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Furxhi I, Perucca M, Koivisto AJ, Bengalli R, Mantecca P, Nicosia A, Burrueco-Subirà D, Vázquez-Campos S, Lahive E, Blosi M, de Ipiña JL, Oliveira J, Carriere M, Vineis C, Costa A. A roadmap towards safe and sustainable by design nanotechnology: Implementation for nano-silver-based antimicrobial textile coatings production by ASINA project. Comput Struct Biotechnol J 2024; 25:127-142. [PMID: 39040658 PMCID: PMC11262112 DOI: 10.1016/j.csbj.2024.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 07/24/2024] Open
Abstract
This report demonstrates a case study within the ASINA project, aimed at instantiating a roadmap with quantitative metrics for Safe(r) and (more) Sustainable by Design (SSbD) options. We begin with a description of ASINA's methodology across the product lifecycle, outlining the quantitative elements within: Physical-Chemical Features (PCFs), Key Decision Factors (KDFs), and Key Performance Indicators (KPIs). Subsequently, we delve in a proposed decision support tool for implementing the SSbD objectives across various dimensions-functionality, cost, environment, and human health safety-within a broader European context. We then provide an overview of the technical processes involved, including design rationales, experimental procedures, and tools/models developed within ASINA in delivering nano-silver-based antimicrobial textile coatings. The result is pragmatic, actionable metrics intended to be estimated and assessed in future SSbD applications and to be adopted in a common SSbD roadmap aligned with the EU's Green Deal objectives. The methodological approach is transparently and thoroughly described to inform similar projects through the integration of KPIs into SSbD and foster data-driven decision-making. Specific results and project data are beyond this work's scope, which is to demonstrate the ASINA roadmap and thus foster SSbD-oriented innovation in nanotechnology.
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Affiliation(s)
- Irini Furxhi
- CNR-ISSMC Istituto di Scienza e Tecnologia dei Materiali Ceramici, Via Granarolo, 64, 48018 Faenza, RA, Italy
| | - Massimo Perucca
- Project HUB360, C.so Laghi 22, 10051 Avigliana, Turin, Italy
| | - Antti Joonas Koivisto
- APM Air Pollution Management, Mattilanmäki 38, FI-33610 Tampere, Finland
- INAR Institute for Atmospheric and Earth System Research, University of Helsinki, PL 64, UHEL, FI-00014 Helsinki, Finland
- ARCHE Consulting, Liefkensstraat 35D, Wondelgem B-9032, Belgium
| | - Rossella Bengalli
- POLARIS Research Center, Dept. of Earth and Environmental Sciences, University of Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Paride Mantecca
- POLARIS Research Center, Dept. of Earth and Environmental Sciences, University of Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Alessia Nicosia
- CNR-ISAC Institute of Atmospheric Sciences and Climate, Via Gobetti 101, 40129 Bologna, Italy
| | | | | | - Elma Lahive
- Centre for Ecology & Hydrology (UKCEH), England, United Kingdom
| | - Magda Blosi
- CNR-ISSMC Istituto di Scienza e Tecnologia dei Materiali Ceramici, Via Granarolo, 64, 48018 Faenza, RA, Italy
| | - Jesús Lopez de Ipiña
- TECNALIA Research and Innovation - Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Alava, Leonardo Da Vinci 11, 01510 Miñano, Spain
| | - Juliana Oliveira
- CeNTI - Centre of Nanotechnology and Smart Materials, Rua Fernando Mesquita 2785, 4760-034 Vila Nova de Famalicão, Portugal
| | - Marie Carriere
- CEA, CNRS, Univ. Grenoble Alpes, Grenoble INP, IRIG, SYMMES, Grenoble 38000, France
| | - Claudia Vineis
- CNR-STIIMA Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato, Italy
| | - Anna Costa
- CNR-ISSMC Istituto di Scienza e Tecnologia dei Materiali Ceramici, Via Granarolo, 64, 48018 Faenza, RA, Italy
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Varsou DD, Kolokathis PD, Antoniou M, Sidiropoulos NK, Tsoumanis A, Papadiamantis AG, Melagraki G, Lynch I, Afantitis A. In silico assessment of nanoparticle toxicity powered by the Enalos Cloud Platform: Integrating automated machine learning and synthetic data for enhanced nanosafety evaluation. Comput Struct Biotechnol J 2024; 25:47-60. [PMID: 38646468 PMCID: PMC11026727 DOI: 10.1016/j.csbj.2024.03.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/22/2024] [Accepted: 03/23/2024] [Indexed: 04/23/2024] Open
Abstract
The rapid advance of nanotechnology has led to the development and widespread application of nanomaterials, raising concerns regarding their potential adverse effects on human health and the environment. Traditional (experimental) methods for assessing the nanoparticles (NPs) safety are time-consuming, expensive, and resource-intensive, and raise ethical concerns due to their reliance on animals. To address these challenges, we propose an in silico workflow that serves as an alternative or complementary approach to conventional hazard and risk assessment strategies, which incorporates state-of-the-art computational methodologies. In this study we present an automated machine learning (autoML) scheme that employs dose-response toxicity data for silver (Ag), titanium dioxide (TiO2), and copper oxide (CuO) NPs. This model is further enriched with atomistic descriptors to capture the NPs' underlying structural properties. To overcome the issue of limited data availability, synthetic data generation techniques are used. These techniques help in broadening the dataset, thus improving the representation of different NP classes. A key aspect of this approach is a novel three-step applicability domain method (which includes the development of a local similarity approach) that enhances user confidence in the results by evaluating the prediction's reliability. We anticipate that this approach will significantly expedite the nanosafety assessment process enabling regulation to keep pace with innovation, and will provide valuable insights for the design and development of safe and sustainable NPs. The ML model developed in this study is made available to the scientific community as an easy-to-use web-service through the Enalos Cloud Platform (www.enaloscloud.novamechanics.com/sabydoma/safenanoscope/), facilitating broader access and collaborative advancements in nanosafety.
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Affiliation(s)
- Dimitra-Danai Varsou
- NovaMechanics MIKE, Piraeus 18545, Greece
- Entelos Institute, Larnaca 6059, Cyprus
| | | | | | | | - Andreas Tsoumanis
- Entelos Institute, Larnaca 6059, Cyprus
- NovaMechanics Ltd, Nicosia 1070, Cyprus
| | - Anastasios G. Papadiamantis
- Entelos Institute, Larnaca 6059, Cyprus
- NovaMechanics Ltd, Nicosia 1070, Cyprus
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Georgia Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, Vari 16672, Greece
| | - Iseult Lynch
- Entelos Institute, Larnaca 6059, Cyprus
- School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT Birmingham, UK
| | - Antreas Afantitis
- NovaMechanics MIKE, Piraeus 18545, Greece
- Entelos Institute, Larnaca 6059, Cyprus
- NovaMechanics Ltd, Nicosia 1070, Cyprus
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Furxhi I, Faccani L, Zanoni I, Brigliadori A, Vespignani M, Costa AL. Design rules applied to silver nanoparticles synthesis: A practical example of machine learning application. Comput Struct Biotechnol J 2024; 25:20-33. [PMID: 38444982 PMCID: PMC10914561 DOI: 10.1016/j.csbj.2024.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 02/12/2024] [Accepted: 02/14/2024] [Indexed: 03/07/2024] Open
Abstract
The synthesis of silver nanoparticles with controlled physicochemical properties is essential for governing their intended functionalities and safety profiles. However, synthesis process involves multiple parameters that could influence the resulting properties. This challenge could be addressed with the development of predictive models that forecast endpoints based on key synthesis parameters. In this study, we manually extracted synthesis-related data from the literature and leveraged various machine learning algorithms. Data extraction included parameters such as reactant concentrations, experimental conditions, as well as physicochemical properties. The antibacterial efficiencies and toxicological profiles of the synthesized nanoparticles were also extracted. In a second step, based on data completeness, we employed regression algorithms to establish relationships between synthesis parameters and desired endpoints and to build predictive models. The models for core size and antibacterial efficiency were trained and validated using a cross-validation approach. Finally, the features' impact was evaluated via Shapley values to provide insights into the contribution of features to the predictions. Factors such as synthesis duration, scale of synthesis and the choice of capping agents emerged as the most significant predictors. This study demonstrated the potential of machine learning to aid in the rational design of synthesis process and paves the way for the safe-by-design principles development by providing insights into the optimization of the synthesis process to achieve the desired properties. Finally, this study provides a valuable dataset compiled from literature sources with significant time and effort from multiple researchers. Access to such datasets notably aids computational advances in the field of nanotechnology.
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Affiliation(s)
- Irini Furxhi
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
- Transgero Limited, Limerick, Ireland
| | - Lara Faccani
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
| | - Ilaria Zanoni
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
| | - Andrea Brigliadori
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
| | - Maurizio Vespignani
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
| | - Anna Luisa Costa
- CNR-ISSMC (Former ISTEC), National Research Council of Italy-Institute of Science, Technology and Sustainability for Ceramics, Faenza, Italy
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Malakouti-Nejad M, Monti D, Burgalassi S, Bardania H, Elahi E, Morshedi D. A comparison between the effects of two liposome-encapsulated bevacizumab formulations on ocular neovascularization inhibition. Colloids Surf B Biointerfaces 2024; 234:113708. [PMID: 38141384 DOI: 10.1016/j.colsurfb.2023.113708] [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: 08/27/2023] [Revised: 11/26/2023] [Accepted: 12/12/2023] [Indexed: 12/25/2023]
Abstract
Bevacizumab (BVZ), an anti-VEGF antibody, has demonstrated reliable outcomes in the treatment of irritating ocular neovascularization. Frequent intravitreal injections are necessitated due to rapid clearance and short local accessibility. We recruited liposome as a highly prevailing drug delivery system to enhance drug availability. Two liposome formulations were characterized and their in vitro stability was analyzed. The toxicity of the formulations on some ocular cell lines was also evaluated. In addition, the anti-angiogenic effects of formulations were examined. Drug permeation was measured across ARPE-19 and HCE cell lines as in vitro cellular barrier models. Results revealed that NLP-DOPE-BVZ acquired high stability at 4 °C, 24 °C, and 37 °C for 45 days. It also showed more capacity to entrap BVZ in NLP-DOPE-BVZ (DEE% 69.1 ± 1.4 and DLE% 55.66 ± 1.15) as compared to NLP-BVZ (DEE% 43.57 ± 14.64, and DLE% 37.72 ± 12.01). Although both formulations inhibited the migration and proliferation of HUVECs, NLP-DOPE-BVZ was more effective at inhibiting angiogenesis. Furthermore, NLP-DOPE-BVZ better crossed our established barrier cellular models. Based on the findings, the inclusion of DOPE in NLPs has significantly enhanced the features of drug carriers. This makes them a potential candidate for treating ocular neovascularization and other related ailments.
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Affiliation(s)
- Maryam Malakouti-Nejad
- Bioprocess Engineering Department, Institute of Industrial and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
| | - Daniela Monti
- Department of Pharmacy, University of Pisa, via Bonanno 33, 56126 Pisa, Italy
| | - Susi Burgalassi
- Department of Pharmacy, University of Pisa, via Bonanno 33, 56126 Pisa, Italy
| | - Hassan Bardania
- Cellular and Molecular Research Center, Yasuj University of Medical Sciences, Yasuj, Iran
| | - Elahe Elahi
- School of Biology, College of Science, University of Tehran, Tehran, Iran
| | - Dina Morshedi
- Bioprocess Engineering Department, Institute of Industrial and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.
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Sudheshwar A, Apel C, Kümmerer K, Wang Z, Soeteman-Hernández LG, Valsami-Jones E, Som C, Nowack B. Learning from Safe-by-Design for Safe-and-Sustainable-by-Design: Mapping the current landscape of Safe-by-Design reviews, case studies, and frameworks. ENVIRONMENT INTERNATIONAL 2024; 183:108305. [PMID: 38048736 DOI: 10.1016/j.envint.2023.108305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/31/2023] [Accepted: 11/03/2023] [Indexed: 12/06/2023]
Abstract
With the introduction of the European Commission's "Safe and Sustainable-by-Design" (SSbD) framework, the interest in understanding the implications of safety and sustainability assessments of chemicals, materials, and processes at early-innovation stages has skyrocketed. Our study focuses on the "Safe-by-Design" (SbD) approach from the nanomaterials sector, which predates the SSbD framework. In this assessment, SbD studies have been compiled and categorized into reviews, case studies, and frameworks. Reviews of SbD tools have been further classified as quantitative, qualitative, or toolboxes and repositories. We assessed the SbD case studies and classified them into three categories: safe(r)-by-modeling, safe(r)-by-selection, or safe(r)-by-redesign. This classification enabled us to understand past SbD work and subsequently use it to define future SSbD work so as to avoid confusion and possibilities of "SSbD-washing" (similar to greenwashing). Finally, the preexisting SbD frameworks have been studied and contextualized against the SSbD framework. Several key recommendations for SSbD based on our analysis can be made. Knowledge gained from existing approaches such as SbD, green and sustainable chemistry, and benign-by-design approaches needs to be preserved and effectively transferred to SSbD. Better incorporation of chemical and material functionality into the SSbD framework is required. The concept of lifecycle thinking and the stage-gate innovation model need to be reconciled for SSbD. The development of high-throughput screening models is critical for the operationalization of SSbD. We conclude that the rapid pace of both SbD and SSbD development necessitates a regular mapping of the newly published literature that is relevant to this field.
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Affiliation(s)
- Akshat Sudheshwar
- Empa - Swiss Federal Laboratories for Material Science and Technology, Technology and Society Laboratory, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland
| | - Christina Apel
- Leuphana University of Lüneburg, Institute of Sustainable Chemistry, Lüneburg, Germany
| | - Klaus Kümmerer
- Leuphana University of Lüneburg, Institute of Sustainable Chemistry, Lüneburg, Germany; International Sustainable Chemistry Collaborative Centre (ISC3), Bonn, Germany
| | - Zhanyun Wang
- Empa - Swiss Federal Laboratories for Material Science and Technology, Technology and Society Laboratory, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland
| | - Lya G Soeteman-Hernández
- National Institute for Public Health and the Environment (RIVM), Center for Safety of Substances and Products, Bilthoven, The Netherlands
| | | | - Claudia Som
- Empa - Swiss Federal Laboratories for Material Science and Technology, Technology and Society Laboratory, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland
| | - Bernd Nowack
- Empa - Swiss Federal Laboratories for Material Science and Technology, Technology and Society Laboratory, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland.
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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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