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Review Article

Predicting the toxicity of nanoparticles using artificial intelligence tools: a systematic review

, , , &
Pages 62-77 | Received 05 Dec 2022, Accepted 26 Feb 2023, Published online: 08 Mar 2023

References

  • Ahn, T.-K., D. H. Lee, T.-S. Kim, S. Choi, J. B. Oh, G. Ye, and S. Lee. 2018. “Modification of Titanium Implant and Titanium Dioxide for Bone Tissue Engineering.” Novel Biomaterials for Regenerative Medicine 1077: 355–368.
  • Ajdary, M., M. A. Moosavi, M. Rahmati, M. Falahati, M. Mahboubi, A. Mandegary, S. Jangjoo, R. Mohammadinejad, and R. S.Varma. 2018. “Health Concerns of Various Nanoparticles: A Review of Their in Vitro and in Vivo Toxicity.” Nanomaterials. 8 (9): 634. doi:10.3390/nano8090634.
  • Aydın, A., H. Sipahi, and M. Charehsaz. 2012. “Nanoparticles Toxicity and Their Routes of Exposures.” In: Recent Advances in Novel Drug Carrier Systems. Croatia: InTech. 483–500.
  • Bahadar, H., F. Maqbool, K. Niaz, and M. Abdollahi. 2016. “Toxicity of Nanoparticles and an Overview of Current Experimental Models.” Iran Biomedical Journal. 20: 1–11.
  • Ban, Z., P. Yuan, F. Yu, T. Peng, Q. Zhou, and X. Hu. 2020. “Machine Learning Predicts the Functional Composition of the Protein Corona and the Cellular Recognition of Nanoparticles.” Proceedings of the National Academy of Sciences of the United States of America. 117 (19): 10492–10499. doi:10.1073/pnas.1919755117.
  • Bhatia, S. 2016. “Nanoparticles Types, Classification, Characterization, Fabrication Methods and Drug Delivery Applications.” In: Natural Polymer Drug Delivery Systems: Nanoparticles, Plants, and Algae, Edited by Bhatia, S, 33-93. Cham: Springer International Publishing.
  • Bogdanska, A., O. L. Gobbo, Y. Volkov, and A. Prina-Mello. 2021. “3D Volume Segmentation and Reconstruction. Supervised Image Classification and Automated Quantification of Superparamagnetic Iron Oxide Nanoparticles in Histology Slides for Safety Assessment.” Nanotoxicology 15 (9): 1151–1167. doi:10.1080/17435390.2021.1991502.
  • Chaudhary, R. G., G. S. Bhusari, A. D. Tiple, A. R. Rai, S. R. Somkuvar, A. K. Potbhare, T. L. Lambat, P. P. Ingle, and A. A. J. C. P. D. Abdala. 2019. “Metal/Metal Oxide Nanoparticles: Toxicity, Applications, and Future Prospects.” Current Pharmaceutical Design 25 (37): 4013–4029. doi:10.2174/1381612825666191111091326.
  • Chellaram, C., G. Murugaboopathi, A. John, R. Sivakumar, S. Ganesan, S. Krithika, and G. Priya. 2014. “Significance of Nanotechnology in Food Industry.” APCBEE Procedia 8: 109–113. doi:10.1016/j.apcbee.2014.03.010.
  • Chen, H., O. Engkvist, Y. Wang, M. Olivecrona, and T. Blaschke. 2018. “The Rise of Deep Learning in Drug Discovery.” Drug Discovery Today. 23 (6): 1241–1250. doi:10.1016/j.drudis.2018.01.039.
  • Concu, R., V. V. Kleandrova, A. Speck-Planche, and M. 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.
  • DAS Chagas Moura, M., E. Zio, I. D. Lins, E. J. R. E. Droguett, and S. Safety. 2011. “Failure and Reliability Prediction by Support Vector Machines Regression of Time Series Data.” Reliability Engineering & System Safety 96 (11): 1527–1534.
  • Elsaesser, A., and C. V. Howard. 2012. “Toxicology of Nanoparticles.” Advanced Drug Delivery Reviews 64 (2): 129–137. doi:10.1016/j.addr.2011.09.001.
  • Fadeel, B., and A. E. Garcia-Bennett. 2010. “Better Safe than Sorry: Understanding the Toxicological Properties of Inorganic Nanoparticles Manufactured for Biomedical Applications.” Advanced Drug Delivery Reviews 62 (3): 362–374. doi:10.1016/j.addr.2009.11.008.
  • Fard, J. K., S. Jafari, and M. A. Eghbal. 2015. “A Review of Molecular Mechanisms Involved in Toxicity of Nanoparticles.” Advanced Pharmaceutical Bulletin 5 (4): 447–454. doi:10.15171/apb.2015.061.
  • Furxhi, I., and F. Murphy. 2020. “Predicting in Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning.” International Journal of Molecular Sciences 21 (15): 5280. doi:10.3390/ijms21155280.
  • Furxhi, I., F. Murphy, M. Mullins, and C. A. Poland. 2019a. “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.
  • Furxhi, I., F. Murphy, C. A. Poland, B. Sheehan, M. Mullins, and P. Mantecca. 2019b. “Application of Bayesian Networks in Determining Nanoparticle-Induced Cellular Outcomes Using Transcriptomics.” Nanotoxicology 13 (6): 827–848. doi:10.1080/17435390.2019.1595206.
  • Gatoo, M. A., S. Naseem, M. Y. Arfat, A. Mahmood Dar, K. Qasim, and S. Zubair. 2014. “Physicochemical Properties of Nanomaterials: implication in Associated Toxic Manifestations.” BioMed Research International 2014: 1–8. 2014. doi:10.1155/2014/498420.
  • Gernand, J. M., and E. A. Casman. 2013. Selecting Nanoparticle Properties to Mitigate Risks to Workers and the Public: A Machine Learning Modeling Framework to Compare Pulmonary Toxicity Risks of Nanomaterials. In ASME International Mechanical Engineering Congress and Exposition, vol. 56444, p. V015T12A016. American Society of Mechanical Engineers.
  • Gernand, J. M., and E. A. Casman. 2016. “Nanoparticle Characteristic Interaction Effects on Pulmonary Toxicity: A Random Forest Modeling Framework to Compare Risks of Nanomaterial Variants.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B: Mechanical Engineering 2(2):021002.
  • Gul, G., R. Yildirim, and N. Ileri-Ercan. 2021. “Cytotoxicity Analysis of Nanoparticles by Association Rule Mining.” Environmental Science: Nano 8 (4): 937–949. doi:10.1039/D0EN01240H.
  • Halder, A. K., A. Melo, and M. Cordeiro. 2020. “A Unified in Silico Model Based on Perturbation Theory for Assessing the Genotoxicity of Metal Oxide Nanoparticles.” Chemosphere 244: 125489. doi:10.1016/j.chemosphere.2019.125489.
  • 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.
  • Huang, Y., X. Li, J. Cao, X. Wei, Y. Li, Z. Wang, X. Cai, R. Li, and J. Chen. 2022. “Use of Dissociation Degree in Lysosomes to Predict Metal Oxide Nanoparticle Toxicity in Immune Cells: Machine Learning Boosts Nano-Safety Assessment.” Environment International. 164: 107258. doi:10.1016/j.envint.2022.107258.
  • Jha, S. K., T. H. Yoon, and Z. Pan. 2018. “Multivariate Statistical Analysis for Selecting Optimal Descriptors in the Toxicity Modeling of Nanomaterials.” Computers in Biology and Medicine 99: 161–172. doi:10.1016/j.compbiomed.2018.06.012.
  • Jones, D. E., H. Ghandehari, and J. C. Facelli. 2016. “A Review of the Applications of Data Mining and Machine Learning for the Prediction of Biomedical Properties of Nanoparticles.” Computer Methods and Programs in Biomedicine 132: 93–103. doi:10.1016/j.cmpb.2016.04.025.
  • Kakoty, V., K. Sarathlal, M. Pandey, S. K. Dubey, P. Kesharwani, and R. Taliyan. 2022. “Biological Toxicity of Nanoparticles.” Nanoparticle Therapeutics Elsevier. 603-628. Academic Press.
  • Kesharwani, P., H. Choudhury, J. G. Meher, M. Pandey, and B. Gorain. 2019. “Dendrimer-Entrapped Gold Nanoparticles as Promising Nanocarriers for Anticancer Therapeutics and Imaging.” Progress in Materials Science 103: 484–508. doi:10.1016/j.pmatsci.2019.03.003.
  • Kitchenham, B., O. Pearl Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman. 2009. “Systematic Literature Reviews in Software Engineering – a Systematic Literature Review.” Information and Software Technology 51 (1): 7–15. doi:10.1016/j.infsof.2008.09.009.
  • Kleandrova, V. V., F. Luan, H. González-Díaz, J. M. Ruso, A. Melo, A. Speck-Planche, and M. N. D. Cordeiro. 2014. “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.
  • Kotzabasaki, M. I., I. Sotiropoulos, and H. Sarimveis. 2020. “QSAR Modeling of the Toxicity Classification of Superparamagnetic Iron Oxide Nanoparticles (SPIONs) in Stem-Cell Monitoring Applications: An Integrated Study from Data Curation to Model Development.” RSC Advances 10 (9): 5385–5391. doi:10.1039/C9RA09475J.
  • 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 : An International Journal Published for the British Industrial Biological Research Association 112: 507–517. doi:10.1016/j.fct.2017.08.008.
  • Li, J., Y. Tian, Y. Zhu, T. Zhou, J. Li, K. Ding, and J. J. A. I. I. M. Li. 2020. “A Multicenter Random Forest Model for Effective Prognosis Prediction in Collaborative Clinical Research Network.” 103: 101814.
  • Liu, R., B. France, S. George, R. Rallo, H. Zhang, T. Xia, A. E. Nel, K. Bradley, and Y. Cohen. 2014. “Association Rule Mining of Cellular Responses Induced by Metal and Metal Oxide Nanoparticles.” Analyst 139 (5): 943–953. doi:10.1039/C3AN01409F.
  • Luan, F., V. V. Kleandrova, H. González-Díaz, J. M. Ruso, A. Melo, A. Speck-Planche, and M. N. D. 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.
  • Mamoshina, P., A. Vieira, E. Putin, and A. Zhavoronkov. 2016. “Applications of Deep Learning in Biomedicine.” Molecular Pharmaceutics 13 (5): 1445–1454. doi:10.1021/acs.molpharmaceut.5b00982.
  • Maynard, Andrew D., Robert J. Aitken, Tilman Butz, Vicki Colvin, Ken Donaldson, Günter Oberdörster, Martin A. Philbert, et al. 2006. “Safe Handling of Nanotechnology.” Nature 444 (7117): 267–269. doi:10.1038/444267a.
  • Mintz, Y., and R. Brodie. 2019. “Introduction to Artificial Intelligence in Medicine.” Minimally Invasive Therapy & Allied Technologies : MITAT : Official Journal of the Society for Minimally Invasive Therapy 28 (2): 73–81. doi:10.1080/13645706.2019.1575882.
  • Moher, D., A. Liberati, J. Tetzlaff, and D. G. Altman, 2009. “Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement.” PLoS Medicine 6 (7): e1000097. doi:10.1371/journal.pmed.1000097.
  • Mühlfeld, C. 2008. “Translocation and Cellular Entering Mechanisms of Nanoparticles in the Respiratory Tract.” Swiss Medical Weekly 138 (2728): 387-391. doi:10.4414/smw.2008.12153.
  • Nadeem, M., R. Khan, N. Shah, I. R. Bangash, B. H. Abbasi, C. Hano, C. Liu, et al. 2021. “A Review of Microbial Mediated Iron Nanoparticles (IONPs) and Its Biomedical Applications.” Nanomaterials [Online 12 (1): 130. doi:10.3390/nano12010130.
  • Nel, A., T. Xia, L. Madler, and N. Li. 2006. “Toxic Potential of Materials at the Nanolevel.” Science 311 (5761): 622–627. doi:10.1126/science.1114397.
  • Nikolova, M. P., and M. S. J. B. Chavali. 2020. “Metal Oxide Nanoparticles as Biomedical Materials.” 5: 27.
  • Olczak, J., J. Pavlopoulos, J. Prijs, F. F. A. Ijpma, J. N. Doornberg, C. Lundström, J. Hedlund, and M. Gordon. 2021. “Presenting Artificial Intelligence, Deep Learning, and Machine Learning Studies to Clinicians and Healthcare Stakeholders: An Introductory Reference with a Guideline and a Clinical AI Research (CAIR) Checklist Proposal.” Acta Orthopaedica 92 (5): 513–525. doi:10.1080/17453674.2021.1918389.
  • Ouzzani, M., H. Hammady, Z. Fedorowicz, and A. Elmagarmid. 2016. “Rayyan—a Web and Mobile App for Systematic Reviews.” Systematic Reviews 5 (1): 210. doi:10.1186/s13643-016-0384-4.
  • 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.
  • Papadiamantis, A. G., J. Janes, E. Voyiatzis, L. Sikk, J. Burk, P. Burk, A. Tsoumanis, et al. 2020. “Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform.” Nanomaterials 10 (10): 2017. doi:10.3390/nano10102017.
  • Pisner, D. A., and D. M. Schnyer. 2020. “Support Vector Machine.” In Machine Learning, 101-121. Academic Press.
  • Pourali, P., S. H. Badiee, S. Manafi, T. Noorani, A. Rezaei, and B. Yahyaei. 2017. “Biosynthesis of Gold Nanoparticles by Two Bacterial and Fungal Strains, Bacillus cereus and Fusarium oxysporum, and Assessment and Comparison of Their Nanotoxicity in Vitro by Direct and Indirect Assays.” Electronic Journal of Biotechnology 29: 86–93. doi:10.1016/j.ejbt.2017.07.005.
  • Pyrgiotakis, G., O. E. Kundakcioglu, P. M. Pardalos, and B. M. Moudgil. 2011. “Raman Spectroscopy and Support Vector Machines for Quick Toxicological Evaluation of Titania Nanoparticles.” Journal of Raman Spectroscopy 42 (6): 1222–1231. doi:10.1002/jrs.2839.
  • Raj, J. S., and J. Vijitha Ananthi. 2019. “Recurrent Neural. Networks and Nonlinear Prediction in Support Vector Machines.” Journal of Soft Computing Paradigm (JSCP). 1(1): 33–40.
  • Rodriguez-Galiano, V., M. Sanchez-Castillo, M. Chica-Olmo, and M. J. O. G. R. Chica-Rivas 2015. “Machine Learning Predictive Models for Mineral Prospectivity: An Evaluation of Neural Networks, Random Forest, Regression Trees and Support Vector Machines.” Ore Geology Reviews. 71: 804–818.
  • Rothen-Rutishauser, B., C. Mühlfeld, F. Blank, C. Musso, and P. Gehr. 2007. “Translocation of Particles and Inflammatory Responses after Exposure to Fine Particles and Nanoparticles in an Epithelial Airway Model.” Particle and Fibre Toxicology 4 (1): 9–9. doi:10.1186/1743-8977-4-9.
  • Sangaiya, P., and R. Jayaprakash. 2018. “A Review on Iron Oxide Nanoparticles and Their Biomedical Applications.” Journal of Superconductivity and Novel Magnetism 31 (11): 3397–3413. doi:10.1007/s10948-018-4841-2.
  • Sani, A., C. Cao, D. J. B. Cui, and B. Reports. 2021. “Toxicity of Gold Nanoparticles (AuNPs): A Review.” Biochemistry and Biophysics Reports 26: 100991. doi:10.1016/j.bbrep.2021.100991.
  • Sawicki, K., M. Czajka, M. Matysiak-Kucharek, B. Fal, B. Drop, S. Męczyńska-Wielgosz, K. Sikorska, M. Kruszewski, and L. J. N. R. Kapka-Skrzypczak. 2019. “Toxicity of Metallic Nanoparticles in the Central Nervous System.” Nanotechnology Reviews. 8 (1): 175–200.
  • Sidharta, S., and A. V. D. Sano, 2018. PROCEEDING International Conference Technopreneur and Education 2018.
  • Singh, R., A. Sharma, J. Saji, A. Umapathi, S. Kumar, and H. K. Daima. 2022. “Smart Nanomaterials for Cancer Diagnosis and Treatment.” Nano Convergence. 9 (1): 1–39. doi:10.1186/s40580-022-00313-x.
  • 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.
  • Sizochenko, N., M. Syzochenko, N. Fjodorova, B. Rasulev, and J. Leszczynski. 2019. “Evaluating Genotoxicity of Metal Oxide Nanoparticles: Application of Advanced Supervised and Unsupervised Machine Learning Techniques.” Ecotoxicology and Environmental Safety 185: 109733. doi:10.1016/j.ecoenv.2019.109733.
  • Speck-Planche, A., V. V. Kleandrova, F. Luan, and M. N. Ds Cordeiro. 2015. “Computational Modeling in Nanomedicine: Prediction of Multiple Antibacterial Profiles of Nanoparticles Using a Quantitative Structure–Activity Relationship Perturbation Model.” Nanomedicine 10 (2): 193–204. doi:10.2217/nnm.14.96.
  • Spyropoulos, C., C. Psevdos, and E. C. Marcoulaki. 2020. Toxicity Assessment for Safe-by-Design Nanomaterials Using Advanced Data Analytics. In: Proceedings of the 29th European Safety And Reliability Conference (ESREL 2019), Beer, M. & Zio, E., eds., Research Publishing Services, 1048–1055.
  • Subramanian, N., and A. Palaniappan 2021. “NanoTox: Development of a Parsimonious in Silico Model for Toxicity Assessment of Metal-Oxide Nanoparticles Using Physicochemical Features.” ACS Omega 6 (17): 11729–11739. doi:10.1021/acsomega.1c01076.
  • Tortella, G., O. Rubilar, N. Durán, M. Diez, M. Martínez, J. Parada, and A. J. J. O. H. M. Seabra. 2020. “Silver Nanoparticles: Toxicity in Model Organisms as an Overview of Its Hazard for Human Health and the Environment.” Journal of Hazardous Materials 390: 121974. doi:10.1016/j.jhazmat.2019.121974.
  • Toschi, N., S. Ciulli, S. Diciotti, A. Duggento, M. Guerrisi, A. Magrini, L. Campagnolo, and A. Pietroiusti. 2016. “Forecasting Nanoparticle Toxicity Using Nonlinear Predictive Regressor Learning Systems.” Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2016, : 137–140. doi:10.1109/EMBC.2016.7590659.
  • Touw, W. G., J. R. Bayjanov, L. Overmars, L. Backus, J. Boekhorst, M. Wels, and S. A. J. B. I. B. Van Hijum 2013. “Data Mining in the Life Sciences with Random Forest: A Walk in the Park or Lost in the Jungle?” Briefings in Bioinformatics 14 (3): 315–326. doi:10.1093/bib/bbs034.
  • Trinh, T. X., M. K. Ha, J. S. Choi, H. G. Byun, and T. H. Yoon. 2018. “Curation of Datasets, Assessment of Their Quality and Completeness, and nanoSAR Classification Model Development for Metallic Nanoparticles.” Environmental Science: Nano 5 (8): 1902–1910. doi:10.1039/C8EN00061A.
  • Vakili-Ghartavol, R., A. A. Momtazi-Borojeni, Z. Vakili-Ghartavol, H. T. Aiyelabegan, M. R. Jaafari, S. M. Rezayat, and S. J. A. C. Arbabi Bidgoli, 2020. “Toxicity Assessment of Superparamagnetic Iron Oxide Nanoparticles in Different Tissues.” Artificial Cells, Nanomedicine, and Biotechnology. 48 (1): 443–451.
  • Xiao-Ming, M., S. Mi, L. Yue, L. Yin-Jin, L. Fang, G. Long-Hua, Q. BIN, L. Zhen-Yu, and C. Guo-Nan. 2018. “Progress of Visual Biosensor Based on Gold Nanoparticles.” Chinese Journal of Analytical Chemistry 46: 1–10.
  • Yang, J., Y. Li, Q. Liu, L. Li, A. Feng, T. Wang, S. Zheng, A. Xu, and J. Lyu. 2020. “Brief Introduction of Medical Database and Data Mining Technology in Big Data Era.” Journal of Evidence-Based Medicine 13 (1): 57–69. doi:10.1111/jebm.12373.
  • Yin, L., and Z. Zhong. 2020. “Nanoparticles.” In: Biomaterials Science, edited by Wagner, W. R., Sakiyama-Elbert, S. E., Zhang, G. & Yaszemski, M. J., 453-483 (Fourth Edition). Academic Press.
  • Yu, F. B., C. H. Wei, P. Deng, T. Peng, and X. G. Hu. 2021. “Deep Exploration of Random Forest Model Boosts the Interpretability of Machine Learning Studies of Complicated Immune Responses and Lung Burden of Nanoparticles.” Science Advances 7 (22): eabf4130. doi:10.1126/sciadv.abf4130.
  • Zheng, T., W. Xie, L. Xu, X. He, Y. Zhang, M. You, G. Yang, and Y. J. I. J. O. M. I. Chen. 2017. “A Machine Learning-Based Framework to Identify Type 2 Diabetes through Electronic Health Records.” International Journal of Medical Informatics 97: 120–127. doi:10.1016/j.ijmedinf.2016.09.014.
  • Zoroddu, M. A., S. Medici, A. Ledda, V. M. Nurchi, J. I. Lachowicz, and M. Peana. 2014. “Toxicity of Nanoparticles.” Current Medicinal Chemistry 21 (33): 3837–3853. doi:10.2174/0929867321666140601162314.

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