Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials
Abstract
1. Introduction
2. Literature Survey
3. Material Flow Analysis and Multimedia Environmental Models
4. Physiologically Based Toxicokinetics Models
5. Quantitative Nanostructure–Activity Relationships
6. Meta-Analysis
7. Concluding Remarks, Challenges, and Perspectives
Funding
Acknowledgments
Conflicts of Interest
References
- European Commission. Third European Report on Science & Technology Indicators; EU Publications Office: Luxembourg, 2003. [Google Scholar]
- McWilliams, A. The Maturing Nanotechnology Market: Products and Applications; NAN031G, Global Markets; BBC Research: Wellesley, MA, USA, 2016. [Google Scholar]
- Global nanotechnology market (by component and applications), funding & investment, patent analysis and 27 companies profile & recent developments—Forecast to 2024. iGATE Res. 2018, 4520812.
- Haase, A.; Klaessig, F. EU US Roadmap Nanoinformatics 2030; EU Nanosafety Cluster, 2018. [Google Scholar] [CrossRef]
- Markiewicz, M.; Kumirska, J.; Lynch, I.; Matzke, M.; Köser, J.; Bemowsky, S.; Docter, D.; Stauber, R.; Westmeier, D.; Stolte, S. Changing environments and biomolecule coronas: Consequences and challenges for the design of environmentally acceptable engineered nanoparticles. Green Chem. 2018, 20, 4133–4168. [Google Scholar] [CrossRef]
- Abbas, Q.; Yousaf, B.; Ullah, H.; Ali, M.U.; Ok, Y.S.; Rinklebe, J. Environmental transformation and nano-toxicity of engineered nano-particles (ENPs) in aquatic and terrestrial organisms. Crit. Rev. Environ. Sci. Technol. 2020, 50, 2523–2581. [Google Scholar] [CrossRef]
- Domingues, C.; Santos, A.; Alvarez-Lorenzo, C.; Concheiro, A.; Jarak, I.; Veiga, F.; Barbosa, I.; Dourado, M.; Figueiras, A. Where is nano today and where is it headed? A review of nanomedicine and the dilemma of nanotoxicology. ACS Nano 2022, 16, 9994–10041. [Google Scholar] [CrossRef] [PubMed]
- Steffen, W.; Richardson, K.; Rockstrom, J.; Cornell, S.E.; Fetzer, I.; Bennett, E.M.; Biggs, R.; Carpenter, S.R.; de Vries, W.; de Wit, C.A.; et al. Planetary boundaries: Guiding human development on a changing planet. Science 2015, 347, 1259855. [Google Scholar] [CrossRef] [PubMed]
- Persson, L.; Almroth, B.M.C.; Collins, C.D.; Cornell, S.; de Wit, C.A.; Diamond, M.L.; Fantke, P.; Hassellov, M.; MacLeod, M.; Ryberg, M.W.; et al. Outside the safe operating space of the planetary boundary for novel entities. Environ. Sci. Technol. 2022, 56, 1510–1521. [Google Scholar] [CrossRef]
- Rockstrom, J.; Steffen, W.; Noone, K.; Persson, A.; Chapin, F.S.; Lambin, E.F.; Lenton, T.M.; Scheffer, M.; Folke, C.; Schellnhuber, H.J.; et al. A safe operating space for humanity. Nature 2009, 461, 472–475. [Google Scholar] [CrossRef]
- Nel, A.; Xia, T.; Madler, L.; Li, N. Toxic potential of materials at the nanolevel. Science 2006, 311, 622–627. [Google Scholar] [CrossRef]
- Valsami-Jones, E.; Lynch, I. How safe are nanomaterials? Science 2015, 350, 388–389. [Google Scholar] [CrossRef]
- Hochella, M.F.; Mogk, D.W.; Ranville, J.; Allen, I.C.; Luther, G.W.; Marr, L.C.; McGrail, B.P.; Murayama, M.; Qafoku, N.P.; Rosso, K.M.; et al. Natural, incidental, and engineered nanomaterials and their impacts on the Earth system. Science 2019, 363, eaau8299. [Google Scholar] [CrossRef]
- Zhang, T.T.; Zhu, X.; Guo, J.H.; Gu, A.Z.; Li, D.; Chen, J.M. Toxicity assessment of nano-zno exposure on the human intestinal microbiome, metabolic functions, and resistome using an in vitro colon simulator. Environ. Sci. Technol. 2021, 55, 6884–6896. [Google Scholar] [CrossRef] [PubMed]
- Yu, S.J.; Lai, Y.J.; Dong, L.J.; Lie, J.F. Intracellular dissolution of silver nanoparticles: Evidence from double stable isotope tracing. Environ. Sci. Technol. 2019, 53, 10218–10226. [Google Scholar] [CrossRef] [PubMed]
- Azimzada, A.; Jreije, I.; Hadioui, M.; Shaw, P.; Farner, J.M.; Wilkinson, K.J. Quantification and characterization of ti-, ce-, and ag-nanoparticles in global surface waters and precipitation. Environ. Sci. Technol. 2021, 55, 9836–9844. [Google Scholar] [CrossRef] [PubMed]
- Cohen, Y.; Rallo, R.; Liu, R.; Liu, H.H. In silico analysis of nanomaterials hazard and risk. Acc. Chem. Res. 2013, 46, 802–812. [Google Scholar] [CrossRef] [PubMed]
- Bottini, A.A.; Hartung, T. Food for thought... on the economics of animal testing. Altex-Altern. Zu Tierexp. 2009, 26, 3–16. [Google Scholar] [CrossRef] [PubMed]
- Sistare, F.D.; Morton, D.; Alden, C.; Christensen, J.; Keller, D.; De Jonghe, S.; Storer, R.D.; Reddy, M.V.; Kraynak, A.; Trela, B.; et al. An analysis of pharmaceutical experience with decades of rat carcinogenicity testing: Support for a proposal to modify current regulatory guidelines. Toxicol. Pathol. 2011, 39, 716–744. [Google Scholar] [CrossRef] [PubMed]
- Halappanavar, S.; Nymark, P.; Krug, H.F.; Clift, M.J.D.; Rothen-Rutishauser, B.; Vogel, U. Non-animal strategies for toxicity assessment of nanoscale materials: Role of adverse outcome pathways in the selection of endpoints. Small 2021, 17, e2007628. [Google Scholar] [CrossRef]
- Hartung, T.; Hoffmann, S. Food for thought... on in silico methods in toxicology. Altex-Altern. Zu Tierexp. 2009, 26, 155–166. [Google Scholar] [CrossRef]
- Kavlock, R.; Dix, D. Computational toxicology as implemented by the us epa: Providing high throughput decision support tools for screening and assessing chemical exposure, hazard and risk. J. Toxicol. Environ. Health-Part B-Crit. Rev. 2010, 13, 197–217. [Google Scholar] [CrossRef]
- Valerio, L.G. In silico toxicology for the pharmaceutical sciences. Toxicol. Appl. Pharmacol. 2009, 241, 356–370. [Google Scholar] [CrossRef]
- Tang, W.H.; Chen, J.W.; Wang, Z.Y.; Xie, H.B.; Hong, H.X. Deep learning for predicting toxicity of chemicals: A mini review. J. Environ. Sci. Health C 2018, 36, 252–271. [Google Scholar] [CrossRef] [PubMed]
- Suhendra, E.; Chang, C.H.; Hou, W.C.; Hsieh, Y.C. A Review on the environmental fate models for predicting the distribution of engineered nanomaterials in surface Waters. Int. J. Mol. Sci. 2020, 21, 4554. [Google Scholar] [CrossRef] [PubMed]
- Kostewicz, E.S.; Aarons, L.; Bergstrand, M.; Bolger, M.B.; Galetin, A.; Hatley, O.; Jamei, M.; Lloyd, R.; Pepin, X.; Rostami-Hodjegan, A.; et al. PBPK models for the prediction of in vivo performance of oral dosage forms. Eur. J. Pharm. Sci. 2014, 57, 300–321. [Google Scholar] [CrossRef] [PubMed]
- Sager, J.E.; Yu, J.J.; Ragueneau-Majlessi, I.; Isoherranen, N. Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: A systematic review of published models, applications, and model verification. Drug Metab. Dispos. 2015, 43, 1823–1837. [Google Scholar] [CrossRef] [PubMed]
- Cherkasov, A.; Muratov, E.N.; Fourches, D.; Varnek, A.; Baskin, I.I.; Cronin, M.; Dearden, J.; Gramatica, P.; Martin, Y.C.; Todeschini, R.; et al. QSAR modeling: Where have you been? where are you going to? J. Med. Chem. 2014, 57, 4977–5010. [Google Scholar] [CrossRef]
- Huang, Y.; Li, X.H.; Xu, S.J.; Zheng, H.Z.; Zhang, L.L.; Chen, J.W.; Hong, H.X.; Kusko, R.; Li, R.B. Quantitative structure-activity relationship models for predicting inflammatory potential of metal oxide nanoparticles. Environ. Health Perspect. 2020, 128, 67010. [Google Scholar] [CrossRef]
- Heo, S.; Safder, U.; Yoo, C. Deep learning driven QSAR model for environmental toxicology: Effects of endocrine disrupting chemicals on human health. Environ. Pollut. 2019, 253, 29–38. [Google Scholar] [CrossRef]
- Vamathevan, J.; Clark, D.; Czodrowski, P.; Dunham, I.; Ferran, E.; Lee, G.; Li, B.; Madabhushi, A.; Shah, P.; Spitzer, M.; et al. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 2019, 18, 463–477. [Google Scholar] [CrossRef]
- Singh, A.V.; 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. Adv. Healthc. Mater. 2020, 9, e1901862. [Google Scholar] [CrossRef]
- Hadrup, N.; Zhernovkov, V.; Jacobsen, N.R.; Voss, C.; Strunz, M.; Ansari, M.; Schiller, H.B.; Halappanavar, S.; Poulsen, S.S.; Kholodenko, B.; et al. Acute phase response as a biological mechanism-of-action of (nano)particle-induced cardiovascular disease. Small 2020, 16, e1907476. [Google Scholar] [CrossRef]
- Maynard, A.D.; Warheit, D.B.; Philbert, M.A. the new toxicology of sophisticated materials: Nanotoxicology and beyond. Toxicol. Sci. 2011, 120, S109–S129. [Google Scholar] [CrossRef] [PubMed]
- Gatoo, M.A.; Naseem, S.; Arfat, M.Y.; Dar, A.M.; Qasim, K.; Zubair, S. Physicochemical properties of nanomaterials: Implication in associated toxic manifestations. BioMed Res. Int. 2014, 2014, 498420. [Google Scholar] [CrossRef] [PubMed]
- Zhang, H.Y.; Ji, Z.X.; Xia, T.; Meng, H.; Low-Kam, C.; Liu, R.; Pokhrel, S.; Lin, S.J.; Wang, X.; Liao, Y.P.; et al. Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammation. ACS Nano 2012, 6, 4349–4368. [Google Scholar] [CrossRef]
- Utembe, W.; Clewell, H.; Sanabria, N.; Doganis, P.; Gulumian, M. Current approaches and techniques in physiologically based pharmacokinetic (pbpk) modelling of nanomaterials. Nanomaterials 2020, 10, 1267. [Google Scholar] [CrossRef] [PubMed]
- Furxhi, I.; Murphy, F.; Mullins, M.; Arvanitis, A.; Poland, C.A. Practices and trends of machine learning application in nanotoxicology. Nanomaterials 2020, 10, 116. [Google Scholar] [CrossRef] [PubMed]
- Caballero-Guzman, A.; Nowack, B. A critical review of engineered nanomaterial release data: Are current data useful for material flow modeling? Environ. Pollut. 2016, 213, 502–517. [Google Scholar] [CrossRef]
- Dale, A.L.; Casman, E.A.; Lowry, G.V.; Lead, J.R.; Viparelli, E.; Baalousha, M. Modeling nanomaterial environmental fate in aquatic systems. Environ. Sci. Technol. 2015, 49, 2587–2593. [Google Scholar] [CrossRef] [PubMed]
- Li, M.; Zou, P.; Tyner, K.; Lee, S. Physiologically based pharmacokinetic (PBPK) modeling of pharmaceutical nanoparticles. AAPS J. 2017, 19, 26–42. [Google Scholar] [CrossRef]
- Yuan, D.F.; He, H.; Wu, Y.; Fan, J.H.; Cao, Y.G. Physiologically based pharmacokinetic modeling of nanoparticles. J. Pharm. Sci. 2019, 108, 58–72. [Google Scholar] [CrossRef]
- Chen, G.C.; Peijnenburg, W.; Xiao, Y.L.; Vijver, M.G. Current Knowledge on the use of computational toxicology in hazard assessment of metallic engineered nanomaterials. Int. J. Mol. Sci. 2017, 18, 1504. [Google Scholar] [CrossRef]
- Burello, E. Review of (Q)SAR models for regulatory assessment of nanomaterials risks. Nanoimpact 2017, 8, 48–58. [Google Scholar] [CrossRef]
- von der Kammer, F.; Ferguson, P.L.; Holden, P.A.; Masion, A.; Rogers, K.R.; Klaine, S.J.; Koelmans, A.A.; Horne, N.; Unrine, J.M. Analysis of engineered nanomaterials in complex matrices (environment and biota): General considerations and conceptual case studies. Environ. Toxicol. Chem. 2012, 31, 32–49. [Google Scholar] [CrossRef] [PubMed]
- Nowack, B. Evaluation of environmental exposure models for engineered nanomaterials in a regulatory context. Nanoimpact 2017, 8, 38–47. [Google Scholar] [CrossRef]
- Mueller, N.C.; Nowack, B. Exposure modeling of engineered nanoparticles in the environment. Environ. Sci. Technol. 2008, 42, 4447–4453. [Google Scholar] [CrossRef] [PubMed]
- Sun, T.Y.; Conroy, G.; Donner, E.; Hungerbuhler, K.; Lombi, E.; Nowack, B. Probabilistic modelling of engineered nanomaterial emissions to the environment: A spatio-temporal approach. Environ. Sci. Nano 2015, 2, 340–351. [Google Scholar] [CrossRef]
- Gottschalk, F.; Scholz, R.W.; Nowack, B. Probabilistic material flow modeling for assessing the environmental exposure to compounds: Methodology and an application to engineered nano-TiO2 particles. Environ. Model. Softw. 2010, 25, 320–332. [Google Scholar] [CrossRef]
- Kuenen, J.; Pomar-Portillo, V.; Vilchez, A.; Visschedijk, A.; van der Gon, H.D.; Vázquez-Campos, S.; Nowack, B.; Adam, V. Inventory of country-specific emissions of engineered nanomaterials throughout the life cycle. Environ. Sci. Nano 2020, 7, 3824–3839. [Google Scholar] [CrossRef]
- Adam, V.; Caballero-Guzman, A.; Nowack, B. Considering the forms of released engineered nanomaterials in probabilistic material flow analysis. Environ. Pollut. 2018, 243, 17–27. [Google Scholar] [CrossRef]
- Zheng, Y.F.; Nowack, B. Size-Specific, Dynamic, Probabilistic material flow analysis of titanium dioxide releases into the environment. Environ. Sci. Technol. 2021, 55, 2392–2402. [Google Scholar] [CrossRef]
- Muller, E.; Hilty, L.M.; Widmer, R.; Schluep, M.; Faulstich, M. Modeling metal stocks and flows: A review of dynamic material flow analysis methods. Environ. Sci. Technol. 2014, 48, 2102–2113. [Google Scholar] [CrossRef]
- Bornhoft, N.A.; Sun, T.Y.; Hilty, L.M.; Nowack, B. A dynamic probabilistic material flow modeling method. Environ. Model. Softw. 2016, 76, 69–80. [Google Scholar] [CrossRef]
- Sun, T.Y.; Bornhoft, N.A.; Hungerbuhler, K.; Nowack, B. Dynamic probabilistic modeling of environmental emissions of engineered nanomaterials. Environ. Sci. Technol. 2016, 50, 4701–4711. [Google Scholar] [CrossRef] [PubMed]
- Sun, T.Y.; Mitrano, D.M.; Bornhoft, N.A.; Scheringer, M.; Hungerbuhler, K.; Nowack, B. Envisioning nano release dynamics in a changing world: Using dynamic probabilistic modeling to assess future environmental emissions of engineered nanomaterials. Environ. Sci. Technol. 2017, 51, 2854–2863. [Google Scholar] [CrossRef] [PubMed]
- Rajkovic, S.; Bornhöft, N.A.; van der Weijden, R.; Nowack, B.; Adam, V. Dynamic probabilistic material flow analysis of engineered nanomaterials in European waste treatment systems. Waste Manag. 2020, 113, 118–131. [Google Scholar] [CrossRef] [PubMed]
- Wang, Y.; Nowack, B. Dynamic probabilistic material flow analysis of nano-SiO2, nano iron oxides, nano-CeO2, nano-Al2O3, and quantum dots in seven European regions. Environ. Pollut. 2018, 235, 589–601. [Google Scholar] [CrossRef] [PubMed]
- Garner, K.L.; Suh, S.; Keller, A.A. Assessing the risk of engineered nanomaterials in the environment: Development and application of the nanofate model. Environ. Sci. Technol. 2017, 51, 5541–5551. [Google Scholar] [CrossRef] [PubMed]
- Liu, H.H.; Cohen, Y. Multimedia environmental distribution of engineered nanomaterials. Environ. Sci. Technol. 2014, 48, 3281–3292. [Google Scholar] [CrossRef]
- Meesters, J.A.J.; Koelmans, A.A.; Quik, J.T.K.; Hendriks, A.J.; van de Meentt, D. Multimedia modeling of engineered nanoparticles with simplebox4nano: Model definition and evaluation. Environ. Sci. Technol. 2014, 48, 5726–5736. [Google Scholar] [CrossRef]
- Parker, N.; Keller, A.A. Variation in regional risk of engineered nanoparticles: NanoTiO as a case study. Environ. Sci. Nano 2019, 6, 444–455. [Google Scholar] [CrossRef]
- Meesters, J.A.J.; Quik, J.T.K.; Koelmans, A.A.; Hendriks, A.J.; van de Meent, D. Multimedia environmental fate and speciation of engineered nanoparticles: A probabilistic modeling approach. Environ. Sci. Nano 2016, 3, 715–727. [Google Scholar] [CrossRef]
- Khalil, F.; Laer, S. Physiologically based pharmacokinetic modeling: Methodology, applications, and limitations with a focus on its role in pediatric drug development. J. Biomed. Biotechnol. 2011, 2011, 907461. [Google Scholar] [CrossRef]
- Lu, M.G.; Al-Jamal, K.T.; Kostarelos, K.; Reineke, J. Physiologically based pharmacokinetic modeling of nanoparticles. ACS Nano 2010, 4, 6303–6317. [Google Scholar] [CrossRef]
- Lee, H.A.; Leavens, T.L.; Mason, S.E.; Monteiro-Riviere, N.A.; Riviere, J.E. Comparison of quantum dot biodistribution with a blood-flow-limited physiologically based pharmacokinetic model. Nano Lett. 2009, 9, 794–799. [Google Scholar] [CrossRef] [PubMed]
- Lankveld, D.P.K.; Oomen, A.G.; Krystek, P.; Neigh, A.; Troost-de Jong, A.; Noorlander, C.W.; Van Eijkeren, J.C.H.; Geertsma, R.E.; De Jong, W.H. The kinetics of the tissue distribution of silver nanoparticles of different sizes. Biomaterials 2010, 31, 8350–8361. [Google Scholar] [CrossRef] [PubMed]
- Aborig, M.; Malik, P.R.V.; Nambiar, S.; Chelle, P.; Darko, J.; Mutsaers, A.; Edginton, A.N.; Fleck, A.; Osei, E.; Wettig, S. Biodistribution and physiologically-based pharmacokinetic modeling of gold nanoparticles in mice with interspecies extrapolation. Pharmaceutics 2019, 11, 179. [Google Scholar] [CrossRef] [PubMed]
- Bachler, G.; von Goetz, N.; Hungerbuhler, K. Using physiologically based pharmacokinetic (PBPK) modeling for dietary risk assessment of titanium dioxide (TiO2) nanoparticles. Nanotoxicology 2015, 9, 373–380. [Google Scholar] [CrossRef] [PubMed]
- Carlander, U.; Moto, T.P.; Desalegn, A.A.; Yokel, R.A.; Johanson, G. Physiologically based pharmacokinetic modeling of nanoceria systemic distribution in rats suggests dose- and route-dependent biokinetics. Int. J. Nanomed. 2018, 13, 2631–2646. [Google Scholar] [CrossRef]
- Chen, W.Y.; Cheng, Y.H.; Hsieh, N.H.; Wu, B.C.; Chou, W.C.; Ho, C.C.; Chen, J.K.; Liao, C.M.; Lin, P. Physiologically based pharmacokinetic modeling of zinc oxide nanoparticles and zinc nitrate in mice. Int. J. Nanomed. 2015, 10, 6277–6292. [Google Scholar] [CrossRef]
- Kumar, M.; Kulkarni, P.; Liu, S.F.; Chemuturi, N.; Shah, D.K. Nanoparticle biodistribution coefficients: A quantitative approach for understanding the tissue distribution of nanoparticles. Adv. Drug Deliv. Rev. 2023, 194, 114708. [Google Scholar] [CrossRef]
- Kutumova, E.O.; Akberdin, I.R.; Kiselev, I.N.; Sharipov, R.N.; Egorova, V.S.; Syrocheva, A.O.; Parodi, A.; Zamyatnin, A.A.; Kolpakov, F.A. Physiologically based pharmacokinetic modeling of nanoparticle biodistribution: A review of existing models, simulation software, and data analysis tools. Int. J. Mol. Sci. 2022, 23, 12560. [Google Scholar] [CrossRef]
- Gakis, G.P.; Krikas, A.; Neofytou, P.; Tran, L.; Charitidis, C. Modelling the biodistribution of inhaled gold nanoparticles in rats with interspecies extrapolation to humans. Toxicol. Appl. Pharmacol. 2022, 457, 116322. [Google Scholar] [CrossRef] [PubMed]
- Dubaj, T.; Kozics, K.; Sramkova, M.; Manova, A.; Bastus, N.G.; Moriones, O.H.; Kohl, Y.; Dusinska, M.; Runden-Pran, E.; Puntes, V.; et al. Pharmacokinetics of PEGylated gold nanoparticles: In vitro-in vivo correlation. Nanomaterials 2022, 12, 511. [Google Scholar] [CrossRef] [PubMed]
- Cheng, Y.H.; Riviere, J.E.; Monteiro-Riviere, N.A.; Lin, Z.M. Probabilistic risk assessment of gold nanoparticles after intravenous administration by integrating and toxicity with physiologically based pharmacokinetic modeling. Nanotoxicology 2018, 12, 453–469. [Google Scholar] [CrossRef] [PubMed]
- Liang, X.W.; Wang, H.L.; Grice, J.E.; Li, L.; Liu, X.; Xu, Z.P.; Roberts, M.S. Physiologically based pharmacokinetic model for long-circulating inorganic nanoparticles. Nano Lett. 2016, 16, 939–945. [Google Scholar] [CrossRef] [PubMed]
- Deng, L.J.; Liu, H.; Ma, Y.S.; Miao, Y.F.; Fu, X.L.; Deng, Q.H. Endocytosis mechanism in physiologically-based pharmacokinetic modeling of nanoparticles. Toxicol. Appl. Pharmacol. 2019, 384, 114765. [Google Scholar] [CrossRef]
- Chou, W.C.; Cheng, Y.H.; Riviere, J.E.; Monteiro-Riviere, N.A.; Kreyling, W.G.; Lin, Z.M. Development of a multi-route physiologically based pharmacokinetic (PBPK) model for nanomaterials: A comparison between a traditional versus a new route-specific approach using gold nanoparticles in rats. Part. Fibre Toxicol. 2022, 19, 47. [Google Scholar] [CrossRef]
- Rosário, F.; Creylman, J.; Verheyen, G.; Van Miert, S.; Santos, C.; Hoet, P.; Oliveira, H. Impact of particle size on toxicity, tissue distribution and excretion kinetics of subchronic intratracheal instilled silver nanoparticles in mice. Toxics 2022, 10, 260. [Google Scholar] [CrossRef]
- Muratov, E.N.; Bajorath, J.; Sheridan, R.P.; Tetko, I.V.; Filimonov, D.; Poroikov, V.; Oprea, T.I.; Baskin, I.I.; Varnek, A.; Roitberg, A.; et al. QSAR without borders. Chem. Soc. Rev. 2020, 49, 3716. [Google Scholar] [CrossRef]
- Yan, X.L.; Yue, T.T.; Winkler, D.A.; Yin, Y.G.; Zhu, H.; Jiang, G.B.; Yan, B. Converting nanotoxicity data to information using artificial intelligence and simulation. Chem. Rev. 2023, 123, 8575–8637. [Google Scholar] [CrossRef]
- Weininger, D. Smiles, a chemical language and information-system.1. Introduction to methodology and encoding rules. J. Chem. Inf. Comp. Sci. 1988, 28, 31–36. [Google Scholar] [CrossRef]
- Toropova, A.P.; Toropov, A.A. Nanomaterials: Quasi-SMILES as a flexible basis for regulation and environmental risk assessment. Sci. Total Environ. 2022, 823, 153747. [Google Scholar] [CrossRef] [PubMed]
- Toropova, A.P.; Toropov, A.A. QSPR and nano-QSPR: What is the difference? J. Mol. Struct. 2019, 1182, 141–149. [Google Scholar] [CrossRef]
- Trinh, T.X.; Choi, J.S.; Jeon, H.; Byun, H.G.; Yoon, T.H.; Kim, J. Quasi-SMILES-based nano-quantitative structure-activity relationship model to predict the cytotoxicity of multiwalled carbon nanotubes to human lung cells. Chem. Res. Toxicol. 2018, 31, 183–190. [Google Scholar] [CrossRef] [PubMed]
- Choi, J.S.; Trinh, T.X.; Yoon, T.H.; Kim, J.; Byun, H.G. Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials. Chemosphere 2019, 217, 243–249. [Google Scholar] [CrossRef] [PubMed]
- Bunmahotama, W.; Vijver, M.G.; Peijnenburg, W. Development of a quasi-quantitative structure-activity relationship model for prediction of the immobilization response of exposed to metal-based nanomaterials. Environ. Toxicol. Chem. 2022, 41, 1439–1450. [Google Scholar] [CrossRef]
- Roy, J.; Roy, K. Evaluating metal oxide nanoparticle (MeOx NP) toxicity with different types of nano descriptors mainly focusing on simple periodic table-based descriptors: A mini-review. Environ. Sci. Nano 2023, 10, 2989–3011. [Google Scholar] [CrossRef]
- Roy, J.; Ojha, P.K.; Roy, K. Risk assessment of heterogeneous TiO2-based engineered nanoparticles (NPs): A QSTR approach using simple periodic table based descriptors. Nanotoxicology 2019, 13, 701–716. [Google Scholar] [CrossRef]
- De, P.; Kar, S.; Roy, K.; Leszczynski, J. Second generation periodic table-based descriptors to encode toxicity of metal oxide nanoparticles to multiple species: QSTR modeling for exploration of toxicity mechanisms. Environ. Sci. Nano 2018, 5, 2742–2760. [Google Scholar] [CrossRef]
- Wang, W.Y.; Sedykh, A.; Sun, H.N.; Zhao, L.L.; Russo, D.P.; Zhou, H.Y.; Yan, B.; Zhu, H. Predicting nano-bio interactions by integrating nanoparticle libraries and quantitative nanostructure activity relationship modeling. ACS Nano 2017, 11, 12641–12649. [Google Scholar] [CrossRef]
- Chew, A.K.; Pedersen, J.A.; Van Lehn, R.C. Predicting the physicochemical properties and biological activities of monolayer-protected gold nanoparticles using simulation-derived descriptors. ACS Nano 2022, 16, 6282–6292. [Google Scholar] [CrossRef]
- Yan, X.L.; Sedykh, A.; Wang, W.Y.; Zhao, X.L.; Yan, B.; Zhu, H. In silico profiling nanoparticles: Predictive nanomodeling using universal nanodescriptors and various machine learning approaches. Nanoscale 2019, 11, 8352–8362. [Google Scholar] [CrossRef] [PubMed]
- Yan, X.L.; Zhang, J.; Russo, D.P.; Zhu, H.; Yan, B. Prediction of nano-bio interactions through convolutional neural network analysis of nanostructure images. ACS Sustain. Chem. Eng. 2020, 8, 19096–19104. [Google Scholar] [CrossRef]
- Singh, K.P.; Gupta, S. Nano-QSAR modeling for predicting biological activity of diverse nanomaterials. RSC Adv. 2014, 4, 13215–13230. [Google Scholar] [CrossRef]
- Gurevitch, J.; Koricheva, J.; Nakagawa, S.; Stewart, G. Meta-analysis and the science of research synthesis. Nature 2018, 555, 175–182. [Google Scholar] [CrossRef] [PubMed]
- Gernand, J.M.; Casman, E.A. A meta-analysis of carbon nanotube pulmonary toxicity studies-how physical dimensions and impurities affect the toxicity of carbon nanotubes. Risk Anal. 2014, 34, 583–597. [Google Scholar] [CrossRef] [PubMed]
- Oh, E.; Liu, R.; Nel, A.; Gemill, K.B.; Bilal, M.; Cohen, Y.; Medintz, I.L. Meta-analysis of cellular toxicity for cadmium-containing quantum dots. Nat. Nanotechnol. 2016, 11, 479–486. [Google Scholar] [CrossRef] [PubMed]
- Bilal, M.; Oh, E.; Liu, R.; Breger, J.C.; Medintz, I.L.; Cohen, Y. Bayesian network resource for meta-analysis: Cellular toxicity of quantum dots. Small 2019, 15, e1900510. [Google Scholar] [CrossRef] [PubMed]
- Labouta, H.I.; Asgarian, N.; Rinker, K.; Cramb, D.T. Meta-analysis of nanoparticle cytotoxicity via data-mining the literature. ACS Nano 2019, 13, 1583–1594. [Google Scholar] [CrossRef]
- Gul, G.; Yildirim, R.; Ileri-Ercan, N. Cytotoxicity analysis of nanoparticles by association rule mining. Environ. Sci. Nano 2021, 8, 937–949. [Google Scholar] [CrossRef]
- Cui, Y.H.; Chen, J.W.; Wang, Z.Y.; Wang, J.Y.; Allen, D.T. Coupled dynamic material flow, multimedia environmental model, and ecological risk analysis for chemical management: A Di(2-ethylhexhyl) phthalate case in China. Environ. Sci. Technol. 2022, 56, 11006–11016. [Google Scholar] [CrossRef]
- Nowack, B.; Baalousha, M.; Bornhöft, N.; Chaudhry, Q.; Cornelis, G.; Cotterill, J.; Gondikas, A.; Hassellöv, M.; Lead, J.; Mitrano, D.M.; et al. Progress towards the validation of modeled environmental concentrations of engineered nanomaterials by analytical measurements. Environ. Sci. Nano 2015, 2, 421–428. [Google Scholar] [CrossRef]
- Svendsen, C.; Walker, L.A.; Matzke, M.; Lahive, E.; Harrison, S.; Crossley, A.; Park, B.; Lofts, S.; Lynch, I.; Vázquez-Campos, S.; et al. Key principles and operational practices for improved nanotechnology environmental exposure assessment. Nat. Nanotechnol. 2020, 15, 731–742. [Google Scholar] [CrossRef] [PubMed]
- van den Brink, N.W.; Kokalj, A.J.; Silva, P.V.; Lahive, E.; Norrfors, K.; Baccaro, M.; Khodaparast, Z.; Loureiro, S.; Drobne, D.; Cornelis, G.; et al. Tools and rules for modelling uptake and bioaccumulation of nanomaterials in invertebrate organisms. Environ. Sci. Nano 2019, 6, 1985–2001. [Google Scholar] [CrossRef]
- Tenzer, S.; Docter, D.; Kuharev, J.; Musyanovych, A.; Fetz, V.; Hecht, R.; Schlenk, F.; Fischer, D.; Kiouptsi, K.; Reinhardt, C.; et al. Rapid formation of plasma protein corona critically affects nanoparticle pathophysiology. Nat. Nanotechnol. 2013, 8, 772–781. [Google Scholar] [CrossRef] [PubMed]
- Chou, W.C.; Chen, Q.R.; Yuan, L.; Cheng, Y.H.; He, C.L.; Monteiro-Riviere, N.A.; Riviere, J.E.; Lin, Z.M. An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice. J. Control. Release 2023, 361, 53–63. [Google Scholar] [CrossRef] [PubMed]
- Li, J.; Wang, C.X.; Yue, L.; Chen, F.R.; Cao, X.S.; Wang, Z.Y. Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review. Ecotoxicol. Environ. Saf. 2022, 243, 113955. [Google Scholar] [CrossRef] [PubMed]
- Ji, Z.W.; Guo, W.J.; Wood, E.L.; Liu, J.; Sakkiah, S.; Xu, X.M.; Patterson, T.A.; Hong, H.X. Machine Learning Models for Predicting Cytotoxicity of Nanomaterials. Chem. Res. Toxicol. 2022, 35, 125–139. [Google Scholar] [CrossRef]
- Yan, X.L.; Sedykh, A.; Wang, W.Y.; Yan, B.; Zhu, H. Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations. Nat. Commun. 2020, 11, 2519. [Google Scholar] [CrossRef]
- Kim, S.; Chen, J.; Cheng, T.J.; Gindulyte, A.; He, J.; He, S.Q.; Li, Q.L.; Shoemaker, B.A.; Thiessen, P.A.; Yu, B.; et al. PubChem in 2021: New data content and improved web interfaces. Nucleic Acids Res. 2021, 49, D1388–D1395. [Google Scholar] [CrossRef]
- Wyrzykowska, E.; Mikolajczyk, A.; Lynch, I.; Jeliazkova, N.; Kochev, N.; Sarimveis, H.; Doganis, P.; Karatzas, P.; Afantitis, A.; Melagraki, G.; et al. Representing and describing nanomaterials in predictive nanoinformatics. Nat. Nanotechnol. 2022, 17, 924–932. [Google Scholar] [CrossRef]
Model Categories | Methods or Name | ENMs | Environmental Compartments | Technological Compartments | Ref. |
---|---|---|---|---|---|
Material flow analysis | Deterministic | Ag, TiO2, CNT | Air, water, and soil | Waste incineration plants, landfills, and sewage treatment plants | [47] |
Probabilistic | Ag, TiO2, ZnO | Air, natural and urban soil, sludge-treated soil, surface water | Production and manufacturing, consumption, recycling, landfill, waste incineration plant | [50] | |
Probabilistic | Ag, TiO2 | Surface water, soil, air | Production, manufacturing, use, wastewater system, solid waste management, export | [51] | |
Probabilistic, dynamic | TiO2 | Air, natural and urban soil, subsurface, sludge-treated soil, surface soil | Production, manufacturing, consumption, in-use stock, release, wastewater management, solid waste management | [52] | |
Probabilistic, dynamic | TiO2, ZnO, Ag, CNT | Atmosphere, natural and urban soil, sewage sludge treated soil, surface waters, and sediment | Landfills, sewage treatment plants, waste incineration plants, recycling and export | [55] | |
Probabilistic, dynamic | TiO2, ZnO, Ag, CNT | Air, soil, surface water, and sediment | Production/manufacturing, wastewater treatment, waste incineration, landfill, and recycling | [56] | |
Probabilistic, dynamic | CNT, Ag, TiO2, ZnO | Air, soil, surface water, and sediment | Production/manufacturing, wastewater treatment, waste incineration, landfill, and recycling | [57] | |
Probabilistic, dynamic | SiO2, iron oxides, CeO2, Al2O3, quantum dots | Air, soil, water, sediment | Production, manufacturing, sewage treatment plants, waste incineration plants, landfills, export and recycling | [58] | |
Multimedia environmental models | MendNano | Al2O3, CeO2, CuO, Fe3O4, TiO2, ZnO, Ag, nanoclays, SiO2, CNT | Air, water, soil, sediment, biota | - | [60] |
SimpleBox4Nano | TiO2 | Air, rain, surface waters, soil, and sediment | - | [61] | |
nanoFate | CeO2, CuO, TiO2, and ZnO | Atmosphere, soil, water, sediment | - | [59] | |
nanoFate | TiO2 | Air, freshwater, marine, natural soil, urban soil, agricultural soil, biosolids soil | [62] | ||
SimpleBox4Nano | CeO2, TiO2, ZnO | Atmosphere, water, sediment, soil | [63] |
ENMs | Species | Exposure Routes | Model Structures | Ref. |
---|---|---|---|---|
Gold | Rats | Intraperitoneal injection | Heart, kidneys, muscle, skin, brain, adipose, gonads, liver, stomach, spleen, pancreas, small intestine, large intestine, bone, and lungs (each organ includes 4 sub-compartments: plasma, vascular endothelium, macrophages, and interstitial space) | [68] |
Cerium dioxide | Rats | Intravenous administration | Blood, liver, spleen, lung, kidney, heart, brain, bone marrow, and other tissues | [70] |
Gold | Rats | Inhalation | Blood, liver, kidney, heart, brain, lymph nodes, spleen, gastrointestinal tract, olfactory, and tracheobronchial | [74] |
Gold | Human | Intravenous injection | Liver, venous plasma, kidney, and skin | [76] |
Quantum dot | Mice | Intravenous injection | Lung, liver, kidney, spleen, and the rest of the body, blood (each organ includes 3 subcompartments: vascular space, phagocytic cells, and tissue) | [77] |
Glycol-coated gold | Mice | Intravenous injection | Blood, liver, spleen, kidneys, lungs, brain, and rest of the body tissues | [78] |
Gold | Rats | Intravenous, oral gavage, intratracheal instillation, and endotracheal inhalation | Blood, lungs, liver, spleen, gastrointestinal tract, kidneys, and remaining tissues | [79] |
Silver | Mice | Intratracheal instilled | Lung, spleen, kidney, liver, brain, heart, and blood | [80] |
Model Categories | ENMs | Endpoints | Algorithms | Data Size | Descriptors | Performance | Ref. |
---|---|---|---|---|---|---|---|
TiO2 | Cytotoxicity | Linear regression | 34 | First generation of simple periodic table-based descriptors | R2 = 0.922–0.926 | [90] | |
Metal oxide | Cytotoxicity | Multiple linear regression | 12 | Second generation of simple periodic table-based descriptors | R2 = 0.88 | [91] | |
Gold | Cell uptake | k nearest neighbors | 34 | Theoretical descriptors based on virtual structures | R2 > 0.918 | [92] | |
Gold | Cell uptake | LASSO and random forest regression | 154 | Simulation-derived descriptors | r = 0.9 | [93] | |
Gold | (1) Enzyme binding affinities; (2) cellular uptake; (3) cellular uptake. | Random forest and k nearest neighbor | (1) 47; (2) 41; (3) 71 | Universal nanodescriptors | (1) R2cv = 0.9; (2) R2cv = 0.92; (3) R2cv = 0.84. | [94] | |
Gold; platinum; palladium. | Cellular uptake and protein adsorption | Convolutional neural network | 147 | Nanoparticle images | R2 > 0.68 | [95] | |
(1) Metal cores; (2) metal; (3) metal oxide; (4) carbon nanotubes. | (1) ATP content, apoptosis, mitochondrial membrane potential; (2) cell uptake; (3) bacteria cytotoxicity; (4) cell cytotoxicity. | Ensemble learning | (1) 51; (2) 109; (3) 17; (4) 80 | Chemistry Development Kit, ChemSpider | (1) CCC = 0.961; (2) CCC = 0.932; (3) CCC = 0.974; (4) CCC = 0.932. | [96] | |
Meta-analysis | Carbon nanotube | Pulmonary toxicity | Random forest | 136 | 20 experimental conditions; 17 nanoparticle properties; 4 experimental endpoints. | No data | [98] |
Cadmium quantum dots | Cellular toxicity | Random forest | 1741 | 24 qualitative/quantitative attributes on material properties and experimental conditions | R2 = 0.92 | [99] | |
Cadmium quantum dots | Cellular toxicity | Bayesian network | 3028 | 15 categorical and 3 quantitative attributes, including quantum dot properties, surface properties, experimental conditions, and biological conditions | R2 > 0.81 | [100] | |
Organic and inorganic nanoparticles | Cytotoxicity | Decision trees | 2896 | Nanoparticle-related features, cell-related features, methodological parameters | ACC > 87.9% | [101] | |
Inorganic, organic, and carbon based | Cytotoxicity | Association rule mining | 4111 | 15 qualitative and 10 quantitative attributes | No data | [102] |
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Tang, W.; Zhang, X.; Hong, H.; Chen, J.; Zhao, Q.; Wu, F. Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials. Nanomaterials 2024, 14, 155. https://doi.org/10.3390/nano14020155
Tang W, Zhang X, Hong H, Chen J, Zhao Q, Wu F. Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials. Nanomaterials. 2024; 14(2):155. https://doi.org/10.3390/nano14020155
Chicago/Turabian StyleTang, Weihao, Xuejiao Zhang, Huixiao Hong, Jingwen Chen, Qing Zhao, and Fengchang Wu. 2024. "Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials" Nanomaterials 14, no. 2: 155. https://doi.org/10.3390/nano14020155
APA StyleTang, W., Zhang, X., Hong, H., Chen, J., Zhao, Q., & Wu, F. (2024). Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials. Nanomaterials, 14(2), 155. https://doi.org/10.3390/nano14020155