Exploring the Relationships Between Physiochemical Properties of Nanoparticles and Cell Damage to Combat Cancer Growth Using Simple Periodic Table-based Descriptors

Overview

Journal Beilstein J Nanotechnol

Specialty Biotechnology

Date 2024 Mar 20

PMID 38505811

Authors

Joyita Roy

Kunal Roy

Affiliations

Soon will be listed here.

Abstract

A comprehensive knowledge of the physical and chemical properties of nanomaterials (NMs) is necessary to design them effectively for regulated use. Although NMs are utilized in therapeutics, their cytotoxicity has attracted great attention. Nanoscale quantitative structure-property relationship (nano-QSPR) models can help in understanding the relationship between NMs and the biological environment and provide new ways for modeling the structural properties and bio-toxic effects of NMs. The goal of the study is to construct fully validated property-based models to extract relevant features for estimating and influencing the zeta potential and obtaining the toxicity profile regarding cell damage in the treatment of cancer cells. To achieve this, QSPR modeling was first performed with 18 metal oxide (MeOx) NMs to measure their materials properties using periodic table-based descriptors. The features obtained were later applied for zeta potential calculation (imputation for sparse data) for MeOx NMs that lack such information. To further clarify the influence of the zeta potential on cell damage, a QSPR model was developed with 132 MeOx NMs to understand the possible mechanisms of cell damage. The results showed that zeta potential, along with seven other descriptors, had the potential to influence oxidative damage through free radical accumulation, which could lead to changes in the survival rate of cancerous cells. The developed QSPR and quantitative structure-activity relationship models also give hints regarding safer design and toxicity assessment of MeOx NMs.

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References

Bossy-Wetzel E, Schwarzenbacher R, Lipton S . Molecular pathways to neurodegeneration. Nat Med. 2004; 10 Suppl:S2-9. DOI: 10.1038/nm1067. View

Huang Y, Li X, Xu S, Zheng H, Zhang L, Chen J . Quantitative Structure-Activity Relationship Models for Predicting Inflammatory Potential of Metal Oxide Nanoparticles. Environ Health Perspect. 2020; 128(6):67010. PMC: 7292395. DOI: 10.1289/EHP6508. View

Ge Y, Zhang Y, Xia J, Ma M, He S, Nie F . Effect of surface charge and agglomerate degree of magnetic iron oxide nanoparticles on KB cellular uptake in vitro. Colloids Surf B Biointerfaces. 2009; 73(2):294-301. DOI: 10.1016/j.colsurfb.2009.05.031. View

Sengottiyan S, Mikolajczyk A, Jagiello K, Swirog M, Puzyn T . Core, Coating, or Corona? The Importance of Considering Protein Coronas in nano-QSPR Modeling of Zeta Potential. ACS Nano. 2023; 17(3):1989-1997. PMC: 9933600. DOI: 10.1021/acsnano.2c06977. View

Puzyn T, Rasulev B, Gajewicz A, Hu X, Dasari T, Michalkova A . Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. Nat Nanotechnol. 2011; 6(3):175-8. DOI: 10.1038/nnano.2011.10. View

Kar S, Gajewicz A, Puzyn T, Roy K, Leszczynski J . Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: a mechanistic QSTR approach. Ecotoxicol Environ Saf. 2014; 107:162-9. DOI: 10.1016/j.ecoenv.2014.05.026. View

Service R . Genetics and medicine. Recruiting genes, proteins for a revolution in diagnostics. Science. 2003; 300(5617):236-9. DOI: 10.1126/science.300.5617.236. View

Choi J, Trinh T, Yoon T, Kim J, Byun H . Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials. Chemosphere. 2018; 217:243-249. DOI: 10.1016/j.chemosphere.2018.11.014. View

Roy J, Roy K . Understanding mechanism governing the inflammatory potential of metal oxide nanoparticles using periodic table-based descriptors: a nano-QSAR approach. SAR QSAR Environ Res. 2023; 34(6):459-474. DOI: 10.1080/1062936X.2023.2227557. View

10.

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(24):11808-11818. DOI: 10.1039/c9nr01162e. View

11.

Walter M, Allen L, de la Vega de Leon A, Webb S, Gillet V . Analysis of the benefits of imputation models over traditional QSAR models for toxicity prediction. J Cheminform. 2022; 14(1):32. PMC: 9172131. DOI: 10.1186/s13321-022-00611-w. View

12.

Dobson C . Experimental investigation of protein folding and misfolding. Methods. 2004; 34(1):4-14. DOI: 10.1016/j.ymeth.2004.03.002. View

13.

Qi R, Pan Y, Cao J, Jia Z, Jiang J . The cytotoxicity of nanomaterials: Modeling multiple human cells uptake of functionalized magneto-fluorescent nanoparticles via nano-QSAR. Chemosphere. 2020; 249:126175. DOI: 10.1016/j.chemosphere.2020.126175. View

14.

Gutlein M, Helma C, Karwath A, Kramer S . A Large-Scale Empirical Evaluation of Cross-Validation and External Test Set Validation in (Q)SAR. Mol Inform. 2016; 32(5-6):516-28. DOI: 10.1002/minf.201200134. View

15.

Roy K, Ambure P, Kar S . How Precise Are Our Quantitative Structure-Activity Relationship Derived Predictions for New Query Chemicals?. ACS Omega. 2019; 3(9):11392-11406. PMC: 6645132. DOI: 10.1021/acsomega.8b01647. View

16.

Wang N, Hsu C, Zhu L, Tseng S, Hsu J . Influence of metal oxide nanoparticles concentration on their zeta potential. J Colloid Interface Sci. 2013; 407:22-8. DOI: 10.1016/j.jcis.2013.05.058. View

17.

Rivera Gil P, Oberdorster G, Elder A, Puntes V, Parak W . Correlating physico-chemical with toxicological properties of nanoparticles: the present and the future. ACS Nano. 2010; 4(10):5527-31. DOI: 10.1021/nn1025687. View

18.

Toropova A, Toropov A, Benfenati E, Korenstein R, Leszczynska D, Leszczynski J . Optimal nano-descriptors as translators of eclectic data into prediction of the cell membrane damage by means of nano metal-oxides. Environ Sci Pollut Res Int. 2014; 22(1):745-57. DOI: 10.1007/s11356-014-3566-4. View

19.

Kraegeloh A, Suarez-Merino B, Sluijters T, Micheletti C . Implementation of Safe-by-Design for Nanomaterial Development and Safe Innovation: Why We Need a Comprehensive Approach. Nanomaterials (Basel). 2018; 8(4). PMC: 5923569. DOI: 10.3390/nano8040239. View

20.

Roy J, Ojha P, Roy K . Risk assessment of heterogeneous TiO-based engineered nanoparticles (NPs): a QSTR approach using simple periodic table based descriptors. Nanotoxicology. 2019; 13(5):701-716. DOI: 10.1080/17435390.2019.1593543. View