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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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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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Antoniou M, Papavasileiou KD, Melagraki G, Dondero F, Lynch I, Afantitis A. Development of a Robust Read-Across Model for the Prediction of Biological Potency of Novel Peroxisome Proliferator-Activated Receptor Delta Agonists. Int J Mol Sci 2024; 25:5216. [PMID: 38791255 PMCID: PMC11121726 DOI: 10.3390/ijms25105216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/02/2024] [Accepted: 05/03/2024] [Indexed: 05/26/2024] Open
Abstract
A robust predictive model was developed using 136 novel peroxisome proliferator-activated receptor delta (PPARδ) agonists, a distinct subtype of lipid-activated transcription factors of the nuclear receptor superfamily that regulate target genes by binding to characteristic sequences of DNA bases. The model employs various structural descriptors and docking calculations and provides predictions of the biological activity of PPARδ agonists, following the criteria of the Organization for Economic Co-operation and Development (OECD) for the development and validation of quantitative structure-activity relationship (QSAR) models. Specifically focused on small molecules, the model facilitates the identification of highly potent and selective PPARδ agonists and offers a read-across concept by providing the chemical neighbours of the compound under study. The model development process was conducted on Isalos Analytics Software (v. 0.1.17) which provides an intuitive environment for machine-learning applications. The final model was released as a user-friendly web tool and can be accessed through the Enalos Cloud platform's graphical user interface (GUI).
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Affiliation(s)
- Maria Antoniou
- Department of Chemoinformatics, NovaMechanics Ltd., Nicosia 1046, Cyprus; (M.A.); (K.D.P.)
- Department of ChemoInformatics, NovaMechanics MIKE, 18545 Piraeus, Greece
- Entelos Institute, Larnaca 6059, Cyprus; (F.D.); (I.L.)
| | - Konstantinos D. Papavasileiou
- Department of Chemoinformatics, NovaMechanics Ltd., Nicosia 1046, Cyprus; (M.A.); (K.D.P.)
- Department of ChemoInformatics, NovaMechanics MIKE, 18545 Piraeus, Greece
- Entelos Institute, Larnaca 6059, Cyprus; (F.D.); (I.L.)
| | - Georgia Melagraki
- Division of Physical Sciences & Applications, Hellenic Military Academy, 16672 Vari, Greece;
| | - Francesco Dondero
- Entelos Institute, Larnaca 6059, Cyprus; (F.D.); (I.L.)
- Department of Science and Technological Innovation, Università del Piemonte Orientale, 15121 Alessandria, Italy
| | - Iseult Lynch
- Entelos Institute, Larnaca 6059, Cyprus; (F.D.); (I.L.)
- School of Geography, Earth and Environmental Sciences, University of Birmingham Edgbaston, Birmingham B15 2TT, UK
| | - Antreas Afantitis
- Department of Chemoinformatics, NovaMechanics Ltd., Nicosia 1046, Cyprus; (M.A.); (K.D.P.)
- Department of ChemoInformatics, NovaMechanics MIKE, 18545 Piraeus, Greece
- Entelos Institute, Larnaca 6059, Cyprus; (F.D.); (I.L.)
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4
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Rosner A, Ballarin L, Barnay-Verdier S, Borisenko I, Drago L, Drobne D, Concetta Eliso M, Harbuzov Z, Grimaldi A, Guy-Haim T, Karahan A, Lynch I, Giulia Lionetto M, Martinez P, Mehennaoui K, Oruc Ozcan E, Pinsino A, Paz G, Rinkevich B, Spagnuolo A, Sugni M, Cambier S. A broad-taxa approach as an important concept in ecotoxicological studies and pollution monitoring. Biol Rev Camb Philos Soc 2024; 99:131-176. [PMID: 37698089 DOI: 10.1111/brv.13015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 08/23/2023] [Accepted: 08/28/2023] [Indexed: 09/13/2023]
Abstract
Aquatic invertebrates play a pivotal role in (eco)toxicological assessments because they offer ethical, cost-effective and repeatable testing options. Additionally, their significance in the food chain and their ability to represent diverse aquatic ecosystems make them valuable subjects for (eco)toxicological studies. To ensure consistency and comparability across studies, international (eco)toxicology guidelines have been used to establish standardised methods and protocols for data collection, analysis and interpretation. However, the current standardised protocols primarily focus on a limited number of aquatic invertebrate species, mainly from Arthropoda, Mollusca and Annelida. These protocols are suitable for basic toxicity screening, effectively assessing the immediate and severe effects of toxic substances on organisms. For more comprehensive and ecologically relevant assessments, particularly those addressing long-term effects and ecosystem-wide impacts, we recommended the use of a broader diversity of species, since the present choice of taxa exacerbates the limited scope of basic ecotoxicological studies. This review provides a comprehensive overview of (eco)toxicological studies, focusing on major aquatic invertebrate taxa and how they are used to assess the impact of chemicals in diverse aquatic environments. The present work supports the use of a broad-taxa approach in basic environmental assessments, as it better represents the natural populations inhabiting various ecosystems. Advances in omics and other biochemical and computational techniques make the broad-taxa approach more feasible, enabling mechanistic studies on non-model organisms. By combining these approaches with in vitro techniques together with the broad-taxa approach, researchers can gain insights into less-explored impacts of pollution, such as changes in population diversity, the development of tolerance and transgenerational inheritance of pollution responses, the impact on organism phenotypic plasticity, biological invasion outcomes, social behaviour changes, metabolome changes, regeneration phenomena, disease susceptibility and tissue pathologies. This review also emphasises the need for harmonised data-reporting standards and minimum annotation checklists to ensure that research results are findable, accessible, interoperable and reusable (FAIR), maximising the use and reusability of data. The ultimate goal is to encourage integrated and holistic problem-focused collaboration between diverse scientific disciplines, international standardisation organisations and decision-making bodies, with a focus on transdisciplinary knowledge co-production for the One-Health approach.
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Affiliation(s)
- Amalia Rosner
- Israel Oceanographic and Limnological Research, National Institute of Oceanography, PO 2336 Sha'ar Palmer 1, Haifa, 3102201, Israel
| | - Loriano Ballarin
- Department of Biology, University of Padova, via Ugo Bassi 58/B, Padova, I-35121, Italy
| | - Stéphanie Barnay-Verdier
- Sorbonne Université; CNRS, INSERM, Université Côte d'Azur, Institute for Research on Cancer and Aging Nice, 28 avenue Valombrose, Nice, F-06107, France
| | - Ilya Borisenko
- Faculty of Biology, Department of Embryology, Saint Petersburg State University, Universitetskaya embankment 7/9, Saint Petersburg, 199034, Russia
| | - Laura Drago
- Department of Biology, University of Padova, via Ugo Bassi 58/B, Padova, I-35121, Italy
| | - Damjana Drobne
- Department of Biology, Biotechnical Faculty, University of Ljubljana, Večna pot 111, Ljubljana, 1111, Slovenia
| | - Maria Concetta Eliso
- Department of Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Naples, 80121, Italy
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy
| | - Zoya Harbuzov
- Israel Oceanographic and Limnological Research, National Institute of Oceanography, PO 2336 Sha'ar Palmer 1, Haifa, 3102201, Israel
- Leon H. Charney School of Marine Sciences, Department of Marine Biology, University of Haifa, 199 Aba Koushy Ave., Haifa, 3498838, Israel
| | - Annalisa Grimaldi
- Department of Biotechnology and Life Sciences, University of Insubria, Via J. H. Dunant, Varese, 3-21100, Italy
| | - Tamar Guy-Haim
- Israel Oceanographic and Limnological Research, National Institute of Oceanography, PO 2336 Sha'ar Palmer 1, Haifa, 3102201, Israel
| | - Arzu Karahan
- Middle East Technical University, Institute of Marine Sciences, Erdemli-Mersin, PO 28, 33731, Turkey
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, UK
| | - Maria Giulia Lionetto
- Department of Biological and Environmental Sciences and Technologies, University of Salento, via prov. le Lecce -Monteroni, Lecce, I-73100, Italy
- NBFC, National Biodiversity Future Center, Piazza Marina, 61, Palermo, I-90133, Italy
| | - Pedro Martinez
- Department de Genètica, Microbiologia i Estadística, Universitat de Barcelona, Av. Diagonal 643, Barcelona, 08028, Spain
- Institut Català de Recerca i Estudis Avançats (ICREA), Passeig de Lluís Companys, Barcelona, 08010, Spain
| | - Kahina Mehennaoui
- Environmental Research and Innovation (ERIN) Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, Belvaux, L-4422, Luxembourg
| | - Elif Oruc Ozcan
- Faculty of Arts and Science, Department of Biology, Cukurova University, Balcali, Saricam, Adana, 01330, Turkey
| | - Annalisa Pinsino
- National Research Council, Institute of Translational Pharmacology (IFT), National Research Council (CNR), Via Ugo La Malfa 153, Palermo, 90146, Italy
| | - Guy Paz
- Israel Oceanographic and Limnological Research, National Institute of Oceanography, PO 2336 Sha'ar Palmer 1, Haifa, 3102201, Israel
| | - Baruch Rinkevich
- Israel Oceanographic and Limnological Research, National Institute of Oceanography, PO 2336 Sha'ar Palmer 1, Haifa, 3102201, Israel
| | - Antonietta Spagnuolo
- Department of Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Naples, 80121, Italy
| | - Michela Sugni
- Department of Environmental Science and Policy, University of Milan, Via Celoria 26, Milan, 20133, Italy
| | - Sébastien Cambier
- Environmental Research and Innovation (ERIN) Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, Belvaux, L-4422, Luxembourg
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5
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Varsou DD, Sarimveis H. Deimos: A novel automated methodology for optimal grouping. Application to nanoinformatics case studies. Mol Inform 2023; 42:e2300019. [PMID: 37258455 DOI: 10.1002/minf.202300019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 05/05/2023] [Accepted: 05/31/2023] [Indexed: 06/02/2023]
Abstract
In this study we present deimos, a computational methodology for optimal grouping, applied on the read-across prediction of engineered nanomaterials' (ENMs) toxicity-related properties. The method is based on the formulation and the solution of a mixed-integer optimization program (MILP) problem that automatically and simultaneously performs feature selection, defines the grouping boundaries according to the response variable and develops linear regression models in each group. For each group/region, the characteristic centroid is defined in order to allocate untested ENMs to the groups. The deimos MILP problem is integrated in a broader optimization workflow that selects the best performing methodology between the standard multiple linear regression (MLR), the least absolute shrinkage and selection operator (LASSO) models and the proposed deimos multiple-region model. The performance of the suggested methodology is demonstrated through the application to benchmark ENMs datasets and comparison with other predictive modelling approaches. However, the proposed method can be applied to property prediction of other than ENM chemical entities and it is not limited to ENMs toxicity prediction.
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Affiliation(s)
- Dimitra-Danai Varsou
- School of Chemical Engineering, National Technical University of Athens, 157 80, Athens, Greece
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 157 80, Athens, Greece
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Reilly K, Ellis LJA, Davoudi HH, Supian S, Maia MT, Silva GH, Guo Z, Martinez DST, Lynch I. Daphnia as a model organism to probe biological responses to nanomaterials-from individual to population effects via adverse outcome pathways. FRONTIERS IN TOXICOLOGY 2023; 5:1178482. [PMID: 37124970 PMCID: PMC10140508 DOI: 10.3389/ftox.2023.1178482] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 04/06/2023] [Indexed: 05/02/2023] Open
Abstract
The importance of the cladoceran Daphnia as a model organism for ecotoxicity testing has been well-established since the 1980s. Daphnia have been increasingly used in standardised testing of chemicals as they are well characterised and show sensitivity to pollutants, making them an essential indicator species for environmental stress. The mapping of the genomes of D. pulex in 2012 and D. magna in 2017 further consolidated their utility for ecotoxicity testing, including demonstrating the responsiveness of the Daphnia genome to environmental stressors. The short lifecycle and parthenogenetic reproduction make Daphnia useful for assessment of developmental toxicity and adaption to stress. The emergence of nanomaterials (NMs) and their safety assessment has introduced some challenges to the use of standard toxicity tests which were developed for soluble chemicals. NMs have enormous reactive surface areas resulting in dynamic interactions with dissolved organic carbon, proteins and other biomolecules in their surroundings leading to a myriad of physical, chemical, biological, and macromolecular transformations of the NMs and thus changes in their bioavailability to, and impacts on, daphnids. However, NM safety assessments are also driving innovations in our approaches to toxicity testing, for both chemicals and other emerging contaminants such as microplastics (MPs). These advances include establishing more realistic environmental exposures via medium composition tuning including pre-conditioning by the organisms to provide relevant biomolecules as background, development of microfluidics approaches to mimic environmental flow conditions typical in streams, utilisation of field daphnids cultured in the lab to assess adaption and impacts of pre-exposure to pollution gradients, and of course development of mechanistic insights to connect the first encounter with NMs or MPs to an adverse outcome, via the key events in an adverse outcome pathway. Insights into these developments are presented below to inspire further advances and utilisation of these important organisms as part of an overall environmental risk assessment of NMs and MPs impacts, including in mixture exposure scenarios.
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Affiliation(s)
- Katie Reilly
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Laura-Jayne A. Ellis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Hossein Hayat Davoudi
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Suffeiya Supian
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Marcella T. Maia
- Brazilian Nanotechnology National Laboratory (LNNano), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Brazil
| | - Gabriela H. Silva
- Brazilian Nanotechnology National Laboratory (LNNano), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Brazil
| | - Zhiling Guo
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
- *Correspondence: Zhiling Guo, ; Iseult Lynch,
| | - Diego Stéfani T. Martinez
- Brazilian Nanotechnology National Laboratory (LNNano), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Brazil
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
- *Correspondence: Zhiling Guo, ; Iseult Lynch,
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7
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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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8
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Precision Medicine Approaches with Metabolomics and Artificial Intelligence. Int J Mol Sci 2022; 23:ijms231911269. [PMID: 36232571 PMCID: PMC9569627 DOI: 10.3390/ijms231911269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 09/14/2022] [Accepted: 09/20/2022] [Indexed: 11/18/2022] Open
Abstract
Recent technological innovations in the field of mass spectrometry have supported the use of metabolomics analysis for precision medicine. This growth has been allowed also by the application of algorithms to data analysis, including multivariate and machine learning methods, which are fundamental to managing large number of variables and samples. In the present review, we reported and discussed the application of artificial intelligence (AI) strategies for metabolomics data analysis. Particularly, we focused on widely used non-linear machine learning classifiers, such as ANN, random forest, and support vector machine (SVM) algorithms. A discussion of recent studies and research focused on disease classification, biomarker identification and early diagnosis is presented. Challenges in the implementation of metabolomics–AI systems, limitations thereof and recent tools were also discussed.
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Swirog M, Mikolajczyk A, Jagiello K, Jänes J, Tämm K, Puzyn T. Predicting electrophoretic mobility of TiO 2, ZnO, and CeO 2 nanoparticles in natural waters: The importance of environment descriptors in nanoinformatics models. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 840:156572. [PMID: 35710003 DOI: 10.1016/j.scitotenv.2022.156572] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/16/2022] [Accepted: 06/05/2022] [Indexed: 06/15/2023]
Abstract
Natural and engineered nanoparticles (NPs) entering the environment are influenced by many physicochemical processes and show various behavior in different systems (e.g., natural waters showing different characteristics). Determining the physicochemical characteristics and predicting the behavior of nanoparticles ending up in the natural aquatic environment are key aspects of their risk assessment. Here, we show that the quantitative structure-property relationship modeling method used in nanoinformatics (nano-QSPR) can be successfully applied to predict environmental fate-relevant properties (electrophoretic mobility) of TiO2, ZnO, and CeO2 nanoparticles. However, in contrast to the previous works, we postulate to use, in parallel: (i) the nanoparticles' structure descriptors (S-descriptors) and (ii) the environment descriptors (E-descriptors) as the input variables. Thus, the method should be abbreviated more precisely as nano-QSEPR ("E" stands for the "environment"). As a proof-of-the-concept, we have developed a group of models (including MLR, GA-PLS, PCR, and Meta-Consensus models) with high predictive capabilities (QEXT2 = 0.931 for the GA-PLS model), where the S-descriptors are represented by the core-shell model descriptor and the E-descriptors - by different ambient water features (including ions concentration and the ionic strength). The newly proposed nano-QSEPR modeling scheme can be efficiently used to design safe and sustainable nanomaterials.
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Affiliation(s)
- Marta Swirog
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdańsk, Poland
| | - Alicja Mikolajczyk
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdańsk, Poland; QSAR Lab Sp. Z o. o., Trzy Lipy 3, 80-172, Poland.
| | - Karolina Jagiello
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdańsk, Poland; QSAR Lab Sp. Z o. o., Trzy Lipy 3, 80-172, Poland
| | - Jaak Jänes
- Institute of Chemistry, University of Tartu, Ravila 14A, Tartu 50411, Estonia
| | - Kaido Tämm
- Institute of Chemistry, University of Tartu, Ravila 14A, Tartu 50411, Estonia
| | - Tomasz Puzyn
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdańsk, Poland; QSAR Lab Sp. Z o. o., Trzy Lipy 3, 80-172, Poland.
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10
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Konstantopoulos G, Koumoulos EP, Charitidis CA. Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives. NANOMATERIALS 2022; 12:nano12152646. [PMID: 35957077 PMCID: PMC9370746 DOI: 10.3390/nano12152646] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 02/05/2023]
Abstract
Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.
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Affiliation(s)
- Georgios Konstantopoulos
- RNANO Lab—Research Unit of Advanced, Composite, Nano Materials & Nanotechnology, School of Chemical Engineering, National Technical University of Athens, GR15773 Athens, Greece; (G.K.); (C.A.C.)
| | - Elias P. Koumoulos
- Innovation in Research & Engineering Solutions (IRES), Boulevard Edmond Machtens 79/22, 1080 Brussels, Belgium
- Correspondence:
| | - Costas A. Charitidis
- RNANO Lab—Research Unit of Advanced, Composite, Nano Materials & Nanotechnology, School of Chemical Engineering, National Technical University of Athens, GR15773 Athens, Greece; (G.K.); (C.A.C.)
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Melagraki G. Reducing health & environmental impacts of chemical warfare agents: Computational chemistry contributions. CHEMOSPHERE 2022; 288:132564. [PMID: 34673043 DOI: 10.1016/j.chemosphere.2021.132564] [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: 07/19/2021] [Revised: 10/10/2021] [Accepted: 10/11/2021] [Indexed: 06/13/2023]
Abstract
This review article summarizes advances in computational chemistry and cheminformatics methods and techniques that are used or have potential for use in reducing health and environmental impacts of Chemical Warfare Agents (CWA). These methods, include, but are not limited to, predictive modeling, data mining and virtual screening, similarity searching, molecular docking and dynamics and are briefly presented here. Applications of these in silico approaches, specifically for the protection of personnel and civilians against CWA, but also beyond, are discussed. CWA include toxic chemicals that can cause death, injury, or temporary incapacitation through their chemical action. CWA impose a significant worldwide threat and as such, destruction, remediation as well as protection measurements need to be carefully designed. Towards this goal computational chemistry and cheminformatics can play a key role specifically as far as decontamination, risk assessment and risk management are concerned. Among the wide range of in silico techniques applied for CWA, specific previously published paradigms are presented, including toxicity and property prediction, CWA simulant identification and CWA detoxification. Beyond CWA research, other applications with military interest are briefly presented and emerging trends of potential relevance noted.
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Affiliation(s)
- Georgia Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, Vari, Greece.
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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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Lynch I, Nymark P, Doganis P, Gulumian M, Yoon TH, Martinez DST, Afantitis A. Methods, models, mechanisms and metadata: Introducing the Nanotoxicology collection at F1000Research. F1000Res 2021; 10:1196. [PMID: 34853679 PMCID: PMC8613506 DOI: 10.12688/f1000research.75113.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/03/2021] [Indexed: 11/28/2022] Open
Abstract
Nanotoxicology is a relatively new field of research concerning the study and application of nanomaterials to evaluate the potential for harmful effects in parallel with the development of applications. Nanotoxicology as a field spans materials synthesis and characterisation, assessment of fate and behaviour, exposure science, toxicology / ecotoxicology, molecular biology and toxicogenomics, epidemiology, safe and sustainable by design approaches, and chemoinformatics and nanoinformatics, thus requiring scientists to work collaboratively, often outside their core expertise area. This interdisciplinarity can lead to challenges in terms of interpretation and reporting, and calls for a platform for sharing of best-practice in nanotoxicology research. The F1000Research Nanotoxicology collection, introduced via this editorial, will provide a place to share accumulated best practice, via original research reports including no-effects studies, protocols and methods papers, software reports and living systematic reviews, which can be updated as new knowledge emerges or as the domain of applicability of the method, model or software is expanded. This editorial introduces the Nanotoxicology Collection in
F1000Research. The aim of the collection is to provide an open access platform for nanotoxicology researchers, to support an improved culture of
data sharing and documentation of evolving protocols, biological and computational models, software tools and datasets, that can be applied and built upon to develop predictive models and move towards
in silico nanotoxicology and nanoinformatics. Submissions will be assessed for fit to the collection and subjected to the F1000Research open peer review process.
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Affiliation(s)
- Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, UK
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Stockholm, 17 177, Sweden
| | - Philip Doganis
- School of Chemical Engineering, National Technical University of Athens, Athens, 10682, Greece
| | - Mary Gulumian
- National Health Laboratory Services, 1 Modderfontein Rd, Sandringham, Johannesburg, 2192, South Africa.,Haematology and Molecular Medicine, University of the Witwatersrand, 1 Jan Smuts Ave, Johannesburg, 2000, South Africa.,Water Research Group, Unit for Environmental Sciences and Management Potchefstroom, North West University, Potchefstroom, South Africa
| | - Tae-Hyun Yoon
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul, 04763, South Korea.,Institute of Next Generation Material Design, Hanyang University, Seoul, 04763, South Korea
| | - Diego S T Martinez
- Brazilian Nanotechnology National Laboratory (LNNano), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas,, Sao Paulo, CEP 13083-970, Brazil
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