Beta cyclodextrin stabilized cupric oxide nanoparticles assisted thermal therapy for lung tumor and its effective in vitro anticancer activity

Abstract

The unique physicochemical properties of cupric oxide nanoparticles (CuO NPs) make them suitable for a wide range of therapeutic applications. Here, we synthesized β-cyclodextrin (βCD) capped CuO NPs (CuONPs@βCD) using a simple reduction process. The formation and physicochemical characteristics were identified via different spectroscopic techniques. The CuONPs@βCD displayed antimicrobial activity as good as commercial drugs. Dimethyl thiazolyl tetrazolium bromide (MTT) assay was carried out to assess the anticancer properties of CuONPs@βCD against A549 lung cancer cells. The result demonstrated that the anticancer activity of CuONPs@βCD with IC₅₀ values of 41.06 ± 0.05 and 19.46 µg/mL at 24 and 48-h incubation period, respectively. CuONPs@βCD exhibited anticancer activity on A549 lung cancer cells while having less adverse effects on normal cells. Annexin V-FITC/PI assay, reactive oxygen species (ROS) analysis, disruption of mitochondrial membrane potential (Δψm), and AO/EB apoptosis studies in A549 cells revealed significant apoptotic impact of CuONPs@βCD when compared to the control. Moreover, thermal therapy study of CuONPs@βCD in lung tumor using COMSOL Multiphysics has been reported. Our investigation revealed Case III, where the temperature distribution at the top surface of the tumor is best and may be the most effective way to treat lung cancer. It was found that an incident flux of 8000 Wm^{- 2} for 900 s and an extinction coefficient of 8.266 m^{- 1} for CuONPs@βCD were the best conditions for reaching a temperature of 43.63 °C across the whole tumor area. Thus, these findings open new research opportunities and potential use of CuONPs@βCD for biological applications.

Keywords: Anticancer; Antimicrobial; COMSOL multiphysics; CuONPs@βCD; Lung cancer; Temperature distribution.

Fig. 1

Schematic illustration of the tumor region surrounded by healthy lung tissue. Three cases of spatial distribution of temperature along the depths (Z = 0, 5, and 10 mm) of tumor.

Fig. 2

(a) FT-IR spectrum and (b) XRD pattern of CuONPs@βCD.

Fig. 3

(a) HRTEM micrograph, (b) particle size distribution histogram obtained from TEM images. DLS analysis of CuONPs@βCD (c) particle size distribution (diameter in nm) and (d) zeta potential.

Fig. 4

In vitro antibacterial and antifungal activity of CuONPs@βCD tested against (a) S. aureus; (b) E. coli; (c) C. albicans, and (d) bar graph representing the ZOI of control (100 µL) & CuONPs@βCD (25, 50, 75, and 100 µL).

Fig. 5

The plot of cell viability in (a) Cisplatin, CuO NPs, and CuONPs@βCD (24 h), & (b) CuONPs@βCD and cisplatin (24 and 48 h) against A549 in different concentrations, and (c) CuONPs@βCD and cisplatin (24 and 48 h) against normal cell (HEK293), the values were expressed as mean ± SD for three independent experiments.

Fig. 6

Morphological appearance of A549 hypotriploid alveolar basal epithelial cancer cell in phase contrast microscopy for (a) untreated cells and treated cells with (b) CuO NPs (24 h), (c) cisplatin (24 h), (d) cisplatin (48 h), (e) CuONPs@βCD (24 h), and (f) CuONPs@βCD (48 h).

Fig. 7

Fluorescent image of A549 (a) untreated cell line and treated cells with (b) cisplatin and (c) CuONPs@βCD. Annexin V-FITC/PI double staining of the (d) control (untreated), and after (e) CuONPs@βCD treated with IC₅₀ concentration (24 h) in A549 cell. Scale bar: (a & b) 20 μm and (c) 100 μm.

Fig. 8

Intracellular ROS level in the A549 cells after treatment of (a) control, (b) CuONPs@βCD, (c) proposed anticancer mechanism of CuONPs@βCD, & MMP level in the A549 cells after treatment of (d) control, and (e) CuONPs@βCD. The data are presented as the means ± SD. n ≥ 3 regions with a total of 1500–2000 cells analyzed.

Fig. 9

Variation in temperature (°C) at the bottom (Z) of 0 mm of lung tumorous tissue. By varying (a) extinction coefficient, (b) time, and (c) incident flux.

Fig. 10

Variation in temperature at the center of the tumor depth (Z) of 5 mm. By varying (a) extinction coefficient, (b) time, and (c) incident flux.

Fig. 11

Variation in temperature at the top surface of the lung tumor depth (Z) of 10 mm. By varying (a) extinction coefficient, (b) time, and (c) incident flux.

Fig. 12

Surface temperature plot from COMSOL at the surface of lung tumor tissue in xz and xy planes at irradiation time of (a) 60 s, (b) 120 s, and (c) 180 s by varying the time.

Fig. 13

(a) Schematic illustration of spherical tumor geometrical region surrounded by healthy lung tissue. Three cases of spatial distribution of temperature along the depths (Z = 0, 5, and 10 mm) of tumor. Variation in temperature (°C) at the bottom (Z) of 0 mm of lung tumorous tissue. By varying (b) extinction coefficient, (c) time, and (d) incident flux.

Fig. 14

(a) Schematic illustration of a tumor location (nanoparticle gel) surrounded by healthy tissue (plain gel). (b) Temperature fluctuation was measured for a gel embedded with NPs. The incident flux of irradiation was kept as 25,000 Wm^− 2 for 120 s. Blood perfusion and metabolic parameters were chosen as zero to simulate the temperatures within the gel. (c) Simulated spatial temperature within tumor tissue. Tumor blood perfusion was kept as 9.1 × 10⁻⁴ s⁻¹, while tissue perfusion was 1 × 10⁻³ s⁻¹. Metabolic heat generation within tumor and tissue was kept as 1091 Wm^− 3. Spatial distribution of temperature along the depth Z = 4 mm of tumor. Also, comparison with Soni et al. model at Z = 4 mm using current modeling method.