Nanosafety: An Evolving Concept to Bring the Safest Possible Nanomaterials to Society and Environment
Abstract
:1. Introduction
2. Nanotoxicology
3. Immunotoxicity
4. Genotoxicity and Epigenetics
4.1. Nanogenotoxicity
4.2. Nanoepigenetics
5. Advanced Models for In Vitro Testing
5.1. 3D Cultures
5.2. Organs-on-Chips
5.3. Multiple-Organs-on-Chips
5.4. Sensor Integration with Microphysiological Models
6. In Silico Tools in Nanotoxicology
7. Life Cycle Assessment and Nanosafety
8. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Conventional Methodology | Observed Interference | Proposed Solution | |
---|---|---|---|
Cause | Result/Interpretation | ||
MTT reduction LDH leakage WST reduction | NM optical density; NM aggregation in cell medium | Falsely increased viability | Sample centrifugation after cell lysis |
NM redox activity | Falsely decreased viability | None | |
ELISA (cytokine release) | Protein adsorption to NMs | Falsely decreased cytokine production | Add serum proteins to NM suspension |
Comet assay | Interference enzyme activity | Falsely decreased genotoxicity | None |
ROS quantification (H2DCF-DA) | NM redox activity | Falsely increased ROS levels | None |
NMs quench fluorescence; NMs scatter emitted fluorescence | Falsely decreased ROS levels | Sample centrifugation after cell lysis |
Genotoxicity Marker | Assays | References |
---|---|---|
Gene mutation | Bacterial reverse mutation (Ames test) | OECD TG 471 |
In vitro mammalian mutagenicity assay: mouse lymphoma (L5178Y) TK+/-assay | OECD TG 490 | |
In vitro mammalian mutagenicity assay: HPRT assay | OECD TG 476 | |
In vivo gene mutation assay (transgenic rodent somatic and germ cell gene mutation) | OECD TG 488 | |
Chromosomal damage assays | In vitro chromosomal aberration assay | OECD TG 473 |
In vitro MN assay | OECD TG 487 | |
In vivo (mammalian bone marrow) chromosomal aberration test | OECD TG 475 | |
In vivo MN assay (mammalian erythrocyte MN) | OECD TG 474 | |
DNA damage (strand-break and DNA adduct) | In vitro comet assay Modified in vitro comet assay with DNA repair enzymes (e.g., OGG1, FPG) | JaCVAM EURL-ECVAM/ICCVAM [94,99] |
In vivo (mammalian alkaline) comet assay | OECD TG 489 | |
DNA damage (DNA adduct) | HPLC/MS; ELISA | [104,109,110] |
DNA damage response and repair | The γH2AX and 53BP1 foci count assay | [108,111] |
Multiplex array for DNA repair activity | [109,110] | |
FM-HCR assay | [112] |
Epigenetic Endpoints | Specific Epigenetic Markers | Analytical Methods | References |
---|---|---|---|
DNA methylation | Global DNA methylation screening (5mc, 5hmC, 6mA, etc.) | HPLC/MS, ELISA, methylation-sensitive comet assay, pyrosequencing (repetitive sequences LINE-1 or Alu) | [121,122,123,124] |
Gene-specific promoter methylation | Methylation-specific PCR | [121,125] | |
Differentially methylated regions (whole-genome sequencing) | MPS, DNA methylation-specific microarrays, MeDIP followed by sequencing | [121,126] | |
Histone modification | Whole genome (specific histone marker) | ChIP with DNA microarray, ChIP-Seq, ChIP-Chip | [127,128] |
Gene-specific histone (specific) modification | ChIP-qPCR | [128,129] | |
Global histone modification markers | HPLC/MS, ELISA, immunostaining, immunoblotting | [130,131] | |
Noncoding RNAs | Whole genome | RNA-seq, microarray | [132,133] |
Gene-specific | qPCR | [134] |
Advanced Cell Models | Cell Types | Nanomaterial Exposure Conditions | Sensorization | Toxicological Assays | Key Biological Outcomes |
---|---|---|---|---|---|
Heart microphysiological system | NRVMs | TiO2 NPs at 10 and 100 μg·mL−1 and Ag NPs at 50 μg·mL−1 | Electrical sensors | LDH assay, MTT assay | The high-dose exposure of TiO2 NPs (100 μg·mL−1) demonstrated impaired contractile function and damaged tissue structure after 48 h of exposure. Ag NP exposure caused cytotoxicity [169] |
Blood–brain barrier on a chip | HAs and HUVECs | INPM exposure at 0, 5, 10, 20, and 40 μg·mL−1 | - | ROS detection assay, CCK8 assay | The INPM could potentially activate several inflammatory pathways that directly damage brain structures and further lead to neurological diseases [170] |
Liver on a chip | PRHs | 10 nm Fe3O4 NPs | - | - | Perfusion of Fe3O4 NPs results in the reduction in albumin and urea production, indicating potential liver injury [167] |
Lung on a chip | HPAEpiC, HUVECs, and THP-1 | PM2.5 exposure at 200 and 400 μg·mL−1 | - | Immunofluorescence staining assay, FITC-dextran permeability assay, ELISA | A low concentration of PM2.5 causes limited cytotoxicity, but a higher concentration of PM2.5 (>200 μg·mL−1) could significantly increase the ROS generation, apoptosis, and inflammation responses of epithelial cells and endothelial cells on the barrier and attachments of monocytes to the vessels [171] |
BEAS-2B and HUVECs | CSE at 10, 20, and 50 μg·mL−1 | - | RT-PCR, ELISA, Western blotting | Lung on a chip enables the study of nanoparticle adsorption during various breathing frequencies, puff profiles of smoking, breath-holding patterns during inhalation and exhalation, and particle deposition in the lungs and the respiratory tract [154] | |
Placenta barrier on a chip | BeWo | 20 nm SiO2 and TiO2 NPs, and 80 nm ZnO NPs for 24 h | Membrane-bound impedance sensor array | ROS detection assay | SiO2 and TiO2 NPs induced no loss in barrier integrity. In contrast, ZnO NPs displayed severe acute cytotoxicity already after 4 h [172] |
BeWo and HUVECs | TiO2 NPs exposure at 50 and 200 μg·mL−1 | - | Immunofluorescence staining assay, ROS detection assay | Gradually increased cell death with increasing concentrations of NPs, thereby potentially leading to placental membrane rupture [173] | |
Gut/liver on a chip | Caco-2, HT29-MTX + HepG2, C3A | 50 nm carboxylated PS NPs | - | AST assay | Gut/liver chip model demonstrates compounding effects of interorgan crosstalk between gut and the liver in facilitating NP toxicity [167] |
Lung/liver/kidney on a chip | A549 + HepG2 + TH-1 | Ag, Au-PEG, TiO2, and SiO2-FITC NPs | TEER measurements | Live/dead assay | The interconnection of the different modules aims at the simulation of whole-body exposure and response. SiO2-FITC NPs showed a cytotoxic effect on TH-1 after 12 h, which could be due to the interaction of NPs with cancerous cells releasing a substance that may have induced a cytotoxic effect [174,175] |
Nanomaterials | Descriptors | Models 1 | Main Goal |
---|---|---|---|
FD | 204 molecular descriptors generated from the QSAR analyzing tools of BIOVIA Discovery Studio | LinReg | Predict the physicochemical properties of FDs that promote their cytotoxic effects/anticancer activity [193] |
Metal NPs | 24 physicochemical descriptors and toxicity data | MLR | Predict the toxicity and design the structures of metal NPs with low toxicity [194] |
Metal oxide NPs | 61 periodic table descriptors | MLR | Predict and investigate the essential descriptors responsible for the cytotoxicity of metal oxide NPs on E. coli cells under different conditions [195] |
Gold NPs | Structural information (i.e., Dragon descriptors) of the surface ligands | MLR | Predict possible relationships between the oxidative reactivity of gold NPs and their cytotoxicity [196] |
Carbon NPs | Physicochemical descriptors (molecular weight, overall surface area, volume, specific surface area, and sum of degrees) | Orthogonal PLS regression | Predict the interaction between carbon NPs and SARS-CoV-2 RNA fragments [197] |
Amine-containing heterolipid NPs | 116 physicochemical descriptors | PLS regression coupled with stepwise forward algorithm | Predict the pKa of the amine-containing heterolipid NPs [198] |
Metal oxide NPs | Quantum-mechanical computations (such as molecular geometries), physicochemical descriptors (such as zeta-potential in water), and periodic table descriptors (such as electronegativity of each atom) | PLS regression, DecTrees, SVM, and logReg | Predict the inflammatory potential of metal oxide NPs [199] |
Functionalized magneto-fluorescent NPs | Norm index descriptors (describing the structural characteristics of the involved NPs) | RF | Predict the cellular uptake of functionalized magneto-fluorescent NPs to PaCa2 cells. Provide guidance for the design and manufacture of safer nanomaterials [200] |
Metal and metal oxide NPs | Structural information (such as core structure and material type), supported by physicochemical descriptors (such as zeta potential and average agglomerate size in media) | DecTrees, GBR, KNN, LinReg, RF, SVM, and XGBoost | Predict the cytotoxicity of metal and metal oxide NPs in zebrafish embryos [201] |
Virtual carbon NP library | 126 nanodescriptors (such as electronegativity of each atom) | KNN and RF | Predict cytotoxicity and inflammatory responses induced by PM2.5 [202] |
Functionalized magneto-fluorescent NPs | Improved optimal quasi-SMILES-based descriptors | MC | Predict the cellular uptake of functionalized magneto-fluorescent NPs to PaCa2 and HUVEC cell lines [203] |
Gold NPs | Optimal quasi-SMILES-based descriptors | MC | Predict the cellular uptake of gold NPs to A549 cells [204] |
Functionalized magneto-fluorescent NPs | Optimal quasi-SMILES-based descriptors | MC | Develop self-consistent predictive models for the cellular uptake of functionalized magneto-fluorescent NPs to PaCa2 cells [205] |
Metal oxide NPs | Optimal quasi-SMILES-based descriptors | MC | Predict the cell viability of different cell lines when exposed to metal oxide NPs [206] |
ZnO NPs | Optimal quasi-SMILES-based descriptors | MC | Predict the toxicity of ZnO NPs in rats via intraperitoneal injections [207] |
Metal oxide NPs | Optimal quasi-SMILES-based descriptors | MC | Predict the cell viability (expressed in %) and cytotoxicity (categorized as true or false) of different cell lines when exposed to 7 types of metal oxide NPs [208] |
Cadmium QDs | Optimal quasi-SMILES-based descriptors | MC | Predict hepatic cell viability when exposed to cadmium QDs [209] |
FDs | Structural information (such as polarizability), optimal quasi-SMILES-based descriptors, and physicochemical properties (obtained from Data Warrior) | MC and CPANN | Predict the binding score activity for 169 FDs related to 5 proteins classified as antidiabetes targets [210] |
Metal-based nanomaterials | Optimal quasi-SMILES-based descriptors | MC | Predict the response of Daphnia magna when exposed to metal-based nanomaterials [211] |
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Lebre, F.; Chatterjee, N.; Costa, S.; Fernández-de-Gortari, E.; Lopes, C.; Meneses, J.; Ortiz, L.; Ribeiro, A.R.; Vilas-Boas, V.; Alfaro-Moreno, E. Nanosafety: An Evolving Concept to Bring the Safest Possible Nanomaterials to Society and Environment. Nanomaterials 2022, 12, 1810. https://doi.org/10.3390/nano12111810
Lebre F, Chatterjee N, Costa S, Fernández-de-Gortari E, Lopes C, Meneses J, Ortiz L, Ribeiro AR, Vilas-Boas V, Alfaro-Moreno E. Nanosafety: An Evolving Concept to Bring the Safest Possible Nanomaterials to Society and Environment. Nanomaterials. 2022; 12(11):1810. https://doi.org/10.3390/nano12111810
Chicago/Turabian StyleLebre, Filipa, Nivedita Chatterjee, Samantha Costa, Eli Fernández-de-Gortari, Carla Lopes, João Meneses, Luís Ortiz, Ana R. Ribeiro, Vânia Vilas-Boas, and Ernesto Alfaro-Moreno. 2022. "Nanosafety: An Evolving Concept to Bring the Safest Possible Nanomaterials to Society and Environment" Nanomaterials 12, no. 11: 1810. https://doi.org/10.3390/nano12111810