Nanotoxicology data for in silico tools: a literature review

References

• Baharifar, H., and A. Amani. 2016. “Cytotoxicity of Chitosan/Streptokinase Nanoparticles as a Function of Size: An Artificial Neural Networks Study.” Nanomedicine: Nanotechnology, Biology and Medicine 12 (1): 171–180. doi:10.1016/j.nano.2015.09.002.
   PubMed Web of Science ®Google Scholar
• Ban, Z., Q. Zhou, A. Sun, L. Mu, and X. Hu. 2018. “Screening Priority Factors Determining and Predicting the Reproductive Toxicity of Various Nanoparticles.” Environmental Science & Technology 52: 9666–9676. doi:10.1021/acs.est.8b02757.
   PubMed Web of Science ®Google Scholar
• Basant, N., and S. Gupta. 2017. “Multi-Target QSTR Modeling for Simultaneous Prediction of Multiple Toxicity Endpoints of Nano-Metal Oxides.” Nanotoxicology 11 (3): 339–350. doi:10.1080/17435390.2017.1302612.
   PubMed Web of Science ®Google Scholar
• Basei, G., D. Hristozov, L. Lamon, A. Zabeo, N. Jeliazkova, G. Tsiliki, A. Marcomini, and A. Torsello. 2019. “Making Use of Available and Emerging Data to Predict the Hazards of Engineered Nanomaterials by Means of In Silico Tools: A Critical Review.” NanoImpact 13: 76–99. doi:10.1016/j.impact.2019.01.003.
   Web of Science ®Google Scholar
• Bigdeli, A., M. R. Hormozi-Nezhad, and H. Parastar. 2015. “Using Nano-QSAR to Determine the Most Responsible Factor(s) in Gold Nanoparticle Exocytosis.” RSC Advances 5 (70): 57030–57037. doi:10.1039/C5RA06198A.
   Web of Science ®Google Scholar
• Bilal, M., E. Oh, R. Liu, J. C. Breger, I. L. Medintz, and Y. Cohen. 2019. “Bayesian Network Resource for Meta-Analysis: Cellular Toxicity of Quantum Dots.” Small 15 (34): 0:1900510. doi:10.1002/smll.201900510.
   Web of Science ®Google Scholar
• Brownlee, J. 2013. A Tour of Machine Learning Algorithms. Accessed September 2019. http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
   Google Scholar
• Burgdorf, T., A. H. Piersma, R. Landsiedel, R. Clewell, N. Kleinstreuer, M. Oelgeschläger, B. Desprez, et al. 2019. “Workshop on the Validation and Regulatory Acceptance of Innovative 3R Approaches in Regulatory Toxicology – Evolution Versus Revolution.” Toxicology in Vitro 59: 1–11. doi:10.1016/j.tiv.2019.03.039.
   PubMed Web of Science ®Google Scholar
• Bygd, H. C., K. D. Forsmark, and K. M. Bratlie. 2015. “Altering In Vivo Macrophage Responses with Modified Polymer Properties.” Biomaterials 56: 187–197. doi:10.1016/j.biomaterials.2015.03.042.
   PubMed Web of Science ®Google Scholar
• Casals, E., M. F. Gusta, J. Piella, G. Casals, W. Jiménez, and V. Puntes. 2017. “Intrinsic and Extrinsic Properties Affecting Innate Immune Responses to Nanoparticles: The Case of Cerium Oxide.” Frontiers in Immunology 8: 970–970.
   PubMed Web of Science ®Google Scholar
• Chau, Y. T., and C. W. Yap. 2012. “Quantitative Nanostructure–Activity Relationship Modelling of Nanoparticles.” RSC Advances 2 (22): 8489–8496. doi:10.1039/c2ra21489j.
   Web of Science ®Google Scholar
• Chen, G., W. J. G. M. Peijnenburg, V. Kovalishyn, and M. G. Vijver. 2016. “Development of Nanostructure–Activity Relationships Assisting the Nanomaterial Hazard Categorization for Risk Assessment and Regulatory Decision-Making.” RSC Advances 6 (57): 52227–52235. doi:10.1039/C6RA06159A.
   Web of Science ®Google Scholar
• Chen, R., J. Qiao, R. Bai, Y. Zhao, and C. Chen. 2018. “Intelligent Testing Strategy and Analytical Techniques for the Safety Assessment of Nanomaterials.” Analytical and Bioanalytical Chemistry 410 (24): 6051–6066. doi:10.1007/s00216-018-0940-y.
   PubMed Web of Science ®Google Scholar
• Choi, J.-S., M. K. Ha, T. X. Trinh, T. H. Yoon, and H.-G. Byun. 2018. “Towards a Generalized Toxicity Prediction Model for Oxide Nanomaterials Using Integrated Data from Different Sources.” Scientific Reports 8 (1): 6110. doi:10.1038/s41598-018-24483-z.
   PubMedGoogle Scholar
• Choi, J.-S., T. X. Trinh, T.-H. Yoon, J. Kim, and H.-G. Byun. 2019. “Quasi-QSAR for Predicting the Cell Viability of Human Lung and Skin Cells Exposed to Different Metal Oxide Nanomaterials.” Chemosphere 217: 243–249. doi:10.1016/j.chemosphere.2018.11.014.
   PubMed Web of Science ®Google Scholar
• Christen, V., M. Camenzind, and K. Fent. 2014. “Silica Nanoparticles Induce Endoplasmic Reticulum Stress Response, Oxidative Stress and Activate the Mitogen-Activated Protein Kinase (MAPK) Signaling Pathway.” Toxicology Reports 1: 1143–1151. doi:10.1016/j.toxrep.2014.10.023.
   PubMedGoogle Scholar
• Clausen, L. P. W., and S. F. Hansen. 2018. “The Ten Decrees of Nanomaterials Regulations.” Nature Nanotechnology 13 (9): 766–768. doi:10.1038/s41565-018-0256-2.
   PubMed Web of Science ®Google Scholar
• Concu, R., V. V. Kleandrova, A. Speck-Planche, and M. N. D. S. Cordeiro. 2017. “Probing the Toxicity of Nanoparticles: A Unified In Silico Machine Learning Model Based on Perturbation Theory.” Nanotoxicology 11 (7): 891–906. doi:10.1080/17435390.2017.1379567.
   PubMed Web of Science ®Google Scholar
• Córdoba, M., and A. Zambon. 2017. “How to Handle Nanomaterials? The Re-Entry of Individuals into the Philosophy of Chemistry.” Foundations of Chemistry 19 (3): 185–196. doi:10.1007/s10698-017-9283-6.
   Web of Science ®Google Scholar
• De, P., S. Kar, K. Roy, and J. Leszczynski. 2018. “Second Generation Periodic Table-Based Descriptors to Encode Toxicity of Metal Oxide Nanoparticles to Multiple Species: QSTR Modeling for Exploration of Toxicity Mechanisms.” Environmental Science: Nano 5: 2742–2760. doi:10.1039/C8EN00809D.
   Web of Science ®Google Scholar
• Donaldson, K., A. Schinwald, F. Murphy, W.-S. Cho, R. Duffin, L. Tran, and C. Poland. 2013. “The Biologically Effective Dose in Inhalation Nanotoxicology.” Accounts of Chemical Research 46 (3): 723–732. doi:10.1021/ar300092y.
   PubMed Web of Science ®Google Scholar
• ECHA. 2017a. Non-Animal Approaches. Current Status of Regulatory Applicability under the REACH, CLP and Biocidal Products Regulations. Helsinki, Finland: European Chemicals Agency.
   Google Scholar
• ECHA. 2017b. The Use of Alternatives to Testing on Animals for the REACH Regulation. Helsinki, Finland: European Chemicals Agency.
   Google Scholar
• Elmore, S. 2007. “Apoptosis: A Review of Programmed Cell Death.” Toxicological Pathology 35 (4): 495–516. doi:10.1080/01926230701320337.
   PubMed Web of Science ®Google Scholar
• Epa, V. C., F. R. Burden, C. Tassa, R. Weissleder, S. Shaw, and D. A. Winkler. 2012. “Modeling Biological Activities of Nanoparticles.” Nano Letters 12 (11): 5808–5812. doi:10.1021/nl303144k.
   PubMed Web of Science ®Google Scholar
• Fourches, D., D. Pu, C. Tassa, R. Weissleder, S. Y. Shaw, R. J. Mumper, and A. Tropsha. 2010. “Quantitative Nanostructure − Activity Relationship Modeling.” ACS Nano. 4 (10): 5703–5712. doi:10.1021/nn1013484.
   PubMed Web of Science ®Google Scholar
• França, T. F., and J. M. Monserrat. 2018. “Reproducibility Crisis in Science or Unrealistic Expectations?” EMBO Reports 19 (6): e46008. doi:10.15252/embr.201846008.
   PubMed Web of Science ®Google Scholar
• Furxhi, I., F. Murphy, M. Mullins, A. Arvanitis, and C. A. Poland. 2020. “Practices and Trends of Machine Learning Application in Nanotoxicology.” Nanomaterials 10 (1): 116. doi:10.3390/nano10010116.
   Web of Science ®Google Scholar
• Furxhi, I., F. Murphy, M. Mullins, and C. A. Poland. 2019. “Machine Learning Prediction of Nanoparticle In Vitro Toxicity: A Comparative Study of Classifiers and Ensemble-Classifiers Using the Copeland Index.” Toxicology Letters 312: 157–166. doi:10.1016/j.toxlet.2019.05.016.
   PubMed Web of Science ®Google Scholar
• Furxhi, I., F. Murphy, C. A. Poland, B. Sheehan, M. Mullins, and P. Mantecca. 2019. “Application of Bayesian Networks in Determining Nanoparticle-Induced Cellular Outcomes Using Transcriptomics.” Nanotoxicology 13 (6): 827–848. doi:10.1080/17435390.2019.1595206.
   PubMed Web of Science ®Google Scholar
• Furxhi, I., F. Murphy, B. Sheehan, M. Mullins, and P. Mantecca. 2018. “Predicting Nanomaterials Toxicity Pathways Based on Genome-Wide Transcriptomics Studies Using Bayesian Networks.” Paper presented at the 2018 IEEE 18th International Conference on Nanotechnology (IEEE-NANO), Cork, Ireland, July 23–26. doi:10.1109/NANO.2018.8626300.
   Google Scholar
• Gajewicz, A., T. Puzyn, K. Odziomek, P. Urbaszek, A. Haase, C. Riebeling, A. Luch, et al. 2018. “Decision Tree Models to Classify Nanomaterials according to the DF4nanoGrouping Scheme.” Nanotoxicology 12 (1): 1–17. doi:10.1080/17435390.2017.1415388.
   PubMed Web of Science ®Google Scholar
• George, S., T. Xia, R. Rallo, Y. Zhao, Z. Ji, S. Lin, X. Wang, et al. 2011. “Use of a High-Throughput Screening Approach Coupled with In Vivo Zebrafish Embryo Screening to Develop Hazard Ranking for Engineered Nanomaterials.” ACS Nano. 5 (3): 1805–1817. doi:10.1021/nn102734s.
   PubMed Web of Science ®Google Scholar
• Gerber, A., M. Bundschuh, D. Klingelhofer, and D. A. Groneberg. 2013. “Gold Nanoparticles: Recent Aspects for Human Toxicology.” Journal of Occupational Medicine and Toxicology 8 (1): 32–32. doi:10.1186/1745-6673-8-32.
   PubMedGoogle Scholar
• Gernand, J. M., and E. A. Casman. 2014. “A Meta-Analysis of Carbon Nanotube Pulmonary Toxicity Studies—How Physical Dimensions and Impurities Affect the Toxicity of Carbon Nanotubes.” Risk Analysis 34 (3): 583–597. doi:10.1111/risa.12109.
   PubMed Web of Science ®Google Scholar
• Gharagheizi, F., and R. F. Alamdari. 2008. “A Molecular-Based Model for Prediction of Solubility of C60 Fullerene in Various Solvents.” Fullerenes, Nanotubes and Carbon Nanostructures 16 (1): 40–57. doi:10.1080/15363830701779315.
   Web of Science ®Google Scholar
• Ghorbanzadeh, M., M. H. Fatemi, and M. Karimpour. 2012. “Modeling the Cellular Uptake of Magnetofluorescent Nanoparticles in Pancreatic Cancer Cells: A Quantitative Structure Activity Relationship Study.” Industrial & Engineering Chemistry Research 51: 10712–10718. doi:10.1021/ie3006947.
   Web of Science ®Google Scholar
• Giusti, A., R. Atluri, R. Tsekovska, A. Gajewicz, M. D. Apostolova, C. L. Battistelli, E. A. J. Bleeker, et al. 2019. “Nanomaterial Grouping: Existing Approaches and Future Recommendations.” NanoImpact 16: 100182. doi:10.1016/j.impact.2019.100182.
   Web of Science ®Google Scholar
• González-Durruthy, M., L. C. Alberici, C. Curti, Z. Naal, D. T. Atique-Sawazaki, J. M. Vázquez-Naya, H. González-Díaz, and C. R. Munteanu. 2017. “Experimental–Computational Study of Carbon Nanotube Effects on Mitochondrial Respiration: In Silico Nano-QSPR Machine Learning Models Based on New Raman Spectra Transform with Markov–Shannon Entropy Invariants.” Journal of Chemical Information and Modeling 57 (5): 1029–1044. doi:10.1021/acs.jcim.6b00458.
   PubMed Web of Science ®Google Scholar
• Grodzinski, P., C. H. Liu, C. M. Hartshorn, S. A. Morris, and L. M. Russell. 2019. “NCI Alliance for Nanotechnology in Cancer – From Academic Research to Clinical Interventions.” Biomedical Microdevices 21 (2): 32. doi:10.1007/s10544-019-0360-6.
   PubMed Web of Science ®Google Scholar
• Ha, M. K., T. X. Trinh, J. S. Choi, D. Maulina, H. G. Byun, and T. H. Yoon. 2018. “Toxicity Classification of Oxide Nanomaterials: Effects of Data Gap Filling and PChem Score-Based Screening Approaches.” Scientific Reports 8 (1): 3141. doi:10.1038/s41598-018-21431-9.
   PubMedGoogle Scholar
• Haase, A., and F. Klaessig. 2018. “EU US Roadmap Nanoinformatics 2030.” EU Nanosafety Cluster. doi:10.5281/zenodo.1486012.
   Google Scholar
• Harper, B., D. Thomas, S. Chikkagoudar, N. Baker, K. Tang, A. Heredia-Langner, R. Lins, and S. Harper. 2015. “Comparative Hazard Analysis and Toxicological Modeling of Diverse Nanomaterials Using the Embryonic Zebrafish (EZ) Metric of Toxicity.” Journal of Nanoparticle Research 17 (6): 250. doi:10.1007/s11051-015-3051-0.
   PubMed Web of Science ®Google Scholar
• Helma, C., M. Rautenberg, and D. Gebele. 2017. “Nano-Lazar: Read Across Predictions for Nanoparticle Toxicities with Calculated and Measured Properties.” Frontiers in Pharmacology 8: 377. doi:10.3389/fphar.2017.00377.
   PubMed Web of Science ®Google Scholar
• Hendren, C. O., C. M. Powers, M. D. Hoover, and S. L. Harper. 2015. “The Nanomaterial Data Curation Initiative: A Collaborative Approach to Assessing, Evaluating, and Advancing the State of the Field.” Beilstein Journal of Nanotechnology 6: 1752–1762. doi:10.3762/bjnano.6.179.
   PubMed Web of Science ®Google Scholar
• Herrmann, K., and K. Jayne. 2019. Animal Experimentation: Working towards a Paradigm Change. Leiden, the Netherlands: Brill.
   Google Scholar
• Hu, X., S. Cook, P. Wang, and H.-M. Hwang. 2009. “In Vitro Evaluation of Cytotoxicity of Engineered Metal Oxide Nanoparticles.” Science of the Total Environment 407 (8): 3070–3072. doi:10.1016/j.scitotenv.2009.01.033.
   PubMed Web of Science ®Google Scholar
• Jagiello, K., M. Grzonkowska, M. Swirog, L. Ahmed, B. Rasulev, A. Avramopoulos, M. G. Papadopoulos, J. Leszczynski, and T. Puzyn. 2016. “Advantages and Limitations of Classic and 3D QSAR Approaches in Nano-QSAR Studies Based on Biological Activity of Fullerene Derivatives.” Journal of Nanoparticle Research 18 (9): 256. doi:10.1007/s11051-016-3564-1.
   PubMed Web of Science ®Google Scholar
• Jones, D. E., H. Ghandehari, and J. C. Facelli. 2015. “Predicting Cytotoxicity of PAMAM Dendrimers Using Molecular Descriptors.” Beilstein Journal of Nanotechnology 6: 1886–1896. doi:10.3762/bjnano.6.192.
   PubMed Web of Science ®Google Scholar
• Karcher, S., E. L. Willighagen, J. Rumble, F. Ehrhart, C. T. Evelo, M. Fritts, S. Gaheen, et al. 2018. “Integration Among Databases and Data Sets to Support Productive Nanotechnology: Challenges and Recommendations.” NanoImpact 9: 85–101. doi:10.1016/j.impact.2017.11.002.
   PubMed Web of Science ®Google Scholar
• Kleandrova, V. V., F. Luan, H. González-Díaz, J. M. Ruso, A. Melo, A. Speck-Planche, and M. N. D. S. Cordeiro. 2014a. “Computational Ecotoxicology: Simultaneous Prediction of Ecotoxic Effects of Nanoparticles under Different Experimental Conditions.” Environment International 73: 288–294. doi:10.1016/j.envint.2014.08.009.
   PubMed Web of Science ®Google Scholar
• Kleandrova, V. V., F. Luan, H. González-Díaz, J. M. Ruso, A. Speck-Planche, and M. N. D. S. Cordeiro. 2014b. “Computational Tool for Risk Assessment of Nanomaterials: Novel QSTR-Perturbation Model for Simultaneous Prediction of Ecotoxicity and Cytotoxicity of Uncoated and Coated Nanoparticles under Multiple Experimental Conditions.” Environmental Science & Technology 48: 14686–14694. doi:10.1021/es503861x.
   PubMed Web of Science ®Google Scholar
• Kovalishyn, V., N. Abramenko, I. Kopernyk, L. Charochkina, L. Metelytsia, I. V. Tetko, W. Peijnenburg, and L. Kustov. 2018. “Modelling the Toxicity of a Large Set of Metal and Metal Oxide Nanoparticles Using the OCHEM Platform.” Food and Chemical Toxicology 112: 507–517. doi:10.1016/j.fct.2017.08.008.
   PubMed Web of Science ®Google Scholar
• Kraegeloh, A., B. Suarez-Merino, T. Sluijters, and C. Micheletti. 2018. “Implementation of Safe-by-Design for Nanomaterial Development and Safe Innovation: Why We Need a Comprehensive Approach.” Nanomaterials 8 (4): 239. doi:10.3390/nano8040239.
   Web of Science ®Google Scholar
• Lamon, L., K. Aschberger, D. Asturiol, A. Richarz, and A. Worth. 2018b. “Grouping of Nanomaterials to Read-Across Hazard Endpoints: A Review.” Nanotoxicology 13(1): 100–118. doi:10.1080/17435390.2018.1506060.
   PubMed Web of Science ®Google Scholar
• Lamon, L., D. Asturiol, A. Richarz, E. Joossens, R. Graepel, K. Aschberger, and A. Worth. 2018a. “Grouping of Nanomaterials to Read-Across Hazard Endpoints: From Data Collection to Assessment of the Grouping Hypothesis by Application of Chemoinformatic Techniques.” Particle and Fibre Toxicology 15 (1): 37. doi:10.1186/s12989-018-0273-1.
   PubMedGoogle Scholar
• Lamon, L., D. Asturiol, A. Vilchez, R. Ruperez-Illescas, J. Cabellos, A. Richarz, and A. Worth. 2019. “Computational Models for the Assessment of Manufactured Nanomaterials: Development of Model Reporting Standards and Mapping of the Model Landscape.” Computational Toxicology 9: 143–151. doi:10.1016/j.comtox.2018.12.002.
   PubMedGoogle Scholar
• Le, T. C., H. Yin, R. Chen, Y. Chen, L. Zhao, P. S. Casey, C. Chen, and D. A. Winkler. 2016. “An Experimental and Computational Approach to the Development of ZnO Nanoparticles That Are Safe by Design.” Small 12 (26): 3568–3577. doi:10.1002/smll.201600597.
   PubMed Web of Science ®Google Scholar
• Li, M., P. Zou, K. Tyner, and S. Lee. 2017. “Physiologically Based Pharmacokinetic (PBPK) Modeling of Pharmaceutical Nanoparticles.” AAPS Journal 19 (1): 26–42. doi:10.1208/s12248-016-0010-3.
   PubMed Web of Science ®Google Scholar
• Liu, R., W. Jiang, C. D. Walkey, W. C. W. Chan, and Y. Cohen. 2015. “Prediction of Nanoparticles-Cell Association Based on Corona Proteins and Physicochemical Properties.” Nanoscale 7 (21): 9664–9675. doi:10.1039/C5NR01537E.
   PubMed Web of Science ®Google Scholar
• Liu, R., R. Rallo, S. George, Z. Ji, S. Nair, A. E. Nel, and Y. Cohen. 2011. “Classification NanoSAR Development for Cytotoxicity of Metal Oxide Nanoparticles.” Small 7 (8): 1118–1126. doi:10.1002/smll.201002366.
   PubMed Web of Science ®Google Scholar
• Liu, X., K. Tang, S. Harper, B. Harper, J. A. Steevens, and R. Xu. 2013. “Predictive Modeling of Nanomaterial Exposure Effects in Biological Systems.” International Journal of Nanomedicine 8 (Suppl 1): 31–43.
   PubMedGoogle Scholar
• Luan, F., V. V. Kleandrova, H. González-Díaz, J. M. Ruso, A. Melo, A. Speck-Planche, and M. N. D. S. Cordeiro. 2014. “Computer-Aided Nanotoxicology: Assessing Cytotoxicity of Nanoparticles under Diverse Experimental Conditions by Using a Novel QSTR-Perturbation Approach.” Nanoscale 6 (18): 10623–10630. doi:10.1039/C4NR01285B.
   PubMed Web of Science ®Google Scholar
• Marchese Robinson, R. L., M. T. D. Cronin, A.-N. Richarz, and R. Rallo. 2015. “An ISA-TAB-Nano Based Data Collection Framework to Support Data-Driven Modelling of Nanotoxicology.” Beilstein Journal of Nanotechnology 6: 1978–1999. doi:10.3762/bjnano.6.202.
   PubMed Web of Science ®Google Scholar
• Marvin, H. J. P., Y. Bouzembrak, E. M. Janssen, M. van der Zande, F. Murphy, B. Sheehan, M. Mullins, and H. Bouwmeester. 2017. “Application of Bayesian Networks for Hazard Ranking of Nanomaterials to Support Human Health Risk Assessment.” Nanotoxicology 11 (1): 123–133. doi:10.1080/17435390.2016.1278481.
   PubMed Web of Science ®Google Scholar
• McWilliams, A. 2017. “Markets of Magnitude Singapore, Malaysia, Thailand, and Indonesia Take the Lead in Southeast Asian Ceramic Technology Research, Development, and Cross-Border Collaboration.” American Ceramic Society Bulletin, October/November 2017.
   Google Scholar
• Melagraki, G., and A. Afantitis. 2014. “Enalos InSilicoNano Platform: An Online Decision Support Tool for the Design and Virtual Screening of Nanoparticles.” RSC Advances 4 (92): 50713–50725. doi:10.1039/C4RA07756C.
   Web of Science ®Google Scholar
• Mikolajczyk, A., A. Gajewicz, E. Mulkiewicz, B. Rasulev, M. Marchelek, M. Diak, S. Hirano, A. Zaleska-Medynska, and T. Puzyn. 2018. “Nano-QSAR Modeling for Ecosafe Design of Heterogeneous TiO2-Based Nano-Photocatalysts.” Environmental Science: Nano 5: 1150–1160. doi:10.1039/C8EN00085A.
   Web of Science ®Google Scholar
• Mikolajczyk, A., A. Gajewicz, B. Rasulev, N. Schaeublin, E. Maurer-Gardner, S. Hussain, J. Leszczynski, and T. Puzyn. 2015. “Zeta Potential for Metal Oxide Nanoparticles: A Predictive Model Developed by a Nano-Quantitative Structure–Property Relationship Approach.” Chemistry of Materials 27 (7): 2400–2407. doi:10.1021/cm504406a.
   Web of Science ®Google Scholar
• Murphy, F., B. Sheehan, M. Mullins, H. Bouwmeester, H. J. P. Marvin, Y. Bouzembrak, A. L. Costa, R. Das, V. Stone, and S. A. M. Tofail. 2016. “A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks.” Nanoscale Research Letters 11 (1): 503–503. doi:10.1186/s11671-016-1724-y.
   PubMedGoogle Scholar
• Nikota, J., A. Williams, C. L. Yauk, H. Wallin, U. Vogel, and S. Halappanavar. 2015. “Meta-Analysis of Transcriptomic Responses as a Means to Identify Pulmonary Disease Outcomes for Engineered Nanomaterials.” Particle and Fibre Toxicology 13 (1): 25. doi:10.1186/s12989-016-0137-5.
   Google Scholar
• OECD. 2004. Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models. OECD Series on Testing and Assessment, No. 69. Paris: OECD Publishing. http://doi.org/10.1787/9789264085442-en.
   Google Scholar
• OECD. 2009. Guidance Document on the Validation of (Quantitative)Structure-Activity Relationships [(Q)SAR] Models. Paris: OECD Publishing. http://doi.org/10.1787/9789264085442-en.
   Google Scholar
• Oh, E., R. Liu, A. Nel, K. B. Gemill, M. Bilal, Y. Cohen, and I. L. Medintz. 2016. “Meta-Analysis of Cellular Toxicity for Cadmium-Containing Quantum Dots.” Nature Nanotechnology 11 (5): 479–486. doi:10.1038/nnano.2015.338.
   PubMed Web of Science ®Google Scholar
• Oksel, C., C. Y. Ma, J. J. Liu, T. Wilkins, and X. Z. Wang. 2015. “(Q)SAR Modelling of Nanomaterial Toxicity: A Critical Review.” Particuology 21: 1–19. doi:10.1016/j.partic.2014.12.001.
   Web of Science ®Google Scholar
• Oksel, C., C. Y. Ma, J. J. Liu, T. Wilkins, and X. Z. Wang. 2017. “Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity.” In Modelling the Toxicity of Nanoparticles, edited by L. Tran, M. A. Bañares and R. Rallo, 103–142. Cham: Springer International Publishing.
   Google Scholar
• Oksel, C., D. A. Winkler, C. Y. Ma, T. Wilkins, and X. Z. Wang. 2016. “Accurate and Interpretable NanoSAR Models from Genetic Programming-Based Decision Tree Construction Approaches.” Nanotoxicology 10 (7): 1001–1012. doi:10.3109/17435390.2016.1161857.
   PubMed Web of Science ®Google Scholar
• Papa, E., J. P. Doucet, and A. Doucet-Panaye. 2015. “Linear and Non-Linear Modelling of the Cytotoxicity of TiO2 and ZnO Nanoparticles by Empirical Descriptors.” SAR and Qsar in Environmental Research. 26 (7-9): 647–665. doi:10.1080/1062936X.2015.1080186.
   PubMed Web of Science ®Google Scholar
• Papa, E., J. P. Doucet, A. Sangion, and A. Doucet-Panaye. 2016. “Investigation of the Influence of Protein Corona Composition on Gold Nanoparticle Bioactivity Using Machine Learning Approaches.” SAR and QSAR in Environmental Research 27 (7): 521–538. doi:10.1080/1062936X.2016.1197310.
   PubMed Web of Science ®Google Scholar
• Patel, T., D. Telesca, C. Low-Kam, Z. Ji, H. Zhang, T. Xia, J. I. Zinc, and A. E. Nel. 2014. “Relating Nanoparticle Properties to Biological Outcomes in Exposure Escalation Experiments.” Environmetrics 25 (1): 57–68. doi:10.1002/env.2246.
   PubMed Web of Science ®Google Scholar
• Pathakoti, K., M.-J. Huang, J. D. Watts, X. He, and H.-M. Hwang. 2014. “Using Experimental Data of Escherichia coli to Develop a QSAR Model for Predicting the Photo-Induced Cytotoxicity of Metal Oxide Nanoparticles.” Journal of Photochemistry and Photobiology B: Biology 130: 234–240. doi:10.1016/j.jphotobiol.2013.11.023.
   PubMed Web of Science ®Google Scholar
• Petrova, T., B. F. Rasulev, A. A. Toropov, D. Leszczynska, and J. Leszczynski. 2011. “Improved Model for Fullerene C60 Solubility in Organic Solvents Based on Quantum-Chemical and Topological Descriptors.” Journal of Nanoparticle Research 13 (8): 3235–3247. doi:10.1007/s11051-011-0238-x.
   Web of Science ®Google Scholar
• Poland, C. A., M. R. Miller, R. Duffin, and F. Cassee. 2014. “The Elephant in the Room: Reproducibility in Toxicology.” Particle and Fibre Toxicology 11 (1): 42. doi:10.1186/s12989-014-0042-8.
   PubMedGoogle Scholar
• Powers, C. M., K. A. Mills, S. A. Morris, F. Klaessig, S. Gaheen, N. Lewinski, and COgilvie Hendren. 2015. “Nanocuration Workflows: Establishing Best Practices for Identifying, Inputting, and Sharing Data to Inform Decisions on Nanomaterials.” Beilstein Journal of Nanotechnology 6: 1860–1871. doi:10.3762/bjnano.6.189.
   PubMed Web of Science ®Google Scholar
• Puzyn, T., N. Jeliazkova, H. Sarimveis, R. L. Marchese Robinson, V. Lobaskin, R. Rallo, A.-N. Richarz, et al. 2018. “Perspectives from the NanoSafety Modelling Cluster on the Validation Criteria for (Q)SAR Models Used in Nanotechnology.” Food and Chemical Toxicology 112: 478–494. doi:10.1016/j.fct.2017.09.037.
   PubMed Web of Science ®Google Scholar
• Puzyn, T., B. Rasulev, A. Gajewicz, X. Hu, T. P. Dasari, A. Michalkova, H.-M. Hwang, A. Toropov, D. Leszczynska, and J. Leszczynski. 2011. “Using Nano-QSAR to Predict the Cytotoxicity of Metal Oxide Nanoparticles.” Nature Nanotechnology 6 (3): 175–178. doi:10.1038/nnano.2011.10.
   PubMed Web of Science ®Google Scholar
• Quik, J. T. K., M. Bakker, D. van de Meent, M. Poikkimäki, M. Dal Maso, and W. Peijnenburg. 2018. “Directions in QPPR Development to Complement the Predictive Models Used in Risk Assessment of Nanomaterials.” NanoImpact 11: 58–66. doi:10.1016/j.impact.2018.02.003.
   Web of Science ®Google Scholar
• Ribeiro, A. R., P. E. Leite, P. Falagan-Lotsch, F. Benetti, C. Micheletti, H. C. Budtz, N. R. Jacobsen, et al. 2017. “Challenges on the Toxicological Predictions of Engineered Nanoparticles.” NanoImpact 8: 59–72. doi:10.1016/j.impact.2017.07.006.
   Web of Science ®Google Scholar
• Saini, B., and S. Srivastava. 2018. “Nanotoxicity Prediction Using Computational Modelling - Review and Future Directions.” IOP Conference Series: Materials Science and Engineering 348: 012005. doi:10.1088/1757-899X/348/1/012005.
   Google Scholar
• Sayes, C., and I. Ivanov. 2010. “Comparative Study of Predictive Computational Models for Nanoparticle-Induced Cytotoxicity.” Risk Analysis 30 (11): 1723–1734. doi:10.1111/j.1539-6924.2010.01438.x.
   PubMed Web of Science ®Google Scholar
• Schwarz-Plaschg, C., A. Kallhoff, and I. Eisenberger. 2017. “Making Nanomaterials Safer by Design?” NanoEthics 11 (3): 277–281. doi:10.1007/s11569-017-0307-4.
   Web of Science ®Google Scholar
• Shao, C.-Y., S.-Z. Chen, B.-H. Su, Y. J. Tseng, E. X. Esposito, and A. J. Hopfinger. 2013. “Dependence of QSAR Models on the Selection of Trial Descriptor Sets: A Demonstration Using Nanotoxicity Endpoints of Decorated Nanotubes.” Journal of Chemical Information and Modeling 53 (1): 142–158. doi:10.1021/ci3005308.
   PubMed Web of Science ®Google Scholar
• Shaw, S. Y., E. C. Westly, M. J. Pittet, A. Subramanian, S. L. Schreiber, and R. Weissleder. 2008. “Perturbational Profiling of Nanomaterial Biologic Activity.” Proceedings of the National Academy of Sciences 105 (21): 7387–7392. doi:10.1073/pnas.0802878105.
   PubMed Web of Science ®Google Scholar
• Singh, K. P., and S. Gupta. 2014. “Nano-QSAR Modeling for Predicting Biological Activity of Diverse Nanomaterials.” RSC Adv. 4 (26): 13215–13230. doi:10.1039/C4RA01274G.
   Web of Science ®Google Scholar
• Sizochenko, N., A. Mikolajczyk, K. Jagiello, T. Puzyn, J. Leszczynski, and B. Rasulev. 2018. “How the Toxicity of Nanomaterials Towards Different Species Could Be Simultaneously Evaluated: A Novel Multi-Nano-Read-Across Approach.” Nanoscale 10 (2): 582–591. doi:10.1039/C7NR05618D.
   PubMed Web of Science ®Google Scholar
• Sizochenko, Natalia, Bakhtiyor Rasulev, Agnieszka Gajewicz, Victor Kuz'min, Tomasz Puzyn, and Jerzy Leszczynski. 2014. “From Basic Physics to Mechanisms of Toxicity: The “Liquid Drop” Approach Applied to Develop Predictive Classification Models for Toxicity of Metal Oxide Nanoparticles.” Nanoscale 6 (22): 13986–13993. doi:10.1039/C4NR03487B.
   PubMed Web of Science ®Google Scholar
• Speck-Planche, A., V. V. Kleandrova, F. Luan, and M. N. Cordeiro. 2015. “Computational Modeling in Nanomedicine: Prediction of Multiple Antibacterial Profiles of Nanoparticles Using a Quantitative Structure-Activity Relationship Perturbation Model.” Nanomedicine (Nanomedicine) 10 (2): 193–204. doi:10.2217/nnm.14.96.
   PubMed Web of Science ®Google Scholar
• Tamvakis, A., C.-N. Anagnostopoulos, G. Tsirtsis, A. D. Niros, and S. Spatharis. 2018. “Optimized Classification Predictions with a New Index Combining Machine Learning Algorithms.” International Journal on Artificial Intelligence Tools 27 (03): 1850012. doi:10.1142/S0218213018500124.
   Google Scholar
• Thomas, D. G., S. Gaheen, S. L. Harper, M. Fritts, F. Klaessig, E. Hahn-Dantona, D. Paik, et al. 2013. “ISA-TAB-Nano: A Specification for Sharing Nanomaterial Research Data in Spreadsheet-Based Format.” BMC Biotechnology 13 (1): 2. doi:10.1186/1472-6750-13-2.
   PubMedGoogle Scholar
• Toropova, A. P., and A. A. Toropov. 2013. “Optimal Descriptor as a Translator of Eclectic Information into the Prediction of Membrane Damage by Means of Various TiO2 Nanoparticles.” Chemosphere 93 (10): 2650–2655. doi:10.1016/j.chemosphere.2013.09.089.
   PubMed Web of Science ®Google Scholar
• Toropova, A. P., A. A. Toropov, and E. Benfenati. 2015. “A quasi-QSPR Modelling for the Photocatalytic Decolourization Rate Constants and Cellular Viability (CV%) of Nanoparticles by CORAL.” SAR and Qsar in Environmental Research 26 (1): 29–40. doi:10.1080/1062936X.2014.984327.
   PubMed Web of Science ®Google Scholar
• Toropova, A. P., A. A. Toropov, E. Benfenati, R. Korenstein, D. Leszczynska, and J. Leszczynski. 2015. “Optimal Nano-Descriptors as Translators of Eclectic Data into Prediction of the Cell Membrane Damage by Means of Nano Metal-Oxides.” Environmental Science and Pollution Research International 22 (1): 745–757. doi:10.1007/s11356-014-3566-4.
   PubMed Web of Science ®Google Scholar
• Toropov, A. A., and A. P. Toropova. 2014. “Optimal Descriptor as a Translator of Eclectic Data into Endpoint Prediction: Mutagenicity of Fullerene as a Mathematical Function of Conditions.” Chemosphere 104: 262–264. doi:10.1016/j.chemosphere.2013.10.079.
   PubMed Web of Science ®Google Scholar
• Toropov, A. A., and A. P. Toropova. 2015. “Quasi-SMILES and nano-QFAR: United Model for Mutagenicity of Fullerene and MWCNT under Different Conditions.” Chemosphere 139: 18–22. doi:10.1016/j.chemosphere.2015.05.042.
   PubMed Web of Science ®Google Scholar
• Toropov, A. A., A. P. Toropova, T. Puzyn, E. Benfenati, G. Gini, D. Leszczynska, and J. Leszczynski. 2013. “QSAR as a Random Event: Modeling of Nanoparticles Uptake in PaCa2 Cancer Cells.” Chemosphere 92 (1): 31–37. doi:10.1016/j.chemosphere.2013.03.012.
   PubMed Web of Science ®Google Scholar
• Trinh, T. X., J. S. Choi, H. Jeon, H. G. Byun, T. H. Yoon, and J. Kim. 2018a. “Quasi-SMILES-Based Nano-Quantitative Structure-Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells.” Chemical Research in Toxicology 31 (3): 183–190. doi:10.1021/acs.chemrestox.7b00303.
   PubMed Web of Science ®Google Scholar
• Trinh, T. X., M. K. Ha, J. S. Choi, H. G. Byun, and T. H. Yoon. 2018b. “Curation of Datasets, Assessment of Their Quality and Completeness, and NanoSAR Classification Model Development for Metallic Nanoparticles.” Environmental Science: Nano 5: 1902–1910. doi:10.1039/C8EN00061A.
   Web of Science ®Google Scholar
• Tsiliki, G., C. R. Munteanu, J. A. Seoane, C. Fernandez-Lozano, H. Sarimveis, and E. L. Willighagen. 2015. “RRegrs: An R Package for Computer-Aided Model Selection with Multiple Regression Models.” Journal of Cheminformatics 7 (1): 46. doi:10.1186/s13321-015-0094-2.
   PubMedGoogle Scholar
• Vance, M. E., T. Kuiken, E. P. Vejerano, S. P. McGinnis, M. F. Hochella Jr., D. Rejeski, and M. S. Hull. 2015. “Nanotechnology in the Real World: Redeveloping the Nanomaterial Consumer Products Inventory.” Beilstein Journal of Nanotechnology 6: 1769–1780. doi:10.3762/bjnano.6.181.
   PubMed Web of Science ®Google Scholar
• Villaverde, J. J., B. Sevilla-Morán, C. López-Goti, J. L. Alonso-Prados, and P. Sandín-España. 2018. “Considerations of Nano-QSAR/QSPR Models for Nanopesticide Risk Assessment within the European Legislative Framework.” Science of the Total Environment 634: 1530–1539. doi:10.1016/j.scitotenv.2018.04.033.
   PubMed Web of Science ®Google Scholar
• Walkey, C. D., J. B. Olsen, F. Song, R. Liu, H. Guo, D. W. H. Olsen, Y. Cohen, A. Emili, and W. C. W. Chan. 2014. “Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles.” ACS Nano 8 (3): 2439–2455. doi:10.1021/nn406018q.
   PubMed Web of Science ®Google Scholar
• Weissleder, R., K. Kelly, E. Y. Sun, T. Shtatland, and L. Josephson. 2005. “Cell-Specific Targeting of Nanoparticles by Multivalent Attachment of Small Molecules.” Nature Biotechnology 23 (11): 1418–1423. doi:10.1038/nbt1159.
   PubMed Web of Science ®Google Scholar
• Winkler, D. A., F. R. Burden, B. Yan, R. Weissleder, C. Tassa, S. Shaw, and V. C. Epa. 2014. “Modelling and Predicting the Biological Effects of Nanomaterials.” SAR and QSAR in Environmental Research 25 (2): 161–172. doi:10.1080/1062936X.2013.874367.
   PubMed Web of Science ®Google Scholar
• Worth, A., K. Aschberger, D. Asturiol Bofill, J. Bessems, K. B. Gerloff, R. Graepel, E. Joossens, L. Lamon, T. Palosaari, A. Richarz. 2017. Evaluation of the Availability and Applicability of Computational Approaches in the Safety Assessment of Nanomaterials: Final Report of the Nanocomput Project. EUR 28617 EN. Luxembourg: Publications Office of the European Union.
   Google Scholar
• Yuan, D., H. He, Y. Wu, J. Fan, and Y. Cao. 2019. “Physiologically Based Pharmacokinetic Modeling of Nanoparticles.” Journal of Pharmaceutical Sciences 108 (1): 58–72. doi:10.1016/j.xphs.2018.10.037.
   PubMed Web of Science ®Google Scholar
• Zhang, H., Z. Ji, T. Xia, H. Meng, C. Low-Kam, R. Liu, S. Pokhrel, et al. 2012. “Use of Metal Oxide Nanoparticle Band Gap to Develop a Predictive Paradigm for Oxidative Stress and Acute Pulmonary Inflammation.” ACS Nano 6 (5): 4349–4368. doi:10.1021/nn3010087.
   PubMed Web of Science ®Google Scholar
• Zhao, F., Y. Zhao, Y. Liu, X. Chang, C. Chen, and Y. Zhao. 2011. “Cellular Uptake, Intracellular Trafficking, and Cytotoxicity of Nanomaterials.” Small 7 (10): 1322–1337. doi:10.1002/smll.201100001.
   PubMed Web of Science ®Google Scholar