Evaluating the toxicity of TiO₂-based nanoparticles to Chinese hamster ovary cells and Escherichia coli: a complementary experimental and computational approach

Scholarly Works

• Gakis, G. P.; Aviziotis, I. G.; Charitidis, C. A. A structure-activity approach towards the toxicity assessment of multicomponent metal oxide nanomaterials. Nanoscale 2023, 15, 16432–16446. doi:10.1039/d3nr03174h
• Slimani, Y.; Almessiere, M. A.; Mohamed, M. J. S.; Hannachi, E.; Caliskan, S.; Akhtar, S.; Baykal, A.; Gondal, M. A. Synthesis of Ce and Sm Co-Doped TiO2 Nanoparticles with Enhanced Photocatalytic Activity for Rhodamine B Dye Degradation. Catalysts 2023, 13, 668. doi:10.3390/catal13040668
• Shirokii, N.; Din, Y.; Petrov, I.; Seregin, Y.; Sirotenko, S.; Razlivina, J.; Serov, N.; Vinogradov, V. Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning. Small (Weinheim an der Bergstrasse, Germany) 2023, 19, e2207106. doi:10.1002/smll.202207106
• Stoliński, F.; Rybińska-Fryca, A.; Gromelski, M.; Mikolajczyk, A.; Puzyn, T. NanoMixHamster: a web-based tool for predicting cytotoxicity of TiO2-based multicomponent nanomaterials toward Chinese hamster ovary (CHO-K1) cells. Nanotoxicology 2022, 16, 276–289. doi:10.1080/17435390.2022.2080609
• Ji, Z.; Guo, W.; Wood, E. L.; Liu, J.; Sakkiah, S.; Xu, X.; Patterson, T. A.; Hong, H. Machine Learning Models for Predicting Cytotoxicity of Nanomaterials. Chemical research in toxicology 2022, 35, 125–139. doi:10.1021/acs.chemrestox.1c00310
• Jeevanandam, J.; Chan, Y. S. In vitro and in vivo toxicity of metal nanoparticles and their drug delivery applications. Applications of Nanotechnology in Drug Discovery and Delivery; Elsevier, 2022; pp 367–421. doi:10.1016/b978-0-12-824408-1.00004-1
• Madi, M.; Tahir, M.; Tasleem, S. Advances in structural modification of perovskite semiconductors for visible light assisted photocatalytic CO2 reduction to renewable solar fuels: A review. Journal of Environmental Chemical Engineering 2021, 9, 106264. doi:10.1016/j.jece.2021.106264
• Toropova, A. P.; Toropov, A. A.; Leszczynski, J.; Sizochenko, N. Using quasi-SMILES for the predictive modeling of the safety of 574 metal oxide nanoparticles measured in different experimental conditions. Environmental toxicology and pharmacology 2021, 86, 103665. doi:10.1016/j.etap.2021.103665
• Sizochenko, N.; Mikolajczyk, A.; Syzochenko, M.; Puzyn, T.; Leszczynski, J. Zeta potentials (ζ) of metal oxide nanoparticles: A meta-analysis of experimental data and a predictive neural networks modeling. NanoImpact 2021, 22, 100317. doi:10.1016/j.impact.2021.100317
• Kar, S.; Leszczynski, J. QSAR and machine learning modeling of toxicity of nanomaterials: a risk assessment approach. Health and Environmental Safety of Nanomaterials; Elsevier, 2021; pp 417–441. doi:10.1016/b978-0-12-820505-1.00016-x
• Kuz'min, V. E.; Ognichenko, L. N.; Sizochenko, N.; Chapkin, V. A.; Stelmakh, S. I.; Shyrykalova, A. O.; Leszczynski, J. Combining Features of Metal Oxide Nanoparticles. Research Anthology on Synthesis, Characterization, and Applications of Nanomaterials; IGI Global, 2021; pp 317–329. doi:10.4018/978-1-7998-8591-7.ch014
• Malankowska, A.; Mikolajczyk, A.; Mędrzycka, J.; Wysocka, I.; Nowaczyk, G.; Jarek, M.; Puzyn, T.; Mulkiewicz, E. The effect of Ag, Au, Pt, and Pd on the surface properties, photocatalytic activity and toxicity of multicomponent TiO2-based nanomaterials. Environmental Science: Nano 2020, 7, 3557–3574. doi:10.1039/d0en00580k
• Singh, A.; Ansari, M. H. D.; Rosenkranz, D.; Maharjan, R. S.; Kriegel, F. L.; Gandhi, K.; Kanase, A.; Singh, R.; Laux, P.; Luch, A. Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine. Advanced healthcare materials 2020, 9, 1901862. doi:10.1002/adhm.201901862
• Hunt, N. J. Handbook of surface-functionalized nanomaterials: safety and legal aspects. Handbook of Functionalized Nanomaterials for Industrial Applications; Elsevier, 2020; pp 945–982. doi:10.1016/b978-0-12-816787-8.00029-6
• Buglak, A. A.; Zherdev, A. V.; Dzantiev, B. B. Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials. Molecules (Basel, Switzerland) 2019, 24, 4537. doi:10.3390/molecules24244537
• Sizochenko, N.; Syzochenko, M.; Fjodorova, N.; Rasulev, B.; Leszczynski, J. Evaluating genotoxicity of metal oxide nanoparticles: Application of advanced supervised and unsupervised machine learning techniques. Ecotoxicology and environmental safety 2019, 185, 109733. doi:10.1016/j.ecoenv.2019.109733
• Tandon, H.; Chakraborty, T.; Suhag, V. A model of atomic compressibility and its application in QSAR domain for toxicological property prediction. Journal of molecular modeling 2019, 25, 1–14. doi:10.1007/s00894-019-4199-9
• Forest, V.; Hochepied, J.-F.; Pourchez, J. Importance of Choosing Relevant Biological End Points To Predict Nanoparticle Toxicity with Computational Approaches for Human Health Risk Assessment. Chemical research in toxicology 2019, 32, 1320–1326. doi:10.1021/acs.chemrestox.9b00022
• Mikolajczyk, A.; Sizochenko, N.; Mulkiewicz, E.; Malankowska, A.; Rasulev, B.; Puzyn, T. A chemoinformatics approach for the characterization of hybrid nanomaterials: safer and efficient design perspective. Nanoscale 2019, 11, 11808–11818. doi:10.1039/c9nr01162e
• Bai, Q.; Lavenas, M.; Vauriot, L.; Le Trequesser, Q.; Hao, J.; Weill, F.; Delville, J.-P.; Delville, M.-H. Hydrothermal Transformation of Titanate Scrolled Nanosheets to Anatase over a Wide pH Range and Contribution of Triethanolamine and Oleic Acid to Control the Morphology. Inorganic chemistry 2019, 58, 2588–2598. doi:10.1021/acs.inorgchem.8b03197

Explore citations to this article at