1
|
Zdybel S, Sosnowska A, Kowalska D, Sommer J, Conrady B, Mester P, Gromelski M, Puzyn T. Hybrid Machine Learning and Experimental Studies of Antiviral Potential of Ionic Liquids against P100, MS2, and Phi6. J Chem Inf Model 2024; 64:1996-2007. [PMID: 38452014 DOI: 10.1021/acs.jcim.3c02037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
Viruses are a group of widespread organisms that are often responsible for very dangerous diseases, as most of them follow a mechanism to multiply and infect their hosts as quickly as possible. Pathogen viruses also mutate regularly, with the result that measures to prevent virus transmission and recover from the disease caused are often limited. The development of new substances is very time-consuming and highly budgeted and requires the sacrifice of many living organisms. Computational chemistry methods allow faster analysis at a much lower cost and, most importantly, reduce the number of living organisms sacrificed experimentally to a minimum. Ionic liquids (ILs) are a group of chemical compounds that could potentially find a wide range of applications due to their potential virucidal activity. In our study, we conducted a complex computational analysis to predict the antiviral activity of ionic liquids against three surrogate viruses: two nonenveloped viruses, Listeria monocytogenes phage P100 and Escherichia coli phage MS2, and one enveloped virus, Pseudomonas syringae phage Phi6. Based on experimental data of toxic activity (logEC90), we assigned activity classes to 154 ILs. Prediction models were created and validated according to the Organization for Economic Co-operation and Development (OECD) recommendations using the Classification Tree method. Further, we performed an external validation of our models through virtual screening on a set of 1277 theoretically generated ionic liquids and then selected 10 active ionic liquids, which were synthesized to verify their activity against the analyzed viruses. Our study proved the effectiveness and efficiency of computational methods to predict the antiviral activity of ionic liquids. Thus, computational models are a cost-effective alternative approach compared with time-consuming experimental studies where live animals are involved.
Collapse
Affiliation(s)
- Szymon Zdybel
- QSAR Lab, ul. Trzy Lipy 3, 80-172 Gdańsk, Poland
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, 80-308 Gdańsk, Poland
| | - Anita Sosnowska
- QSAR Lab, ul. Trzy Lipy 3, 80-172 Gdańsk, Poland
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, 80-308 Gdańsk, Poland
| | | | - Julia Sommer
- Unit of Food Microbiology, Institute of Food Safety, Food Technology and Veterinary Public Health, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine Vienna, Veterinärplatz 1, 1210 Vienna, Austria
| | - Beate Conrady
- Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 8, 1870 Frederiksberg Campus, Copenhagen DK-1870, Denmark
| | - Patrick Mester
- Unit of Food Microbiology, Institute of Food Safety, Food Technology and Veterinary Public Health, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine Vienna, Veterinärplatz 1, 1210 Vienna, Austria
| | | | - Tomasz Puzyn
- QSAR Lab, ul. Trzy Lipy 3, 80-172 Gdańsk, Poland
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, 80-308 Gdańsk, Poland
| |
Collapse
|
2
|
Wittwehr C, Clerbaux LA, Edwards S, Angrish M, Mortensen H, Carusi A, Gromelski M, Lekka E, Virvilis V, Martens M, Bonino da Silva Santos LO, Nymark P. Why adverse outcome pathways need to be FAIR. ALTEX 2024; 41:50-56. [PMID: 37528748 DOI: 10.14573/altex.2307131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 07/25/2023] [Indexed: 08/03/2023]
Abstract
Adverse outcome pathways (AOPs) provide evidence for demonstrating and assessing causality between measurable toxicological mechanisms and human or environmental adverse effects. AOPs have gained increasing attention over the past decade and are believed to provide the necessary steppingstone for more effective risk assessment of chemicals and materials and moving beyond the need for animal testing. However, as with all types of data and knowledge today, AOPs need to be reusable by machines, i.e., machine-actionable, in order to reach their full impact potential. Machine-actionability is supported by the FAIR principles, which guide findability, accessibility, interoperability, and reusability of data and knowledge. Here, we describe why AOPs need to be FAIR and touch on aspects such as the improved visibility and the increased trust that FAIRification of AOPs provides.
Collapse
Affiliation(s)
| | | | | | - Michelle Angrish
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division, U.S. Environmental Protection Agency, Washington DC, USA
| | - Holly Mortensen
- Center for Public Health and Environmental Assessment, Public Health and Integrated Toxicology Division, U.S. Environmental Protection Agency, Durham, NC, USA
| | | | - Maciej Gromelski
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | | | | | - Marvin Martens
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands
| | - Luiz Olavo Bonino da Silva Santos
- GO FAIR Foundation, Leiden, the Netherlands
- Services and Cybersecurity group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente - Enschede, The Netherlands
| | - Penny Nymark
- Karolinska Institutet, Institute of Environmental Medicine, Stockholm, Sweden
| |
Collapse
|
3
|
Gajewicz-Skretna A, Wyrzykowska E, Gromelski M. Quantitative multi-species toxicity modeling: Does a multi-species, machine learning model provide better performance than a single-species model for the evaluation of acute aquatic toxicity by organic pollutants? Sci Total Environ 2023; 861:160590. [PMID: 36473653 DOI: 10.1016/j.scitotenv.2022.160590] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/25/2022] [Accepted: 11/26/2022] [Indexed: 06/17/2023]
Abstract
The toxicological profile of any chemical is defined by multiple endpoints and testing procedures, including representative test species from different trophic levels. While computer-aided methods play an increasingly important role in supporting ecotoxicology research and chemical hazard assessment, most of the recently developed machine learning models are directed towards a single, specific endpoint. To overcome this limitation and accelerate the process of identifying potentially hazardous environmental pollutants, we are introducing an effective approach for quantitative, multi-species modeling. The proposed approach is based on canonical correlation analysis that finds a pair(s) of uncorrelated, linear combinations of the original variables that best defines the overall variability within and between multiple biological responses and predictor variables. Its effectiveness was confirmed by the machine learning model for estimating acute toxicity of diverse organic pollutants in aquatic species from three trophic levels: algae (Pseudokirchneriella subcapitata), daphnia (Daphnia magna), and fish (Oryzias latipes). The multi-species model achieved a favorable predictive performance that were in line with predictive models derived for the aquatic organisms individually. The chemical bioavailability and reactivity parameters (n-octanol/water partition coefficient, chemical potential, and molecular size and volume) were important to accurately predict acute ecotoxicity to the three aquatic organisms. To facilitate the use of this approach, an open-source, Python-based script, named qMTM (quantitative Multi-species Toxicity Modeling) has been provided.
Collapse
Affiliation(s)
- Agnieszka Gajewicz-Skretna
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland.
| | - Ewelina Wyrzykowska
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Maciej Gromelski
- Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| |
Collapse
|
4
|
Ciura K, Fryca I, Gromelski M. Prediction of the retention factor in cetyltrimethylammonium bromide modified micellar electrokinetic chromatography using a machine learning approach. Microchem J 2023. [DOI: 10.1016/j.microc.2023.108393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
|
5
|
Stoliński F, Rybińska-Fryca A, Gromelski M, Mikolajczyk A, Puzyn T. NanoMixHamster: a web-based tool for predicting cytotoxicity of TiO 2-based multicomponent nanomaterials toward Chinese hamster ovary (CHO-K1) cells. Nanotoxicology 2022; 16:276-289. [PMID: 35713578 DOI: 10.1080/17435390.2022.2080609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Nano-QSAR models can be effectively used for prediction of the biological activity of nanomaterials that have not been experimentally tested before. However, their use is associated with the need to have appropriate knowledge and skills in chemoinformatics. Thus, they are mainly aimed at specialists in the field. This significantly limits the potential group of recipients of the developed solutions. In this perspective, the purpose of the presented research was to develop an easily accessible and user-friendly web-based application that could enable the prediction of TiO2-based multicomponent nanomaterials cytotoxicity toward Chinese Hamster Ovary (CHO-K1) cells. The graphical user interface is clear and intuitive and the only information required from the user is the type and concentration of the metals which will be modifying TiO2-based nanomaterial. Thanks to this, the application will be easy to use not only by cheminformatics but also by specialists in the field of nanotechnology or toxicology, who will be able to quickly predict cytotoxicity of desired nanoclusters. We have performed case studies to demonstrate the features and utilities of developed application. The NanoMixHamster application is freely available at https://nanomixhamster.cloud.nanosolveit.eu/.
Collapse
Affiliation(s)
- Filip Stoliński
- QSAR Lab Ltd, Gdansk, Poland.,Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | | | - Alicja Mikolajczyk
- QSAR Lab Ltd, Gdansk, Poland.,Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Tomasz Puzyn
- QSAR Lab Ltd, Gdansk, Poland.,Laboratory of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| |
Collapse
|
6
|
Gromelski M, Stoliński F, Jagiello K, Rybińska-Fryca A, Williams A, Halappanavar S, Vogel U, Puzyn T. AOP173 key event associated pathway predictor - online application for the prediction of benchmark dose lower bound (BMDLs) of a transcriptomic pathway involved in MWCNTs-induced lung fibrosis. Nanotoxicology 2022; 16:183-194. [PMID: 35452346 DOI: 10.1080/17435390.2022.2064250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Nano-QSAR model allows for prediction of the toxicity of materials that have not been experimentally tested before by linking the nano-related structural properties with the biological responses induced by nanomaterials. Prediction of adverse effects caused by substances without having to perform time- and cost-consuming experiments makes QSAR models promising tools for supporting risk assessment. However, very often, newly developed nano-QSAR models are not used in practice due to the complexity of their algorithms, the necessity to have experience in chemoinformatics, and their poor accessibility. In this perspective, the aim of this paper is to encourage developers of the QSAR models to take the effort to prepare user-friendly applications based on predictive models. This would make the developed models accessible to a wider community, and, in effect, promote their further application by regulators and decision-makers. Here, we describe a web-based application that enables to predict the transcriptomic pathway-level response perturbated in the lungs of mice exposed to multiwalled carbon nanotubes. The developed application is freely available at http://aop173-event1.nanoqsar-aop.com/apps/aop_app. It requires only two types of input information related to analyzed nanotubes (their length and diameter) to assess the doses that initiate the inflammation process that may lead to lung fibrosis.
Collapse
Affiliation(s)
| | | | - Karolina Jagiello
- QSAR Lab, ul. Trzy Lipy 3, Gdańsk, Poland.,Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Andrew Williams
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Canada
| | - Sabina Halappanavar
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Canada
| | - Ulla Vogel
- National Research Centre for the Working Environment, Copenhagen, Denmark
| | - Tomasz Puzyn
- QSAR Lab, ul. Trzy Lipy 3, Gdańsk, Poland.,Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| |
Collapse
|
7
|
El Yamani N, Gromelski M, Mariussen E, Wyrzykowska E, Grabarek D, Puzyn T, Dusinska M, Rundén-Pran E. In silico unravelling of descriptors for cytotoxicity and genotoxicity for hazard identification of nanomaterials. Toxicol Lett 2021. [DOI: 10.1016/s0378-4274(21)00606-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
8
|
Rybińska-Fryca A, Gromelski M, Vijver M, Peijnenburg W, Châtel A, Barrick A, Manier N, Kalman J, Navas J, Jagiełło K, Puzyn T. How do the existing read-across frameworks work for nanomaterials? Toxicol Lett 2021. [DOI: 10.1016/s0378-4274(21)00748-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
9
|
Jeliazkova N, Apostolova MD, Andreoli C, Barone F, Barrick A, Battistelli C, Bossa C, Botea-Petcu A, Châtel A, De Angelis I, Dusinska M, El Yamani N, Gheorghe D, Giusti A, Gómez-Fernández P, Grafström R, Gromelski M, Jacobsen NR, Jeliazkov V, Jensen KA, Kochev N, Kohonen P, Manier N, Mariussen E, Mech A, Navas JM, Paskaleva V, Precupas A, Puzyn T, Rasmussen K, Ritchie P, Llopis IR, Rundén-Pran E, Sandu R, Shandilya N, Tanasescu S, Haase A, Nymark P. Towards FAIR nanosafety data. Nat Nanotechnol 2021; 16:644-654. [PMID: 34017099 DOI: 10.1038/s41565-021-00911-6] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 03/28/2021] [Indexed: 06/12/2023]
Abstract
Nanotechnology is a key enabling technology with billions of euros in global investment from public funding, which include large collaborative projects that have investigated environmental and health safety aspects of nanomaterials, but the reuse of accumulated data is clearly lagging behind. Here we summarize challenges and provide recommendations for the efficient reuse of nanosafety data, in line with the recently established FAIR (findable, accessible, interoperable and reusable) guiding principles. We describe the FAIR-aligned Nanosafety Data Interface, with an aggregated findability, accessibility and interoperability across physicochemical, bio-nano interaction, human toxicity, omics, ecotoxicological and exposure data. Overall, we illustrate a much-needed path towards standards for the optimized use of existing data, which avoids duplication of efforts, and provides a multitude of options to promote safe and sustainable nanotechnology.
Collapse
Affiliation(s)
| | - Margarita D Apostolova
- Medical and Biological Research Laboratory, Roumen Tsanev Institute of Molecular Biology, Bulgarian Academy of Sciences, Sofia, Bulgaria
| | | | | | - Andrew Barrick
- Mer Molécules Santé, Université Catholique de l'Ouest, Angers, France
| | | | | | - Alina Botea-Petcu
- Institute of Physical Chemistry 'Ilie Murgulescu' of the Romanian Academy, Bucharest, Romania
| | - Amélie Châtel
- Mer Molécules Santé, Université Catholique de l'Ouest, Angers, France
| | | | - Maria Dusinska
- Department of Environmental Chemistry, Health Effects Laboratory, Norwegian Institute for Air Research, Kjeller, Norway
| | - Naouale El Yamani
- Department of Environmental Chemistry, Health Effects Laboratory, Norwegian Institute for Air Research, Kjeller, Norway
| | - Daniela Gheorghe
- Institute of Physical Chemistry 'Ilie Murgulescu' of the Romanian Academy, Bucharest, Romania
| | - Anna Giusti
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment, Berlin, Germany
| | | | - Roland Grafström
- Department of Toxicology, Misvik Biology, Turku, Finland
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Maciej Gromelski
- Group of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Gdańsk, Poland
- QSAR Lab Ltd, Gdańsk, Poland
| | | | | | | | - Nikolay Kochev
- Ideaconsult Ltd, Sofia, Bulgaria
- Faculty of Chemistry, Department of Analytical Chemistry and Computer Chemistry, University of Plovdiv, Plovdiv, Bulgaria
| | - Pekka Kohonen
- Department of Toxicology, Misvik Biology, Turku, Finland
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Nicolas Manier
- Expertise and Assays in Ecotoxicology Unit, French National Institute for Industrial Environment and Risks, Verneuil-en-Halatte, France
| | - Espen Mariussen
- Department of Environmental Chemistry, Health Effects Laboratory, Norwegian Institute for Air Research, Kjeller, Norway
| | - Agnieszka Mech
- Joint Research Centre, European Commission, Ispra, Italy
| | - José María Navas
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Madrid, Spain
| | - Vesselina Paskaleva
- Ideaconsult Ltd, Sofia, Bulgaria
- Faculty of Chemistry, Department of Analytical Chemistry and Computer Chemistry, University of Plovdiv, Plovdiv, Bulgaria
| | - Aurica Precupas
- Institute of Physical Chemistry 'Ilie Murgulescu' of the Romanian Academy, Bucharest, Romania
| | - Tomasz Puzyn
- Group of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Gdańsk, Poland
- QSAR Lab Ltd, Gdańsk, Poland
| | | | | | | | - Elise Rundén-Pran
- Department of Environmental Chemistry, Health Effects Laboratory, Norwegian Institute for Air Research, Kjeller, Norway
| | - Romica Sandu
- Institute of Physical Chemistry 'Ilie Murgulescu' of the Romanian Academy, Bucharest, Romania
| | - Neeraj Shandilya
- Netherlands Organisation for Applied Scientific Research (TNO), Utrecht, Netherlands
| | - Speranta Tanasescu
- Institute of Physical Chemistry 'Ilie Murgulescu' of the Romanian Academy, Bucharest, Romania
| | - Andrea Haase
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment, Berlin, Germany
| | - Penny Nymark
- Department of Toxicology, Misvik Biology, Turku, Finland.
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
| |
Collapse
|
10
|
Gajewicz-Skretna A, Gromelski M, Wyrzykowska E, Furuhama A, Yamamoto H, Suzuki N. Aquatic toxicity (Pre)screening strategy for structurally diverse chemicals: global or local classification tree models? Ecotoxicol Environ Saf 2021; 208:111738. [PMID: 33396066 DOI: 10.1016/j.ecoenv.2020.111738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 11/23/2020] [Accepted: 11/25/2020] [Indexed: 06/12/2023]
Abstract
With an ever-increasing number of synthetic chemicals being manufactured, it is unrealistic to expect that they will all be subjected to comprehensive and effective risk assessment. A shift from conventional animal testing to computer-aided methods is therefore an important step towards advancing the environmental risk assessments of chemicals. The aims of this study are two-fold: firstly, it examines the relationships between structural and physicochemical features of a diverse set of organic chemicals, and their acute aquatic toxicity towards Daphnia magna and Oryzias latipes using a classification tree approach. Secondly, it compares the efficiency and accuracy of the predictions of two modeling schemes: local models that are inherently restricted to a smaller subset of structurally-related substances, and a global model that covers a wider chemical space and a number of modes of toxic action. The classification tree-based models differentiate the organic chemicals into either 'highly toxic' or 'low to non-toxic' classes, based on internal and external validation criteria. These mechanistically-driven models, which demonstrate good performance, reveal that the key factors driving acute aquatic toxicity are lipophilicity, electrophilic reactivity, molecular polarizability and size. A comparative analysis of the performance of the two modeling schemes indicates that the local models, trained on homogeneous data sets, are less error prone, and therefore superior to the global model. Although the global models showed worse performance metrics compared to the local ones, their applicability domain is much wider, thereby significantly increasing their usefulness in practical applications for regulatory purposes. This demonstrates their advantage over local models and shows they are an invaluable tool for modeling heterogeneous chemical data sets.
Collapse
Affiliation(s)
- Agnieszka Gajewicz-Skretna
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland.
| | - Maciej Gromelski
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Ewelina Wyrzykowska
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Ayako Furuhama
- Division of Genetics and Mutagenesis, National Institute of Health Sciences (NIHS), 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 210-9501, Japan; Center for Health and Environmental Risk Research, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba 305-8506, Japan
| | - Hiroshi Yamamoto
- Center for Health and Environmental Risk Research, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba 305-8506, Japan
| | - Noriyuki Suzuki
- Center for Health and Environmental Risk Research, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba 305-8506, Japan
| |
Collapse
|
11
|
Lynch I, Afantitis A, Exner T, Himly M, Lobaskin V, Doganis P, Maier D, Sanabria N, Papadiamantis AG, Rybinska-Fryca A, Gromelski M, Puzyn T, Willighagen E, Johnston BD, Gulumian M, Matzke M, Green Etxabe A, Bossa N, Serra A, Liampa I, Harper S, Tämm K, Jensen ACØ, Kohonen P, Slater L, Tsoumanis A, Greco D, Winkler DA, Sarimveis H, Melagraki G. Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies? Nanomaterials (Basel) 2020; 10:E2493. [PMID: 33322568 PMCID: PMC7764592 DOI: 10.3390/nano10122493] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 12/05/2020] [Accepted: 12/08/2020] [Indexed: 12/16/2022]
Abstract
Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs (NInChI). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a "nano" extension to the InChI standard.
Collapse
Affiliation(s)
- Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;
| | - Antreas Afantitis
- Nanoinformatics Department, NovaMechanics Ltd., 1666 Nicosia, Cyprus; (A.A.); (A.T.)
| | - Thomas Exner
- Edelweiss Connect GmbH, Hochbergerstrasse 60C, 4057 Basel, Switzerland;
| | - Martin Himly
- Department Biosciences, Paris Lodron University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, Austria;
| | - Vladimir Lobaskin
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland;
| | - Philip Doganis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; (P.D.); (I.L.); (H.S.)
| | - Dieter Maier
- Biomax Informatics AG, Robert-Koch-Str. 2, 82152 Planegg, Germany;
| | - Natasha Sanabria
- National Health Laboratory Services, 1 Modderfontein Rd, Sandringham, Johannesburg 2192, South Africa; (N.S.); (M.G.)
| | - Anastasios G. Papadiamantis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;
- Nanoinformatics Department, NovaMechanics Ltd., 1666 Nicosia, Cyprus; (A.A.); (A.T.)
| | - Anna Rybinska-Fryca
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; (A.R.-F.); (M.G.); (T.P.)
| | - Maciej Gromelski
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; (A.R.-F.); (M.G.); (T.P.)
| | - Tomasz Puzyn
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; (A.R.-F.); (M.G.); (T.P.)
| | - Egon Willighagen
- Department of Bioinformatics—BiGCaT, School of Nutrition and Translational Research in Metabolism, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands;
| | - Blair D. Johnston
- Department Chemicals and Product Safety, Federal Institute for Risk Assessment, Max-Dohrn-Str. 8-10, 10589 Berlin, Germany;
| | - Mary Gulumian
- National Health Laboratory Services, 1 Modderfontein Rd, Sandringham, Johannesburg 2192, South Africa; (N.S.); (M.G.)
- Haematology and Molecular Medicine, University of the Witwatersrand, 1 Jan Smuts Ave, Johannesburg 2000, South Africa
| | - Marianne Matzke
- UK Centre for Ecology and Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford OX10 8BB, UK; (M.M.); (A.G.E.)
| | - Amaia Green Etxabe
- UK Centre for Ecology and Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford OX10 8BB, UK; (M.M.); (A.G.E.)
| | - Nathan Bossa
- LEITAT Technological Center, Circular Economy Business Unit, C/de La Innovació 2, 08225 Terrassa, Barcelona, Spain;
| | - Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (D.G.)
| | - Irene Liampa
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; (P.D.); (I.L.); (H.S.)
| | - Stacey Harper
- School of Chemical, Biological, and Environmental Engineering, Oregon State University, 116 Johnson Hall 105 SW 26th St., Corvallis, OR 97331, USA;
| | - Kaido Tämm
- Institute of Chemistry, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia;
| | - Alexander CØ Jensen
- The National Research Center for the Work Environment, Lersø Parkallé 105, 2100 Copenhagen, Denmark;
| | - Pekka Kohonen
- Misvik Biology OY, Karjakatu 35 B, 20520 Turku, Finland;
| | - Luke Slater
- Institute of Cancer and Genomics, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;
| | - Andreas Tsoumanis
- Nanoinformatics Department, NovaMechanics Ltd., 1666 Nicosia, Cyprus; (A.A.); (A.T.)
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (D.G.)
| | - David A. Winkler
- Institute of Molecular Sciences, La Trobe University, Kingsbury Drive, Bundoora 3086, Australia;
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville 3052, Australia
- School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, UK
- CSIRO Data61, Pullenvale 4069, Australia
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; (P.D.); (I.L.); (H.S.)
| | - Georgia Melagraki
- Nanoinformatics Department, NovaMechanics Ltd., 1666 Nicosia, Cyprus; (A.A.); (A.T.)
| |
Collapse
|
12
|
Isigonis P, Afantitis A, Antunes D, Bartonova A, Beitollahi A, Bohmer N, Bouman E, Chaudhry Q, Cimpan MR, Cimpan E, Doak S, Dupin D, Fedrigo D, Fessard V, Gromelski M, Gutleb AC, Halappanavar S, Hoet P, Jeliazkova N, Jomini S, Lindner S, Linkov I, Longhin EM, Lynch I, Malsch I, Marcomini A, Mariussen E, de la Fuente JM, Melagraki G, Murphy F, Neaves M, Packroff R, Pfuhler S, Puzyn T, Rahman Q, Pran ER, Semenzin E, Serchi T, Steinbach C, Trump B, Vrček IV, Warheit D, Wiesner MR, Willighagen E, Dusinska M. Risk Governance of Emerging Technologies Demonstrated in Terms of its Applicability to Nanomaterials. Small 2020; 16:e2003303. [PMID: 32700469 DOI: 10.1002/smll.202003303] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Indexed: 06/11/2023]
Abstract
Nanotechnologies have reached maturity and market penetration that require nano-specific changes in legislation and harmonization among legislation domains, such as the amendments to REACH for nanomaterials (NMs) which came into force in 2020. Thus, an assessment of the components and regulatory boundaries of NMs risk governance is timely, alongside related methods and tools, as part of the global efforts to optimise nanosafety and integrate it into product design processes, via Safe(r)-by-Design (SbD) concepts. This paper provides an overview of the state-of-the-art regarding risk governance of NMs and lays out the theoretical basis for the development and implementation of an effective, trustworthy and transparent risk governance framework for NMs. The proposed framework enables continuous integration of the evolving state of the science, leverages best practice from contiguous disciplines and facilitates responsive re-thinking of nanosafety governance to meet future needs. To achieve and operationalise such framework, a science-based Risk Governance Council (RGC) for NMs is being developed. The framework will provide a toolkit for independent NMs' risk governance and integrates needs and views of stakeholders. An extension of this framework to relevant advanced materials and emerging technologies is also envisaged, in view of future foundations of risk research in Europe and globally.
Collapse
Affiliation(s)
- Panagiotis Isigonis
- Department of Environmental Sciences, Informatics and Statistics, University Ca' Foscari of Venice, Via Torino 155, Mestre, Venice, 30172, Italy
| | | | | | - Alena Bartonova
- NILU, Norwegian Institute for Air Research, Kjeller, 2007, Norway
| | - Ali Beitollahi
- INIC, Iran Nanotechnology Initiate Council, Tehran, Iran
| | - Nils Bohmer
- Society for Chemical Engineering and Biotechnology (DECHEMA), Theodor-Heuss-Allee 25, Frankfurt am Main, 60486, Germany
| | - Evert Bouman
- NILU, Norwegian Institute for Air Research, Kjeller, 2007, Norway
| | - Qasim Chaudhry
- University of Chester, Parkgate Road, Chester, CH1 4BJ, UK
| | - Mihaela Roxana Cimpan
- Department of Clinical Dentistry, Biomaterials, Faculty of Medicine, University of Bergen, Aarstadveien 19, Bergen, 5009, Norway
| | - Emil Cimpan
- Western Norway University of Applied Sciences, Inndalsveien 28, Bergen, 5063, Norway
| | - Shareen Doak
- Swansea University Medical School, Singleton Park, Swansea, Wales, SA2 8PP, UK
| | - Damien Dupin
- CIDETEC, Paseo Miramón 196, Donostia-San Sebastián, 20014, Spain
| | - Doreen Fedrigo
- ECOS - European Environmental Citizens Organization for Standardization, Rue d'Edimbourg, 26, Brussels, 1050, Belgium
| | - Valérie Fessard
- ANSES Fougères Laboratory, Contaminant Toxicology Unit and Risk Management Support, Unit of Chemicals Assessment, Risk Assessment Department, 14 rue Pierre et Marie Curie, Maisons-Alfort, Cedex 94701, France
| | - Maciej Gromelski
- QSAR Lab Sp. z o.o., al. Grunwaldzka 190/102, Gdańsk, 80-266, Poland
| | - Arno C Gutleb
- LIST, Luxembourg Institute of Science and Technology, Belvaux, Luxembourg
| | - Sabina Halappanavar
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada
| | - Peter Hoet
- KU Leuven, Department of Public Health and Primary Care, Unit of Environment and Health, Leuven, 3000, Belgium
| | - Nina Jeliazkova
- IDEA Ideaconsult Limited Liability Company, Angel Kanchev 4, Sofia, 1000, Bulgaria
| | - Stéphane Jomini
- ANSES Fougères Laboratory, Contaminant Toxicology Unit and Risk Management Support, Unit of Chemicals Assessment, Risk Assessment Department, 14 rue Pierre et Marie Curie, Maisons-Alfort, Cedex 94701, France
| | - Sabine Lindner
- Plastics Europe Deutschland e. V., Mainzer Landstrasse 55, Frankfurt am Main, 60329, Germany
| | - Igor Linkov
- Factor Social Lda., Lisbon, Portugal
- US Army Engineer Research and Development Center and Carnegie Mellon University, Lisbon, Portugal
| | | | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Ineke Malsch
- Malsch TechnoValuation, PO Box 455, Utrecht, AL, 3500, The Netherlands
| | - Antonio Marcomini
- Department of Environmental Sciences, Informatics and Statistics, University Ca' Foscari of Venice, Via Torino 155, Mestre, Venice, 30172, Italy
| | - Espen Mariussen
- NILU, Norwegian Institute for Air Research, Kjeller, 2007, Norway
| | - Jesus M de la Fuente
- Instituto de Ciencia de Materiales de Aragón (ICMA), Consejo Superior de Investigaciones Científicas (CSIC)-Universidad de Zaragoza, C/Pedro Cerbuna 12, Zaragoza, 50009, Spain
| | | | | | - Michael Neaves
- ECOS - European Environmental Citizens Organization for Standardization, Rue d'Edimbourg, 26, Brussels, 1050, Belgium
| | - Rolf Packroff
- Division of 'Hazardous chemicals and biological agents', BAuA - Federal Institute for Occupational Safety and Health, Dortmund, Germany
| | - Stefan Pfuhler
- Procter & Gamble Co., Miami Valley Innovation Center, 11810 East Miami River Road, Cincinnati, OH, 45239 8707, USA
| | - Tomasz Puzyn
- QSAR Lab Sp. z o.o., al. Grunwaldzka 190/102, Gdańsk, 80-266, Poland
- University of Gdansk, Faculty of Chemistry, Group of Environmental Chemometrics, Wita Stwosza 63, Gdańsk, 80-308, Poland
| | | | | | - Elena Semenzin
- Department of Environmental Sciences, Informatics and Statistics, University Ca' Foscari of Venice, Via Torino 155, Mestre, Venice, 30172, Italy
| | - Tommaso Serchi
- LIST, Luxembourg Institute of Science and Technology, Belvaux, Luxembourg
| | - Christoph Steinbach
- Society for Chemical Engineering and Biotechnology (DECHEMA), Theodor-Heuss-Allee 25, Frankfurt am Main, 60486, Germany
| | - Benjamin Trump
- Factor Social Lda., Lisbon, Portugal
- US Army Engineer Research and Development Center and University of Michigan, Lisbon, Portugal
| | - Ivana Vinković Vrček
- Institute for Medical Research and Occupational Health, Analytical Toxicology and Mineral Metabolism Unit, Ksaverska cesta 2, Zagreb, 10 000, Croatia
| | | | - Mark R Wiesner
- Department of Civil and Environmental Engineering, Center for the Environmental Implications of NanoTechnology (CEINT) Duke University, 121 Hudson Hall, Durham, NC, 27708-0287, USA
| | - Egon Willighagen
- Department of Bioinformatics, BiGCaT, NUTRIM, Maastricht University, Maastricht, ER 6229, The Netherlands
| | - Maria Dusinska
- NILU, Norwegian Institute for Air Research, Kjeller, 2007, Norway
| |
Collapse
|
13
|
Jakubus A, Gromelski M, Jagiello K, Puzyn T, Stepnowski P, Paszkiewicz M. Dispersive solid-phase extraction using multi-walled carbon nanotubes combined with liquid chromatography–mass spectrometry for the analysis of β-blockers: Experimental and theoretical studies. Microchem J 2019. [DOI: 10.1016/j.microc.2018.12.063] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
14
|
Jakubus A, Godlewska K, Gromelski M, Jagiello K, Puzyn T, Stepnowski P, Paszkiewicz M. The possibility to use multi-walled carbon nanotubes as a sorbent for dispersive solid phase extraction of selected pharmaceuticals and their metabolites: Effect of extraction condition. Microchem J 2019. [DOI: 10.1016/j.microc.2019.02.051] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
15
|
Judycka U, Jagiello K, Gromelski M, Bober L, Błażejowski J, Puzyn T. Chemometric approach to correlations between retention parameters of non-polar HPLC columns and physicochemical characteristics for ampholytic substances of biological and pharmaceutical relevance. J Chromatogr B Analyt Technol Biomed Life Sci 2018; 1095:8-14. [PMID: 30036737 DOI: 10.1016/j.jchromb.2018.07.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Revised: 06/29/2018] [Accepted: 07/15/2018] [Indexed: 11/25/2022]
Abstract
The correlations between the retention parameters of forty ampholytic, biologically active and/or pharmaceutically relevant substances (obtained for three non-polar HPLC columns at various compositions of mobile phases and pH conditions: 2.5, 7.0, 11.5) and their thirty-two physicochemical (calculated/spectral) characteristics were investigated by applying chemometric methods of analysis. In three cases (among seven cases considered), Quantitative Property-Retention Relation (QPRR) models meeting the predictive capability criteria were developed (the values of R2, Q2CV, Q2Ext were higher than 0.76, 0.66 and 0.67, respectively, while values of RMSEC, RMSECV and RMSEExt were lower than 0.51, 0.65 and 0.65 in each developed model). These models create a useful platform for predicting retention parameters of untested chemicals and, to some extent, gaining pharmaceutically valuable information on the biologically active ampholytic substances based on their properties and the conditions of chromatographic separation.
Collapse
Affiliation(s)
- Urszula Judycka
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308 Gdańsk, Poland; Research Laboratory, Polpharma SA, Pelplińska 19, 80-200 Starogard Gdański, Poland
| | - Karolina Jagiello
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308 Gdańsk, Poland
| | - Maciej Gromelski
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308 Gdańsk, Poland
| | - Leszek Bober
- Research Laboratory, Polpharma SA, Pelplińska 19, 80-200 Starogard Gdański, Poland
| | - Jerzy Błażejowski
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308 Gdańsk, Poland.
| | - Tomasz Puzyn
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308 Gdańsk, Poland.
| |
Collapse
|