NanoSolveIT Project: driving Nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment
journal contributionposted on 06.07.2021, 03:57 by Antreas Afantitis, Georgia Melagraki, Panagiotis Isigonis, Andreas Tsoumanis, Dimitra Danai Varsou, Eugenia Valsami-Jones, Anastasios Papadiamantis, Laura-Jayne A Ellis, Haralambos Sarimveis, Philip Doganis, Pantelis Karatzas, Periklis Tsiros, Irene Liampa, Vladimir Lobaskin, Dario Greco, Angela Serra, Pia Anneli Sofia Kinaret, Laura Aliisa Saarimäki, Roland Grafström, Pekka Kohonen, Penny Nymark, Egon Willighagen, Tomasz Puzyn, Anna Rybinska-Fryca, Alexander Lyubartsev, Keld Alstrup Jensen, Jan Gerit Brandenburg, Stephen Lofts, Claus Svendsen, Samuel Harrison, Dieter Maier, Kaido Tamm, Jaak Jänes, Lauri Sikk, Maria Dusinska, Eleonora Longhin, Elise Rundén-Pran, Espen Mariussen, Naouale El Yamani, Wolfgang Unger, Jörg Radnik, Alexander Tropsha, Yoram Cohen, Jerzy Leszczynski, Christine Ogilvie Hendren, Mark Wiesner, David Winkler, Noriyuki Suzuki, Tae Hyun Yoon, Jang-Sik Choi, Natasha Sanabria, Mary Gulumian, Iseult Lynch
Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational ‘safe-by-design’ approaches to facilitate NM commercialization.
This work has received funding from the European Union's Horizon 2020 research and innovation programme via NanoSolveIT Project under grant agreement No 814572. Tae Hyun Yoon (THY) and Jang-Sik Choi (JSC) also appreciate partial support by the Korean Ministry of Science & ICT (MSIT) and the National Research Foundation (NRF) through the International Cooperative R&D Program (No. 2019K1A3A1A78112959), as a part of the European Union's Horizon 2020 NanoSolveIT Project.
JournalComputational and Structural Biotechnology Journal
Pagination20p. (p. 583-602)
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Science & TechnologyLife Sciences & BiomedicineBiochemistry & Molecular BiologyBiotechnology & Applied MicrobiologyNanoinformaticsComputational toxicologyHazard assessmentEngineered nanomaterials(quantitative) Structure-activity relationshipsIntegrated approach for testing and assessmentSafe-by-designMachine learningRead acrossToxicogenomicsPredictive modellingMETAL-OXIDE NANOPARTICLESINDUCE OXIDATIVE STRESSCARBON NANOTUBESPROTEIN CORONAQUANTITATIVE STRUCTUREPULMONARY INFLAMMATIONDEPENDENT TOXICITYCELLULAR TOXICITYNANOMATERIAL DATAFORCE-FIELD(quantitative) Structure–activity relationshipsAI, Artificial IntelligenceAOPs, Adverse Outcome PathwaysAPI, Application Programming interfaceCG, coarse-grained (model)CNTs, carbon nanotubesFAIR, Findable Accessible Inter-operable and Re-usableGUI, Graphical Processing UnitHOMO-LUMO, Highest Occupied Molecular Orbital Lowest Unoccupied Molecular OrbitalIATA, Integrated Approaches to Testing and AssessmentKE, key eventsMIE, molecular initiating eventsML, machine learningMOA, mechanism (mode) of actionMWCNT, multi-walled carbon nanotubesNMs, nanomaterialsOECD, Organisation for Economic Co-operation and DevelopmentPBPK, Physiologically Based PharmacoKineticsPC, Protein CoronaPChem, PhysicochemicalPTGS, Predictive Toxicogenomics SpaceQC, quantum-chemicalQM, quantum-mechanicalQSAR, quantitative structure-activity relationshipQSPR, quantitative structure-property relationshipRA, risk assessmentREST, Representational State TransferROS, reactive oxygen speciesSAR, structure-activity relationshipSMILES, Simplified Molecular Input Line Entry SystemSOPs, standard operating procedures