Toward Rigorous Materials Production: New Approach Methodologies Have Extensive Potential to Improve Current Safety Assessment Practices.
暂无分享,去创建一个
Mikko Poikkimäki | Wouter Fransman | Mary Gulumian | Pekka Kohonen | Penny Nymark | Roland Grafström | Dario Greco | Keld Alstrup Jensen | Isabel Rodriguez-Llopis | Sabina Halappanavar | Antti Joonas Koivisto | Vesa Hongisto | Susan Dekkers | Remy Franken | Thies Oosterwijk | Rob Stierum | Jorid Birkelund Sørli | Niels Hadrup | Martine Bakker | Amaia García-Bilbao | Karin Sørig Hougaard | Miikka Dal Maso | R. Stierum | D. Greco | K. Jensen | P. Kohonen | R. Grafström | K. Hougaard | S. Halappanavar | N. Hadrup | W. Fransman | V. Hongisto | S. Dekkers | A. Koivisto | P. Nymark | M. Bakker | M. Gulumian | Mikko Poikkimäki | M. Dal Maso | Amaia Garcia-Bilbao | J. Sørli | T. Oosterwijk | R. Franken | I. Rodríguez-Llopis
[1] Pekka Kohonen,et al. Toxic and Genomic Influences of Inhaled Nanomaterials as a Basis for Predicting Adverse Outcome. , 2018, Annals of the American Thoracic Society.
[2] B. Thomsen,et al. A Proposed In Vitro Method to Assess Effects of Inhaled Particles on Lung Surfactant Function. , 2016, American journal of respiratory cell and molecular biology.
[3] Alexander Golbraikh,et al. Integrative chemical-biological read-across approach for chemical hazard classification. , 2013, Chemical research in toxicology.
[4] Georgia Tsiliki,et al. A Data Fusion Pipeline for Generating and Enriching Adverse Outcome Pathway Descriptions , 2017, Toxicological sciences : an official journal of the Society of Toxicology.
[5] Timothy F. Malloy,et al. Policy reforms to update chemical safety testing , 2017, Science.
[6] Enrico Burello,et al. Proposal for a risk banding framework for inhaled low aspect ratio nanoparticles based on physicochemical properties , 2016, Nanotoxicology.
[7] José María Navas,et al. Quality evaluation of human and environmental toxicity studies performed with nanomaterials – the GUIDEnano approach , 2018 .
[8] Ola Spjuth,et al. Cancer biology, toxicology and alternative methods development go hand-in-hand. , 2014, Basic & clinical pharmacology & toxicology.
[9] David Brown,et al. Organ burden and pulmonary toxicity of nano-sized copper (II) oxide particles after short-term inhalation exposure , 2016, Nanotoxicology.
[10] Flemming R. Cassee,et al. On the pivotal role of dose for particle toxicology and risk assessment: exposure is a poor surrogate for delivered dose , 2017, Particle and Fibre Toxicology.
[11] Robert J Kavlock,et al. Accelerating the Pace of Chemical Risk Assessment. , 2018, Chemical research in toxicology.
[12] Mary Gulumian,et al. Label-free in vitro toxicity and uptake assessment of citrate stabilised gold nanoparticles in three cell lines , 2013, Particle and Fibre Toxicology.
[13] Fred A. Wright,et al. A chemical–biological similarity-based grouping of complex substances as a prototype approach for evaluating chemical alternatives† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6gc01147k Click here for additional data file. , 2016, Green chemistry : an international journal and green chemistry resource : GC.
[14] Skylar W. Marvel,et al. ToxPi Graphical User Interface 2.0: Dynamic exploration, visualization, and sharing of integrated data models , 2018, BMC Bioinformatics.
[15] Andrea Haase,et al. Nanomaterial grouping: Existing approaches and future recommendations , 2019, NanoImpact.
[16] Jiri Aubrecht,et al. Validation of Transcriptomics-Based In Vitro Methods. , 2016, Advances in experimental medicine and biology.
[17] Wout Slob,et al. Shape and steepness of toxicological dose–response relationships of continuous endpoints , 2014, Critical reviews in toxicology.
[18] Rong Liu,et al. Implementation of a multidisciplinary approach to solve complex nano EHS problems by the UC Center for the Environmental Implications of Nanotechnology. , 2013, Small.
[19] Kan Shao,et al. Is the assumption of normality or log-normality for continuous response data critical for benchmark dose estimation? , 2013, Toxicology and applied pharmacology.
[20] Sharon Munn,et al. The Adverse Outcome Pathway approach in nanotoxicology , 2017 .
[21] Andrew Worth,et al. Ab initio chemical safety assessment: A workflow based on exposure considerations and non-animal methods , 2017, Computational toxicology.
[22] J. Dearden,et al. QSAR modeling: where have you been? Where are you going to? , 2014, Journal of medicinal chemistry.
[23] Martin Fritts,et al. Integration among databases and data sets to support productive nanotechnology: Challenges and recommendations , 2018, NanoImpact.
[24] Thomas Hartung,et al. Big-data and machine learning to revamp computational toxicology and its use in risk assessment. , 2018, Toxicology research.
[25] Bruce C Allen,et al. Benchmark dose (BMD) modeling: current practice, issues, and challenges , 2018, Critical reviews in toxicology.
[26] Andrew Williams,et al. Meta-analysis of transcriptomic responses as a means to identify pulmonary disease outcomes for engineered nanomaterials , 2015, Particle and Fibre Toxicology.
[27] Christian Micheletti,et al. Implementation of Safe-by-Design for Nanomaterial Development and Safe Innovation: Why We Need a Comprehensive Approach , 2018, Nanomaterials.
[28] Miikka Dal Maso,et al. Comparison of Geometrical Layouts for a Multi-Box Aerosol Model from a Single-Chamber Dispersion Study , 2018 .
[29] M. Vetten,et al. From the Cover: An Investigation of the Genotoxicity and Interference of Gold Nanoparticles in Commonly Used In Vitro Mutagenicity and Genotoxicity Assays , 2017, Toxicological sciences : an official journal of the Society of Toxicology.
[30] Valérie Zuang,et al. A Modular Approach to the ECVAM Principles on Test Validity , 2004, Alternatives to laboratory animals : ATLA.
[31] Andrew Worth,et al. Applying Adverse Outcome Pathways (AOPs) to support Integrated Approaches to Testing and Assessment (IATA). , 2014, Regulatory toxicology and pharmacology : RTP.
[32] Schuur Ag,et al. ConsExpo Web : Consumer exposure models - Model documentation , 2016 .
[33] Thomas Hartung,et al. 21st Century Cell Culture for 21st Century Toxicology. , 2017, Chemical research in toxicology.
[34] Ivan Rusyn,et al. The Impact of Novel Assessment Methodologies in Toxicology on Green Chemistry and Chemical Alternatives , 2018, Toxicological sciences : an official journal of the Society of Toxicology.
[35] Hans Marquart,et al. RISKOFDERM: risk assessment of occupational dermal exposure to chemicals. An introduction to a series of papers on the development of a toolkit. , 2003, The Annals of occupational hygiene.
[36] Bruce C Allen,et al. BMDExpress: a software tool for the benchmark dose analyses of genomic data , 2007, BMC Genomics.
[37] Sijie Lin,et al. Nanomaterials Safer‐by‐Design: An Environmental Safety Perspective , 2018, Advances in Materials.
[38] Margriet Vdz Park,et al. Development of a systematic method to assess similarity between nanomaterials for human hazard evaluation purposes – lessons learnt , 2018, Nanotoxicology.
[39] Andrew Emili,et al. Protein corona fingerprinting predicts the cellular interaction of gold and silver nanoparticles. , 2014, ACS nano.
[40] Detlef Ritter,et al. Air–Liquid Interface In Vitro Models for Respiratory Toxicology Research: Consensus Workshop and Recommendations , 2018, Applied in vitro toxicology.
[41] Saji George,et al. A predictive toxicological paradigm for the safety assessment of nanomaterials. , 2009, ACS nano.
[42] Antonio Marcomini,et al. Frameworks and tools for risk assessment of manufactured nanomaterials. , 2016, Environment international.
[43] Roberto Tagliaferri,et al. INSIdE NANO: a systems biology framework to contextualize the mechanism-of-action of engineered nanomaterials , 2019, Scientific Reports.
[44] Vittorio Fortino,et al. INfORM: Inference of NetwOrk Response Modules , 2018, Bioinform..
[45] Egon L. Willighagen,et al. Introducing WikiPathways as a Data-Source to Support Adverse Outcome Pathways for Regulatory Risk Assessment of Chemicals and Nanomaterials , 2018, Front. Genet..
[46] Kari E. J. Lehtinen,et al. Multicomponent aerosol dynamics model UHMA: model development and validation , 2004 .
[47] Ola Spjuth,et al. Toward the Replacement of Animal Experiments through the Bioinformatics-driven Analysis of ‘Omics’ Data from Human Cell Cultures , 2015, Alternatives to laboratory animals : ATLA.
[48] S. Kežić,et al. Progress and future of in vitro models to study translocation of nanoparticles , 2015, Archives of Toxicology.
[49] Egon L. Willighagen,et al. RRegrs: an R package for computer-aided model selection with multiple regression models , 2015, Journal of Cheminformatics.
[50] Melvin E. Andersen,et al. Developing tools for defining and establishing pathways of toxicity , 2015, Archives of Toxicology.
[51] M Levin,et al. Testing the near field/far field model performance for prediction of particulate matter emissions in a paint factory. , 2015, Environmental science. Processes & impacts.
[52] Min Li,et al. Physiologically Based Pharmacokinetic (PBPK) Modeling of Pharmaceutical Nanoparticles , 2016, The AAPS Journal.
[53] Georgia Tsiliki,et al. toxFlow: A Web-Based Application for Read-Across Toxicity Prediction Using Omics and Physicochemical Data , 2017, J. Chem. Inf. Model..
[54] Aron Walsh,et al. The 2019 materials by design roadmap , 2018, Journal of physics D: Applied physics.
[55] Haralambos Sarimveis,et al. Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment , 2020, Nanomaterials.
[56] Jeremy M. Gernand,et al. Approaches to Develop Alternative Testing Strategies to Inform Human Health Risk Assessment of Nanomaterials , 2016, Risk analysis : an official publication of the Society for Risk Analysis.
[57] Y. Zuo,et al. Biophysical influence of airborne carbon nanomaterials on natural pulmonary surfactant. , 2015, ACS nano.
[58] Agnieszka Gajewicz,et al. What if the number of nanotoxicity data is too small for developing predictive Nano-QSAR models? An alternative read-across based approach for filling data gaps. , 2017, Nanoscale.
[59] Lang Tran,et al. Adoption of in vitro systems and zebrafish embryos as alternative models for reducing rodent use in assessments of immunological and oxidative stress responses to nanomaterials , 2018, Critical reviews in toxicology.
[60] Andrew Williams,et al. Ranking of nanomaterial potency to induce pathway perturbations associated with lung responses , 2019, NanoImpact.
[61] Miikka Dal Maso,et al. Source specific exposure and risk assessment for indoor aerosols. , 2019, The Science of the total environment.
[62] John J. Schlager,et al. Toxicity Evaluation for Safe Use of Nanomaterials: Recent Achievements and Technical Challenges , 2009 .
[63] Dominique Lison,et al. Mechanisms of lung fibrosis induced by carbon nanotubes: towards an Adverse Outcome Pathway (AOP) , 2015, Particle and Fibre Toxicology.
[64] A. Luch,et al. Comparative modeling of exposure to airborne nanoparticles released by consumer spray products , 2016, Nanotoxicology.
[65] Andrey A Toropov,et al. Nano-QSAR in cell biology: Model of cell viability as a mathematical function of available eclectic data. , 2017, Journal of theoretical biology.
[66] Philip Demokritou,et al. An integrated approach for the in vitro dosimetry of engineered nanomaterials , 2014, Particle and Fibre Toxicology.
[67] Raffaella Corvi,et al. 3S – Systematic, Systemic, and Systems Biology and Toxicology , 2019, ALTEX.
[68] Wouter Fransman,et al. LICARA nanoSCAN - A tool for the self-assessment of benefits and risks of nanoproducts. , 2016, Environment international.
[69] Nicklas Raun Jacobsen,et al. Comparative Hazard Identification by a Single Dose Lung Exposure of Zinc Oxide and Silver Nanomaterials in Mice , 2015, PloS one.
[70] Helene Stockmann-Juvala,et al. A theoretical approach for a weighted assessment of the mutagenic potential of nanomaterials , 2017, Nanotoxicology.
[71] Lang Tran,et al. ITS-NANO - Prioritising nanosafety research to develop a stakeholder driven intelligent testing strategy , 2014, Particle and Fibre Toxicology.
[72] Scott C. Wesselkamper,et al. Editor's Highlight: Application of Gene Set Enrichment Analysis for Identification of Chemically Induced, Biologically Relevant Transcriptomic Networks and Potential Utilization in Human Health Risk Assessment , 2017, Toxicological sciences : an official journal of the Society of Toxicology.
[73] D. Dix,et al. Informing Selection of Nanomaterial Concentrations for ToxCast in Vitro Testing Based on Occupational Exposure Potential , 2011, Environmental health perspectives.
[74] Carole L Yauk,et al. Promise and peril in nanomedicine: the challenges and needs for integrated systems biology approaches to define health risk , 2017, Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology.
[75] G. Kaptay,et al. On the size and shape dependence of the solubility of nano-particles in solutions. , 2012, International journal of pharmaceutics.
[76] D BurgoonLyle,et al. The AOPOntology: A Semantic Artificial Intelligence Tool for Predictive Toxicology , 2017 .
[77] S. H. Bennekou,et al. Adverse outcome pathways: opportunities, limitations and open questions , 2017, Archives of Toxicology.
[78] Igor Linkov,et al. Sustainable nanotechnology decision support system: bridging risk management, sustainable innovation and risk governance , 2016, Journal of Nanoparticle Research.
[79] Brian L. Murphy,et al. Modeling Indoor Air Exposure from Short‐Term Point Source Releases , 1996 .
[80] Philip Demokritou,et al. Preparation, characterization, and in vitro dosimetry of dispersed, engineered nanomaterials , 2017, Nature Protocols.
[81] Steffen Foss Hansen,et al. Control banding tools for occupational exposure assessment of nanomaterials — Ready for use in a regulatory context? , 2016 .
[82] Joris T.K. Quik,et al. Directions in QPPR development to complement the predictive models used in risk assessment of nanomaterials , 2018, NanoImpact.
[83] Haralambos Sarimveis,et al. Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology. , 2017, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.
[84] Kee Woei Ng,et al. Emerging in vitro models for safety screening of high-volume production nanomaterials under environmentally relevant exposure conditions. , 2013, Small.
[85] R Damoiseaux,et al. No time to lose--high throughput screening to assess nanomaterial safety. , 2011, Nanoscale.
[86] Peter Wick,et al. Nanoparticle transport across the placental barrier: pushing the field forward! , 2016, Nanomedicine.
[87] Stephen W. Edwards,et al. Systems Biology and Biomarkers of Early Effects for Occupational Exposure Limit Setting , 2015, Journal of occupational and environmental hygiene.
[88] Robert G. Cooper,et al. Stage-gate systems: A new tool for managing new products , 1990 .
[89] Bryant C Nelson,et al. Emerging metrology for high-throughput nanomaterial genotoxicology. , 2017, Mutagenesis.
[90] Egon L. Willighagen,et al. eNanoMapper: harnessing ontologies to enable data integration for nanomaterial risk assessment , 2015, Journal of Biomedical Semantics.
[91] David M. Reif,et al. Incorporating exposure information into the toxicological prioritization index decision support framework. , 2012, The Science of the total environment.
[92] Xiang Wang,et al. Nanomaterial toxicity testing in the 21st century: use of a predictive toxicological approach and high-throughput screening. , 2013, Accounts of chemical research.
[93] C. Austin,et al. Improving the Human Hazard Characterization of Chemicals: A Tox21 Update , 2013, Environmental health perspectives.
[94] Robert G. Cooper,et al. What's Next?: After Stage-Gate , 2014 .
[95] K. Dawson,et al. Microscopy-based high-throughput assays enable multi-parametric analysis to assess adverse effects of nanomaterials in various cell lines , 2017, Archives of Toxicology.
[96] K. Hougaard,et al. Prediction of acute inhalation toxicity using in vitro lung surfactant inhibition. , 2018, ALTEX.
[97] Blanca Suarez-Merino,et al. High throughput toxicity screening and intracellular detection of nanomaterials , 2016, Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology.
[98] E. J. Foster,et al. An in vitro testing strategy towards mimicking the inhalation of high aspect ratio nanoparticles , 2014, Particle and Fibre Toxicology.
[99] Lauren Heine,et al. Advancing alternatives analysis: The role of predictive toxicology in selecting safer chemical products and processes. , 2017, Integrated environmental assessment and management.
[100] Robert Landsiedel,et al. Nanomaterial categorization for assessing risk potential to facilitate regulatory decision-making. , 2015, ACS nano.
[101] Kirk G Scheckel,et al. A comprehensive framework for evaluating the environmental health and safety implications of engineered nanomaterials , 2017, Critical reviews in toxicology.
[102] Andrew Williams,et al. Nano-risk Science: application of toxicogenomics in an adverse outcome pathway framework for risk assessment of multi-walled carbon nanotubes , 2015, Particle and Fibre Toxicology.
[103] Terry W Schultz,et al. The Adverse Outcome Pathway for Skin Sensitisation: Moving Closer to Replacing Animal Testing , 2016, Alternatives to laboratory animals : ATLA.
[104] Joel G Pounds,et al. ISDD: A computational model of particle sedimentation, diffusion and target cell dosimetry for in vitro toxicity studies , 2010, Particle and Fibre Toxicology.
[105] Helinor Johnston,et al. Development of in vitro systems for nanotoxicology: methodological considerations , 2009, Critical reviews in toxicology.
[106] Alexandra Maertens,et al. Integrated testing strategies for safety assessments. , 2013, ALTEX.
[107] Erik Tielemans,et al. Classification of occupational activities for assessment of inhalation exposure. , 2011, The Annals of occupational hygiene.
[108] Georgia Tsiliki,et al. The eNanoMapper database for nanomaterial safety information , 2015, Beilstein journal of nanotechnology.
[109] Pantelis Sopasakis,et al. Jaqpot Quattro: A Novel Computational Web Platform for Modeling and Analysis in Nanoinformatics , 2017, J. Chem. Inf. Model..
[110] Andrew Williams,et al. Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials , 2015, Beilstein journal of nanotechnology.
[111] Fred A. Wright,et al. ToxPi GUI: an interactive visualization tool for transparent integration of data from diverse sources of evidence , 2013, Bioinform..
[112] Mark D. Hoover,et al. The Nanomaterial Data Curation Initiative: A collaborative approach to assessing, evaluating, and advancing the state of the field , 2015, Beilstein journal of nanotechnology.
[113] Paul A Clemons,et al. The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease , 2006, Science.
[114] Christian Micheletti,et al. Safe innovation approach: Towards an agile system for dealing with innovations , 2019, Materials Today Communications.
[115] M. Andersen,et al. Implementing Toxicity Testing in the 21st Century (TT21C): Making safety decisions using toxicity pathways, and progress in a prototype risk assessment. , 2015, Toxicology.
[116] Jelena Srebric,et al. Advanced computational modeling for in vitro nanomaterial dosimetry , 2015, Particle and Fibre Toxicology.
[117] Qasim Chaudhry,et al. Aligning nanotoxicology with the 3Rs: What is needed to realise the short, medium and long-term opportunities? , 2017, Regulatory toxicology and pharmacology : RTP.
[118] Liang Yan,et al. A Safe‐by‐Design Strategy towards Safer Nanomaterials in Nanomedicines , 2019, Advanced materials.
[119] Sijin Liu,et al. Mesoporous carbon nanomaterials induced pulmonary surfactant inhibition, cytotoxicity, inflammation and lung fibrosis. , 2017, Journal of environmental sciences.
[120] Krister Wennerberg,et al. A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury , 2017, Nature Communications.
[121] Susan Wijnhoven,et al. Risk assessment frameworks for nanomaterials: Scope, link to regulations, applicability, and outline for future directions in view of needed increase in efficiency , 2018 .
[122] Georgia Tsiliki,et al. Enriching Nanomaterials Omics Data: An Integration Technique to Generate Biological Descriptors , 2017 .
[123] Jerzy Leszczynski,et al. NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment , 2020, Computational and structural biotechnology journal.
[124] Thomas Hartung,et al. Green toxicology. , 2014, ALTEX.
[125] Katarzyna Odziomek,et al. Decision tree models to classify nanomaterials according to the DF4nanoGrouping scheme , 2018, Nanotoxicology.
[126] Stephen W. Edwards,et al. The Adverse Outcome Pathway: A Multifaceted Framework Supporting 21st Century Toxicology. , 2018, Current opinion in toxicology.
[127] Lisa M. Sweeney,et al. Advances in Inhalation Dosimetry Models and Methods for Occupational Risk Assessment and Exposure Limit Derivation , 2015, Journal of occupational and environmental hygiene.
[128] Mikko Poikkimäki,et al. Ranking of human risk assessment models for manufactured nanomaterials along the Cooper stage-gate innovation funnel using stakeholder criteria , 2020 .
[129] V. Rogiers,et al. Inter-laboratory study of human in vitro toxicogenomics-based tests as alternative methods for evaluating chemical carcinogenicity: a bioinformatics perspective , 2015, Archives of Toxicology.
[130] Thomas Hartung,et al. Perspectives on validation of high-throughput assays supporting 21st century toxicity testing. , 2013, ALTEX.
[131] Ruud Boessen,et al. Evaluation of Decision Rules in a Tiered Assessment of Inhalation Exposure to Nanomaterials. , 2016, The Annals of occupational hygiene.