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Varsou DD, Kolokathis PD, Antoniou M, Sidiropoulos NK, Tsoumanis A, Papadiamantis AG, Melagraki G, Lynch I, Afantitis A. In silico assessment of nanoparticle toxicity powered by the Enalos Cloud Platform: Integrating automated machine learning and synthetic data for enhanced nanosafety evaluation. Comput Struct Biotechnol J 2024; 25:47-60. [PMID: 38646468 PMCID: PMC11026727 DOI: 10.1016/j.csbj.2024.03.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/22/2024] [Accepted: 03/23/2024] [Indexed: 04/23/2024] Open
Abstract
The rapid advance of nanotechnology has led to the development and widespread application of nanomaterials, raising concerns regarding their potential adverse effects on human health and the environment. Traditional (experimental) methods for assessing the nanoparticles (NPs) safety are time-consuming, expensive, and resource-intensive, and raise ethical concerns due to their reliance on animals. To address these challenges, we propose an in silico workflow that serves as an alternative or complementary approach to conventional hazard and risk assessment strategies, which incorporates state-of-the-art computational methodologies. In this study we present an automated machine learning (autoML) scheme that employs dose-response toxicity data for silver (Ag), titanium dioxide (TiO2), and copper oxide (CuO) NPs. This model is further enriched with atomistic descriptors to capture the NPs' underlying structural properties. To overcome the issue of limited data availability, synthetic data generation techniques are used. These techniques help in broadening the dataset, thus improving the representation of different NP classes. A key aspect of this approach is a novel three-step applicability domain method (which includes the development of a local similarity approach) that enhances user confidence in the results by evaluating the prediction's reliability. We anticipate that this approach will significantly expedite the nanosafety assessment process enabling regulation to keep pace with innovation, and will provide valuable insights for the design and development of safe and sustainable NPs. The ML model developed in this study is made available to the scientific community as an easy-to-use web-service through the Enalos Cloud Platform (www.enaloscloud.novamechanics.com/sabydoma/safenanoscope/), facilitating broader access and collaborative advancements in nanosafety.
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Affiliation(s)
- Dimitra-Danai Varsou
- NovaMechanics MIKE, Piraeus 18545, Greece
- Entelos Institute, Larnaca 6059, Cyprus
| | | | | | | | - Andreas Tsoumanis
- Entelos Institute, Larnaca 6059, Cyprus
- NovaMechanics Ltd, Nicosia 1070, Cyprus
| | - Anastasios G. Papadiamantis
- Entelos Institute, Larnaca 6059, Cyprus
- NovaMechanics Ltd, Nicosia 1070, Cyprus
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Georgia Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, Vari 16672, Greece
| | - Iseult Lynch
- Entelos Institute, Larnaca 6059, Cyprus
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Antreas Afantitis
- NovaMechanics MIKE, Piraeus 18545, Greece
- Entelos Institute, Larnaca 6059, Cyprus
- NovaMechanics Ltd, Nicosia 1070, Cyprus
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Furxhi I, Faccani L, Zanoni I, Brigliadori A, Vespignani M, Costa AL. Design rules applied to silver nanoparticles synthesis: A practical example of machine learning application. Comput Struct Biotechnol J 2024; 25:20-33. [PMID: 38444982 PMCID: PMC10914561 DOI: 10.1016/j.csbj.2024.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 02/12/2024] [Accepted: 02/14/2024] [Indexed: 03/07/2024] Open
Abstract
The synthesis of silver nanoparticles with controlled physicochemical properties is essential for governing their intended functionalities and safety profiles. However, synthesis process involves multiple parameters that could influence the resulting properties. This challenge could be addressed with the development of predictive models that forecast endpoints based on key synthesis parameters. In this study, we manually extracted synthesis-related data from the literature and leveraged various machine learning algorithms. Data extraction included parameters such as reactant concentrations, experimental conditions, as well as physicochemical properties. The antibacterial efficiencies and toxicological profiles of the synthesized nanoparticles were also extracted. In a second step, based on data completeness, we employed regression algorithms to establish relationships between synthesis parameters and desired endpoints and to build predictive models. The models for core size and antibacterial efficiency were trained and validated using a cross-validation approach. Finally, the features' impact was evaluated via Shapley values to provide insights into the contribution of features to the predictions. Factors such as synthesis duration, scale of synthesis and the choice of capping agents emerged as the most significant predictors. This study demonstrated the potential of machine learning to aid in the rational design of synthesis process and paves the way for the safe-by-design principles development by providing insights into the optimization of the synthesis process to achieve the desired properties. Finally, this study provides a valuable dataset compiled from literature sources with significant time and effort from multiple researchers. Access to such datasets notably aids computational advances in the field of nanotechnology.
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Affiliation(s)
- Irini Furxhi
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
- Transgero Limited, Limerick, Ireland
| | - Lara Faccani
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
| | - Ilaria Zanoni
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
| | - Andrea Brigliadori
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
| | - Maurizio Vespignani
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
| | - Anna Luisa Costa
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
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3
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Bahl A, Halappanavar S, Wohlleben W, Nymark P, Kohonen P, Wallin H, Vogel U, Haase A. Bioinformatics and machine learning to support nanomaterial grouping. Nanotoxicology 2024:1-28. [PMID: 38949108 DOI: 10.1080/17435390.2024.2368005] [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: 12/28/2023] [Accepted: 06/11/2024] [Indexed: 07/02/2024]
Abstract
Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and risk assessment strategies building on New Approach Methodologies (NAMs) become indispensable. Indeed, the design, the development and implementation of NAMs has been a major topic in a substantial number of research projects. One of the promising strategies that can help to deal with the high number of NMs variants is grouping and read-across. Based on demonstrated structural and physicochemical similarity, NMs can be grouped and assessed together. Within an established NM group, read-across may be performed to fill in data gaps for data-poor variants using existing data for NMs within the group. Establishing a group requires a sound justification, usually based on a grouping hypothesis that links specific physicochemical properties to well-defined hazard endpoints. However, for NMs these interrelationships are only beginning to be understood. The aim of this review is to demonstrate the power of bioinformatics with a specific focus on Machine Learning (ML) approaches to unravel the NM Modes-of-Action (MoA) and identify the properties that are relevant to specific hazards, in support of grouping strategies. This review emphasizes the following messages: 1) ML supports identification of the most relevant properties contributing to specific hazards; 2) ML supports analysis of large omics datasets and identification of MoA patterns in support of hypothesis formulation in grouping approaches; 3) omics approaches are useful for shifting away from consideration of single endpoints towards a more mechanistic understanding across multiple endpoints gained from one experiment; and 4) approaches from other fields of Artificial Intelligence (AI) like Natural Language Processing or image analysis may support automated extraction and interlinkage of information related to NM toxicity. Here, existing ML models for predicting NM toxicity and for analyzing omics data in support of NM grouping are reviewed. Various challenges related to building robust models in the field of nanotoxicology exist and are also discussed.
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Affiliation(s)
- Aileen Bahl
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
- Department of Biological Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
- Freie Universität Berlin, Institute of Pharmacy, Berlin, Germany
| | - Sabina Halappanavar
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Canada
| | - Wendel Wohlleben
- BASF SE, Department Analytical and Material Science and Department Experimental Toxicology and Ecology, Ludwigshafen, Germany
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Håkan Wallin
- Department of Chemical and Biological Risk Factors, National Institute of Occupational Health, Oslo, Norway
- Department of Public Health, Copenhagen University, Copenhagen, Denmark
| | - Ulla Vogel
- National Research Centre for the Working Environment, Copenhagen, Denmark
| | - Andrea Haase
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
- Freie Universität Berlin, Institute of Pharmacy, Berlin, Germany
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4
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Balraadjsing S, J G M Peijnenburg W, Vijver MG. Building species trait-specific nano-QSARs: Model stacking, navigating model uncertainties and limitations, and the effect of dataset size. ENVIRONMENT INTERNATIONAL 2024; 188:108764. [PMID: 38788418 DOI: 10.1016/j.envint.2024.108764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 05/17/2024] [Accepted: 05/19/2024] [Indexed: 05/26/2024]
Abstract
A strong need exists for broadly applicable nano-QSARs, capable of predicting toxicological outcomes towards untested species and nanomaterials, under different environmental conditions. Existing nano-QSARs are generally limited to only a few species but the inclusion of species characteristics into models can aid in making them applicable to multiple species, even when toxicity data is not available for biological species. Species traits were used to create classification- and regression machine learning models to predict acute toxicity towards aquatic species for metallic nanomaterials. Afterwards, the individual classification- and regression models were stacked into a meta-model to improve performance. Additionally, the uncertainty and limitations of the models were assessed in detail (beyond the OECD principles) and it was investigated whether models would benefit from the addition of more data. Results showed a significant improvement in model performance following model stacking. Investigation of model uncertainties and limitations highlighted the discrepancy between the applicability domain and accuracy of predictions. Data points outside of the assessed chemical space did not have higher likelihoods of generating inadequate predictions or vice versa. It is therefore concluded that the applicability domain does not give complete insight into the uncertainty of predictions and instead the generation of prediction intervals can help in this regard. Furthermore, results indicated that an increase of the dataset size did not improve model performance. This implies that larger dataset sizes may not necessarily improve model performance while in turn also meaning that large datasets are not necessarily required for prediction of acute toxicity with nano-QSARs.
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Affiliation(s)
- Surendra Balraadjsing
- Institute of Environmental Sciences (CML), Leiden University, PO Box 9518, 2300 RA Leiden, the Netherlands.
| | - Willie J G M Peijnenburg
- Institute of Environmental Sciences (CML), Leiden University, PO Box 9518, 2300 RA Leiden, the Netherlands; Centre for Safety of Substances and Products, National Institute of Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven, the Netherlands
| | - Martina G Vijver
- Institute of Environmental Sciences (CML), Leiden University, PO Box 9518, 2300 RA Leiden, the Netherlands
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5
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Senanayake RD, Daly CA, Hernandez R. Optimized Bags of Artificial Neural Networks Can Predict the Viability of Organisms Exposed to Nanoparticles. J Phys Chem A 2024; 128:2857-2870. [PMID: 38536900 DOI: 10.1021/acs.jpca.3c07462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Prediction of organismal viability upon exposure to a nanoparticle in varying environments─as fully specified at the molecular scale─has emerged as a useful figure of merit in the design of engineered nanoparticles. We build on our earlier finding that a bag of artificial neural networks (ANNs) can provide such a prediction when such machines are trained with a relatively small data set (with ca. 200 examples). Therein, viabilities were predicted by consensus using the weighted means of the predictions from the bags. Here, we confirm the accuracy and precision of the prediction of nanoparticle viabilities using an optimized bag of ANNs over sets of data examples that had not previously been used in the training and validation process. We also introduce the viability strip, rather than a single value, as the prediction and construct it from the viability probability distribution of an ensemble of ANNs compatible with the data set. Specifically, the ensemble consists of the ANNs arising from subsets of the data set corresponding to different splittings between training and validation, and the different bags (k-folds). A k - 1 k machine uses a single partition (or bag) of k - 1 ANNs each trained on 1/k of the data to obtain a consensus prediction, and a k-bag machine quorum samples the k possible k - 1 k machines available for a given partition. We find that with increasing k in the k-bag or k - 1 k machines, the viability strips become more normally distributed and their predictions become more precise. Benchmark comparisons between ensembles of 4-bag machines and 3 4 fraction machines suggest that the 3 4 fraction machine has similar accuracy while overcoming some of the challenges arising from divergent ANNs in the 4-bag machines.
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Affiliation(s)
- Ravithree D Senanayake
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Clyde A Daly
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Rigoberto Hernandez
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Chemical & Biomolecular Engineering and Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
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6
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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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7
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Yang C, Dong H, Chen Y, Xu L, Chen G, Fan X, Wang Y, Tham YJ, Lin Z, Li M, Hong Y, Chen J. New Insights on the Formation of Nucleation Mode Particles in a Coastal City Based on a Machine Learning Approach. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:1187-1198. [PMID: 38117945 DOI: 10.1021/acs.est.3c07042] [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: 12/22/2023]
Abstract
Atmospheric particles have profound implications for the global climate and human health. Among them, ultrafine particles dominate in terms of the number concentration and exhibit enhanced toxic effects as a result of their large total surface area. Therefore, understanding the driving factors behind ultrafine particle behavior is crucial. Machine learning (ML) provides a promising approach for handling complex relationships. In this study, three ML models were constructed on the basis of field observations to simulate the particle number concentration of nucleation mode (PNCN). All three models exhibited robust PNCN reproduction (R2 > 0.80), with the random forest (RF) model excelling on the test data (R2 = 0.89). Multiple methods of feature importance analysis revealed that ultraviolet (UV), H2SO4, low-volatility oxygenated organic molecules (LOOMs), temperature, and O3 were the primary factors influencing PNCN. Bivariate partial dependency plots (PDPs) indicated that during nighttime and overcast conditions, the presence of H2SO4 and LOOMs may play a crucial role in influencing PNCN. Additionally, integrating additional detailed information related to emissions or meteorology would further enhance the model performance. This pilot study shows that ML can be a novel approach for simulating atmospheric pollutants and contributes to a better understanding of the formation and growth mechanisms of nucleation mode particles.
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Affiliation(s)
- Chen Yang
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
- Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Hesong Dong
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
| | - Yuping Chen
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
- Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Lingling Xu
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
- Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
| | - Gaojie Chen
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
- Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Xiaolong Fan
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
- Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
| | - Yonghong Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, People's Republic of China
| | - Yee Jun Tham
- School of Marine Sciences, Sun Yat-sen University, Zhuhai, Guangdong 519082, People's Republic of China
| | - Ziyi Lin
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
- Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Mengren Li
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
- Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
| | - Youwei Hong
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
- Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
| | - Jinsheng Chen
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
- Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China
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8
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Tang W, Zhang X, Hong H, Chen J, Zhao Q, Wu F. Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:155. [PMID: 38251120 PMCID: PMC10819018 DOI: 10.3390/nano14020155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/08/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024]
Abstract
Although engineered nanomaterials (ENMs) have tremendous potential to generate technological benefits in numerous sectors, uncertainty on the risks of ENMs for human health and the environment may impede the advancement of novel materials. Traditionally, the risks of ENMs can be evaluated by experimental methods such as environmental field monitoring and animal-based toxicity testing. However, it is time-consuming, expensive, and impractical to evaluate the risk of the increasingly large number of ENMs with the experimental methods. On the contrary, with the advancement of artificial intelligence and machine learning, in silico methods have recently received more attention in the risk assessment of ENMs. This review discusses the key progress of computational nanotoxicology models for assessing the risks of ENMs, including material flow analysis models, multimedia environmental models, physiologically based toxicokinetics models, quantitative nanostructure-activity relationships, and meta-analysis. Several challenges are identified and a perspective is provided regarding how the challenges can be addressed.
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Affiliation(s)
- Weihao Tang
- National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, 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
| | - Xuejiao Zhang
- National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, 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
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Rd., Jefferson, AR 72079, USA
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Qing Zhao
- National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, 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
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
| | - Fengchang Wu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
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9
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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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10
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Cui S, Gao Y, Huang Y, Shen L, Zhao Q, Pan Y, Zhuang S. Advances and applications of machine learning and deep learning in environmental ecology and health. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 335:122358. [PMID: 37567408 DOI: 10.1016/j.envpol.2023.122358] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 08/02/2023] [Accepted: 08/08/2023] [Indexed: 08/13/2023]
Abstract
Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecology and health (EEH). Considering the rapid growth and increasing complexity of data in EEH, it is of significance to summarize recent advances and applications of ML and DL in EEH. This review summarized the basic processes and fundamental algorithms of the ML and DL modeling, and indicated the urgent needs of ML and DL in EEH. Recent research hotspots such as environmental ecology and restoration, environmental fate of new pollutants, chemical exposures and risks, chemical hazard identification and control were highlighted. Various applications of ML and DL in EEH demonstrate their versatility and technological revolution, and present some challenges. The perspective of ML and DL in EEH were further outlined to promote the innovative analysis and cultivation of the ML-driven research paradigm.
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Affiliation(s)
- Shixuan Cui
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Yuchen Gao
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yizhou Huang
- Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Lilai Shen
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Qiming Zhao
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yaru Pan
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Shulin Zhuang
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China.
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11
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Furxhi I, Kalapus M, Costa A, Puzyn T. Artificial augmented dataset for the enhancement of nano-QSARs models. A methodology based on topological projections. Nanotoxicology 2023; 17:529-544. [PMID: 37885250 DOI: 10.1080/17435390.2023.2268163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 10/02/2023] [Indexed: 10/28/2023]
Abstract
Nanoinformatics demands accurate predictive models to assess the potential hazards of nanomaterials (NMs). However, limited data availability and the diverse nature of NMs physicochemical properties and their interaction with biological media, hinder the development of robust nano-Quantitative Structure-Activity Relationship (QSAR) models. This article proposes an approach that combines artificially data generation techniques and topological projections to address the challenges of insufficient dataset sizes and their limited representativeness of the chemical space. By leveraging the rich information embedded in the topological features, this methodology enhances the representation of the chemical space, enabling a more an exploration of the structure-activity relationships. We demonstrate the efficacy of our approach through extensive experiments, employing various machine learning regression algorithms to validate the methodology. Finally, we compare two different resampling approaches based on different modeling scenarios. The results showcase a significant improved predictive performance of QSAR models demonstrating a promising strategy to overcome the limitations of small datasets in the field of nanoinformatics. The proposed approach offers noteworthy potential for advancing nanoinformatics research within the nanosafety domain by enabling the development of more accurate predictive models for assessing the potential hazards associated with NMs.
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Affiliation(s)
- Irini Furxhi
- Dept. of Accounting and Finance, Kemmy Business School, University of Limerick, Ireland
- Transgero Limited, Cullinagh, Newcastle West, Co. Limerick, Limerick, Ireland
| | - Michal Kalapus
- Laboratory of Environmental Chemoinformatics, Department of Environmental Chemistry and Radiochemistry, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Anna Costa
- CNR-ISSMC Istituto di Scienza, Tecnologia e Sostenibilità per lo Sviluppo dei Materiali Ceramici, Faenza, Italy
| | - Tomasz Puzyn
- Laboratory of Environmental Chemoinformatics, Department of Environmental Chemistry and Radiochemistry, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
- QSAR Lab Ltd, Gdansk, Poland
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12
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Varsou DD, Sarimveis H. Deimos: A novel automated methodology for optimal grouping. Application to nanoinformatics case studies. Mol Inform 2023; 42:e2300019. [PMID: 37258455 DOI: 10.1002/minf.202300019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 05/05/2023] [Accepted: 05/31/2023] [Indexed: 06/02/2023]
Abstract
In this study we present deimos, a computational methodology for optimal grouping, applied on the read-across prediction of engineered nanomaterials' (ENMs) toxicity-related properties. The method is based on the formulation and the solution of a mixed-integer optimization program (MILP) problem that automatically and simultaneously performs feature selection, defines the grouping boundaries according to the response variable and develops linear regression models in each group. For each group/region, the characteristic centroid is defined in order to allocate untested ENMs to the groups. The deimos MILP problem is integrated in a broader optimization workflow that selects the best performing methodology between the standard multiple linear regression (MLR), the least absolute shrinkage and selection operator (LASSO) models and the proposed deimos multiple-region model. The performance of the suggested methodology is demonstrated through the application to benchmark ENMs datasets and comparison with other predictive modelling approaches. However, the proposed method can be applied to property prediction of other than ENM chemical entities and it is not limited to ENMs toxicity prediction.
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Affiliation(s)
- Dimitra-Danai Varsou
- School of Chemical Engineering, National Technical University of Athens, 157 80, Athens, Greece
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 157 80, Athens, Greece
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13
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Furxhi I, Willighagen E, Evelo C, Costa A, Gardini D, Ammar A. A data reusability assessment in the nanosafety domain based on the NSDRA framework followed by an exploratory quantitative structure activity relationships (QSAR) modeling targeting cellular viability. NANOIMPACT 2023; 31:100475. [PMID: 37423508 DOI: 10.1016/j.impact.2023.100475] [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: 03/28/2023] [Revised: 07/03/2023] [Accepted: 07/04/2023] [Indexed: 07/11/2023]
Abstract
INTRODUCTION The current effort towards the digital transformation across multiple scientific domains requires data that is Findable, Accessible, Interoperable and Reusable (FAIR). In addition to the FAIR data, what is required for the application of computational tools, such as Quantitative Structure Activity Relationships (QSARs), is a sufficient data volume and the ability to merge sources into homogeneous digital assets. In the nanosafety domain there is a lack of FAIR available metadata. METHODOLOGY To address this challenge, we utilized 34 datasets from the nanosafety domain by exploiting the NanoSafety Data Reusability Assessment (NSDRA) framework, which allowed the annotation and assessment of dataset's reusability. From the framework's application results, eight datasets targeting the same endpoint (i.e. numerical cellular viability) were selected, processed and merged to test several hypothesis including universal versus nanogroup-specific QSAR models (metal oxide and nanotubes), and regression versus classification Machine Learning (ML) algorithms. RESULTS Universal regression and classification QSARs reached an 0.86 R2 and 0.92 accuracy, respectively, for the test set. Nanogroup-specific regression models reached 0.88 R2 for nanotubes test set followed by metal oxide (0.78). Nanogroup-specific classification models reached 0.99 accuracy for nanotubes test set, followed by metal oxide (0.91). Feature importance revealed different patterns depending on the dataset with common influential features including core size, exposure conditions and toxicological assay. Even in the case where the available experimental knowledge was merged, the models still failed to correctly predict the outputs of an unseen dataset, revealing the cumbersome conundrum of scientific reproducibility in realistic applications of QSAR for nanosafety. To harness the full potential of computational tools and ensure their long-term applications, embracing FAIR data practices is imperative in driving the development of responsible QSAR models. CONCLUSIONS This study reveals that the digitalization of nanosafety knowledge in a reproducible manner has a long way towards its successful pragmatic implementation. The workflow carried out in the study shows a promising approach to increase the FAIRness across all the elements of computational studies, from dataset's annotation, selection, merging to FAIR modeling reporting. This has significant implications for future research as it provides an example of how to utilize and report different tools available in the nanosafety knowledge system, while increasing the transparency of the results. One of the main benefits of this workflow is that it promotes data sharing and reuse, which is essential for advancing scientific knowledge by making data and metadata FAIR compliant. In addition, the increased transparency and reproducibility of the results can enhance the trustworthiness of the computational findings.
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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.
| | - Egon Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, the Netherlands.
| | - Chris Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, the Netherlands.
| | - Anna Costa
- National Research Council, Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy.
| | - Davide Gardini
- National Research Council, Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy.
| | - Ammar Ammar
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, the Netherlands.
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14
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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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15
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Meneses J, González-Durruthy M, Fernandez-de-Gortari E, Toropova AP, Toropov AA, Alfaro-Moreno E. A Nano-QSTR model to predict nano-cytotoxicity: an approach using human lung cells data. Part Fibre Toxicol 2023; 20:21. [PMID: 37211608 DOI: 10.1186/s12989-023-00530-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 05/01/2023] [Indexed: 05/23/2023] Open
Abstract
BACKGROUND The widespread use of new engineered nanomaterials (ENMs) in industries such as cosmetics, electronics, and diagnostic nanodevices, has been revolutionizing our society. However, emerging studies suggest that ENMs present potentially toxic effects on the human lung. In this regard, we developed a machine learning (ML) nano-quantitative-structure-toxicity relationship (QSTR) model to predict the potential human lung nano-cytotoxicity induced by exposure to ENMs based on metal oxide nanoparticles. RESULTS Tree-based learning algorithms (e.g., decision tree (DT), random forest (RF), and extra-trees (ET)) were able to predict ENMs' cytotoxic risk in an efficient, robust, and interpretable way. The best-ranked ET nano-QSTR model showed excellent statistical performance with R2 and Q2-based metrics of 0.95, 0.80, and 0.79 for training, internal validation, and external validation subsets, respectively. Several nano-descriptors linked to the core-type and surface coating reactivity properties were identified as the most relevant characteristics to predict human lung nano-cytotoxicity. CONCLUSIONS The proposed model suggests that a decrease in the ENMs diameter could significantly increase their potential ability to access lung subcellular compartments (e.g., mitochondria and nuclei), promoting strong nano-cytotoxicity and epithelial barrier dysfunction. Additionally, the presence of polyethylene glycol (PEG) as a surface coating could prevent the potential release of cytotoxic metal ions, promoting lung cytoprotection. Overall, the current work could pave the way for efficient decision-making, prediction, and mitigation of the potential occupational and environmental ENMs risks.
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Affiliation(s)
- João Meneses
- NanoSafety Group, International Iberian Nanotechnology Laboratory, Braga, 4715-330, Portugal
| | | | | | - Alla P Toropova
- Instituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milano, 20156, Italy
| | - Andrey A Toropov
- Instituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milano, 20156, Italy
| | - Ernesto Alfaro-Moreno
- NanoSafety Group, International Iberian Nanotechnology Laboratory, Braga, 4715-330, Portugal.
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16
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Blekos K, Chairetakis K, Lynch I, Marcoulaki E. Principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools. J Cheminform 2023; 15:44. [PMID: 37046286 PMCID: PMC10099932 DOI: 10.1186/s13321-022-00669-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 12/10/2022] [Indexed: 04/14/2023] Open
Abstract
Efficient and machine-readable representations are needed to accurately identify, validate and communicate information of chemical structures. Many such representations have been developed (as, for example, the Simplified Molecular-Input Line-Entry System and the IUPAC International Chemical Identifier), each offering advantages specific to various use-cases. Representation of the multi-component structures of nanomaterials (NMs), though, remains out of scope for all the currently available standards, as the nature of NMs sets new challenges on formalizing the encoding of their structure, interactions and environmental parameters. In this work we identify a set of principles that a NM representation should adhere to in order to provide "machine-friendly" encodings of NMs, i.e. encodings that facilitate machine processing and cooperation with nanoinformatics tools. We illustrate our principles by showing how the recently introduced InChI-based NM representation, might be augmented, in principle, to also encode morphology and mixture properties, distributions of properties, and also to capture auxiliary information and allow data reuse.
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Affiliation(s)
- Kostas Blekos
- Institute of Nuclear and Radiological Sciences and Technology, Energy and Safety, National Centre for Scientific Research "Demokritos", 15341, Agia Paraskevi, Greece
| | - Kostas Chairetakis
- Institute of Nuclear and Radiological Sciences and Technology, Energy and Safety, National Centre for Scientific Research "Demokritos", 15341, Agia Paraskevi, Greece
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Effie Marcoulaki
- Institute of Nuclear and Radiological Sciences and Technology, Energy and Safety, National Centre for Scientific Research "Demokritos", 15341, Agia Paraskevi, Greece.
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17
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Furxhi I, Bengalli R, Motta G, Mantecca P, Kose O, Carriere M, Haq EU, O’Mahony C, Blosi M, Gardini D, Costa A. Data-Driven Quantitative Intrinsic Hazard Criteria for Nanoproduct Development in a Safe-by-Design Paradigm: A Case Study of Silver Nanoforms. ACS APPLIED NANO MATERIALS 2023; 6:3948-3962. [PMID: 36938492 PMCID: PMC10012170 DOI: 10.1021/acsanm.3c00173] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
The current European (EU) policies, that is, the Green Deal, envisage safe and sustainable practices for chemicals, which include nanoforms (NFs), at the earliest stages of innovation. A theoretically safe and sustainable by design (SSbD) framework has been established from EU collaborative efforts toward the definition of quantitative criteria in each SSbD dimension, namely, the human and environmental safety dimension and the environmental, social, and economic sustainability dimensions. In this study, we target the safety dimension, and we demonstrate the journey toward quantitative intrinsic hazard criteria derived from findable, accessible, interoperable, and reusable data. Data were curated and merged for the development of new approach methodologies, that is, quantitative structure-activity relationship models based on regression and classification machine learning algorithms, with the intent to predict a hazard class. The models utilize system (i.e., hydrodynamic size and polydispersity index) and non-system (i.e., elemental composition and core size)-dependent nanoscale features in combination with biological in vitro attributes and experimental conditions for various silver NFs, functional antimicrobial textiles, and cosmetics applications. In a second step, interpretable rules (criteria) followed by a certainty factor were obtained by exploiting a Bayesian network structure crafted by expert reasoning. The probabilistic model shows a predictive capability of ≈78% (average accuracy across all hazard classes). In this work, we show how we shifted from the conceptualization of the SSbD framework toward the realistic implementation with pragmatic instances. This study reveals (i) quantitative intrinsic hazard criteria to be considered in the safety aspects during synthesis stage, (ii) the challenges within, and (iii) the future directions for the generation and distillation of such criteria that can feed SSbD paradigms. Specifically, the criteria can guide material engineers to synthesize NFs that are inherently safer from alternative nanoformulations, at the earliest stages of innovation, while the models enable a fast and cost-efficient in silico toxicological screening of previously synthesized and hypothetical scenarios of yet-to-be synthesized NFs.
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Affiliation(s)
- Irini Furxhi
- Transgero
Ltd, Limerick V42V384, Ireland
- Department
of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick V94T9PX, Ireland
| | - Rossella Bengalli
- Department
of Earth and Environmental Sciences, University
of Milano-Bicocca, Piazza
della Scienza 1, Milano 20126, Italy
| | - Giulia Motta
- Department
of Earth and Environmental Sciences, University
of Milano-Bicocca, Piazza
della Scienza 1, Milano 20126, Italy
| | - Paride Mantecca
- Department
of Earth and Environmental Sciences, University
of Milano-Bicocca, Piazza
della Scienza 1, Milano 20126, Italy
| | - Ozge Kose
- Univ.
Grenoble Alpes, CEA, CNRS, Grenoble INP, IRIG, SYMMES, Grenoble 38000, France
| | - Marie Carriere
- Univ.
Grenoble Alpes, CEA, CNRS, Grenoble INP, IRIG, SYMMES, Grenoble 38000, France
| | - Ehtsham Ul Haq
- Department
of Physics, and Bernal Institute, University
of Limerick, Limerick V94TC9PX, Ireland
| | - Charlie O’Mahony
- Department
of Physics, and Bernal Institute, University
of Limerick, Limerick V94TC9PX, Ireland
| | - Magda Blosi
- Istituto
di Scienza e Tecnologia dei Materiali Ceramici (CNR-ISTEC), Via Granarolo, 64, Faenza 48018, Ravenna, Italy
| | - Davide Gardini
- Istituto
di Scienza e Tecnologia dei Materiali Ceramici (CNR-ISTEC), Via Granarolo, 64, Faenza 48018, Ravenna, Italy
| | - Anna Costa
- Istituto
di Scienza e Tecnologia dei Materiali Ceramici (CNR-ISTEC), Via Granarolo, 64, Faenza 48018, Ravenna, Italy
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18
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Experimental Methods for the Biological Evaluation of Nanoparticle-Based Drug Delivery Risks. Pharmaceutics 2023; 15:pharmaceutics15020612. [PMID: 36839932 PMCID: PMC9959606 DOI: 10.3390/pharmaceutics15020612] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 02/02/2023] [Accepted: 02/09/2023] [Indexed: 02/15/2023] Open
Abstract
Many novel medical therapies use nanoparticle-based drug delivery systems, including nanomaterials through drug delivery systems, diagnostics, or physiologically active medicinal products. The approval of nanoparticles with advanced therapeutic and diagnostic potentials for applications in medication and immunization depends strongly on their synthesizing procedure, efficiency of functionalization, and biological safety and biocompatibility. Nanoparticle biodistribution, absorption, bioavailability, passage across biological barriers, and biodistribution are frequently assessed using bespoke and biological models. These methods largely rely on in vitro cell-based evaluations that cannot predict the complexity involved in preclinical and clinical studies. Therefore, assessing the nanoparticle risk has to involve pharmacokinetics, organ toxicity, and drug interactions manifested at multiple cellular levels. At the same time, there is a need for novel approaches to examine nanoparticle safety risks due to increased constraints on animal exploitation and the demand for high-throughput testing. We focus here on biological evaluation methodologies that provide access to nanoparticle interactions with the organism (positive or negative via toxicity). This work aimed to provide a perception regarding the risks associated with the utilization of nanoparticle-based formulations with a particular focus on assays applied to assess the cytotoxicity of nanomaterials.
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19
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Scott-Fordsmand JJ, Amorim MJB. Using Machine Learning to make nanomaterials sustainable. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 859:160303. [PMID: 36410486 DOI: 10.1016/j.scitotenv.2022.160303] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/06/2022] [Accepted: 11/15/2022] [Indexed: 06/16/2023]
Abstract
Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.
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Affiliation(s)
| | - Mónica J B Amorim
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
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20
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Mirzaei M, Furxhi I, Murphy F, Mullins M. Employing Supervised Algorithms for the Prediction of Nanomaterial's Antioxidant Efficiency. Int J Mol Sci 2023; 24:ijms24032792. [PMID: 36769135 PMCID: PMC9918003 DOI: 10.3390/ijms24032792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/25/2023] [Accepted: 01/29/2023] [Indexed: 02/05/2023] Open
Abstract
Reactive oxygen species (ROS) are compounds that readily transform into free radicals. Excessive exposure to ROS depletes antioxidant enzymes that protect cells, leading to oxidative stress and cellular damage. Nanomaterials (NMs) exhibit free radical scavenging efficiency representing a potential solution for oxidative stress-induced disorders. This study aims to demonstrate the application of machine learning (ML) algorithms for predicting the antioxidant efficiency of NMs. We manually compiled a comprehensive dataset based on a literature review of 62 in vitro studies. We extracted NMs' physico-chemical (P-chem) properties, the NMs' synthesis technique and various experimental conditions as input features to predict the antioxidant efficiency measured by a 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. Following data pre-processing, various regression models were trained and validated. The random forest model showed the highest predictive performance reaching an R2 = 0.83. The attribute importance analysis revealed that the NM's type, core-size and dosage are the most important attributes influencing the prediction. Our findings corroborate with those of the prior research landscape regarding the importance of P-chem characteristics. This study expands the application of ML in the nano-domain beyond safety-related outcomes by capturing the functional performance. Accordingly, this study has two objectives: (1) to develop a model to forecast the antioxidant efficiency of NMs to complement conventional in vitro assays and (2) to underline the lack of a comprehensive database and the scarcity of relevant data and/or data management practices in the nanotechnology field, especially with regards to functionality assessments.
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Affiliation(s)
- Mahsa Mirzaei
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland
| | - Irini Furxhi
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland
- Transgero Limited, Newcastle West, V42V384 Limerick, Ireland
- Correspondence: ; Tel.: +353-85-106-9771
| | - Finbarr Murphy
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland
| | - Martin Mullins
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland
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21
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Ota R, Yamashita F. Application of machine learning techniques to the analysis and prediction of drug pharmacokinetics. J Control Release 2022; 352:961-969. [PMID: 36370876 DOI: 10.1016/j.jconrel.2022.11.014] [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: 03/19/2022] [Revised: 10/23/2022] [Accepted: 11/07/2022] [Indexed: 11/17/2022]
Abstract
In this review, we describe the current status and challenges in applying machine-learning techniques to the analysis and prediction of pharmacokinetic data. The theory of pharmacokinetics has been developed over decades on the basis of physiology and reaction kinetics. Mathematical models allow the reduction of pharmacokinetic data to parameter values, giving insight and understanding into ADME processes and predicting the outcome of different dosing scenarios. However, much information hidden in the data is lost through conceptual simplification with models. It is difficult to use mechanistic models alone to predict diverse pharmacokinetic time profiles, including inter-drug and inter-individual differences, in a cross-sectional manner. Machine learning is a prediction platform that can handle complex phenomena through data-driven analysis. As a resule, machine learning has been successfully adopted in various fields, including image recognition and language processing, and has been used for over two decades in pharmacokinetic research, primarily in the area of quantitative structure-activity relationships for pharmacokinetic parameters. Machine-learning models are generally known to provide better predictive performance than conventional linear models. Owing to the recent success in deep learning, models with new structures are being consistently proposed. These models include transfer learning and generative adversarial networks, which contribute to the effective use of a limited amount of data by diverting existing similar models or generating pseudo-data. How to make such newly emerging machine learning technologies applicable to meet challenges in the pharmacokinetics/pharmacodynamics field is now the key issue.
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Affiliation(s)
- Ryosaku Ota
- Department of Drug Delivery Research, Graduate School of Pharmaceutical Sciences, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
| | - Fumiyoshi Yamashita
- Department of Drug Delivery Research, Graduate School of Pharmaceutical Sciences, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan; Department of Applied Pharmacy and Pharmacokinetics, Graduate School of Pharmaceutical Sciences, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan.
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22
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Balraadjsing S, Peijnenburg WJGM, Vijver MG. Exploring the potential of in silico machine learning tools for the prediction of acute Daphnia magna nanotoxicity. CHEMOSPHERE 2022; 307:135930. [PMID: 35961453 DOI: 10.1016/j.chemosphere.2022.135930] [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: 02/19/2022] [Revised: 07/19/2022] [Accepted: 07/31/2022] [Indexed: 06/15/2023]
Abstract
Engineered nanomaterials (ENMs) are ubiquitous nowadays, finding their application in different fields of technology and various consumer products. Virtually any chemical can be manipulated at the nano-scale to display unique characteristics which makes them appealing over larger sized materials. As the production and development of ENMs have increased considerably over time, so too have concerns regarding their adverse effects and environmental impacts. It is unfeasible to assess the risks associated with every single ENM through in vivo or in vitro experiments. As an alternative, in silico methods can be employed to evaluate ENMs. To perform such an evaluation, we collected data from databases and literature to create classification models based on machine learning algorithms in accordance with the principles laid out by the OECD for the creation of QSARs. The aim was to investigate the performance of various machine learning algorithms towards predicting a well-defined in vivo toxicity endpoint (Daphnia magna immobilization) and also to identify which features are important drivers of D. magna in vivo nanotoxicity. Results indicated highly comparable model performance between all algorithms and predictive performance exceeding ∼0.7 for all evaluated metrics (e.g. accuracy, sensitivity, specificity, balanced accuracy, Matthews correlation coefficient, area under the receiver operator characteristic curve). The random forest, artificial neural network, and k-nearest neighbor models displayed the best performance but this was only marginally better compared to the other models. Furthermore, the variable importance analysis indicated that molecular descriptors and physicochemical properties were generally important within most models, while features related to the exposure conditions produced slightly conflicting results. Lastly, results also indicate that reliable and robust machine learning models can be generated for in vivo endpoints with smaller datasets.
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Affiliation(s)
- Surendra Balraadjsing
- Institute of Environmental Sciences (CML), Leiden University, PO Box 9518, 2300 RA, Leiden, the Netherlands.
| | - Willie J G M Peijnenburg
- Institute of Environmental Sciences (CML), Leiden University, PO Box 9518, 2300 RA, Leiden, the Netherlands; Centre for Safety of Substances and Products, National Institute of Public Health and the Environment (RIVM), PO Box 1, 3720 BA, Bilthoven, the Netherlands
| | - Martina G Vijver
- Institute of Environmental Sciences (CML), Leiden University, PO Box 9518, 2300 RA, Leiden, the Netherlands
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23
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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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24
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Romeo D, Louka C, Gudino B, Wigström J, Wick P. Structure-activity relationship of graphene-related materials: A meta-analysis based on mammalian in vitro toxicity data. NANOIMPACT 2022; 28:100436. [PMID: 36334912 DOI: 10.1016/j.impact.2022.100436] [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: 07/08/2022] [Revised: 09/30/2022] [Accepted: 10/23/2022] [Indexed: 06/16/2023]
Abstract
To support a safe application of graphene-related materials (GRMs) it is necessary to understand the potential negative impacts they could have on human health, in particular on the lung - one of the most sensitive exposure routes. Machine learning (ML) approaches can help analyse the results of multiple toxicity studies to understand the structure-activity relationship and the effect of experimental conditions, thus supporting predictive nanotoxicology. In this work we collected in vitro cytotoxicity data obtained from studies using lung cells; we then fitted multiple regression models to predict this endpoint based on the material properties and experimental conditions. Moreover, the data set was used to calculate the Benchmark Dose Lower Confidence Interval (BMDL), a dose descriptor widely used in risk assessment. Regression and classification models were applied for the prediction of the BMDL value and BMDL range. The analyses show that both cytotoxicity and the BMDL range can be predicted well (Q2 = 0.77 and accuracy = 0.71, respectively). Both physico-chemical characteristics such as the lateral size, number of layers, and functionalization, and experimental conditions such as the assay and media used were important predicting features, confirming the need for thorough characterization and reporting of these parameters.
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Affiliation(s)
- Daina Romeo
- Empa, Swiss Federal Laboratories for Materials Science and Technology, Particles-Biology Interactions Laboratory, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland.
| | - Chrysovalanto Louka
- Empa, Swiss Federal Laboratories for Materials Science and Technology, Particles-Biology Interactions Laboratory, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland
| | - Berenice Gudino
- Chalmers Industriteknik, Applied AI, Sven Hultins gata 1, 41258 Göteborg, Sweden.
| | - Joakim Wigström
- Chalmers Industriteknik, Applied AI, Sven Hultins gata 1, 41258 Göteborg, Sweden.
| | - Peter Wick
- Empa, Swiss Federal Laboratories for Materials Science and Technology, Particles-Biology Interactions Laboratory, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland.
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25
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Wu X, Zhou Q, Mu L, Hu X. Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives. JOURNAL OF HAZARDOUS MATERIALS 2022; 438:129487. [PMID: 35816807 DOI: 10.1016/j.jhazmat.2022.129487] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/16/2022] [Accepted: 06/26/2022] [Indexed: 06/15/2023]
Abstract
Over the past few decades, data-driven machine learning (ML) has distinguished itself from hypothesis-driven studies and has recently received much attention in environmental toxicology. However, the use of ML in environmental toxicology remains in the early stages, with knowledge gaps, technical bottlenecks in data quality, high-dimensional/heterogeneous/small-sample data analysis and model interpretability, and a lack of an in-depth understanding of environmental toxicology. Given the above problems, we review the recent progress in the literature and highlight state-of-the-art toxicological studies using ML (such as learning and predicting toxicity in complicated biosystems and multiple-factor environmental scenarios of long-term and large-scale pollution). Beyond predicting simple biological endpoints by integrating untargeted omics and adverse outcome pathways, ML development should focus on revealing toxicological mechanisms. The integration of data-driven ML with other methods (e.g., omics analysis and adverse outcome pathway frameworks) endows ML with widely promising application in revealing toxicological mechanisms. High-quality databases and interpretable algorithms are urgently needed for toxicology and environmental science. Addressing the core issues and future challenges for ML in this review may narrow the knowledge gap between environmental toxicity and computational science and facilitate the control of environmental risk in the future.
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Affiliation(s)
- Xiaotong Wu
- 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
| | - Li Mu
- Tianjin Key Laboratory of Agro-environment and Safe-product, Key Laboratory for Environmental Factors Control of Agro-product Quality Safety (Ministry of Agriculture and Rural Affairs), Institute of Agro-environmental Protection, Ministry of Agriculture and Rural Affairs, Tianjin 300191, 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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26
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Konstantopoulos G, Koumoulos EP, Charitidis CA. Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives. NANOMATERIALS 2022; 12:nano12152646. [PMID: 35957077 PMCID: PMC9370746 DOI: 10.3390/nano12152646] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 02/05/2023]
Abstract
Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.
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Affiliation(s)
- Georgios Konstantopoulos
- RNANO Lab—Research Unit of Advanced, Composite, Nano Materials & Nanotechnology, School of Chemical Engineering, National Technical University of Athens, GR15773 Athens, Greece; (G.K.); (C.A.C.)
| | - Elias P. Koumoulos
- Innovation in Research & Engineering Solutions (IRES), Boulevard Edmond Machtens 79/22, 1080 Brussels, Belgium
- Correspondence:
| | - Costas A. Charitidis
- RNANO Lab—Research Unit of Advanced, Composite, Nano Materials & Nanotechnology, School of Chemical Engineering, National Technical University of Athens, GR15773 Athens, Greece; (G.K.); (C.A.C.)
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27
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Analysis of Research Status and Development Trend of Nanotoxicology of Liliaceae Medicinal Plants. BIOMED RESEARCH INTERNATIONAL 2022; 2022:9777817. [PMID: 35909474 PMCID: PMC9334102 DOI: 10.1155/2022/9777817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/12/2022] [Accepted: 06/17/2022] [Indexed: 11/17/2022]
Abstract
The research status and development trend of nanotoxicology of Liliaceae medicinal plants were analyzed. In the research, the toxicology of Liliaceae medicinal plants was investigated by the preparation method of silver nanoparticles. By means of spectral curve experiment, the present situation of nanotoxicology of Liliaceae medicinal plants was analyzed, and then its subsequent development trend was analyzed. In this process, Liliaceae medicinal plants could be used effectively, which could create great economic benefits. In the application of the above scheme, the toxicological degradation of Liliaceae medicinal plants could be controlled at about 96%. The high-dose silver nanoparticles could reach 100 μM, and the silver nitrate could reach 10 or 30 μM.
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28
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Basei G, Rauscher H, Jeliazkova N, Hristozov D. A methodology for the automatic evaluation of data quality and completeness of nanomaterials for risk assessment purposes. Nanotoxicology 2022; 16:195-216. [PMID: 35506346 DOI: 10.1080/17435390.2022.2065222] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
This manuscript proposes a methodology to assess the completeness and quality of physicochemical and hazard datasets for risk assessment purposes. The approach is also specifically applicable to similarity assessment as a basis for grouping of (nanoforms of) chemical substances as well as for classification of the substances according to the Classification, Labeling and Packaging regulation. The unique goal of this approach is to assess data quality in such a way that all the steps are automatized, thus reducing reliance on expert judgment. The analysis starts from available (meta)data as provided in the data entry templates developed by the NanoSafety community and used for import into the eNanoMapper database. The methodology is implemented in the templates as a traffic light system-the providers of the data can see in real time the completeness scores calculated by the system for their datasets in green, yellow, or red. This is an interactive feedback feature that is intended to provide an incentive for anyone inserting data into the database to deliver more complete and higher quality datasets. The users of the data can also see this information both in the data entry templates and on the database interface, which enables them to select better datasets for their assessments. The proposed methodology has been partially implemented in the eNanoMapper database and in a Weight of Evidence approach for the regulatory classification of nanomaterials. It was fully implemented in a publicly available online R tool.
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Affiliation(s)
| | - Hubert Rauscher
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | | - Danail Hristozov
- GreenDecision Srl, Mestre, Italy.,East European Research and Innovation Enterprise, Sofia, Bulgaria
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29
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Ji Z, Guo W, Wood EL, Liu J, Sakkiah S, Xu X, Patterson TA, Hong H. Machine Learning Models for Predicting Cytotoxicity of Nanomaterials. Chem Res Toxicol 2022; 35:125-139. [PMID: 35029374 DOI: 10.1021/acs.chemrestox.1c00310] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.
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Affiliation(s)
- Zuowei Ji
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Wenjing Guo
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Erin L Wood
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Jie Liu
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Sugunadevi Sakkiah
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Xiaoming Xu
- Office of Testing and Research, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Tucker A Patterson
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Huixiao Hong
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
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30
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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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31
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Smith CW, Hizir MS, Nandu N, Yigit MV. Algorithmically Guided Optical Nanosensor Selector (AGONS): Guiding Data Acquisition, Processing, and Discrimination for Biological Sampling. Anal Chem 2021; 94:1195-1202. [PMID: 34964601 DOI: 10.1021/acs.analchem.1c04379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Here, we report a biomarker-free detection of various biological targets through a programmed machine learning algorithm and an automated computational selection process termed algorithmically guided optical nanosensor selector (AGONS). The optical data processed/used by algorithms are obtained through a nanosensor array selected from a library of nanosensors through AGONS. The nanosensors are assembled using two-dimensional nanoparticles (2D-nps) and fluorescently labeled single-stranded DNAs (F-ssDNAs) with random sequences. Both 2D-np and F-ssDNA components are cost-efficient and easy to synthesize, allowing for scaled-up data collection essential for machine learning modeling. The nanosensor library was subjected to various target groups, including proteins, breast cancer cells, and lethal-7 (let-7) miRNA mimics. We have demonstrated that AGONS could select the most essential nanosensors while achieving 100% predictive accuracy in all cases. With this approach, we demonstrate that machine learning can guide the design of nanosensor arrays with greater predictive accuracy while minimizing manpower, material cost, computational resources, instrumentation usage, and time. The biomarker-free detection attribute makes this approach readily available for biological targets without any detectable biomarker. We believe that AGONS can guide optical nanosensor array setups, opening broader opportunities through a biomarker-free detection approach for most challenging biological targets.
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Affiliation(s)
- Christopher W Smith
- Department of Chemistry, University at Albany, State University of New York, 1400 Washington Avenue, Albany, New York 12222, United States.,The RNA Institute, University at Albany, State University of New York, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Mustafa Salih Hizir
- Department of Chemistry, University at Albany, State University of New York, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Nidhi Nandu
- Department of Chemistry, University at Albany, State University of New York, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Mehmet V Yigit
- Department of Chemistry, University at Albany, State University of New York, 1400 Washington Avenue, Albany, New York 12222, United States.,The RNA Institute, University at Albany, State University of New York, 1400 Washington Avenue, Albany, New York 12222, United States
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32
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Basei G, Zabeo A, Rasmussen K, Tsiliki G, Hristozov D. A Weight of Evidence approach to classify nanomaterials according to the EU Classification, Labelling and Packaging Regulation criteria. NANOIMPACT 2021; 24:100359. [PMID: 35559818 DOI: 10.1016/j.impact.2021.100359] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 09/13/2021] [Accepted: 09/30/2021] [Indexed: 06/15/2023]
Abstract
In the context of the European Union (EU) Horizon 2020 GRACIOUS project (Grouping, Read-Across, Characterisation and classification framework for regulatory risk assessment of manufactured nanomaterials and Safer design of nano-enabled products), we proposed a quantitative Weight of Evidence (WoE) approach for hazard classification of nanomaterials (NMs). This approach is based on the requirements of the European Regulation on Classification, Labelling and Packaging of Substances and Mixtures (the CLP regulation), which implements the United Nations' Globally Harmonized System of Classification and Labelling of Chemicals (UN GHS) in the European Union. The goal of this WoE methodology is to facilitate classification of NMs according to CLP criteria, following the decision trees defined in ECHA's CLP regulatory guidance. In the WoE, results from heterogeneous studies are weighted according to data quality and completeness criteria, integrated, and then evaluated by expert judgment to obtain a hazard classification, resulting in a coherent and justifiable methodology. Moreover, the probabilistic nature of the proposed approach enables highlighting the uncertainty in the analysis. The proposed methodology involves the following stages: (1) collection of data for different NMs related to the endpoint of interest: each study related to each NM is referred as a Line of Evidence (LoE); (2) computation of weighted scores for each LoE: each LoE is weighted by a score calculated based on data quality and completeness criteria defined in the GRACIOUS project; (3) comparison and integration of the weighed LoEs for each NM: A Monte Carlo resampling approach is adopted to quantitatively and probabilistically integrate the weighted evidence; and (4) assignment of each NM to a hazard class: according to the results, each NM is assigned to one of the classes defined by the CLP regulation. Furthermore, to facilitate the integration and the classification of the weighted LoEs, an online R tool was developed. Finally, the approach was tested against an endpoint relevant to CLP (Aquatic Toxicity) using data retrieved from the eNanoMapper database, results obtained were consistent to results in REACH registration dossiers and in recent literature.
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33
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Hayat H, Nukala A, Nyamira A, Fan J, Wang P. A concise review: the synergy between artificial intelligence and biomedical nanomaterials that empowers nanomedicine. Biomed Mater 2021; 16. [PMID: 34280907 DOI: 10.1088/1748-605x/ac15b2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 07/19/2021] [Indexed: 12/17/2022]
Abstract
Nanomedicine has recently experienced unprecedented growth and development. However, the complexity of operations at the nanoscale introduces a layer of difficulty in the clinical translation of nanodrugs and biomedical nanotechnology. This problem is further exacerbated when engineering and optimizing nanomaterials for biomedical purposes. To navigate this issue, artificial intelligence (AI) algorithms have been applied for data analysis and inference, allowing for a more applicable understanding of the complex interaction amongst the abundant variables in a system involving the synthesis or use of nanomedicine. Here, we report on the current relationship and implications of nanomedicine and AI. Particularly, we explore AI as a tool for enabling nanomedicine in the context of nanodrug screening and development, brain-machine interfaces and nanotoxicology. We also report on the current state and future direction of nanomedicine and AI in cancer, diabetes, and neurological disorder therapy.
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Affiliation(s)
- Hasaan Hayat
- Precision Health Program,, Michigan State University, East Lansing, MI, United States of America.,Lyman Briggs College, Michigan State University, East Lansing, MI, United States of America
| | - Arijit Nukala
- Precision Health Program,, Michigan State University, East Lansing, MI, United States of America.,Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, United States of America
| | - Anthony Nyamira
- Lyman Briggs College, Michigan State University, East Lansing, MI, United States of America
| | - Jinda Fan
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States of America
| | - Ping Wang
- Precision Health Program,, Michigan State University, East Lansing, MI, United States of America.,Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, United States of America
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34
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Guo Z, Chakraborty S, Monikh FA, Varsou DD, Chetwynd AJ, Afantitis A, Lynch I, Zhang P. Surface Functionalization of Graphene-Based Materials: Biological Behavior, Toxicology, and Safe-By-Design Aspects. Adv Biol (Weinh) 2021; 5:e2100637. [PMID: 34288601 DOI: 10.1002/adbi.202100637] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 06/11/2021] [Indexed: 01/08/2023]
Abstract
The increasing exploitation of graphene-based materials (GBMs) is driven by their unique properties and structures, which ignite the imagination of scientists and engineers. At the same time, the very properties that make them so useful for applications lead to growing concerns regarding their potential impacts on human health and the environment. Since GBMs are inert to reaction, various attempts of surface functionalization are made to make them reactive. Herein, surface functionalization of GBMs, including those intentionally designed for specific applications, as well as those unintentionally acquired (e.g., protein corona formation) from the environment and biota, are reviewed through the lenses of nanotoxicity and design of safe materials (safe-by-design). Uptake and toxicity of functionalized GBMs and the underlying mechanisms are discussed and linked with the surface functionalization. Computational tools that can predict the interaction of GBMs behavior with their toxicity are discussed. A concise framing of current knowledge and key features of GBMs to be controlled for safe and sustainable applications are provided for the community.
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Affiliation(s)
- Zhiling Guo
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Swaroop Chakraborty
- Department of Biological Engineering, Indian Institute of Technology, Gandhinagar, Gujarat, 382355, India
| | - Fazel Abdolahpur Monikh
- Department of Environmental & Biological Sciences, University of Eastern Finland, P.O. Box 111, Joensuu, FI-80101, Finland
| | - Dimitra-Danai Varsou
- School of Chemical Engineering, National Technical University of Athens, Athens, 15780, Greece
| | - Andrew J Chetwynd
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Antreas Afantitis
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia, 1046, Cyprus
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Peng Zhang
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
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Mirzaei M, Furxhi I, Murphy F, Mullins M. A Machine Learning Tool to Predict the Antibacterial Capacity of Nanoparticles. NANOMATERIALS (BASEL, SWITZERLAND) 2021; 11:1774. [PMID: 34361160 PMCID: PMC8308172 DOI: 10.3390/nano11071774] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/13/2021] [Accepted: 07/06/2021] [Indexed: 12/22/2022]
Abstract
The emergence and rapid spread of multidrug-resistant bacteria strains are a public health concern. This emergence is caused by the overuse and misuse of antibiotics leading to the evolution of antibiotic-resistant strains. Nanoparticles (NPs) are objects with all three external dimensions in the nanoscale that varies from 1 to 100 nm. Research on NPs with enhanced antimicrobial activity as alternatives to antibiotics has grown due to the increased incidence of nosocomial and community acquired infections caused by pathogens. Machine learning (ML) tools have been used in the field of nanoinformatics with promising results. As a consequence of evident achievements on a wide range of predictive tasks, ML techniques are attracting significant interest across a variety of stakeholders. In this article, we present an ML tool that successfully predicts the antibacterial capacity of NPs while the model's validation demonstrates encouraging results (R2 = 0.78). The data were compiled after a literature review of 60 articles and consist of key physico-chemical (p-chem) properties and experimental conditions (exposure variables and bacterial clustering) from in vitro studies. Following data homogenization and pre-processing, we trained various regression algorithms and we validated them using diverse performance metrics. Finally, an important attribute evaluation, which ranks the attributes that are most important in predicting the outcome, was performed. The attribute importance revealed that NP core size, the exposure dose, and the species of bacterium are key variables in predicting the antibacterial effect of NPs. This tool assists various stakeholders and scientists in predicting the antibacterial effects of NPs based on their p-chem properties and diverse exposure settings. This concept also aids the safe-by-design paradigm by incorporating functionality tools.
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Affiliation(s)
- Mahsa Mirzaei
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (M.M.); (F.M.); (M.M.)
| | - Irini Furxhi
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (M.M.); (F.M.); (M.M.)
- Transgero Limited, Cullinagh, Newcastle West, V42V384 Limerick, Ireland
| | - Finbarr Murphy
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (M.M.); (F.M.); (M.M.)
- Transgero Limited, Cullinagh, Newcastle West, V42V384 Limerick, Ireland
| | - Martin Mullins
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; (M.M.); (F.M.); (M.M.)
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Marcoulaki E, López de Ipiña JM, Vercauteren S, Bouillard J, Himly M, Lynch I, Witters H, Shandilya N, van Duuren-Stuurman B, Kunz V, Unger WES, Hodoroaba VD, Bard D, Evans G, Jensen KA, Pilou M, Viitanen AK, Bochon A, Duschl A, Geppert M, Persson K, Cotgreave I, Niga P, Gini M, Eleftheriadis K, Scalbi S, Caillard B, Arevalillo A, Frejafon E, Aguerre-Chariol O, Dulio V. Blueprint for a self-sustained European Centre for service provision in safe and sustainable innovation for nanotechnology. NANOIMPACT 2021; 23:100337. [PMID: 35559838 DOI: 10.1016/j.impact.2021.100337] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/05/2021] [Accepted: 06/17/2021] [Indexed: 06/15/2023]
Abstract
The coming years are expected to bring rapid changes in the nanotechnology regulatory landscape, with the establishment of a new framework for nano-risk governance, in silico approaches for characterisation and risk assessment of nanomaterials, and novel procedures for the early identification and management of nanomaterial risks. In this context, Safe(r)-by-Design (SbD) emerges as a powerful preventive approach to support the development of safe and sustainable (SSbD) nanotechnology-based products and processes throughout the life cycle. This paper summarises the work undertaken to develop a blueprint for the deployment and operation of a permanent European Centre of collaborating laboratories and research organisations supporting safe innovation in nanotechnologies. The proposed entity, referred to as "the Centre", will establish a 'one-stop shop' for nanosafety-related services and a central contact point for addressing stakeholder questions about nanosafety. Its operation will rely on significant business, legal and market knowledge, as well as other tools developed and acquired through the EU-funded EC4SafeNano project and subsequent ongoing activities. The proposed blueprint adopts a demand-driven service update scheme to allow the necessary vigilance and flexibility to identify opportunities and adjust its activities and services in the rapidly evolving regulatory and nano risk governance landscape. The proposed Centre will play a major role as a conduit to transfer scientific knowledge between the research and commercial laboratories or consultants able to provide high quality nanosafety services, and the end-users of such services (e.g., industry, SMEs, consultancy firms, and regulatory authorities). The Centre will harmonise service provision, and bring novel risk assessment and management approaches, e.g. in silico methodologies, closer to practice, notably through SbD/SSbD, and decisively support safe and sustainable innovation of industrial production in the nanotechnology industry according to the European Chemicals Strategy for Sustainability.
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Affiliation(s)
- Effie Marcoulaki
- National Centre for Scientific Research "Demokritos", PO Box 60037, 15310 Agia Paraskevi, Greece.
| | - Jesús M López de Ipiña
- TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Alava, 01510 Miñano, Spain.
| | | | - Jacques Bouillard
- Institut national de l'environnement industriel et des risques (INERIS), Rue Jacques Taffanel, Parc technologique ALATA, Verneuil-en-Halatte, 60550, France.
| | - Martin Himly
- Paris Lodron University of Salzburg, Kapitelgasse 4/6, 5020 Salzburg, Austria.
| | - Iseult Lynch
- School of Geography, Earth & Environmental Sciences, University of Birmingham, Edgbaston, B15 2TT Birmingham, UK.
| | - Hilda Witters
- VITO NV, Health Unit, Boeretang 200, 2400 Mol, Belgium.
| | - Neeraj Shandilya
- TNO, Research group Risk Analysis for Products in Development (RAPID), Princetonlaan 6, 3584 CB Utrecht, Netherlands.
| | - Birgit van Duuren-Stuurman
- TNO, Research group Risk Analysis for Products in Development (RAPID), Princetonlaan 6, 3584 CB Utrecht, Netherlands.
| | - Valentin Kunz
- Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 44-46, 12203 Berlin, Germany
| | - Wolfgang E S Unger
- Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 44-46, 12203 Berlin, Germany
| | - Vasile-Dan Hodoroaba
- Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 44-46, 12203 Berlin, Germany.
| | - Delphine Bard
- Health & Safety Executive Science and Research Centre, Harpur Hill, Buxton, Derbyshire SK17 9JN, UK.
| | - Gareth Evans
- Health & Safety Executive Science and Research Centre, Harpur Hill, Buxton, Derbyshire SK17 9JN, UK.
| | - Keld Alstrup Jensen
- National Research Center for the Work Environment (NRCWE), Lersø Parkallé 105, 2100 København, Denmark.
| | - Marika Pilou
- National Centre for Scientific Research "Demokritos", PO Box 60037, 15310 Agia Paraskevi, Greece.
| | - Anna-Kaisa Viitanen
- Finnish Institute of Occupational Health (FIOH), P.O. Box 40, FI-00032 Työterveyslaitos, Finland.
| | - Anthony Bochon
- JurisLab, Centre de droit privé, Université Libre de Bruxelles, Avenue F. Roosevelt 50, CP 137, 1050 Bruxelles, Belgium.
| | - Albert Duschl
- Paris Lodron University of Salzburg, Kapitelgasse 4/6, 5020 Salzburg, Austria.
| | - Mark Geppert
- Paris Lodron University of Salzburg, Kapitelgasse 4/6, 5020 Salzburg, Austria.
| | - Karin Persson
- RISE Surface, Process and Formulation, Box 5607, SE-114 86 Stockholm, Sweden.
| | - Ian Cotgreave
- RISE Surface, Process and Formulation, Box 5607, SE-114 86 Stockholm, Sweden.
| | - Petru Niga
- RISE Surface, Process and Formulation, Box 5607, SE-114 86 Stockholm, Sweden.
| | - Maria Gini
- National Centre for Scientific Research "Demokritos", PO Box 60037, 15310 Agia Paraskevi, Greece.
| | | | - Simona Scalbi
- ENEA, Agenzia Nazionale per le nuove tecnologie, l'energia e lo sviluppo sostenibile, SSPT-USER-RISE, Via martiri di monte sole 4, 40129 Bologna, Italy.
| | - Bastien Caillard
- European Risk Management Institute (EU-VRi), Fangelsbachstr. 14, 70178 Stuttgart, Germany.
| | - Alfonso Arevalillo
- TECNALIA, Basque Research and Technology Alliance (BRTA), Area Anardi 5, 20730 Azpeitia, Spain.
| | - Emeric Frejafon
- BRGM, 3 av. Claude-Guillemin, BP 36009, 45100 Orléans Cedex 2, France.
| | - Olivier Aguerre-Chariol
- Institut national de l'environnement industriel et des risques (INERIS), Rue Jacques Taffanel, Parc technologique ALATA, Verneuil-en-Halatte, 60550, France.
| | - Valeria Dulio
- Institut national de l'environnement industriel et des risques (INERIS), Rue Jacques Taffanel, Parc technologique ALATA, Verneuil-en-Halatte, 60550, France.
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Data Shepherding in Nanotechnology. The Initiation. NANOMATERIALS 2021; 11:nano11061520. [PMID: 34201308 PMCID: PMC8230087 DOI: 10.3390/nano11061520] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/25/2021] [Accepted: 06/07/2021] [Indexed: 01/26/2023]
Abstract
In this paper we describe the pragmatic approach of initiating, designing and implementing the Data Management Plan (DMP) and the data FAIRification process in the multidisciplinary Horizon 2020 nanotechnology project, Anticipating Safety Issues at the Design Stage of NAno Product Development (ASINA). We briefly describe the general DMP requirements, emphasizing that the initial steps in the direction towards data FAIRification must be conceptualized and visualized in a systematic way. We demonstrate the use of a generic questionnaire to capture primary data and metadata description from our consortium (data creators/experimentalists and data analysts/modelers). We then display the interactive process with external FAIR data initiatives (data curators/quality assessors), regarding guidance for data and metadata capturing and future integration into repositories. After the preliminary data capturing and FAIRification template is formed, the inner-communication process begins between the partners, which leads to developing case-specific templates. This paper assists future data creators, data analysts, stewards and shepherds engaged in the multi-faceted data shepherding process, in any project, by providing a roadmap, demonstrated in the case of ASINA.
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Ahmad F, Mahmood A, Muhmood T. Machine learning-integrated omics for the risk and safety assessment of nanomaterials. Biomater Sci 2021; 9:1598-1608. [PMID: 33443512 DOI: 10.1039/d0bm01672a] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
With the advancement in nanotechnology, we are experiencing transformation in world order with deep insemination of nanoproducts from basic necessities to advanced electronics, health care products and medicines. Therefore, nanoproducts, however, can have negative side effects and must be strictly monitored to avoid negative outcomes. Future toxicity and safety challenges regarding nanomaterial incorporation into consumer products, including rapid addition of nanomaterials with diverse functionalities and attributes, highlight the limitations of traditional safety evaluation tools. Currently, artificial intelligence and machine learning algorithms are envisioned for enhancing and improving the nano-bio-interaction simulation and modeling, and they extend to the post-marketing surveillance of nanomaterials in the real world. Thus, hyphenation of machine learning with biology and nanomaterials could provide exclusive insights into the perturbations of delicate biological functions after integration with nanomaterials. In this review, we discuss the potential of combining integrative omics with machine learning in profiling nanomaterial safety and risk assessment and provide guidance for regulatory authorities as well.
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Affiliation(s)
- Farooq Ahmad
- College of Engineering and Applied Sciences, Nanjing National Laboratory of Microstructures, Jiangsu Key Laboratory of Artificial Functional Materials, Nanjing University, Nanjing, Jiangsu 210093, China.
| | - Asif Mahmood
- Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion Materials, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, 100081, China
| | - Tahir Muhmood
- State Key Lab of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
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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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40
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Peng L, Chen Z, Long Z, Liu M, Lei L, Wang C, Peng H, Shi Y, Peng Y, Deng Q, Wang S, Zou G, Wan L, Yuan H, He L, Xie Y, Tang Z, Wan N, Gong Y, Hou X, Shen L, Xia K, Li J, Chen C, Qiu R, Klockgether T, Tang B, Jiang H. New Model for Estimation of the Age at Onset in Spinocerebellar Ataxia Type 3. Neurology 2021; 96:e2885-e2895. [PMID: 33893204 DOI: 10.1212/wnl.0000000000012068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Accepted: 03/11/2021] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVES The aim of this study was to develop an appropriate parametric survival model to predict patient's age at onset (AAO) for spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) populations from mainland China. METHODS We compared the efficiency and performance of 6 parametric survival analysis methods (exponential, weibull, log-gaussian, gaussian, log-logistic, and logistic) based on cytosine-adenine-guanine (CAG) repeat length at ATXN3 to predict the probability of AAO in the largest cohort of patients with SCA3/MJD. A set of evaluation criteria, including -2 log-likelihood statistic, Akaike information criterion (AIC), bayesian information criterion (BIC), Nagelkerke R-squared (Nagelkerke R^2), and Cox-Snell residual plot, were used to identify the best model. RESULTS Among these 6 parametric survival models, the logistic model had the lowest -2 log-likelihood (6,560.12), AIC (6,566.12), and BIC (6,566.14) and the highest value of Nagelkerke R^2 (0.54), with the closest graph to the bisector Cox-Snell residual graph. Therefore, the logistic survival model was the best fit to the studied data. Using the optimal logistic survival model, we indicated the age-specific probability distribution of AAO according to the CAG repeat size and current age. CONCLUSIONS We first demonstrated that the logistic survival model provided the best fit for AAO prediction in patients with SCA3/MJD from mainland China. This optimal model can be valuable in clinical and research. However, the rigorous clinical testing and practice of other independent cohorts are needed for its clinical application. A unified model across multiethnic cohorts is worth further exploration by identifying regional differences and significant modifiers in AAO determination.
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Affiliation(s)
- Linliu Peng
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Zhao Chen
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Zhe Long
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Mingjie Liu
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Lijing Lei
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Chunrong Wang
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Huirong Peng
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Yuting Shi
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Yun Peng
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Qi Deng
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Shang Wang
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Guangdong Zou
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Linlin Wan
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Hongyu Yuan
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Lang He
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Yue Xie
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Zhichao Tang
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Na Wan
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Yiqing Gong
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Xuan Hou
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Lu Shen
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Kun Xia
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Jinchen Li
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Chao Chen
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Rong Qiu
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Thomas Klockgether
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Beisha Tang
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany
| | - Hong Jiang
- From the Department of Neurology (L.P., Z.C., M.L., L.L., H.P., Y.S., Y.P., Q.D., S.W., G.Z., L.W., H.Y., L.H., Y.X., Z.T., N.W., Y.G., X.H., L.S., J.L., B.T., H.J.), Department of Pathology (C.W.), National Clinical Research Center for Geriatric Disorders (Z.C., L.S., B.T., H.J.), Xiangya Hospital, Central South University; Department of Neurology (Z.L.), The Second Xiangya Hospital, Central South University; Key Laboratory of Hunan Province in Neurodegenerative Disorders (Z.C., L.S., J.L., B.T., H.J.), Center for Medical Genetics School of Life Sciences (K.X., J.L., C.C.), Hunan Key Laboratory of Medical Genetics (K.X., J.L., C.C.), School of Computer Science and Engineering (R.Q.), and School of Basic Medical Science (H.J.), Central South University, Changsha, Hunan, China; Department of Neurology (T.K.), University of Bonn; and German Center for Neurodegenerative Diseases (DZNE) (T.K.), Bonn, Germany.
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Huang HJ, Lee YH, Hsu YH, Liao CT, Lin YF, Chiu HW. Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing. Int J Mol Sci 2021; 22:4216. [PMID: 33921715 PMCID: PMC8073679 DOI: 10.3390/ijms22084216] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/01/2021] [Accepted: 04/16/2021] [Indexed: 12/12/2022] Open
Abstract
Millions of experimental animals are widely used in the assessment of toxicological or biological effects of manufactured nanomaterials in medical technology. However, the animal consciousness has increased and become an issue for debate in recent years. Currently, the principle of the 3Rs (i.e., reduction, refinement, and replacement) is applied to ensure the more ethical application of humane animal research. In order to avoid unethical procedures, the strategy of alternatives to animal testing has been employed to overcome the drawbacks of animal experiments. This article provides current alternative strategies to replace or reduce the use of experimental animals in the assessment of nanotoxicity. The currently available alternative methods include in vitro and in silico approaches, which can be used as cost-effective approaches to meet the principle of the 3Rs. These methods are regarded as non-animal approaches and have been implemented in many countries for scientific purposes. The in vitro experiments related to nanotoxicity assays involve cell culture testing and tissue engineering, while the in silico methods refer to prediction using molecular docking, molecular dynamics simulations, and quantitative structure-activity relationship (QSAR) modeling. The commonly used novel cell-based methods and computational approaches have the potential to help minimize the use of experimental animals for nanomaterial toxicity assessments.
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Affiliation(s)
- Hung-Jin Huang
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
| | - Yu-Hsuan Lee
- Department of Cosmeceutics, China Medical University, Taichung 406040, Taiwan;
| | - Yung-Ho Hsu
- Division of Nephrology, Department of Internal Medicine, Hsin Kuo Min Hospital, Taipei Medical University, Taoyuan City 320001, Taiwan;
- Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei 11031, Taiwan
| | - Chia-Te Liao
- Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei 11031, Taiwan
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
| | - Yuh-Feng Lin
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei 11031, Taiwan
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
| | - Hui-Wen Chiu
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei 11031, Taiwan
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
- Department of Medical Research, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
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Albalawi F, Hussein MZ, Fakurazi S, Masarudin MJ. Engineered Nanomaterials: The Challenges and Opportunities for Nanomedicines. Int J Nanomedicine 2021; 16:161-184. [PMID: 33447033 PMCID: PMC7802788 DOI: 10.2147/ijn.s288236] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 12/05/2020] [Indexed: 12/14/2022] Open
Abstract
The emergence of nanotechnology as a key enabling technology over the past years has opened avenues for new and innovative applications in nanomedicine. From the business aspect, the nanomedicine market was estimated to worth USD 293.1 billion by 2022 with a perception of market growth to USD 350.8 billion in 2025. Despite these opportunities, the underlying challenges for the future of engineered nanomaterials (ENMs) in nanomedicine research became a significant obstacle in bringing ENMs into clinical stages. These challenges include the capability to design bias-free methods in evaluating ENMs' toxicity due to the lack of suitable detection and inconsistent characterization techniques. Therefore, in this literature review, the state-of-the-art of engineered nanomaterials in nanomedicine, their toxicology issues, the working framework in developing a toxicology benchmark and technical characterization techniques in determining the toxicity of ENMs from the reported literature are explored.
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Affiliation(s)
- Fahad Albalawi
- Department of Medical Laboratory and Blood Bank, King Fahad Specialist Hospital-Tabuk, Tabuk, Saudi Arabia
- Materials Synthesis and Characterization Laboratory, Institute of Advanced Technology (ITMA), Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Mohd Zobir Hussein
- Materials Synthesis and Characterization Laboratory, Institute of Advanced Technology (ITMA), Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Sharida Fakurazi
- Department of Human Anatomy, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
- Natural Medicine and Product Research Laboratory Institute of Bioscience, Serdang, Selangor, Malaysia
| | - Mas Jaffri Masarudin
- Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
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Lynch I, Afantitis A, Greco D, Dusinska M, Banares MA, Melagraki G. Editorial for the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance. NANOMATERIALS 2021; 11:nano11010121. [PMID: 33430326 PMCID: PMC7825746 DOI: 10.3390/nano11010121] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 12/29/2020] [Indexed: 12/12/2022]
Affiliation(s)
- Iseult Lynch
- School of Geography, Earth & Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
- Correspondence:
| | - Antreas Afantitis
- Department of Cheminformatics, NovaMechanics Ltd., Nicosia 1065, Cyprus; (A.A.); (G.M.)
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, 33100 Tampere, Finland;
| | - Maria Dusinska
- Environmental Chemistry Department, Norwegian Institute for Air Research, 2027 Kjeller, Norway;
| | - Miguel A. Banares
- Institute for Catalysis, ICP-CSIC, Marie Curie 2, E-28049 Madrid, Spain;
| | - Georgia Melagraki
- Department of Cheminformatics, NovaMechanics Ltd., Nicosia 1065, Cyprus; (A.A.); (G.M.)
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Basso J, Mendes M, Silva J, Cova T, Luque-Michel E, Jorge AF, Grijalvo S, Gonçalves L, Eritja R, Blanco-Prieto MJ, Almeida AJ, Pais A, Vitorino C. Sorting hidden patterns in nanoparticle performance for glioblastoma using machine learning algorithms. Int J Pharm 2021; 592:120095. [PMID: 33220382 DOI: 10.1016/j.ijpharm.2020.120095] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 11/10/2020] [Accepted: 11/11/2020] [Indexed: 12/29/2022]
Abstract
Cationic compounds have been described to readily penetrate cell membranes. Assigning positive charge to nanosystems, e.g. lipid nanoparticles, has been identified as a key feature to promote electrostatic binding and design ligand-based constructs for tumour targeting. However, their intrinsic high cytotoxicity has hampered their biomedical application. This paper seeks to establish which cationic compounds and properties are compelling for interface modulation, in order to improve the design of tumour targeted nanoparticles against glioblastoma. How can intrinsic features (e.g. nature, structure, conformation) shape efficacy outcomes? In the quest for safer alternative cationic compounds, we evaluate the effects of two novel glycerol-based lipids, GLY1 and GLY2, on the architecture and performance of nanostructured lipid carriers (NLCs). These two molecules, composed of two alkylated chains and a glycerol backbone, differ only in their polar head and proved to be efficient in reversing the zeta potential of the nanosystems to positive values. The use of unsupervised and supervised machine learning (ML) techniques unraveled their structural similarities: in spite of their common backbone, GLY1 exhibited a better performance in increasing zeta potential and cytotoxicity, while decreasing particle size. Furthermore, NLCs containing GLY1 showed a favorable hemocompatible profile, as well as an improved uptake by tumour cells. Summing-up, GLY1 circumvents the intrinsic cytotoxicity of a common surfactant, CTAB, is effective at increasing glioblastoma uptake, and exhibits encouraging anticancer activity. Moreover, the use of ML is strongly incited for formulation design and optimization.
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Affiliation(s)
- João Basso
- Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal; Coimbra Chemistry Centre, Department of Chemistry, University of Coimbra, Rua Larga, 3004-535 Coimbra, Portugal; Centre for Neurosciences and Cell Biology (CNC), University of Coimbra, Faculty of Medicine, Rua Larga, Pólo I, 1st Floor, 3004-504 Coimbra, Portugal
| | - Maria Mendes
- Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal; Coimbra Chemistry Centre, Department of Chemistry, University of Coimbra, Rua Larga, 3004-535 Coimbra, Portugal; Centre for Neurosciences and Cell Biology (CNC), University of Coimbra, Faculty of Medicine, Rua Larga, Pólo I, 1st Floor, 3004-504 Coimbra, Portugal
| | - Jessica Silva
- Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal; Centre for Neurosciences and Cell Biology (CNC), University of Coimbra, Faculty of Medicine, Rua Larga, Pólo I, 1st Floor, 3004-504 Coimbra, Portugal
| | - Tânia Cova
- Coimbra Chemistry Centre, Department of Chemistry, University of Coimbra, Rua Larga, 3004-535 Coimbra, Portugal
| | - Edurne Luque-Michel
- Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, C/Irunlarrea 1, E-31008 Pamplona, Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Andreia F Jorge
- Coimbra Chemistry Centre, Department of Chemistry, University of Coimbra, Rua Larga, 3004-535 Coimbra, Portugal
| | - Santiago Grijalvo
- Institute for Advanced Chemistry of Catalonia (IQAC-CSIC), Jordi Girona 18-26, E-08034 Barcelona, Spain; Networking Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Jordi Girona 18-26, E-08034 Barcelona, Spain
| | - Lídia Gonçalves
- Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, 1649-003 Lisbon, Portugal
| | - Ramon Eritja
- Institute for Advanced Chemistry of Catalonia (IQAC-CSIC), Jordi Girona 18-26, E-08034 Barcelona, Spain; Networking Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Jordi Girona 18-26, E-08034 Barcelona, Spain
| | - María J Blanco-Prieto
- Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, C/Irunlarrea 1, E-31008 Pamplona, Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - António José Almeida
- Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, 1649-003 Lisbon, Portugal
| | - Alberto Pais
- Coimbra Chemistry Centre, Department of Chemistry, University of Coimbra, Rua Larga, 3004-535 Coimbra, Portugal
| | - Carla Vitorino
- Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal; Coimbra Chemistry Centre, Department of Chemistry, University of Coimbra, Rua Larga, 3004-535 Coimbra, Portugal; Centre for Neurosciences and Cell Biology (CNC), University of Coimbra, Faculty of Medicine, Rua Larga, Pólo I, 1st Floor, 3004-504 Coimbra, Portugal.
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Damasco JA, Ravi S, Perez JD, Hagaman DE, Melancon MP. Understanding Nanoparticle Toxicity to Direct a Safe-by-Design Approach in Cancer Nanomedicine. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E2186. [PMID: 33147800 PMCID: PMC7692849 DOI: 10.3390/nano10112186] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 10/26/2020] [Accepted: 10/28/2020] [Indexed: 12/22/2022]
Abstract
Nanomedicine is a rapidly growing field that uses nanomaterials for the diagnosis, treatment and prevention of various diseases, including cancer. Various biocompatible nanoplatforms with diversified capabilities for tumor targeting, imaging, and therapy have materialized to yield individualized therapy. However, due to their unique properties brought about by their small size, safety concerns have emerged as their physicochemical properties can lead to altered pharmacokinetics, with the potential to cross biological barriers. In addition, the intrinsic toxicity of some of the inorganic materials (i.e., heavy metals) and their ability to accumulate and persist in the human body has been a challenge to their translation. Successful clinical translation of these nanoparticles is heavily dependent on their stability, circulation time, access and bioavailability to disease sites, and their safety profile. This review covers preclinical and clinical inorganic-nanoparticle based nanomaterial utilized for cancer imaging and therapeutics. A special emphasis is put on the rational design to develop non-toxic/safe inorganic nanoparticle constructs to increase their viability as translatable nanomedicine for cancer therapies.
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Affiliation(s)
- Jossana A. Damasco
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (J.A.D.); (J.D.P.); (D.E.H.)
| | - Saisree Ravi
- School of Medicine, University of Texas Rio Grande Valley, Edinburg, TX 78539, USA;
| | - Joy D. Perez
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (J.A.D.); (J.D.P.); (D.E.H.)
| | - Daniel E. Hagaman
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (J.A.D.); (J.D.P.); (D.E.H.)
| | - Marites P. Melancon
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (J.A.D.); (J.D.P.); (D.E.H.)
- UT Health Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Setyawati MI, Zhao Z, Ng KW. Transformation of Nanomaterials and Its Implications in Gut Nanotoxicology. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e2001246. [PMID: 32495486 DOI: 10.1002/smll.202001246] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 04/24/2020] [Indexed: 06/11/2023]
Abstract
Ingestion of engineered nanomaterials (ENMs) is inevitable due to their widespread utilization in the agrifood industry. Safety evaluation has become pivotal to identify the consequences on human health of exposure to these ingested ENMs. Much of the current understanding of nanotoxicology in the gastrointestinal tract (GIT) is derived from studies utilizing pristine ENMs. In reality, agrifood ENMs interact with their microenvironment, and undergo multiple physicochemical transformations, such as aggregation/agglomeration, dissolution, speciation change, and surface characteristics alteration, across their life cycle from synthesis to consumption. This work sieves out the implications of ENM transformations on their behavior, stability, and reactivity in food and product matrices and through the GIT, in relation to measured toxicological profiles. In particular, a strong emphasis is given to understand the mechanisms through which these transformations can affect ENM induced gut nanotoxicity.
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Affiliation(s)
- Magdiel Inggrid Setyawati
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Zhitong Zhao
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Kee Woei Ng
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, 02115, USA
- Environmental Chemistry and Materials Centre, Nanyang Environment and Water Research Institute (NEWRI), Nanyang Technological University, 1 Cleantech Loop, CleanTech One, Singapore, 637141, Singapore
- Skin Research Institute of Singapore, Biomedical Science Institutes, Immunos, 8A Biomedical Grove, Singapore, 138648, Singapore
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Winkler DA. Role of Artificial Intelligence and Machine Learning in Nanosafety. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e2001883. [PMID: 32537842 DOI: 10.1002/smll.202001883] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 05/07/2020] [Indexed: 06/11/2023]
Abstract
Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and analysis by modern machine learning (ML) methods. Nanomaterials have not yet fully captured the benefits of automation, so lag behind in the application of ML methods of data analysis. Here, some key developments in, and roadblocks to the application of ML methods are reviewed to model and predict potentially adverse biological and environmental effects of nanomaterials. This work focuses on the diverse ways a range of ML algorithms are applied to understand and predict nanomaterials properties, provides examples of the application of traditional ML and deep learning methods to nanosafety, and provides context and future perspectives on developments that are likely to occur, or need to occur in the near future that allow artificial intelligence to make a deeper contribution to nanosafety.
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Affiliation(s)
- David A Winkler
- La Trobe Institute for Molecular Science, La Trobe University, Kingsbury Drive, Bundoora, 3042, Australia
- CSIRO Data61, 1 Technology Court, Pullenvale, 4069, Australia
- School of Pharmacy, University of Nottingham, Nottingham, NG7 2QL, UK
- Monash Institute of Pharmaceutical Sciences, Monash University, 392 Royal Parade, Parkville, 3052, Australia
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48
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Daly CA, Hernandez R. Optimizing bags of artificial neural networks for the prediction of viability from sparse data. J Chem Phys 2020; 153:054112. [DOI: 10.1063/5.0017229] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Affiliation(s)
- Clyde A. Daly
- Department of Chemistry, The Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Rigoberto Hernandez
- Department of Chemistry, The Johns Hopkins University, Baltimore, Maryland 21218, USA
- Departments of Chemical and Biomolecular Engineering, and Materials Science and Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
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49
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Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning. Int J Mol Sci 2020; 21:ijms21155280. [PMID: 32722414 PMCID: PMC7432486 DOI: 10.3390/ijms21155280] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 07/21/2020] [Accepted: 07/23/2020] [Indexed: 12/31/2022] Open
Abstract
The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models.
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50
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Furxhi I, Murphy F, Mullins M, Arvanitis A, Poland CA. Nanotoxicology data for in silico tools: a literature review. Nanotoxicology 2020; 14:612-637. [PMID: 32100604 DOI: 10.1080/17435390.2020.1729439] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies.
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Affiliation(s)
- Irini Furxhi
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Finbarr Murphy
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Martin Mullins
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Athanasios Arvanitis
- Department of Mechanical Engineering, Environmental Informatics Research Group, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Craig A Poland
- ELEGI/Colt Laboratory, Queen's Medical Research Institute, 47 Little France Crescent, University of Edinburgh, Edinburgh, Scotland
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