Efficient predictions of cytotoxicity of TiO₂-based multi-component nanoparticles using a machine learning-based q-RASAR approach

References

• Banerjee, A., and K. Roy, On Some Novel Similarity-Based Functions Used in the ML-Based q‑RASAR Approach for Efficient Quantitative Predictions of Selected Toxicity End Points2023. Chemical Research in Toxicology https://doi.org/10.1021/acs.chemrestox.2c00374.
   Google Scholar
• Acharya, S., and S. K. Sahoo. 2011. “PLGA Nanoparticles Containing Various Anticancer Agents and Tumour Delivery by EPR Effect.” Advanced Drug Delivery Reviews 63 (3): 170–183. doi:10.1016/j.addr.2010.10.008.
   PubMed Web of Science ®Google Scholar
• Banerjee, A., M. Chatterjee, P. De, and K. Roy. 2022. “Quantitative Predictions from Chemical Read-Across and Their Confidence Measures.” Chemometrics and Intelligent Laboratory Systems 227: 104613. doi:10.1016/j.chemolab.2022.104613.
   Web of Science ®Google Scholar
• Banerjee, A., P. De, V. Kumar, S. Kar, and K. Roy. 2022. “Quick and Efficient Quantitative Predictions of Androgen Receptor Binding Affinity for Screening Endocrine Disruptor Chemicals Using 2D-QSAR and Chemical Read-Across.” Chemosphere 309 (Pt 1): 136579. doi:10.1016/j.chemosphere.2022.136579.
   PubMedGoogle Scholar
• Banerjee, A., and K. Roy. 2022. “First Report of Q-RASAR Modeling Toward an Approach of Easy Interpretability and Efficient Transferability.” Molecular Diversity 26 (5): 2847–2862. doi:10.1007/s11030-022-10478-6.
   PubMed Web of Science ®Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Chatterjee, M., A. Banerjee, P. De, A. Gajewicz, and K. Roy. 2022. “A Novel Quantitative Read-Across Tool Designed Purposefully to Fill the Existing Gaps in Nanosafety Data.” Environmental Science: Nano 9: 189–203. doi:10.1039/D1EN00725D.
   Google Scholar
• Chavali, M. S., and M. P. Nikolova. 2019. “Metal Oxide Nanoparticles and Their Applications in Nanotechnology.” SN Applied Sciences 1 (6): 607. doi:10.1007/s42452-019-0592-3.
   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
• Gajewicz, A., B. Rasulev, T. C. Dinadayalane, P. Urbaszek, T. Puzyn, D. Leszczynska, and J. Leszczynski. 2012. “Advancing Risk Assessment of Engineered Nanomaterials: Application of Computational Approaches.” Advanced Drug Delivery Reviews 64 (15): 1663–1693. doi:10.1016/j.addr.2012.05.014.
   PubMed Web of Science ®Google Scholar
• García-Fernández, A. J. 2020. “Ecotoxicological Risk Assessment in the Context of Different EU Regulations.” In Ecotoxicological QSARs. Methods in Pharmacology and Toxicology, edited by K. Roy. New York: Humana. doi:10.1007/978-1-0716-0150-1_1.
   Google Scholar
• Golbraikh, A., and A. Tropsha. 2002. “Beware of q2!” Journal of Molecular Graphics & Modelling 20 (4): 269–276. doi:10.1016/S1093-3263(01)00123-1.
   PubMed Web of Science ®Google Scholar
• Gramatica, P. 2013. “On the Development and Validation of QSAR Models.” In Computational Toxicology. Methods in Molecular Biology, edited by B. Reisfeld and A. Mayeno, Vol. 930. Totowa, NJ: Humana Press. doi:10.1007/978-1-62703-059-5_21.
   Google Scholar
• Kar, S., A. Gajewicz, T. Puzyn, K. Roy, and J. Leszczynski. 2014. “Periodic Table-Based Descriptors to Encode Cytotoxicity Profile of Metal Oxide Nanoparticles: A Mechanistic QSTR Approach.” Ecotoxicology and Environmental Safety 107: 162–169. doi:10.1016/j.ecoenv.2014.05.026.
   PubMed Web of Science ®Google Scholar
• Karlsson, H. L., M. S. Toprak, and B. Fadeel. 2022. “Toxicity of Metal and Metal Oxide Nanoparticles.” In Handbook on the Toxicology of Metals. 5th ed., edited by G. F. Nordberg and M. Costa. New York, NY: Academic Press. doi:10.1016/B978-0-12-823292-7.00002-4.
   Google Scholar
• Kirklin, S., J. E. Saal, B. Meredig, A. Thompson, J. W. Doak, M. Aykol, S. Rühl, and C. Wolverton. 2015. “The Open Quantum Materials Database (OQMD): Assessing the Accuracy of DFT Formation Energies.” NPJ Computational Materials 1 (1): 15010. doi:10.1038/npjcompumats.2015.10.
   Google Scholar
• Kluyver, T., B. Ragan-Kelley, F. Pérez, B. E. Granger, M. Bussonnier, J. Frederic, K. Kelley, et al. 2016. “Jupyter Notebooks - A Publishing Format for Reproducible Computational Workflows.” In Positioning and Power in Academic Publishing: Players, Agents and Agendas, edited by F. Loizides and B. Schmidt, 87–90. Amsterdam: IOS Press. doi:10.3233/978-1-61499-649-1-87.
   Google Scholar
• Kovarich, S., L. Ceriani, M. F. Gatnik, A. Bassan, and M. Pavan. 2019. “Filling Data Gaps by Read-Across: A Mini Review on Its Application, Developments and Challenges.” Molecular Informatics 38 (8–9): e1800121. doi:10.1002/minf.201800121.
   PubMedGoogle Scholar
• Luechtefeld, T., D. Marsh, C. Rowlands, and T. Hartung. 2018. “Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility.” Toxicological Sciences : An Official Journal of the Society of Toxicology 165 (1): 198–212. doi:10.1093/toxsci/kfy152.
   PubMed Web of Science ®Google Scholar
• Manuja, A., B. Kumar, R. Kumar, D. Chhabra, M. Ghosh, M. Manuja, B. Brar, Y. Pal, B. N. Tripathi, and M. Prasad. 2021. “Metal/Metal Oxide Nanoparticles: Toxicity Concerns Associated with Their Physical State and Remediation for Biomedical Applications.” Toxicology Reports 8: 1970–1978. doi:10.1016/j.toxrep.2021.11.020.
   PubMedGoogle 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 Journal 5: 1150–1160. doi:10.1039/c8en00085a.
   Google Scholar
• Mikolajczyk, A., N. Sizochenko, E. Mulkiewicz, A. Malankowska, B. Rasulev, and T. Puzyn. 2019. “A Chemoinformatics Approach for the Characterization of Hybrid Nanomaterials: Safer and Efficient Design Perspective.” Nanoscale 11 (24): 11808–11818. doi:10.1039/C9NR01162E.
   PubMedGoogle 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
• Roy, K., R. N. Das, P. Ambure, and R. B. Aher. 2016. “Be Aware of Error Measures. Further Studies on Validation of Predictive QSAR Models.” Chemometrics and Intelligent Laboratory Systems 152: 18–33. doi:10.1016/j.chemolab.2016.01.008.
   Web of Science ®Google Scholar
• Roy, K., S. Kar, and R. N. Das. 2015. Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment. New York: Elsevier Inc. doi:10.1016/C2014-0-00286-9.
   Google Scholar
• Roy, J., and K. Roy. 2022a. “Risk Assessment and Data Gap Filling of Toxicity of Metal Oxide Nanoparticles (Meox Nps) Used in Nanomedicines: A Mechanistic QSAR Approach.” Environmental Science: Nano 9: 3456–3470. doi:10.1039/D2EN00303A.
   Google Scholar
• Roy, J., and K. Roy. 2022b. “Modeling and Mechanistic Understanding of Cytotoxicity of Metal Oxide Nanoparticles (Meoxnps) to Escherichia coli: Categorization and Data Gap Filling for Untested Metal Oxides.” Nanotoxicology 16 (2): 152–164. doi:10.1080/17435390.2022.2038299.
   PubMed Web of Science ®Google Scholar
• Roy, J., and K. Roy. 2022c. “Nano-Read-Across Predictions of Toxicity of Metal Oxide Engineered Nanoparticles (MeOx ENPS) Used in Nanopesticides to BEAS-2B and RAW 264.7 Cells.” Nanotoxicology. 16 (5): 629–644. doi:10.1080/17435390.2022.2132887.
   PubMedGoogle Scholar
• Roy, J., and K. Roy. 2021. “Assessment of Toxicity of Metal Oxide and Hydroxide Nanoparticles Using the QSAR Modeling Approach.” Environmental Science: Nano 8: 3395–3407. doi:10.1039/D1EN00733E.
   Google Scholar
• Saal, J. E., S. Kirklin, M. Aykol, B. Meredig, and C. Wolverton. 2013. “Materials Design and Discovery with High-Throughput Density Functional Theory.” The Open Quantum Materials Database (OQMD): JOM. 65: 1501–1509. doi:10.1007/s11837-013-0755-4.
   Google Scholar
• Sengul, A. B., and E. Asmatulu. 2020. “Toxicity of Metal and Metal Oxide Nanoparticles: A Review.” Environmental Chemistry Letters 18 (5): 1659–1683. doi:10.1007/s10311-020-01033-6.
   Web of Science ®Google Scholar
• Varnek, A., and I. Baskin. 2012. “Machine Learning Methods for Property Prediction in Chemoinformatics: Quo Vadis?” Journal of Chemical Information and Modeling 52 (6): 1413–1437. doi:10.1021/ci200409x.
   PubMed Web of Science ®Google Scholar
• Zhang, S., A. Golbraikh, S. Oloff, H. Kohn, and A. Tropsha. 2006. “A Novel Automated Lazy Learning QSAR (ALL-QSAR) Approach: Method Development, Applications and Virtual Screening of Chemical Databases Using Validated ALL-QSAR Models.” Journal of Chemical Information and Modeling 46 (5): 1984–1995. doi:10.1021/ci060132x.
   PubMed Web of Science ®Google Scholar