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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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2
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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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3
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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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4
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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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5
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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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6
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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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7
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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: 1] [Impact Index Per Article: 1.0] [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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8
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Griego A, Scarpa E, De Matteis V, Rizzello L. Nanoparticle delivery through the BBB in central nervous system tuberculosis. IBRAIN 2023; 9:43-62. [PMID: 37786519 PMCID: PMC10528790 DOI: 10.1002/ibra.12087] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 12/16/2022] [Accepted: 12/19/2022] [Indexed: 10/04/2023]
Abstract
Recent advances in Nanotechnology have revolutionized the production of materials for biomedical applications. Nowadays, there is a plethora of nanomaterials with potential for use towards improvement of human health. On the other hand, very little is known about how these materials interact with biological systems, especially at the nanoscale level, mainly because of the lack of specific methods to probe these interactions. In this review, we will analytically describe the journey of nanoparticles (NPs) through the brain, starting from the very first moment upon injection. We will preliminarily provide a brief overlook of the physicochemical properties of NPs. Then, we will discuss how these NPs interact with the body compartments and biological barriers, before reaching the blood-brain barrier (BBB), the last gate guarding the brain. Particular attention will be paid to the interaction with the biomolecular, the bio-mesoscopic, the (blood) cellular, and the tissue barriers, with a focus on the BBB. This will be framed in the context of brain infections, especially considering central nervous system tuberculosis (CNS-TB), which is one of the most devastating forms of human mycobacterial infections. The final aim of this review is not a collection, nor a list, of current literature data, as it provides the readers with the analytical tools and guidelines for the design of effective and rational NPs for delivery in the infected brain.
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Affiliation(s)
- Anna Griego
- Department of Pharmaceutical SciencesUniversity of MilanMilanItaly
- The National Institute of Molecular Genetics (INGM)MilanItaly
| | - Edoardo Scarpa
- Department of Pharmaceutical SciencesUniversity of MilanMilanItaly
- The National Institute of Molecular Genetics (INGM)MilanItaly
| | - Valeria De Matteis
- Department of Mathematics and Physics “Ennio De Giorgi”University of SalentoLecceItaly
| | - Loris Rizzello
- Department of Pharmaceutical SciencesUniversity of MilanMilanItaly
- The National Institute of Molecular Genetics (INGM)MilanItaly
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9
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Precision Nanotoxicology in Drug Development: Current Trends and Challenges in Safety and Toxicity Implications of Customized Multifunctional Nanocarriers for Drug-Delivery Applications. Pharmaceutics 2022; 14:pharmaceutics14112463. [PMID: 36432653 PMCID: PMC9697541 DOI: 10.3390/pharmaceutics14112463] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/06/2022] [Accepted: 11/13/2022] [Indexed: 11/17/2022] Open
Abstract
The dire need for the assessment of human and environmental endangerments of nanoparticulate material has motivated the formulation of novel scientific tools and techniques to detect, quantify, and characterize these nanomaterials. Several of these paradigms possess enormous possibilities for applications in many of the realms of nanotoxicology. Furthermore, in a large number of cases, the limited capabilities to assess the environmental and human toxicological outcomes of customized and tailored multifunctional nanoparticles used for drug delivery have hindered their full exploitation in preclinical and clinical settings. With the ever-compounded availability of nanoparticulate materials in commercialized settings, an ever-arising popular debate has been egressing on whether the social, human, and environmental costs associated with the risks of nanomaterials outweigh their profits. Here we briefly review the various health, pharmaceutical, and regulatory aspects of nanotoxicology of engineered multifunctional nanoparticles in vitro and in vivo. Several aspects and issues encountered during the safety and toxicity assessments of these drug-delivery nanocarriers have also been summarized. Furthermore, recent trends implicated in the nanotoxicological evaluations of nanoparticulate matter in vitro and in vivo have also been discussed. Due to the absence of robust and rigid regulatory guidelines, researchers currently frequently encounter a larger number of challenges in the toxicology assessment of nanocarriers, which have also been briefly discussed here. Nanotoxicology has an appreciable and significant part in the clinical translational development as well as commercialization potential of nanocarriers; hence these aspects have also been touched upon. Finally, a brief overview has been provided regarding some of the nanocarrier-based medicines that are currently undergoing clinical trials, and some of those which have recently been commercialized and are available for patients. It is expected that this review will instigate an appreciable interest in the research community working in the arena of pharmaceutical drug development and nanoformulation-based drug delivery.
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10
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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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11
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Li J, Wang C, Yue L, Chen F, Cao X, Wang Z. Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 243:113955. [PMID: 35961199 DOI: 10.1016/j.ecoenv.2022.113955] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/11/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Given the rapid development of nanotechnology, it is crucial to understand the effects of nanoparticles on living organisms. However, it is laborious to perform toxicological tests on a case-by-case basis. Quantitative structure-activity relationship (QSAR) is an effective computational technique because it saves time, costs, and animal sacrifice. Therefore, this review presents general procedures for the construction and application of nano-QSAR models of metal-based and metal-oxide nanoparticles (MBNPs and MONPs). We also provide an overview of available databases and common algorithms. The molecular descriptors and their roles in the toxicological interpretation of MBNPs and MONPs are systematically reviewed and the future of nano-QSAR is discussed. Finally, we address the growing demand for novel nano-specific descriptors, new computational strategies to address the data shortage, in situ data for regulatory concerns, a better understanding of the physicochemical properties of NPs with bioactivity, and, most importantly, the design of nano-QSAR for real-life environmental predictions rather than laboratory simulations.
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Affiliation(s)
- Jing Li
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Chuanxi Wang
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Le Yue
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Feiran Chen
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Xuesong Cao
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Zhenyu Wang
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China.
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12
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Mullins M, Himly M, Llopis IR, Furxhi I, Hofer S, Hofstätter N, Wick P, Romeo D, Küehnel D, Siivola K, Catalán J, Hund-Rinke K, Xiarchos I, Linehan S, Schuurbiers D, Bilbao AG, Barruetabeña L, Drobne D. (Re)Conceptualizing decision-making tools in a risk governance framework for emerging technologies-the case of nanomaterials. ENVIRONMENT SYSTEMS & DECISIONS 2022; 43:3-15. [PMID: 35912374 PMCID: PMC9309004 DOI: 10.1007/s10669-022-09870-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/06/2022] [Indexed: 12/03/2022]
Abstract
The utility of decision-making tools for the risk governance of nanotechnology is at the core of this paper. Those working in nanotechnology risk management have been prolific in creating such tools, many derived from European FP7 and H2020-funded projects. What is less clear is how such tools might assist the overarching ambition of creating a fair system of risk governance. In this paper, we reflect upon the role that tools might and should play in any system of risk governance. With many tools designed for the risk governance of this emerging technology falling into disuse, this paper provides an overview of extant tools and addresses their potential shortcomings. We also posit the need for a data readiness tool. With the EUs NMP13 family of research consortia about to report to the Commission on ways forward in terms of risk governance of this domain, this is a timely intervention on an important element of any risk governance system.
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Affiliation(s)
- Martin Mullins
- Transgero Limited, Cullinagh, Newcastle West, Co., Limerick, Ireland
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Martin Himly
- Department of Biosciences, Paris Lodron University of Salzburg (PLUS), 5020 Salzburg, Austria
| | - Isabel Rodríguez Llopis
- GAIKER Technology Centre, Basque Research and Technology Alliance, (BRTA) ES, Gipuzkoa, Spain
| | - Irini Furxhi
- Transgero Limited, Cullinagh, Newcastle West, Co., Limerick, Ireland
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Sabine Hofer
- Department of Biosciences, Paris Lodron University of Salzburg (PLUS), 5020 Salzburg, Austria
| | - Norbert Hofstätter
- Department of Biosciences, Paris Lodron University of Salzburg (PLUS), 5020 Salzburg, Austria
| | - Peter Wick
- Particles-Biology Interactions Laboratory, Empa, Swiss Federal Laboratories for Materials Science and Technology, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland
| | - Daina Romeo
- Particles-Biology Interactions Laboratory, Empa, Swiss Federal Laboratories for Materials Science and Technology, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland
| | - Dana Küehnel
- Department Bioanalytical Ecotoxicology (BIOTOX), Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318 Leipzig, Germany
| | - Kirsi Siivola
- Finnish Institute of Occupational Health, Työterveyslaitos, Box 40, 00032 Helsinki, Finland
| | - Julia Catalán
- Finnish Institute of Occupational Health, Työterveyslaitos, Box 40, 00032 Helsinki, Finland
- Department of Anatomy, Embryology and Genetics, University of Zaragoza, Saragossa, Spain
| | - Kerstin Hund-Rinke
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Auf dem Aberg 1, 57392 Schmallenberg, Germany
| | - Ioannis Xiarchos
- Research Lab of Advanced Composite, Nanomaterials, and Nanotechnology (R-NanoLab), School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechniou str, 15780 Zographos, Athens Greece
| | - Shona Linehan
- Management, Cairnes School of Business and Economics, National University of Ireland Galway, Galway, Ireland
| | - Daan Schuurbiers
- De Proeffabriek Josef Israelslaan 63, NL-6813 JB Arnhem, The Netherlands
| | - Amaia García Bilbao
- GAIKER Technology Centre, Basque Research and Technology Alliance, (BRTA) ES, Gipuzkoa, Spain
| | - Leire Barruetabeña
- GAIKER Technology Centre, Basque Research and Technology Alliance, (BRTA) ES, Gipuzkoa, Spain
| | - Damjana Drobne
- Department Biology, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
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13
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Kad A, Pundir A, Arya SK, Puri S, Khatri M. Meta-analysis of in-vitro cytotoxicity evaluation studies of zinc oxide nanoparticles: Paving way for safer innovations. Toxicol In Vitro 2022; 83:105418. [PMID: 35724836 DOI: 10.1016/j.tiv.2022.105418] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/03/2022] [Accepted: 06/14/2022] [Indexed: 02/02/2023]
Abstract
Nano-based products have shown their daunting presence in several sectors. Among them, Zinc Oxide (ZnO) nanoparticles wangled the reputation of providing "next-generation solutions" and are being utilized in plethora of products. Their widespread application has led to increased exposure of these particles, raising concerns regarding toxicological repercussions to the human health and environment. The diversity, complexity, and heterogeneity in the available literature, along with correlation of befitting attributes, makes it challenging to develop one systematic framework to predict this toxicity. The present study aims at developing predictive modelling framework to tap the prospective features responsible for causing cytotoxicity in-vitro on exposure to ZnO nanoparticles. Rigorous approach was used to mine the information from complete body of evidence published to date. The attributes, features and experimental conditions were systematically extracted to unmask the effect of varied features. 1240 data points from 76 publications were obtained, containing 14 qualitative and quantitative attributes, including physiochemical properties of nanoparticles, cell culture and experimental parameters to perform meta-analysis. For the first time, the efforts were made to investigate the degree of significance of attributes accountable for causing cytotoxicity on exposure to ZnO nanoparticles. We show that in-vitro cytotoxicity is closely related with dose concentration of nanoparticles, followed by exposure time, disease state of the cell line and size of these nanoparticles among other attributes.
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Affiliation(s)
- Anaida Kad
- Department of Biotechnology, University Institute of Engineering and Technology, Panjab University, Sector-25, Chandigarh 160014, India
| | - Archit Pundir
- Department of Biotechnology, University Institute of Engineering and Technology, Panjab University, Sector-25, Chandigarh 160014, India
| | - Shailendra Kumar Arya
- Department of Biotechnology, University Institute of Engineering and Technology, Panjab University, Sector-25, Chandigarh 160014, India
| | - Sanjeev Puri
- Department of Biotechnology, University Institute of Engineering and Technology, Panjab University, Sector-25, Chandigarh 160014, India
| | - Madhu Khatri
- Department of Biotechnology, University Institute of Engineering and Technology, Panjab University, Sector-25, Chandigarh 160014, India; Wellcome trustTrust/DBT IA Early Career Fellow Panjab University, Chandigarh 160014, India.
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14
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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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
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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15
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Forest V. Experimental and Computational Nanotoxicology-Complementary Approaches for Nanomaterial Hazard Assessment. NANOMATERIALS 2022; 12:nano12081346. [PMID: 35458054 PMCID: PMC9031966 DOI: 10.3390/nano12081346] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/07/2022] [Accepted: 04/12/2022] [Indexed: 12/25/2022]
Abstract
The growing development and applications of nanomaterials lead to an increasing release of these materials in the environment. The adverse effects they may elicit on ecosystems or human health are not always fully characterized. Such potential toxicity must be carefully assessed with the underlying mechanisms elucidated. To that purpose, different approaches can be used. First, experimental toxicology consisting of conducting in vitro or in vivo experiments (including clinical studies) can be used to evaluate the nanomaterial hazard. It can rely on variable models (more or less complex), allowing the investigation of different biological endpoints. The respective advantages and limitations of in vitro and in vivo models are discussed as well as some issues associated with experimental nanotoxicology. Perspectives of future developments in the field are also proposed. Second, computational nanotoxicology, i.e., in silico approaches, can be used to predict nanomaterial toxicity. In this context, we describe the general principles, advantages, and limitations especially of quantitative structure–activity relationship (QSAR) models and grouping/read-across approaches. The aim of this review is to provide an overview of these different approaches based on examples and highlight their complementarity.
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Affiliation(s)
- Valérie Forest
- Mines Saint-Etienne, Univ Lyon, Univ Jean Monnet, Etablissement Français du Sang, INSERM, U1059 Sainbiose, Centre CIS, F-42023 Saint-Etienne, France
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16
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Đorđević S, Gonzalez MM, Conejos-Sánchez I, Carreira B, Pozzi S, Acúrcio RC, Satchi-Fainaro R, Florindo HF, Vicent MJ. Current hurdles to the translation of nanomedicines from bench to the clinic. Drug Deliv Transl Res 2022; 12:500-525. [PMID: 34302274 PMCID: PMC8300981 DOI: 10.1007/s13346-021-01024-2] [Citation(s) in RCA: 71] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/29/2021] [Indexed: 02/07/2023]
Abstract
The field of nanomedicine has significantly influenced research areas such as drug delivery, diagnostics, theranostics, and regenerative medicine; however, the further development of this field will face significant challenges at the regulatory level if related guidance remains unclear and unconsolidated. This review describes those features and pathways crucial to the clinical translation of nanomedicine and highlights considerations for early-stage product development. These include identifying those critical quality attributes of the drug product essential for activity and safety, appropriate analytical methods (physical, chemical, biological) for characterization, important process parameters, and adequate pre-clinical models. Additional concerns include the evaluation of batch-to-batch consistency and considerations regarding scaling up that will ensure a successful reproducible manufacturing process. Furthermore, we advise close collaboration with regulatory agencies from the early stages of development to assure an aligned position to accelerate the development of future nanomedicines.
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Affiliation(s)
- Snežana Đorđević
- Polymer Therapeutics Laboratory, Prince Felipe Research Center (CIPF), Eduardo Primo Yúfera 3, 46012, Valencia, Av, Spain
| | - María Medel Gonzalez
- Polymer Therapeutics Laboratory, Prince Felipe Research Center (CIPF), Eduardo Primo Yúfera 3, 46012, Valencia, Av, Spain
| | - Inmaculada Conejos-Sánchez
- Polymer Therapeutics Laboratory, Prince Felipe Research Center (CIPF), Eduardo Primo Yúfera 3, 46012, Valencia, Av, Spain
| | - Barbara Carreira
- Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, Avenida Professor Gama Pinto, 1649-003, Lisboa, Portugal
| | - Sabina Pozzi
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, 69978, Tel Aviv, Israel
| | - Rita C Acúrcio
- Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, Avenida Professor Gama Pinto, 1649-003, Lisboa, Portugal
| | - Ronit Satchi-Fainaro
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, 69978, Tel Aviv, Israel.
- Sagol School of Neuroscience, Tel Aviv University, 69978, Tel Aviv, Israel.
| | - Helena F Florindo
- Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, Avenida Professor Gama Pinto, 1649-003, Lisboa, Portugal.
| | - María J Vicent
- Polymer Therapeutics Laboratory, Prince Felipe Research Center (CIPF), Eduardo Primo Yúfera 3, 46012, Valencia, Av, Spain.
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17
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Furxhi I, Perucca M, Blosi M, Lopez de Ipiña J, Oliveira J, Murphy F, Costa AL. ASINA Project: Towards a Methodological Data-Driven Sustainable and Safe-by-Design Approach for the Development of Nanomaterials. Front Bioeng Biotechnol 2022; 9:805096. [PMID: 35155410 PMCID: PMC8832976 DOI: 10.3389/fbioe.2021.805096] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 12/20/2021] [Indexed: 12/27/2022] Open
Abstract
The novel chemical strategy for sustainability calls for a Sustainable and Safe-by-Design (SSbD) holistic approach to achieve protection of public health and the environment, industrial relevance, societal empowerment, and regulatory preparedness. Based on it, the ASINA project expands a data-driven Management Methodology (ASINA-SMM) capturing quality, safety, and sustainability criteria across the Nano-Enabled Products’ (NEPs) life cycle. We base the development of this methodology through value chains of highly representative classes of NEPs in the market, namely, (i) self-cleaning/air-purifying/antimicrobial coatings and (ii) nano-structured capsules delivering active phases in cosmetics. These NEPs improve environmental quality and human health/wellness and have innovative competence to industrial sectors such as healthcare, textiles, cosmetics, and medical devices. The purpose of this article is to visually exhibit and explain the ASINA approach, which allows identifying, combining, and addressing the following pillars: environmental impact, techno-economic performance, functionality, and human and environmental safety when developing novel NEPs, at an early stage. A metamodel supports the above by utilizing quality data collected throughout the NEPs’ life cycle, for maximization of functionality (to meet stakeholders needs) and nano-safety (regulatory obligations) and for the minimization of costs (to meet business requirements) and environmental impacts (to achieve sustainability). Furthermore, ASINA explores digitalization opportunities (digital twins) to speed the nano-industry translation into automatic progress towards economic, social, environmental, and governance sustainability.
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Affiliation(s)
- Irini Furxhi
- Transgero Limited, Limerick, Ireland
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
- *Correspondence: Irini Furxhi, ; Massimo Perucca, ; Anna Luisa Costa,
| | - Massimo Perucca
- PROJECT-SAS, Faenza, Italy
- *Correspondence: Irini Furxhi, ; Massimo Perucca, ; Anna Luisa Costa,
| | - Magda Blosi
- National Research Council, Institute of Science and Technology for Ceramics, Faenza, Italy
| | - Jesús Lopez de Ipiña
- TECNALIA Research and Innovation—Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Alava, Miñano, Spain
| | - Juliana Oliveira
- CeNTI—Centre of Nanotechnology and Smart Materials, Vila Nova de Famalicão, Portugal
| | - Finbarr Murphy
- Transgero Limited, Limerick, Ireland
- Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland
| | - Anna Luisa Costa
- National Research Council, Institute of Science and Technology for Ceramics, Faenza, Italy
- *Correspondence: Irini Furxhi, ; Massimo Perucca, ; Anna Luisa Costa,
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18
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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: 5] [Impact Index Per Article: 2.5] [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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19
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Bardhan N. Nanomaterials in diagnostics, imaging and delivery: Applications from COVID-19 to cancer. MRS COMMUNICATIONS 2022; 12:1119-1139. [PMID: 36277435 PMCID: PMC9576318 DOI: 10.1557/s43579-022-00257-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/01/2022] [Indexed: 05/09/2023]
Abstract
ABSTRACT In the past two decades, the emergence of nanomaterials for biomedical applications has shown tremendous promise for changing the paradigm of all aspects of disease management. Nanomaterials are particularly attractive for being a modularly tunable system; with the ability to add functionality for early diagnostics, drug delivery, therapy, treatment and monitoring of patient response. In this review, a survey of the landscape of different classes of nanomaterials being developed for applications in diagnostics and imaging, as well as for the delivery of prophylactic vaccines and therapeutics such as small molecules and biologic drugs is undertaken; with a particular focus on COVID-19 diagnostics and vaccination. Work involving bio-templated nanomaterials for high-resolution imaging applications for early cancer detection, as well as for optimal cancer treatment efficacy, is discussed. The main challenges which need to be overcome from the standpoint of effective delivery and mitigating toxicity concerns are investigated. Subsequently, a section is included with resources for researchers and practitioners in nanomedicine, to help tailor their designs and formulations from a clinical perspective. Finally, three key areas for researchers to focus on are highlighted; to accelerate the development and clinical translation of these nanomaterials, thereby unleashing the true potential of nanomedicine in healthcare.
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Affiliation(s)
- Neelkanth Bardhan
- The Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 500 Main St., Cambridge, 02142 MA USA
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, 02139 MA USA
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20
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Leudjo Taka A, Tata CM, Klink MJ, Mbianda XY, Mtunzi FM, Naidoo EB. A Review on Conventional and Advanced Methods for Nanotoxicology Evaluation of Engineered Nanomaterials. Molecules 2021; 26:6536. [PMID: 34770945 PMCID: PMC8588160 DOI: 10.3390/molecules26216536] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/12/2021] [Accepted: 10/21/2021] [Indexed: 01/17/2023] Open
Abstract
Nanotechnology can be defined as the field of science and technology that studies material at nanoscale (1-100 nm). These nanomaterials, especially carbon nanostructure-based composites and biopolymer-based nanocomposites, exhibit excellent chemical, physical, mechanical, electrical, and many other properties beneficial for their application in many consumer products (e.g., industrial, food, pharmaceutical, and medical). The current literature reports that the increased exposure of humans to nanomaterials could toxicologically affect their environment. Hence, this paper aims to present a review on the possible nanotoxicology assays that can be used to evaluate the toxicity of engineered nanomaterials. The different ways humans are exposed to nanomaterials are discussed, and the recent toxicity evaluation approaches of these nanomaterials are critically assessed.
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Affiliation(s)
- Anny Leudjo Taka
- Department of Chemistry/Biotechnology, Vaal University of Technology, Vanderbijlpark 1900, South Africa; (F.M.M.); (E.B.N.)
- Institute of Chemical & Biotechnology, Vaal University of Technology, Southern Gauteng Science and Technology Park, Sebokeng 1983, South Africa
| | - Charlotte Mungho Tata
- Department of Chemical Sciences, University of Johannesburg, Doornfontein, Johannesburg 2028, South Africa; (C.M.T.); (X.Y.M.)
- Department of Biochemistry, University of Bamenda, Bambili 00237, Cameroon
| | - Michael John Klink
- Department of Chemistry/Biotechnology, Vaal University of Technology, Vanderbijlpark 1900, South Africa; (F.M.M.); (E.B.N.)
| | - Xavier Yangkou Mbianda
- Department of Chemical Sciences, University of Johannesburg, Doornfontein, Johannesburg 2028, South Africa; (C.M.T.); (X.Y.M.)
| | - Fanyana Moses Mtunzi
- Department of Chemistry/Biotechnology, Vaal University of Technology, Vanderbijlpark 1900, South Africa; (F.M.M.); (E.B.N.)
- Institute of Chemical & Biotechnology, Vaal University of Technology, Southern Gauteng Science and Technology Park, Sebokeng 1983, South Africa
| | - Eliazer Bobby Naidoo
- Department of Chemistry/Biotechnology, Vaal University of Technology, Vanderbijlpark 1900, South Africa; (F.M.M.); (E.B.N.)
- Institute of Chemical & Biotechnology, Vaal University of Technology, Southern Gauteng Science and Technology Park, Sebokeng 1983, South Africa
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21
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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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22
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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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23
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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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24
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Boots TE, Kogel AM, Drew NM, Kuempel ED. Utilizing literature-based rodent toxicology data to derive potency estimates for quantitative risk assessment. Nanotoxicology 2021; 15:740-760. [PMID: 34087078 DOI: 10.1080/17435390.2021.1918278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Evaluating the potential occupational health risk of engineered nanomaterials is an ongoing need. The objective of this meta-analysis, which consisted of 36 studies containing 86 materials, was to assess the availability of published in vivo rodent pulmonary toxicity data for a variety of nanoscale and microscale materials and to derive potency estimates via benchmark dose modeling. Additionally, the potency estimates based on particle mass lung dose associated with acute pulmonary inflammation were used to group materials based on toxicity. The commonalities among the physicochemical properties of the materials in each group were also explored. This exploration found that a material's potency tended to be associated primarily with the material class based on chemical composition and form (e.g. carbon nanotubes, TiO2, ZnO) rather than with particular physicochemical properties. Limitations in the data available precluded a more extensive analysis of these associations. Issues such as data reporting and appropriate experimental design for use in quantitative risk assessment are the main reasons publications were excluded from these analyses and are discussed.
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Affiliation(s)
- Theresa E Boots
- Health Effect Laboratory Division (HELD), BioAnalytics Branch (BB), National Institute for Occupational Safety and Health (NIOSH), Morgantown, WV, USA
| | - Alyssa M Kogel
- Formerly Oak Ridge Associated Universities/Oak Ridge Institute for Science and Education, at NIOSH, Oak Ridge, TN, USA
| | - Nathan M Drew
- Division of Science Integration (DSI), Emerging Technologies Branch (ETB), NIOSH, Cincinnati, OH, USA
| | - Eileen D Kuempel
- Division of Science Integration (DSI), Emerging Technologies Branch (ETB), NIOSH, Cincinnati, OH, USA
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25
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Yu H, Luo D, Dai L, Cheng F. In silico nanosafety assessment tools and their ecosystem-level integration prospect. NANOSCALE 2021; 13:8722-8739. [PMID: 33960351 DOI: 10.1039/d1nr00115a] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Engineered nanomaterials (ENMs) have tremendous potential in many fields, but their applications and commercialization are difficult to widely implement due to their safety concerns. Recently, in silico nanosafety assessment has become an important and necessary tool to realize the safer-by-design strategy of ENMs and at the same time to reduce animal tests and exposure experiments. Here, in silico nanosafety assessment tools are classified into three categories according to their methodologies and objectives, including (i) data-driven prediction for acute toxicity, (ii) fate modeling for environmental pollution, and (iii) nano-biological interaction modeling for long-term biological effects. Released ENMs may cross environmental boundaries and undergo a variety of transformations in biological and environmental media. Therefore, the potential impacts of ENMs must be assessed from a multimedia perspective and with integrated approaches considering environmental and biological effects. Ecosystems with biodiversity and an abiotic environment may be used as an excellent integration platform to assess the community- and ecosystem-level nanosafety. In this review, the advances and challenges of in silico nanosafety assessment tools are carefully discussed. Furthermore, their integration at the ecosystem level may provide more comprehensive and reliable nanosafety assessment by establishing a site-specific interactive system among ENMs, abiotic environment, and biological communities.
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Affiliation(s)
- Hengjie Yu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Dan Luo
- Department of Biological and Environmental Engineering, Cornell University, Ithaca, New York 14853, USA
| | - Limin Dai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Fang Cheng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
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26
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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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27
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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: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/01/2021] [Accepted: 04/16/2021] [Indexed: 12/12/2022] Open
Abstract
Millions of experimental animals are widely used in the assessment of toxicological or biological effects of manufactured nanomaterials in medical technology. However, the animal consciousness has increased and become an issue for debate in recent years. Currently, the principle of the 3Rs (i.e., reduction, refinement, and replacement) is applied to ensure the more ethical application of humane animal research. In order to avoid unethical procedures, the strategy of alternatives to animal testing has been employed to overcome the drawbacks of animal experiments. This article provides current alternative strategies to replace or reduce the use of experimental animals in the assessment of nanotoxicity. The currently available alternative methods include in vitro and in silico approaches, which can be used as cost-effective approaches to meet the principle of the 3Rs. These methods are regarded as non-animal approaches and have been implemented in many countries for scientific purposes. The in vitro experiments related to nanotoxicity assays involve cell culture testing and tissue engineering, while the in silico methods refer to prediction using molecular docking, molecular dynamics simulations, and quantitative structure-activity relationship (QSAR) modeling. The commonly used novel cell-based methods and computational approaches have the potential to help minimize the use of experimental animals for nanomaterial toxicity assessments.
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Affiliation(s)
- Hung-Jin Huang
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
| | - Yu-Hsuan Lee
- Department of Cosmeceutics, China Medical University, Taichung 406040, Taiwan;
| | - Yung-Ho Hsu
- Division of Nephrology, Department of Internal Medicine, Hsin Kuo Min Hospital, Taipei Medical University, Taoyuan City 320001, Taiwan;
- Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei 11031, Taiwan
| | - Chia-Te Liao
- Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei 11031, Taiwan
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
| | - Yuh-Feng Lin
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei 11031, Taiwan
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
| | - Hui-Wen Chiu
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei 11031, Taiwan
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
- Department of Medical Research, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
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28
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Oksel Karakus C, Bilgi E, Winkler DA. Biomedical nanomaterials: applications, toxicological concerns, and regulatory needs. Nanotoxicology 2020; 15:331-351. [PMID: 33337941 DOI: 10.1080/17435390.2020.1860265] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Advances in cutting-edge technologies such as nano- and biotechnology have created an opportunity for re-engineering existing materials and generating new nano-scale products that can function beyond the limits of conventional ones. While the step change in the properties and functionalities of these new materials opens up new possibilities for a broad range of applications, it also calls for structural modifications to existing safety assessment processes that are primarily focused on bulk material properties. Decades after the need to modify existing risk management practices to include nano-specific behaviors and exposure pathways was recognized, relevant policies for evaluating, and controlling health risks of nano-enabled materials is still lacking. This review provides an overview of current progress in the field of nanobiotechnology rather than intentions and aspirations, summarizes long-recognized but still unresolved issues surrounding materials safety at the nanoscale, and discusses key barriers preventing generation and integration of reliable data in bio/nano-safety domain. Particular attention is given to nanostructured materials that are commonly used in biomedical applications.
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Affiliation(s)
| | - Eyup Bilgi
- Department of Bioengineering, Izmir Institute of Technology, Izmir, Turkey
| | - David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Australia.,Latrobe Institute for Molecular Science, La Trobe University, Bundoora, Australia.,School of Pharmacy, University of Nottingham, Nottingham, UK.,CSIRO Data61, Pullenvale, Australia
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29
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Krebs J, McKeague M. Green Toxicology: Connecting Green Chemistry and Modern Toxicology. Chem Res Toxicol 2020; 33:2919-2931. [DOI: 10.1021/acs.chemrestox.0c00260] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Johanna Krebs
- Pharmacology and Therapeutics, Faculty of Medicine, McGill University, 3655 Promenade Sir William Osler, Montreal, Quebec, Canada H3G 1Y6
- Department of Health Sciences and Technology, ETH Zürich, Universitätstrasse 2, Zurich, Switzerland CH 8092
| | - Maureen McKeague
- Pharmacology and Therapeutics, Faculty of Medicine, McGill University, 3655 Promenade Sir William Osler, Montreal, Quebec, Canada H3G 1Y6
- Faculty of Science, Chemistry, McGill University, 801 Sherbrooke Street West, Montreal, Quebec, Canada H3A 0B8
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30
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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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31
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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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32
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Cho YL, Tan HWS, Saquib Q, Ren Y, Ahmad J, Wahab R, He W, Bay BH, Shen HM. Dual role of oxidative stress-JNK activation in autophagy and apoptosis induced by nickel oxide nanoparticles in human cancer cells. Free Radic Biol Med 2020; 153:173-186. [PMID: 32353482 DOI: 10.1016/j.freeradbiomed.2020.03.027] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 03/25/2020] [Indexed: 02/08/2023]
Abstract
Nickel oxide nanoparticles (NiO-NPs) are an important group of nanoparticles with increasing applications in many aspects of industry. At present, there is evidence demonstrating the cytotoxic characteristics of NiO-NPs, while the involvement of autophagy in the cytotoxicity of NiO-NPs has not been reported. In this study, we aimed to study the role of autophagy in the cytotoxicity of NiO-NPs and the underlying regulatory mechanisms. First, we provided evidence that NiO-NPs induce autophagy in human cancer cells. Second, we found that the enhanced autophagic flux by NiO-NPs via the generation of intracellular reactive oxygen species (ROS) from mitochondria and the subsequent activation of the JNK pathway. Third, we demonstrated that the activation of JNK is a main force in mediating NiO-NPs-induced apoptosis. Finally, we demonstrated that the autophagic response plays an important protective role against the cytotoxic effect of NiO-NPs. Therefore, this study identifies the dual role of oxidative stress-JNK activation in the biological effects of NiO-NPs via promoting autophagy and mediating apoptosis. Understanding the protective role of autophagy and the underlying mechanism is important for the potential application of NiO-NPs in the biomedical industry.
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Affiliation(s)
- Yik-Lam Cho
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Hayden Weng Siong Tan
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore.
| | - Quaiser Saquib
- Zoology Department, College of Sciences, King Saud University, Riyadh, Saudi Arabia.
| | - Yi Ren
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Javed Ahmad
- Zoology Department, College of Sciences, King Saud University, Riyadh, Saudi Arabia.
| | - Rizwan Wahab
- Zoology Department, College of Sciences, King Saud University, Riyadh, Saudi Arabia.
| | - Weifeng He
- State Key Laboratory of Trauma, Burn and Combined Injury, Institute of Burn Research, Southwest Hospital, Army Medical University, Chongqing, China.
| | - Boon-Huat Bay
- Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Han-Ming Shen
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore; Faculty of Health Sciences, University of Macau, Macau.
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33
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Reduction of Health Care-Associated Infections (HAIs) with Antimicrobial Inorganic Nanoparticles Incorporated in Medical Textiles: An Economic Assessment. NANOMATERIALS 2020; 10:nano10050999. [PMID: 32456213 PMCID: PMC7279532 DOI: 10.3390/nano10050999] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 05/08/2020] [Accepted: 05/21/2020] [Indexed: 01/27/2023]
Abstract
Health care-associated infections (HAIs) affect millions of patients annually with up to 80,000 affected in Europe on any given day. This represents a significant societal and economic burden. Staff training, hand hygiene, patient identification and isolation and controlled antibiotic use are some of the standard ways to reduce HAI incidence but this is time consuming and subject and subject to rigorous implementation. In addition, the lack of antimicrobial activity of some disinfectants against healthcare-associated pathogens may also affect the efficacy of disinfection practices. Textiles are an attractive substrate for pathogens because of contact with the human body with the attendant warmth and moisture. Textiles and surfaces coated with engineered nanomaterials (ENMs) have shown considerable promise in reducing the microbial burden on those surfaces. Studies have also shown that this antimicrobial affect can reduce the incidence of HAIs. For all of the promising research, there has been an absence of study on the economic effectiveness of ENM coated materials in a healthcare setting. This article examines the relative economic efficacy of ENM coated materials against an antiseptic approach. The goal is to establish the economic efficacy of the widespread usage of ENM coated materials in a healthcare setting. In the absence of detailed and segregated costs, benefits and control variables over at least cross sectional data or time series, an aggregated approach is warranted. This approach, while relying on some supposition allows for a comparison with similar data regarding standard treatment to reduce HAIs and provides a reasonable economic comparison. We find that while, relative to antiseptics, ENM coated textiles represent a significant clinical advantage, they can also offer considerable cost savings.
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