Safety Evaluation of Nanotechnology Products

Abstract

Nanomaterials are now being used in a wide variety of biomedical applications. Medical and health-related issues, however, have raised major concerns, in view of the potential risks of these materials against tissue, cells, and/or organs and these are still poorly understood. These particles are able to interact with the body in countless ways, and they can cause unexpected and hazardous toxicities, especially at cellular levels. Therefore, undertaking in vitro and in vivo experiments is vital to establish their toxicity with natural tissues. In this review, we discuss the underlying mechanisms of nanotoxicity and provide an overview on in vitro characterizations and cytotoxicity assays, as well as in vivo studies that emphasize blood circulation and the in vivo fate of nanomaterials. Our focus is on understanding the role that the physicochemical properties of nanomaterials play in determining their toxicity.

Keywords: apoptosis; biodistribution; cell viability; in vivo fate; nanomaterials; nanomedicine; nanoparticles toxicity; necrosis; oxidative stress; toxicity assessment.

Figure 1

Real-time tracking of autophagosome formation in intestinal cells of C-QD-injected worms. (a–c), (d–f), (g–i), (j–l), and (m–o) are the fluorescent microscopy images of LGG-1::GFP and C QDs in intestinal cells of a C-QD-injected worm at 60 min, 120 min, 180 min, 300 min, and 360 min post-microinjection, respectively; (p–r) present enlarged views of the boxed areas in (m–o); blue arrows indicate the aggregated LGG-1::GFP; white arrows point to the internalized QDs; the red arrow in (c) indicates the injection site in the intestinal cells; (s) quantitative analysis of the aggregated LGG-1::GFP puncta subjected to C QDs or H₂O treatment over 6 h. LGG-1 is the worm ortholog of the vacuolar protein Atg8/MAP-LC3). Reproduced with permission from [31], Elsevier, 2015.

Figure 2

SNH induced less apoptosis and necrosis than CNT. (a,b) Western blot analyses of (a) PARP and (b) caspase-3 cleavages after different nanocarbon incubations. (c,d) Quantitative cleavage ratio measurements of (c) PARP and (d) caspase-3 in nanocarbon-incubated cells according to the integrated optic density (IOD) value, detected based on Western blot imaging (n = 3). (e) Western blot analyses of caspase-8 and caspase-9 cleavages after nanocarbon incubations. (f,g) Cytotoxicity detection of different nanocarbons with and without two caspase inhibitors, (f) Z-DEVD-FMK and (g) Z-VAD-FMK) (n = 4). (h) Transmission electron microscopy images of dead cells caused by different nanocarbons. Red arrows show the typical necrosis characteristics of cells. Scale bar: 5 μm. (i) Intracellular ATP detection after nanocarbon incubations (n = 4). (j) Immunoblot analysis of extracellular and intracellular HMGB1 after cellular incubations with different nanocarbons. (k) Quantitative ratio of extracellular HMGB1 to intracellular HMGB1 according to the IOD detection based on WB imaging (n = 3). (l) Flow cytometry analysis of cells based on the Annexin V/PI assay after nanocarbon incubations. (m) Quantitative comparison of apoptosis and necrosis caused by different nanocarbons detected using an apoptosis/necrosis assay kit (n = 4). In (c,d,f,g,i,k,m), data are presented as means ± s.d. Statistical significances were calculated by Student’s t-test. In (c,d,k), and (m), data were compared with control (Ctrl) and SNH groups separately. Versus Ctrl: * p < 0.05, ** p < 0.01, # p < 0.005, ## p < 0.001. Versus SNH: † p < 0.05, †† p < 0.01, ‡ p < 0.005, ‡‡ p < 0.001. The values in brackets denote the data ratios compared to SNH group. In f, g, data were compared with no-inhibitor-added groups for each type of nanocarbons: # p < 0.005; ## p < 0.001. Reproduced with permission from [34], Nature, 2018. Abbreviations: PARP, poly-ADP-ribose polymerase; Z-DEVD-FMK, specific caspase-3 inhibitor; Z-VAD-FMK, pan-caspase inhibitor; MNT, multi-walled carbon nanotubes, SNT, single-walled carbon nanotubes.

Figure 3

Qualitative images of nanoparticle-mediated ENP DNA damage in TK6 cells using both CometChip and standard comet assays. (A) Media-treated control cells. (B) TK6 cells were exposed to industrially relevant ENPs at concentrations of 5, 10, and 20 μg/ml for 4 h and evaluated using CometChip technology. The expanded view illustrates the morphology of the comet structure induced from 4 h exposure of zinc oxide ENP in TK6, revealing significant DNA damage. (C) Positive control cells treated with H2O2 (100 μM) for 20 min. (D) Traditional comet assay of TK6 cells treated with ZnO (20 μg/ml) for 4 h for comparison to CometChip qualitative assessments. Horizontal scale bar represents 100 μm. Reproduced with permission from [52], American Chemical Society, 2014.

Figure 4

In vitro characterization methods. (a) Scanning electron microscopy (SEM) images of the different sites (a–c) in the oral SCC slide, in a nanometric resolution. The GNRs appear as bright rods. The nanoparticles’ concentration gradually decreases from the tumor to the healthy sites. Scale bar is 1 μm. Reproduced with permission from [66], American Chemical Society, 2016. (b) Transmission electron microscopy (TEM) of RAW264.7 cells treated with media (control) or treated with different concentrations of porous and nonporous SNPs for 4 h. The SNPs were taken up by cells and localized inside vesicles. The dose-dependent increase of the cellular association of SNPs in both types of nanoparticles is visualized. Red arrows indicate particles inside cells. Particles were not observed inside the nucleus. Reproduced with permission from [69], Elsevier, 2017. (c) Atomic force microscopy (AFM) imaging of CUR-AuNCs treated with HeLa cells in different intervals of time, 0, 24, and 48 h. Reproduced with permission from [74], American Chemical Society, 2018. (d) Confocal laser scanning microscopy (CLSM) imaging of DCs after incubation with free OVA and MAN-ALG/ALG=OVA NPs. Reproduced with permission from [77], Elsevier, 2017. (e) 3D dark field microscopy of AuNPs in mouse intestine and kidney tissues, with (1) surface mapping of the 3D image of intestinal tissue containing 50-nm AuNPs, showing the morphology of villi. (2) Smaller segment of the image from (1). (3) 3D maximum intensity projection (MIP) of the same region of intestinal tissue showing the arrangement of blood vessels and the distribution of AuNPs. (4) Position of the 2D section in (5) showing the distribution of AuNPs within a single villus. (6, 7) 3D maximum intensity projection of blood vessels and AuNPs within kidney tissue with brightly stained glomeruli visible. (8) Location of 2D sections of (9) and (10) showing the local distribution of AuNPs within and around a glomerulus. Scale bars indicate 200 µm for (1), (2), (3), (4), (6), (7), (8) and 100 μm for (5), (9), (10). Reproduced with permission from [81], Royal Society of Chemistry, 2017. (f) Light scattering microscopy of size histograms for 6-stranded cubes. Reproduced with permission from [85], Nature, 2010.

Figure 5

A zebrafish model for liposome biodistribution. (a) Schematic of liposome injection and quantification in zebrafish. Fluorescently labeled liposomes (1 mM total lipids containing 1 mol% Rhod-PE) were injected into the duct of Cuvier at 54 hpf. Confocal microscopy was performed in a defined region (boxed) caudal to the yolk extension at 1, 8, 24, and 48 h after injection. (b) Whole-embryo view of liposome distribution in kdrl:GFP transgenic embryos, 1 hpi with three different liposome formulations (AmBisome, EndoTAG-1, and Myocet). (c) High-resolution imaging allows the quantification of liposomes in the circulation (measured in the lumen of the dorsal aorta (white box)) and liposome association with different blood vessel types. CHT-EC: caudal hematopoietic tissue endothelial cells, DLAV: dorsal longitudinal anastomotic vessel. ISV: intersegmental vessel. (d) Tissue=level view of liposome distribution in kdrl:gfp transgenic embryos, 1 h and 8 h after injection, with three different liposome formulations and a single confocal section through the dorsal aorta (DA) at 1 h after injection. (e) Quantification of liposome levels in circulation based on mean rhodamine fluorescence intensity in the lumen of the dorsal aorta at 1, 8, 24, and 48 h after injection (error bars: standard deviation.) n = 6 individually injected embryos per formulation per time point (in two experiments). (f) Quantification of liposome levels associated with venous vs. arterial endothelial cells based on rhodamine fluorescence intensity, associated with caudal vein (CV) vs. DA at 8 h after injection. (g) Quantification of extravascular liposome levels based on rhodamine fluorescence intensity outside of the vasculature between the DLAV and DA at 8 h after injection. (h) Quantification of liposome levels associated with the vessel wall based on rhodamine fluorescence intensity, associated with all endothelial cells relative to rhodamine fluorescence intensity in circulation at 1 h after injection. (f−h) Bar height represents median values, dots represent individual data points, and brackets indicate significantly different values (*: p < 0.05, **: p < 0.01, ***: p < 0.001), based on Kruskal−Wallis and Dunn’s tests with the Bonferroni correction for multiple testing. n = 12 individually injected embryos per group (in 2 experiments). (i) Whole-embryo view of liposome distribution in kdrl:GFP transgenic embryos, 1 h after injection with DOPG and DSPC liposomes. Liposome accumulation for both formulations was observed in the primitive head sinus (PHS), common cardinal vein (CCV), posterior cardinal vein (PCV), and caudal vein (CV). (j) Tissue-level view of liposome distribution in kdrl:GFP transgenic embryos, 1 h after injection with DOPG and DSPC liposomes at 102 hpf. Liposome accumulation was observed in the entire caudal vein (CV), but only on the dorsal side of the PCV (dPCV, arrows). Reproduced with permission from [156], American Chemical Society, 2018.

Figure 6

Renal clearance of different-sized AuNCs and schematic diagram of the glomerular filtration membrane. (a) whole-body X-ray images of mice after being intravenously (i.v.) injected with Au_10-11, Au₁₈, or Au₂₅ at 40 min p.i. Although all three different AuNCs were cleared through the kidneys into the bladder, the smallest, Au_10-11, shows much longer kidney retention than Au18, which in turn shows a longer kidney retention than Au₂₅, even though there is only a seven-atom difference among these three AuNCs. LK, left kidney; RK, right kidney. (b) X-ray intensity bladder-to-kidney ratios of Au_10-11, Au₁₈, and Au₂₅ at 40 min p.i., clearly showing that more Au_10-11 and Au₁₈ were retained in the kidneys than Au₂₅. * p < 0.05, based on one-way ANOVA (n = 3 for Au_10–11 and Au₂₅; n = 4 for Au₁₈). (c) Renal clearance efficiency of Au_10–11, Au₁₅, Au₁₈, and Au₂₅ at 0–2 h and 2–24 h after i.v. injection (n = 3 for Au_10–11, Au₁₅ and Au₂₅; n = 6 for Au₁₈). (d) Renal clearance efficiencies of Au_10–11, Au₁₅, Au₁₈ and Au₂₅, 1.7 nm (Au₂₀₁), 2.5 nm (Au₆₄₀), and 6 nm (Au₈₈₅₆) GS-AuNPs at 24 h p.i. versus number of gold atoms. Below Au₂₅, the renal clearance efficiency decreased exponentially with the decreasing number of gold atoms in the NPs. (e) The glomerulus, an important component of renal filtration, is composed of kidney blood vessels, the glomerular filtration membrane, and Bowman’s space. The glomerular filtration membrane is composed of multiple layers: endothelial glycocalyx, endothelial cell, glomerular basement membrane (GBM), and podocyte. Podocytes are covered by a 200-nm glycocalyx. Generally, the fenestration between endothelial cells is 70–90 nm, the GBM junction is 2–8 nm, and the podocyte slits are in the range of 4–11 nm. With the combination of these layers, the size threshold for kidney filtration is ~6 nm. NPs or proteins with a hydrodynamic diameter (HD) of <6 nm can pass through the glomerular filtration membrane readily, but it is difficult for large particles to cross through. Reproduced with permission from [189] Nature, 2017.

Figure 7

Representative (a) PET images and (b) overlaid coronal PA and ultrasound (US) images that illustrate size-dependent uptake in popliteal lymph nodes (LNs) and sciatic LNs at different time points post-injection. Quantitative analysis of the total PDI NP PET signal with uptake of different-sized NPs in (c) popliteal LNs and (d) sciatic LNs. The LN mapping is visualized after the footpad injection of PDI NPs at (e) 20 min and (f) 120 min post-injection. All white arrows in figures represent popliteal LNs, red arrows represent sciatic LNs, and white arrowheads represent injection sites. Reproduced with permission from [249], American Chemical Society, 2017.

Figure 8

The effect of NP surface charge on their interaction with neurons: confocal microscope images of primary hippocampal neural cells incubated with 1 nM of negatively charged fluorescent QRs after 10 min of incubation at RT. Neurites and dendrites are covered by negatively charged QRs (A, green signal). Yellow represents the combination of NPs (green signal) and a neuronal marker (VGAT, red signal) and highlights the healthy condition of the entire neural network and the colocalization between QRs and synapses (MCC = 0.45 ± 0.06). (B) The same neuronal culture incubated with QRs that were identical in shape and size, but with a positive zeta potential; note the absence of QR fluorescence. These results are independent of QR size. Quantum rods (QRs), Manders’ correlation coefficient (MCC). Reproduced with permission from [276], American Chemical Society, 2017.

Figure 9

Penetration of coumarin-6-labeled NsP and Pep-NPs in 3D glioma spheroids. Z-stack images were obtained, starting at the top of the spheroids at 20-mm intervals, depicting the penetration of NPs (A) and Pep-NPs (C) for a total of 300 mm into the spheroids. Quantitative analysis of the penetration depth of NPs (B) and Pep-NPs (D). Pep-NPs penetrated much deeper, with a distance of 120.00 μm in tumor spheroids, and the fluorescence was distributed more extensively, whereas the fluorescence was mainly located at the edge of the spheroid for unmodified NPs, and the penetration depth was 80.00 μm. Reproduced with permission from [297], Elsevier, 2014.

Figure 10

Schematic illustration of hydrophobicity-adaptive nanogels for programmed anticancer drug delivery. (A) Construction of the nanogels. (B) Characterization of the nanogels in response to the tumor microenvironment. (C) Schematic illustration of the in vivo transport process of the nanogels during anticancer drug delivery. Reproduced with permission from [317], Elsevier, 2018.

Figure 11

(a) Accumulation of 16-AuNP-C10-CN4-5:5 and 16-AuNP-PEG2000 in KB tumors in BALB/c nude mice at 24, 48, and 72 h post-injection. (b) Tumor uptake normalized at 48 and 72 h post-injection to that at 24 h (error bars represent mean (SD (n = 3); asterisk indicates significant difference, * p < 0.05). (c–f) Representative TEM images of sections of KB tumor tissue after injection with AuNPs for 24 h: 16-AuNP-C10-CN4-5:5 located in the interstitium (c) and lysosome of tumor cells (e) and 16-AuNP-PEG2000 located in the interstitium (d) and lysosome of tumor cells (f); red arrows indicate the AuNPs. PH-responsive aggregation was observed in acidic tumor spaces (c), and AuNPs appeared to be internalized effectively in tumor cells as aggregates in a similar manner to particles internalized in KB cells in vitro (e). Furthermore, two types of AuNPs exhibited different aggregation behaviors (d), and lysosomes of tumor cells revealed the presence of only a few small aggregates of 16-AuNP-PEG2000 (f). Each mouse was injected with 100μg of AuNPs. Reproduced with permission from [332], American Chemical Society, 2013.