlong Tan
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China; (H.S.); (F.Y.); (J.C.); (X.T.); (B.Y.); (J.J.)
| | - Beilei Yuan
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China; (H.S.); (F.Y.); (J.C.); (X.T.); (B.Y.); (J.J.)
| | - Juncheng Jiang
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China; (H.S.); (F.Y.); (J.C.); (X.T.); (B.Y.); (J.J.)
- School of Environment & Safety Engineering, Changzhou University, Changzhou 213164, China
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Kar S, Pathakoti K, Tchounwou PB, Leszczynska D, Leszczynski J. Evaluating the cytotoxicity of a large pool of metal oxide nanoparticles to Escherichia coli: Mechanistic understanding through In Vitro and In Silico studies. CHEMOSPHERE 2021; 264:128428. [PMID: 33022504 PMCID: PMC7919734 DOI: 10.1016/j.chemosphere.2020.128428] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/23/2020] [Accepted: 09/21/2020] [Indexed: 05/25/2023]
Abstract
The toxic effect of eight metal oxide nanoparticles (MONPs) on Escherichia coli was experimentally evaluated following standard bioassay protocols. The obtained cytotoxicity ranking of these studied MONPs is Er2O3, Gd2O3, CeO2, Co2O3, Mn2O3, Co3O4, Fe3O4/WO3 (in descending order). The computed EC50 values from experimental data suggested that Er2O3 and Gd2O3 were the most acutely toxic MONPs to E. coli. To identify the mechanism of toxicity of these 8 MONPs along with 17 other MONPs from our previous study, we employed seven classifications and machine learning (ML) algorithms including linear discriminant analysis (LDA), naïve bayes (NB), multinomial logistic regression (MLogitR), sequential minimal optimization (SMO), AdaBoost, J48, and random forest (RF). We also employed 1st and 2nd generation periodic table descriptors developed by us (without any sophisticated computing facilities) along with experimentally analyzed Zeta-potential, to model the cytotoxicity of these MONPs. Based on qualitative validation metrics, the LDA model appeared to be the best among the 7 tested models. The core environment of metal defined by the ratio of the number of core electrons to the number of valence electrons and the electronegativity count of oxygen showed a positive impact on toxicity. The identified properties were important for understanding the mechanisms of nanotoxicity and for predicting the potential environmental risk associated with MONPs exposure. The developed models can be utilized for environmental risk assessment of any untested MONP to E. coli, thereby providing a scientific basis for the design and preparation of safe nanomaterials.
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Affiliation(s)
- Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA
| | - Kavitha Pathakoti
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA; RCMI Center for Environmental Health, Department of Biology, Jackson State University, Jackson, MS, 39217, USA
| | - Paul B Tchounwou
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA; RCMI Center for Environmental Health, Department of Biology, Jackson State University, Jackson, MS, 39217, USA
| | - Danuta Leszczynska
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA; Department of Civil and Environmental Engineering, Jackson State University, Jackson, MS, 39217, USA
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA.
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Abdelsattar AS, Dawoud A, Helal MA. Interaction of nanoparticles with biological macromolecules: a review of molecular docking studies. Nanotoxicology 2020; 15:66-95. [PMID: 33283572 DOI: 10.1080/17435390.2020.1842537] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The high frequency of using engineered nanoparticles in various medical applications entails a deep understanding of their interaction with biological macromolecules. Molecular docking simulation is now widely used to study the binding of different types of nanoparticles with proteins and nucleic acids. This helps not only in understanding the mechanism of their biological action but also in predicting any potential toxicity. In this review, the computational techniques used in studying the nanoparticles interaction with biological macromolecules are covered. Then, a comprehensive overview of the docking studies performed on various types of nanoparticles will be offered. The implication of these predicted interactions in the biological activity and/or toxicity is also discussed for each type of nanoparticles.
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Affiliation(s)
- Abdallah S Abdelsattar
- Center for X-Ray and Determination of Structure of Matter, Zewail City of Science and Technology, Giza, Egypt
| | - Alyaa Dawoud
- Faculty of Pharmacy and Biotechnology, German University in Cairo, Cairo, Egypt
| | - Mohamed A Helal
- Biomedical Sciences Program, University of Science and Technology, Zewail City of Science and Technology, Giza, Egypt.,Medicinal Chemistry Department, Faculty of Pharmacy, Suez Canal University, Ismailia, Egypt
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Peng T, Wei C, Yu F, Xu J, Zhou Q, Shi T, Hu X. Predicting nanotoxicity by an integrated machine learning and metabolomics approach. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 267:115434. [PMID: 32841907 DOI: 10.1016/j.envpol.2020.115434] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 08/11/2020] [Accepted: 08/12/2020] [Indexed: 06/11/2023]
Abstract
Predicting the biological responses to engineered nanoparticles (ENPs) is critical to their environmental health assessment. The disturbances of metabolic pathways reflect the global profile of biological responses to ENPs but are difficult to predict due to the highly heterogeneous data from complicated biological systems and various ENP properties. Herein, integrating multiple machine learning models and metabolomics enabled accurate prediction of the disturbance of metabolic pathways induced by 33 ENPs. Screening nine typical properties of ENPs identified type and size as the top features determining the effects on metabolic pathways. Similarity network analysis and decision tree models overcame the highly heterogeneous data sources to visualize and judge the occurrence of metabolic pathways depending on the sorting priority features. The model accuracy was verified by animal experiments and reached 75%-100%, even for the prediction of ENPs outside of databases. The models also predicted metabolic pathway-related histopathology. This work provides an approach for the quick assessment of environmental health risks induced by known and unknown ENPs.
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Affiliation(s)
- Ting Peng
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Changhong Wei
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Fubo Yu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Jing Xu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Qixing Zhou
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Tonglei Shi
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Xiangang Hu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China.
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Ahmadi S, Toropova AP, Toropov AA. Correlation intensity index: mathematical modeling of cytotoxicity of metal oxide nanoparticles. Nanotoxicology 2020; 14:1118-1126. [DOI: 10.1080/17435390.2020.1808252] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Shahin Ahmadi
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Alla P. Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Andrey A. Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
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Qi R, Pan Y, Cao J, Jia Z, Jiang J. The cytotoxicity of nanomaterials: Modeling multiple human cells uptake of functionalized magneto-fluorescent nanoparticles via nano-QSAR. CHEMOSPHERE 2020; 249:126175. [PMID: 32078856 DOI: 10.1016/j.chemosphere.2020.126175] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 02/04/2020] [Accepted: 02/09/2020] [Indexed: 06/10/2023]
Abstract
The vast majority of nanomaterials have attracted an upsurge of interest since their discovery and considerable researches are being carried out about their adverse outcomes for human health and the environment. In this study, two regression-based quantitative structure-activity relationship models for nanoparticles (nano-QSAR) were established to predict the cellular uptakes of 109 functionalized magneto-fluorescent nanoparticles to pancreatic cancer cells (PaCa2) and human umbilical vein endothelial cells (HUVEC) lines, respectively. The improved SMILES-based optimal descriptors encoded with certain easily available physicochemical properties were proposed to describe the molecular structure characteristics of the involved nanoparticles, and the Monte Carlo method was used for calculating the improved SMILES-based optimal descriptors. Both developed nano-QSAR models for cellular uptake prediction provided satisfactory statistical results, with the squared correlation coefficient (R2) being 0.852 and 0.905 for training sets, and 0.822 and 0.885 for test sets, respectively. Both models were rigorously validated and further extensively compared to literature models. Predominant physicochemical features responsible for cellular uptake were identified by model interpretation. The proposed models could be reasonably expected to provide guidance for synthesizing or choosing safer, more suitable surface modifiers of desired properties prior to their biomedical applications.
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Affiliation(s)
- Ronghua Qi
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 210009, China
| | - Yong Pan
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 210009, China.
| | - Jiakai Cao
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 210009, China
| | - Zhenhua Jia
- Institute of Advanced Synthesis, School of Chemistry and Molecular Engineering, Nanjing Tech University, Nanjing, 210009, China
| | - Juncheng Jiang
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 210009, China
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Drug design by machine-trained elastic networks: predicting Ser/Thr-protein kinase inhibitors' activities. Mol Divers 2020; 25:899-909. [PMID: 32222890 DOI: 10.1007/s11030-020-10074-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 03/11/2020] [Indexed: 12/23/2022]
Abstract
An elastic network model (ENM) represents a molecule as a matrix of pairwise atomic interactions. Rich in coded information, ENMs are hereby proposed as a novel tool for the prediction of the activity of series of molecules, with widely different chemical structures, but a common biological activity. The new approach is developed and tested using a set of 183 inhibitors of serine/threonine-protein kinase enzyme (Plk3) which is an enzyme implicated in the regulation of cell cycle and tumorigenesis. The elastic network (EN) predictive model is found to exhibit high accuracy and speed compared to descriptor-based machine-trained modeling. EN modeling appears to be a highly promising new tool for the high demands of industrial applications such as drug and material design.
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30
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Ahmadi S. Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria. CHEMOSPHERE 2020; 242:125192. [PMID: 31677509 DOI: 10.1016/j.chemosphere.2019.125192] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 10/17/2019] [Accepted: 10/21/2019] [Indexed: 06/10/2023]
Abstract
Several types of metal oxide nanoparticles (MO-NPs) are often utilized as one of the novel class of materials in the pharmaceutical industry and human health. The wide use of MO-NPs forces an enhanced understanding of their potential impact on human health and the environment. The research aims to investigate and develop a nano-QFAR (nano-quantitative feature activity relationship) model applying the quasi-SMILES such as cell line, assay, time exposition, concentration, nanoparticles size and metal oxide type for prediction of cell viability (%) of MO-NPs. The total set of 83 quasi-SMILES of MO-NPs divided into training, validation and test sets randomly three times. The statistical model results based on the balance of correlation target function (TF1) and index of ideality correlation target function (TF2) and the Monte Carlo optimization were compared. The comparison of two target function results indicated that TF2 improves the predictability of models. The significance of various eclectic features of both increase and decrease of cell viability (%) is provided. Mechanistic interpretation of significant factors for the model are proposed as well. The sufficient statistical quality of three nano-QFAR models based on TF2 reveals that the developed models can be efficiency for predictions of the cell viability (%) of MO-NPs.
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Affiliation(s)
- Shahin Ahmadi
- Department of Chemistry, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
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31
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Examining the in vivo pulmonary toxicity of engineered metal oxide nanomaterials using a genetic algorithm-based dose-response-recovery clustering model. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.comtox.2019.100113] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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32
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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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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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34
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Duan Y, Coreas R, Liu Y, Bitounis D, Zhang Z, Parviz D, Strano M, Demokritou P, Zhong W. Prediction of protein corona on nanomaterials by machine learning using novel descriptors. NANOIMPACT 2020; 17:10.1016/j.impact.2020.100207. [PMID: 32104746 PMCID: PMC7043407 DOI: 10.1016/j.impact.2020.100207] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Effective in silico methods to predict protein corona compositions on engineered nanomaterials (ENMs) could help elucidate the biological outcomes of ENMs in biosystems without the need for conducting lengthy experiments for corona characterization. However, the physicochemical properties of ENMs, used as the descriptors in current modeling methods, are insufficient to represent the complex interactions between ENMs and proteins. Herein, we utilized the fluorescence change (FC) from fluorescamine labeling on a protein, with or without the presence of the ENM, as a novel descriptor of the ENM to build machine learning models for corona formation. FCs were significantly correlated with the abundance of the corresponding proteins in the corona on diverse classes of ENMs, including metal and metal oxides, nanocellulose, and 2D ENMs. Prediction models established by the random forest algorithm using FCs as the ENM descriptors showed better performance than the conventional descriptors, such as ENM size and surface charge, in the prediction of corona formation. Moreover, they were able to predict protein corona formation on ENMs with very heterogeneous properties. We believe this novel descriptor can improve in silico studies of corona formation, leading to a better understanding on the protein adsorption behaviors of diverse ENMs in different biological matrices. Such information is essential for gaining a comprehensive view of how ENMs interact with biological systems in ENM safety and sustainability assessments.
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Affiliation(s)
- Yaokai Duan
- Department of Chemistry, University of California, Riverside, CA 92507, United States
| | - Roxana Coreas
- Department of Environmental Toxicology Graduate Program, University of California, Riverside, CA 92507, United States
| | - Yang Liu
- Department of Environmental Toxicology Graduate Program, University of California, Riverside, CA 92507, United States
| | - Dimitrios Bitounis
- Center for Nanotechnology and Nanotoxicology, HSPH-NIEHS Nanosafety Center, Department of Environmental Health, Harvard T. H. Chan School of Public Health, Harvard University, 665 Huntington, Boston, MA 02115, USA
| | - Zhenyuan Zhang
- Center for Nanotechnology and Nanotoxicology, HSPH-NIEHS Nanosafety Center, Department of Environmental Health, Harvard T. H. Chan School of Public Health, Harvard University, 665 Huntington, Boston, MA 02115, USA
| | - Dorsa Parviz
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue 66-570b, Cambridge, MA 02139, USA
| | - Michael Strano
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue 66-570b, Cambridge, MA 02139, USA
| | - Philip Demokritou
- Center for Nanotechnology and Nanotoxicology, HSPH-NIEHS Nanosafety Center, Department of Environmental Health, Harvard T. H. Chan School of Public Health, Harvard University, 665 Huntington, Boston, MA 02115, USA
| | - Wenwan Zhong
- Department of Chemistry, University of California, Riverside, CA 92507, United States
- Department of Environmental Toxicology Graduate Program, University of California, Riverside, CA 92507, United States
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Buglak AA, Zherdev AV, Dzantiev BB. Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials. Molecules 2019; 24:molecules24244537. [PMID: 31835808 PMCID: PMC6943593 DOI: 10.3390/molecules24244537] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 11/24/2019] [Accepted: 12/10/2019] [Indexed: 12/12/2022] Open
Abstract
Although nanotechnology is a new and rapidly growing area of science, the impact of nanomaterials on living organisms is unknown in many aspects. In this regard, it is extremely important to perform toxicological tests, but complete characterization of all varying preparations is extremely laborious. The computational technique called quantitative structure–activity relationship, or QSAR, allows reducing the cost of time- and resource-consuming nanotoxicity tests. In this review, (Q)SAR cytotoxicity studies of the past decade are systematically considered. We regard here five classes of engineered nanomaterials (ENMs): Metal oxides, metal-containing nanoparticles, multi-walled carbon nanotubes, fullerenes, and silica nanoparticles. Some studies reveal that QSAR models are better than classification SAR models, while other reports conclude that SAR is more precise than QSAR. The quasi-QSAR method appears to be the most promising tool, as it allows accurately taking experimental conditions into account. However, experimental artifacts are a major concern in this case.
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Affiliation(s)
- Andrey A. Buglak
- A. N. Bach Institute of Biochemistry, Research Center of Biotechnology, Russian Academy of Sciences, Leninsky Prospect 33, 119071 Moscow, Russia; (A.V.Z.); (B.B.D.)
- Physical Faculty, St. Petersburg State University, 7/9 Universitetskaya Naberezhnaya, 199034 St. Petersburg, Russia
- Correspondence: ; Tel.: +7-(495)-954-27-32
| | - Anatoly V. Zherdev
- A. N. Bach Institute of Biochemistry, Research Center of Biotechnology, Russian Academy of Sciences, Leninsky Prospect 33, 119071 Moscow, Russia; (A.V.Z.); (B.B.D.)
- Institute of Physiologically Active Compounds, Russian Academy of Sciences, Severny Proezd 1, 142432 Chernogolovka, Moscow Region, Russia
| | - Boris B. Dzantiev
- A. N. Bach Institute of Biochemistry, Research Center of Biotechnology, Russian Academy of Sciences, Leninsky Prospect 33, 119071 Moscow, Russia; (A.V.Z.); (B.B.D.)
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36
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Yan L, Zhao F, Wang J, Zu Y, Gu Z, Zhao Y. A Safe-by-Design Strategy towards Safer Nanomaterials in Nanomedicines. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2019; 31:e1805391. [PMID: 30701603 DOI: 10.1002/adma.201805391] [Citation(s) in RCA: 82] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 09/13/2018] [Indexed: 05/25/2023]
Abstract
The marriage of nanotechnology and medicine offers new opportunities to fight against human diseases. Benefiting from their unique optical, thermal, magnetic, or redox properties, a wide range of nanomaterials have shown potential in applications such as diagnosis, drug delivery, or tissue repair and regeneration. Despite the considerable success achieved over the past decades, the newly emerging nanomedicines still suffer from an incomplete understanding of their safety risks, and of the relationships between their physicochemical characteristics and safety profiles. Herein, the most important categories of nanomaterials with clinical potential and their toxicological mechanisms are summarized, and then, based on this available information, an overview of the principles in developing safe-by-design nanomaterials for medical applications and of the recent progress in this field is provided. These principles may serve as a starting point to guide the development of more effective safe-by-design strategies and to help identify the major knowledge and skill gaps.
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Affiliation(s)
- Liang Yan
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Nanoscience National Center for Nanoscience and Technology of China, Beijing, 100190, China
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Feng Zhao
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Nanoscience National Center for Nanoscience and Technology of China, Beijing, 100190, China
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Jing Wang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Nanoscience National Center for Nanoscience and Technology of China, Beijing, 100190, China
| | - Yan Zu
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Nanoscience National Center for Nanoscience and Technology of China, Beijing, 100190, China
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhanjun Gu
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Nanoscience National Center for Nanoscience and Technology of China, Beijing, 100190, China
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
- College of Materials Science and Optoelectronic Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuliang Zhao
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Nanoscience National Center for Nanoscience and Technology of China, Beijing, 100190, China
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
- College of Materials Science and Optoelectronic Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology of China, Beijing, 100190, China
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Forest V, Hochepied JF, Pourchez J. Importance of Choosing Relevant Biological End Points To Predict Nanoparticle Toxicity with Computational Approaches for Human Health Risk Assessment. Chem Res Toxicol 2019; 32:1320-1326. [PMID: 31243983 DOI: 10.1021/acs.chemrestox.9b00022] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Because it is impossible to assess in vitro or in vivo the toxicity of all nanoparticles available on the market on a case-by-case basis, computational approaches have been proposed as useful alternatives to predict in silico the hazard potential of engineered nanoparticles. Despite promising results, a major issue associated with these mathematical models lies in the a priori choice of the physicochemical descriptors and the biological end points. We performed a thorough bibliographic survey on the biological end points used for nanotoxicology purposes and compared them between experimental and computational approaches. They were found to be disparate: while conventional in vitro nanotoxicology assays usually investigate a large array of biological effects using eukaryotic cells (cytotoxicity, pro-inflammatory response, oxidative stress, genotoxicity), computational studies mostly focus on cell viability and also include studies on prokaryotic cells. We may thus wonder the relevance of building complex mathematical models able to predict accurately a biological end point if this latter is not the most relevant to support human health risk assessment. The choice of biological end points clearly deserves to be more carefully discussed. This could bridge the gap between experimental and computational nanotoxicology studies and allow in silico predictive models to reach their full potential.
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Affiliation(s)
- Valérie Forest
- Mines Saint-Etienne, Univ Lyon, Univ Jean Monnet , INSERM, U 1059 Sainbiose, Centre CIS , F-42023 Saint-Etienne , France
| | - Jean-François Hochepied
- MINES ParisTech , PSL Research University , MAT - Centre des matériaux, CNRS UMR 7633 , BP 87 91003 Evry , France.,UCP, ENSTA ParisTech , Université Paris-Saclay , 828 bd des Maréchaux , 91762 Palaiseau cedex , France
| | - Jérémie Pourchez
- Mines Saint-Etienne, Univ Lyon, Univ Jean Monnet , INSERM, U 1059 Sainbiose, Centre CIS , F-42023 Saint-Etienne , France
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38
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Lamon L, Asturiol D, Vilchez A, Ruperez-Illescas R, Cabellos J, Richarz A, Worth A. Computational models for the assessment of manufactured nanomaterials: Development of model reporting standards and mapping of the model landscape. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2019; 9:143-151. [PMID: 31008416 PMCID: PMC6472618 DOI: 10.1016/j.comtox.2018.12.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 12/05/2018] [Accepted: 12/11/2018] [Indexed: 01/31/2023]
Abstract
Different types of computational models have been developed for predicting the biokinetics, environmental fate, exposure levels and toxicological effects of chemicals and manufactured nanomaterials (MNs). However, these models are not described in a consistent manner in the scientific literature, which is one of the barriers to their broader use and acceptance, especially for regulatory purposes. Quantitative structure-activity relationships (QSARs) are in silico models based on the assumption that the activity of a substance is related to its chemical structure. These models can be used to provide information on (eco)toxicological effects in hazard assessment. In an environmental risk assessment, environmental exposure models can be used to estimate the predicted environmental concentration (PEC). In addition, physiologically based kinetic (PBK) models can be used in various ways to support a human health risk assessment. In this paper, we first propose model reporting templates for systematically and transparently describing models that could potentially be used to support regulatory risk assessments of MNs, for example under the REACH regulation. The model reporting templates include (a) the adaptation of the QSAR Model Reporting Format (QMRF) to report models for MNs, and (b) the development of a model reporting template for PBK and environmental exposure models applicable to MNs. Second, we show the usefulness of these templates to report different models, resulting in an overview of the landscape of available computational models for MNs.
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Affiliation(s)
- L. Lamon
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - D. Asturiol
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - A. Vilchez
- Leitat Technological Center, c/de la Innovació 2, Terrassa, Barcelona, Spain
| | - R. Ruperez-Illescas
- Leitat Technological Center, c/de la Innovació 2, Terrassa, Barcelona, Spain
| | - J. Cabellos
- Leitat Technological Center, c/de la Innovació 2, Terrassa, Barcelona, Spain
| | - A. Richarz
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - A. Worth
- European Commission, Joint Research Centre (JRC), Ispra, Italy
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39
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Wang J, Wang Y, Huang Y, Peijnenburg WJG, Chen J, Li X. Development of a nano-QSPR model to predict band gaps of spherical metal oxide nanoparticles. RSC Adv 2019; 9:8426-8434. [PMID: 35518709 PMCID: PMC9061875 DOI: 10.1039/c8ra10226k] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 03/06/2019] [Indexed: 01/11/2023] Open
Abstract
Antibacterial activities and cytotoxicity of metal oxide nanoparticles are determined by their special band structures, which also influence their potential ecological risks. Traditional experimental determination of the band gap is time-consuming, while the accuracy of theoretical computation depends on the selected algorithm, for which higher precision algorithms, being more expensive, can give a more accurate band gap. Therefore, in this study, a quantitative structure–property relationship (QSPR) model, highlighting the influence of crystalline type and material size, was developed to predict the band gap of metal oxide nanoparticles rapidly and accurately. The structural descriptors for metal oxide nanoparticles were generated via quantum chemistry computations, among which heat of formation and beta angle of the unit cell were the most important parameters influencing band gaps. The developed model shows great robustness and predictive ability (R2 = 0.848, RMSE = 0.378 eV, RMSECV = 0.478 eV, QEXT2 = 0.814, RMSEP = 0.408 eV). The current study can assist in screening the ecological risks of existing metal oxide nanoparticles and may act as a reference for newly designed materials. Antibacterial activities and cytotoxicity of metal oxide nanoparticles are determined by their special band structures, which also influence their potential ecological risks.![]()
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Affiliation(s)
- Jiaxing Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE)
- School of Environmental Science and Technology
- Dalian University of Technology
- Dalian 116024
- China
| | - Ya Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE)
- School of Environmental Science and Technology
- Dalian University of Technology
- Dalian 116024
- China
| | - Yang Huang
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE)
- School of Environmental Science and Technology
- Dalian University of Technology
- Dalian 116024
- China
| | - Willie J. G. M. Peijnenburg
- Institute of Environmental Sciences
- Leiden University
- 2300 RA Leiden
- The Netherlands
- National Institute of Public Health and the Environment
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE)
- School of Environmental Science and Technology
- Dalian University of Technology
- Dalian 116024
- China
| | - Xuehua Li
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE)
- School of Environmental Science and Technology
- Dalian University of Technology
- Dalian 116024
- China
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Golbamaki A, Golbamaki N, Sizochenko N, Rasulev B, Leszczynski J, Benfenati E. Genotoxicity induced by metal oxide nanoparticles: a weight of evidence study and effect of particle surface and electronic properties. Nanotoxicology 2018; 12:1113-1129. [PMID: 29888633 DOI: 10.1080/17435390.2018.1478999] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
The genetic toxicology of nanomaterials is a crucial toxicology issue and one of the least investigated topics. Substantially, the genotoxicity of metal oxide nanomaterials' data is resulting from in vitro comet assay. Current contributions to the genotoxicity data assessed by the comet assay provide a case-by-case evaluation of different types of metal oxides. The existing inconsistency in the literature regarding the genotoxicity testing data requires intelligent assessment strategies, such as weight of evidence evaluation. Two main tasks were performed in the present study. First, the genotoxicity data from comet assay for 16 noncoated metal oxide nanomaterials with different core composition were collected. An evaluation criterion was applied to establish which of these individual lines of evidence were of sufficient quality and what weight could have been given to them in inferring genotoxic results. The collected data were surveyed on (1) minimum necessary characterization points for nanomaterials and (2) principals of correct comet assay testing for nanomaterials. Second, in this study the genotoxicity effect of metal oxide nanomaterials was investigated by quantitative nanostructure-activity relationship approach. A set of quantum-chemical descriptors was developed for all investigated metal oxide nanomaterials. A classification model based on decision tree was developed for the investigated dataset. Thus, three descriptors were identified as the most responsible factors for genotoxicity effect: heat of formation, molecular weight, and surface area of the oxide cluster based on the conductor-like screening model. Conclusively, the proposed genotoxicity assessment strategy is useful to prioritize the study of the nanomaterials for further risk assessment evaluations.
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Affiliation(s)
- Azadi Golbamaki
- a Department of Environmental Health Sciences , Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | - Nazanin Golbamaki
- a Department of Environmental Health Sciences , Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
| | - Natalia Sizochenko
- b Interdisciplinary Center for Nanotoxicity , Jackson State University , Jackson , MS , USA.,c Department of Computer Science , Dartmouth College, Sudikoff Lab , Hanover , NH , USA
| | - Bakhtiyor Rasulev
- b Interdisciplinary Center for Nanotoxicity , Jackson State University , Jackson , MS , USA.,d Department of Coatings and Polymeric Materials , North Dakota State University , Fargo , ND , USA
| | - Jerzy Leszczynski
- b Interdisciplinary Center for Nanotoxicity , Jackson State University , Jackson , MS , USA
| | - Emilio Benfenati
- a Department of Environmental Health Sciences , Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy
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41
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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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42
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Trinh TX, Choi JS, Jeon H, Byun HG, Yoon TH, Kim J. Quasi-SMILES-Based Nano-Quantitative Structure-Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells. Chem Res Toxicol 2018; 31:183-190. [PMID: 29439565 DOI: 10.1021/acs.chemrestox.7b00303] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Quantitative structure-activity relationship (QSAR) models for nanomaterials (nano-QSAR) were developed to predict the cytotoxicity of 20 different types of multiwalled carbon nanotubes (MWCNTs) to human lung cells by using quasi-SMILES. The optimal descriptors, recorded as quasi-SMILES, were encoded to represent the physicochemical properties and experimental conditions for the MWCNTs from 276 data records collected from previously published studies. The quasi-SMILES used to build the optimal descriptors were (i) diameter, (ii) length, (iii) surface area, (iv) in vitro toxicity assay, (v) cell line, (vi) exposure time, and (vii) dose. The model calculations were performed by using the Monte Carlo method and computed with CORAL software ( www.insilico.eu/coral ). The quasi-SMILES-based nano-QSAR model provided satisfactory statistical results ( R2 for internal validation data sets: 0.60-0.80; R2pred for external validation data sets: 0.81-0.88). The model showed potential for use in the estimation of human lung cell viability after exposure to MWCNTs with the following properties: diameter, 12-74 nm; length, 0.19-20.25 μm; surface area, 11.3-380.0 m2/g; and dose, 0-200 ppm.
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Affiliation(s)
- Tung Xuan Trinh
- Department of Chemistry, College of Natural Sciences , Hanyang University , Seoul 04763 , Republic of Korea
| | - Jang-Sik Choi
- Division of Electronics, Information and Communication Engineering , Kangwon National University , Samcheok , Kangwon-do 24341 , Republic of Korea
| | - Hyunpyo Jeon
- Environmental Safety Group , Korea Institute of Science and Technology (KIST) Europe , Campus E 7.1 , D-66123 Saarbruecken , Germany
| | - Hyung-Gi Byun
- Division of Electronics, Information and Communication Engineering , Kangwon National University , Samcheok , Kangwon-do 24341 , Republic of Korea
| | - Tae-Hyun Yoon
- Department of Chemistry, College of Natural Sciences , Hanyang University , Seoul 04763 , Republic of Korea
| | - Jongwoon Kim
- Environmental Safety Group , Korea Institute of Science and Technology (KIST) Europe , Campus E 7.1 , D-66123 Saarbruecken , Germany
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43
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Toxicity Classification of Oxide Nanomaterials: Effects of Data Gap Filling and PChem Score-based Screening Approaches. Sci Rep 2018; 8:3141. [PMID: 29453389 PMCID: PMC5816655 DOI: 10.1038/s41598-018-21431-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 02/05/2018] [Indexed: 12/14/2022] Open
Abstract
Development of nanotoxicity prediction models is becoming increasingly important in the risk assessment of engineered nanomaterials. However, it has significant obstacles caused by the wide heterogeneities of published literature in terms of data completeness and quality. Here, we performed a meta-analysis of 216 published articles on oxide nanoparticles using 14 attributes of physicochemical, toxicological and quantum-mechanical properties. Particularly, to improve completeness and quality of the extracted dataset, we adapted two preprocessing approaches: data gap-filling and physicochemical property based scoring. Performances of nano-SAR classification models revealed that the dataset with the highest score value resulted in the best predictivity with compromise in its applicability domain. The combination of physicochemical and toxicological attributes was proved to be more relevant to toxicity classification than quantum-mechanical attributes. Overall, by adapting these two preprocessing methods, we demonstrated that meta-analysis of nanotoxicity literatures could provide an effective alternative for the risk assessment of engineered nanomaterials.
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44
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Synthesis and characterization of biocompatible zinc oxide nanorod doped-titanium dioxide nanosheet. J IND ENG CHEM 2018. [DOI: 10.1016/j.jiec.2017.08.047] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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45
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Varsou DD, Tsiliki G, Nymark P, Kohonen P, Grafström R, Sarimveis H. toxFlow: A Web-Based Application for Read-Across Toxicity Prediction Using Omics and Physicochemical Data. J Chem Inf Model 2017; 58:543-549. [DOI: 10.1021/acs.jcim.7b00160] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Dimitra-Danai Varsou
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Georgia Tsiliki
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Misvik Biology Oy, 20520 Turku, Finland
| | - Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Misvik Biology Oy, 20520 Turku, Finland
| | - Roland Grafström
- Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Misvik Biology Oy, 20520 Turku, Finland
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
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46
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Gajewicz A, Puzyn T, Odziomek K, Urbaszek P, Haase A, Riebeling C, Luch A, Irfan MA, Landsiedel R, van der Zande M, Bouwmeester H. Decision tree models to classify nanomaterials according to the DF4nanoGrouping scheme. Nanotoxicology 2017; 12:1-17. [DOI: 10.1080/17435390.2017.1415388] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Agnieszka Gajewicz
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Tomasz Puzyn
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Katarzyna Odziomek
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Piotr Urbaszek
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Andrea Haase
- German Federal Institute for Risk Assessment (BfR), Department of Chemical and Product Safety, Berlin, Germany
| | - Christian Riebeling
- German Federal Institute for Risk Assessment (BfR), Department of Chemical and Product Safety, Berlin, Germany
| | - Andreas Luch
- German Federal Institute for Risk Assessment (BfR), Department of Chemical and Product Safety, Berlin, Germany
| | - Muhammad A. Irfan
- Department of Experimental Toxicology and Ecology, BASF SE, Ludwigshafen, Germany
| | - Robert Landsiedel
- Department of Experimental Toxicology and Ecology, BASF SE, Ludwigshafen, Germany
| | | | - Hans Bouwmeester
- RIKILT – Wageningen University and Research, Wageningen, The Netherlands
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47
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Chen G, Vijver MG, Xiao Y, Peijnenburg WJGM. A Review of Recent Advances towards the Development of (Quantitative) Structure-Activity Relationships for Metallic Nanomaterials. MATERIALS 2017; 10:ma10091013. [PMID: 28858269 PMCID: PMC5615668 DOI: 10.3390/ma10091013] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2017] [Revised: 08/08/2017] [Accepted: 08/28/2017] [Indexed: 11/16/2022]
Abstract
Gathering required information in a fast and inexpensive way is essential for assessing the risks of engineered nanomaterials (ENMs). The extension of conventional (quantitative) structure-activity relationships ((Q)SARs) approach to nanotoxicology, i.e., nano-(Q)SARs, is a possible solution. The preliminary attempts of correlating ENMs' characteristics to the biological effects elicited by ENMs highlighted the potential applicability of (Q)SARs in the nanotoxicity field. This review discusses the current knowledge on the development of nano-(Q)SARs for metallic ENMs, on the aspects of data sources, reported nano-(Q)SARs, and mechanistic interpretation. An outlook is given on the further development of this frontier. As concluded, the used experimental data mainly concern the uptake of ENMs by different cell lines and the toxicity of ENMs to cells lines and Escherichia coli. The widely applied techniques of deriving models are linear and non-linear regressions, support vector machine, artificial neural network, k-nearest neighbors, etc. Concluded from the descriptors, surface properties of ENMs are seen as vital for the cellular uptake of ENMs; the capability of releasing ions and surface redox properties of ENMs are of importance for evaluating nanotoxicity. This review aims to present key advances in relevant nano-modeling studies and stimulate future research efforts in this quickly developing field of research.
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Affiliation(s)
- Guangchao Chen
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
| | - Martina G Vijver
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
| | - Yinlong Xiao
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
| | - Willie J G M Peijnenburg
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
- Centre for Safety of Substances and Products, National Institute of Public Health and the Environment (RIVM), 3720 BA Bilthoven, The Netherlands.
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48
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Chen G, Peijnenburg W, Xiao Y, Vijver MG. Current Knowledge on the Use of Computational Toxicology in Hazard Assessment of Metallic Engineered Nanomaterials. Int J Mol Sci 2017; 18:ijms18071504. [PMID: 28704975 PMCID: PMC5535994 DOI: 10.3390/ijms18071504] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Revised: 07/07/2017] [Accepted: 07/10/2017] [Indexed: 11/16/2022] Open
Abstract
As listed by the European Chemicals Agency, the three elements in evaluating the hazards of engineered nanomaterials (ENMs) include the integration and evaluation of toxicity data, categorization and labeling of ENMs, and derivation of hazard threshold levels for human health and the environment. Assessing the hazards of ENMs solely based on laboratory tests is time-consuming, resource intensive, and constrained by ethical considerations. The adoption of computational toxicology into this task has recently become a priority. Alternative approaches such as (quantitative) structure-activity relationships ((Q)SAR) and read-across are of significant help in predicting nanotoxicity and filling data gaps, and in classifying the hazards of ENMs to individual species. Thereupon, the species sensitivity distribution (SSD) approach is able to serve the establishment of ENM hazard thresholds sufficiently protecting the ecosystem. This article critically reviews the current knowledge on the development of in silico models in predicting and classifying the hazard of metallic ENMs, and the development of SSDs for metallic ENMs. Further discussion includes the significance of well-curated experimental datasets and the interpretation of toxicity mechanisms of metallic ENMs based on reported models. An outlook is also given on future directions of research in this frontier.
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Affiliation(s)
- Guangchao Chen
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
| | - Willie Peijnenburg
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
- Centre for Safety of Substances and Products, National Institute of Public Health and the Environment (RIVM), Bilthoven, 3720 BA Bilthoven, The Netherlands.
| | - Yinlong Xiao
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
| | - Martina G Vijver
- Institute of Environmental Sciences, Leiden University, 2300 RA Leiden, The Netherlands.
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49
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Boukhvalov DW, Yoon TH. Development of Theoretical Descriptors for Cytotoxicity Evaluation of Metallic Nanoparticles. Chem Res Toxicol 2017. [PMID: 28651428 DOI: 10.1021/acs.chemrestox.7b00026] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Motivated by the recent development of quantitative structure-activity relationship (QSAR) methods in the area of nanotoxicology, we proposed an approach to develop additional descriptors based on results of first-principles calculations. For the evaluation of the biochemical activity of metallic nanoparticles, we consider two processes: ion extraction from the surface of a specimen to aqueous media and water dissociation on the surface. We performed calculations for a set of metals (Al, Fe, Cu, Ag, Au, and Pt). Taking into account the diversity of atomic structures of real metallic nanoparticles, we performed calculations for different models such as (001) and (111) surfaces, nanorods, and two different cubic nanoparticles of 0.6 and 0.3 nm size. Significant energy dependence of the processes from the selected model of nanoparticle suggests that for the correct description we should combine the calculations for several representative models. In addition to the descriptors of chemical activity of the metallic nanoparticles for the two studied processes, we propose descriptors for taking into account the dependence of chemical activity from the size and shape of nanoparticles. Routes to minimization of computational costs for these calculations are also discussed.
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Affiliation(s)
- D W Boukhvalov
- Department of Chemistry, Hanyang University , 17 Haengdang-dong, Seongdong-gu, Seoul 04763, Korea
| | - T H Yoon
- Department of Chemistry, Hanyang University , 17 Haengdang-dong, Seongdong-gu, Seoul 04763, Korea
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50
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Lin S, Mortimer M, Chen R, Kakinen A, Riviere JE, Davis TP, Ding F, Ke PC. NanoEHS beyond Toxicity - Focusing on Biocorona. ENVIRONMENTAL SCIENCE. NANO 2017; 7:1433-1454. [PMID: 29123668 PMCID: PMC5673284 DOI: 10.1039/c6en00579a] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
The first phase of environmental health and safety of nanomaterials (nanoEHS) studies has been mainly focused on evidence-based investigations that probe the impact of nanoparticles, nanomaterials and nano-enabled products on biological and ecological systems. The integration of multiple disciplines, including colloidal science, nanomaterial science, chemistry, toxicology/immunology and environmental science, is necessary to understand the implications of nanotechnology for both human health and the environment. While strides have been made in connecting the physicochemical properties of nanomaterials with their hazard potential in tiered models, fundamental understanding of nano-biomolecular interactions and their implications for nanoEHS is largely absent from the literature. Research on nano-biomolecular interactions within the context of natural systems not only provides important clues for deciphering nanotoxicity and nanoparticle-induced pathology, but also presents vast new opportunities for screening beneficial material properties and designing greener products from bottom up. This review highlights new opportunities concerning nano-biomolecular interactions beyond the scope of toxicity.
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Affiliation(s)
- Sijie Lin
- College of Environmental Science and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, 1239 Siping Road, Shanghai 200092, China
| | - Monika Mortimer
- Bren School of Environmental Science and Management, Earth Research Institute and University of California Center for the Environmental Implications of Nanotechnology (UC CEIN), University of California, Santa Barbara, California 93106, United States
| | - Ran Chen
- Nanotechnology Innovation Center of Kansas State, Kansas State University, Manhattan, Kansas 66506, United States
| | - Aleksandr Kakinen
- ARC Center of Excellence in Convergent Bio-Nano Science and Technology, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC 3052, Australia
| | - Jim E. Riviere
- Nanotechnology Innovation Center of Kansas State, Kansas State University, Manhattan, Kansas 66506, United States
| | - Thomas P. Davis
- ARC Center of Excellence in Convergent Bio-Nano Science and Technology, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC 3052, Australia
- Department of Chemistry, University of Warwick, Gibbet Hill, Coventry, CV4 7AL, United Kingdom
| | - Feng Ding
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, United States
| | - Pu Chun Ke
- ARC Center of Excellence in Convergent Bio-Nano Science and Technology, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC 3052, Australia
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