Purpose: This study presents a novel and sustainable approach to cancer therapy by combining halloysite nanotubes (HNTs), green-synthesized copper oxide (CuO) nanoparticles, and curcumin (CUR). We demonstrate that the green synthesis of CuO nanoparticles, when combined with CUR and incorporated into HNTs, enhances the delivery and anticancer effects of CUR. This innovative complex could address some of the critical limitations of current anticancer therapies such as poor pharmacokinetics and drug resistance by providing a controlled release mechanism and leveraging the benefits of combination therapy.
Methods: We synthesized CuO using green synthesis with lemon peel extract and used this synthesized CuO to formulate a complex with HNT and CUR (CHC). Comprehensive characterization was conducted using UV-visible spectroscopy, scanning electron microscopy-energy-dispersive X-ray spectroscopy (SEM–EDX), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and Fourier-transform infrared spectroscopy (FTIR). We also examined the release kinetics of the formulation. In vitro experiments were performed to evaluate the anticancer effects of the complex on HMVII, HepG2, and MCF-7 cancer cell lines. In addition, an in silico docking assessment and a 10 ns molecular dynamics simulation were conducted to determine the interaction between CUR and HNTs.
Results: The characterization of HNTs loaded with CUR showed a drug loading efficiency of 3%–5% and an encapsulation efficiency of 15%–20%. Drug release kinetics were best described by the Hixson–Crowell model for CHC-50 and CHC-20, with R2 = 0.9897 and R2 = 0.9900 respectively. CHC-10 fit the Higuchi model (R2 = 0.8838), while free CUR fit the Korsmeyer–Peppas model (R2 = 0.9212). The formulations demonstrated significant anticancer effects across all tested cell lines, with CHC-10 showing the lowest IC50 value of 10.43 μg/mL. The CHC formulations exhibited enhanced delivery and maintained significant anticancer activity compared to CUR across HepG2, MCF-7, and HMVII cell lines, with lower IC50 values after UV exposure. Molecular docking analysis revealed a CUR–HNT binding score of −3.803, with the complex remaining stable over a 10 ns simulation.
Conclusion: This study demonstrates the successful integration of green-synthesized CuO nanoparticles with CUR-loaded HNTs as a novel approach to cancer therapy. The enhanced anticancer effects of the CHC-10 formulation, coupled with the complex’s stability, suggest significant potential for improving cancer treatment outcomes. This innovative, sustainable approach addresses key limitations of current therapies, potentially offering more effective and patient-friendly treatments with reduced side effects. Our findings pave the way for further development of targeted, environmentally conscious cancer therapies.
1. Introduction
Cancer represents a complex group of diseases characterized by abnormal cell proliferation and metastasis, posing a significant global health challenge [1–4]. The Global Burden of Cancer Study (GLOBOCAN) reported approximately 19.3 million new cancer cases and 10.0 million cancer-related deaths in 2020, with projections indicating an increase to 20 million new cases annually by 2025 [4, 5]. This rising incidence underscores the urgent need for more effective and less toxic therapeutic strategies.
While chemotherapy remains a cornerstone of cancer treatment, it faces substantial challenges, including drug resistance and severe adverse effects such as alopecia, hematotoxicity such as thrombocytopenia and neutropenia, and cardiotoxicity such as pericarditis and myocarditis associated with drugs such as doxorubicin [6–8]; [9]. These limitations have spurred research into innovative drug delivery systems, with nanoparticle (NP)-based approaches showing promise due to their favorable pharmacokinetic profiles, tumor-targeting capabilities, and potential to mitigate side effects and drug resistance [10–12].
Among the emerging nanocarriers, halloysite nanotubes (HNTs) have garnered significant interest. These naturally occurring aluminosilicate clays with tubular structures offer versatility in drug delivery applications [13–15]. Recent studies have demonstrated the potential of HNTs to enhance drug loading efficiency, optimize release kinetics [16, 17], improve biocompatibility [18], and facilitate targeted delivery [19, 20]. Research by the authors from [21] highlighted the capacity of HNTs for sustained drug release, laying the groundwork for further advancements in cancer therapeutics. Recent studies have shown potential for effective delivery of combination therapy for cancer using halloysites [22–25].
Curcumin (CUR), a natural compound found in the spice turmeric, has been widely studied for its medicinal properties, particularly its anticancer effects. It inhibits cancer cell growth by lowering the modulation of antiapoptotic gene products, activating caspases, and upregulating cancer-suppressive genes such as P53 [9]. Also, the anticancer properties of copper (II) oxide (CuO) have been reported [26] CuO NPs (CuONPs) have shown high therapeutic potential against several cancers: breast cancer, cervical cancer, colon cancer, gastric cancer, lung cancer, and leukemias [27]. While various NP-based drug delivery systems have been explored, the combination of HNTs, green-synthesized CuO, and CUR has not been extensively studied. Combining these two compounds through a single delivery system could enhance cancer therapy. This study fills this gap by exploring the synergistic effects of these materials for improved cancer therapy.
Building upon previous research, we developed a novel combinatorial approach integrating HNTs, green-synthesized copper oxide (CuO) NPs, and CUR for cancer therapy. Lemon peel extract was used as a biological reducing and capping agent in the green synthesis of CuO, producing biocompatible NPs with anticancer potential. This strategy leveraged the synergistic effects of HNTs’ versatility in drug delivery [28, 29], the well-documented anticancer potential of CuO [26, 27, 30], and the multifaceted cancer growth inhibition mechanisms of CUR [31–33]. Using both computational and experimental tools, we investigated the in vitro efficacy, drug release kinetics, and photodynamic potential of the HNT–CuO–CUR nanocomposite. This integrated approach was designed to overcome key limitations of current drug delivery systems, potentially improving efficacy while reducing toxicity in cancer treatment.
Green synthesis of NPs, particularly using plant-based materials, offers a more sustainable and eco-friendlier alternative to conventional methods that often rely on toxic chemicals. In this study, lemon peel extract was employed as a capping agent to synthesize CuO NPs, which were then combined with CUR to create a biocompatible nanocomposite. Computational methods were also used to investigate the molecular mechanisms of curcumin’s interaction with HNTs, providing deeper insight into the sustained release mechanism and synergistic effects of this combination and its role in enhancing anticancer efficacy.
Our rigorous in vitro investigations provided a comprehensive understanding of the anticancer properties of the nanocomposite, addressing the urgent need for innovative solutions to reduce the global cancer burden. These findings not only demonstrate the therapeutic potential of this approach but also highlight its relevance to sustainable nanomedical research, paving the way for future studies aimed at developing more targeted and eco-friendly cancer therapies. This research contributes to ongoing efforts to advance cancer treatment, potentially offering new hope for patients.
2. Materials and Methods
Green synthesis using lemon peel extract as a capping agent was chosen for its eco-friendly nature and ability to produce biocompatible NPs, aligning with the study’s goals of developing sustainable cancer therapies [34–36].
CUR (99% purity), HNTs Al2Si2O5(OH)4·2H2O (99.5% purity) with a diameter of 30–70 nm and length of 1–3 μm, trypsin thiazolyl blue tetrazolium bromide, sodium hydroxide, and copper chloride were purchased from Sigma-Aldrich (United States of America). Trypan blue solution (0.4% in PBS), fetal bovine serum (FBS), Dulbecco’s modified Eagle’s Medium (DMEM), penicillin/streptomycin, 3-(4,5-dimethyl-thiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT), and ethanol were obtained from the Noguchi Memorial Institute for Medical Research and the Department of Material Science and Engineering, University of Ghana. Lemon fruits were purchased from a local market in Accra, Ghana, and sent to the Department of Plant and Environmental Biology, University of Ghana, for identification and authentication.
2.1. Preparation of Lemon Peel Extracts
Fresh lemon fruits were washed with distilled water, peeled, cut into small pieces, and dried in the oven for 5 h at a temperature of 105°C. The dried lemon fruit was then ground into a fine powder using a hand mill. Afterward, 20 g of the powder was dissolved in 300 mL of distilled water in a beaker. The prepared solution was then stirred continuously for 3 h using a magnetic stirrer. The mixture was placed in a water bath at 60°C for 1 h. The prepared solution was then filtered using a Whatman filter paper to obtain the lemon peel extract which was stored at room temperature (to avoid any reaction with other compounds). This extract was used as a capping agent for the synthesis of CuO.
2.2. Preparation and Characterization of CuO and CUR–CuO–HNT
A volume of 90 mL of the lemon peel extract was measured and heated at 200°C for 10 min, and stirring was performed at 200 rpm. Afterward, 4.5 g of copper (II) chloride (CuCl2) was added to the lemon peel extracts and the pH of the solution was recorded. A 2 M solution of sodium hydroxide (NaOH) was added dropwise to the solutions while heating and stirring at 200°C and 200 rpm, respectively. The solution was expected to change color from green to yellowish green, which indicated the formation of CuO NPs. At that point, the pH of the solution was recorded (11–13) and the addition of NaOH was discontinued. A sample of the solution was taken through ultraviolet (UV)-visible spectroscopy. The solution was then heated and stirred for an additional 2 h to obtain a reddish-brown color, which is characteristic of CuO NPs. The solution was centrifuged at 6000 rpm and the supernatant was discarded. The powder (CuO NPs) was washed with distilled water multiple times to remove residual lemon extract and NaOH. The powder was then dried in a vacuum desiccator for 2 h to retrieve the dried CuO NPs. The final CuO NPs obtained were calcined at 200°C for 1 h to obtain a more crystalline form of the NPs. The particles were then characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), UV-visible spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA). The preparation of CuO–HNT was performed as initially described except that 6 g of the HNTs was weighed and added to the 90 mL extract before the addition of 4.5 g of CuCl2.
2.3. Preparation of CUR-Loaded CuO–HNTs (CUR–CuO–HNTs)
The loading method adopted for this study is similar to the procedure reported by Nyankson et al. [29]. A 5 mg/mL concentration of CUR in ethanol solution was prepared by dissolving 25 mg of CUR in 5 mL of absolute ethanol. A sample of the 5 mg/mL stock solution was diluted to 5 μg/mL through serial dilution and the resulting solution was analyzed with a UV-vis spectrophotometer. 200 mg of the CuO–HNTs’ composite was then dispersed in 1 mL of 5 mg/mL CUR solution and the mixture was sonicated for about 30 min. The sonicated CuO–HNTs composite in the CUR solution was placed in a vacuum chamber and subsequently subjected to repetitive vacuum suction and release cycles until the fizzing stopped. The vacuum suction and release cycles were applied to ensure that the CUR solution filled the lumen of the halloysite nanotubes. The resulting composite mixture was vacuum dried for 3 h to obtain a dry powder of CUR-loaded CuO–HNTs. From the vacuum suction loading method adopted, it is likely that some of the CUR may be deposited on the outer surface of the HNTs. The CUR that was not loaded into the lumen of the HNTs and hence deposited on the surface of the HNTs was washed off by centrifuging the CUR-loaded CuO–HNTs in the ethanol–water mixture at 6000 rpm for 20 min. The washing was repeated until the absorbance at the CUR wavelength (430 nm) of the supernatant was negligible. The CuO–HNTs loaded with the CUR were labeled as CUR–CuO–HNTs.
Three different formulations were prepared with varying concentrations of CuO, halloysite, and CUR with varying concentrations of CuO, HNT, and CUR. These formulations are shown in Table 1.
Table 1.
Composition of formulations prepared with varying concentrations of CuO, HNT, and curcumin.
Formulation
Percentage of CuO (%)
Percentage of HNT (%)
Percentage of curcumin (%)
50% CuO + HNT + curcumin (CHC-50)
50
30
∼20
20% CuO + HNT + curcumin (CHC-20)
20
60
∼20
10% CuO + HNT + curcumin (CHC-10)
10
70
∼20
2.4. CUR Loading and Encapsulation Efficiency
To determine the CUR content in the nanocomposite, ca. 10 mg of CUR–CuO–HNT was crushed in a mortar, and 10 mL phosphate-buffered saline (PBS) was added. After this, the CUR–CuO–HNT–PBS was transferred into an Eppendorf tube. The CUR–CuO–HNTs–PBS mixture was centrifuged at 10, 000 rpm for a specific amount of time (10 min). An amount of the supernatant was sampled and diluted with DI water to obtain a 20 μg/mL concentration. The amount of CUR in the CUR–CuO–HNTs composite was estimated by using UV-vis to measure the absorbance at 430 nm. The loading efficiency and encapsulation efficiency were estimated using the following equations:
()
()
2.5. In Vitro Release and Kinetics of CUR
The in vitro release assessment was conducted in a controlled environment at room temperature (37 ± 1°C). A predetermined amount (10–20 mg) of the CUR–CuO–HNT complex was placed in a beaker containing 50mL of PBS (pH 7.4) and ethanol (2:1 ratio) as the dissolution medium, with 0.5% of Tween 80 added to enhance CUR solubility. The beaker was sealed to prevent evaporation during the experiment. This approach is similar to the one reported by the authors in [37].
1mL aliquots were pipetted from the release medium at regular intervals, with the first aliquot taken immediately after adding the complex to the dissolution medium (t = 0 h). Subsequent aliquots were collected every 2 h for a total duration of 24 h to capture the release profile of CUR over time. After each sampling, an equal volume of fresh medium was added to maintain a constant volume.
The concentration of CUR in each collected aliquot was determined using UV-visible spectroscopy (wavelength 430 nm). A standard curve was prepared using known concentrations of CUR in the dissolution medium to correlate absorbance values with CUR concentration. The release profile of CUR over time was plotted based on the concentration data obtained. The cumulative release percentage at each time point was calculated by using the following equation:
()
After obtaining the in vitro release data, it was fitted to various mathematical models to analyze the release kinetics (Dash et al., 2010). The following models were applied: zero-order, first-order, Higuchi, Hixson–Crowell, and Korsmeyer–Peppas. Each model represented a different release mechanism, and the fitting process was conducted to determine the best-fit model that described the release.
2.6. Cytotoxicity Assay
In brief, MTT assay was used to access the anticancer potential of CUR–CuO–HNT against human liver cancer cell lines (HepG2), Michigan Cancer Foundation-7 (MCF-7) breast cancer cell lines, and Human Melanoma Vagina-II (HMVII) cell lines [38–40]. These cell lines were cultured in 96-well culture plates at 105 cells/per well containing 100 μL of RPMI 1640. The cells were maintained at 37°C in an incubator with 5% CO2. They were then plated in a 96-well plate at a density of 5000 cells/cm2 and left to adhere overnight. A range of CUR solution and CUR–CuO–HNT concentrations (2.12, 4.24, 8.51, 16.98, 33.97, and 67.93 μM) was prepared. The various concentrations of CUR–CuO–HNT and CUR were added to the 96-well plate and incubated for 24 h, with polyvinylpyrrolidone and sodium dodecyl sulfate (SDS) serving as negative controls. Following incubation, 20 μL of the MTT solution (5 g/L) was added to each well and left for 4 h to allow formazan crystal formation through mitochondrial dehydrogenase activity. The medium was subsequently removed, and 150 μL of dimethyl sulfoxide (DMSO) was added to each well. The plates were then agitated for 15 min at room temperature to dissolve the formazan crystals. The optical density at 570 nm was measured using a microplate reader. All procedures were performed under sterile conditions. The cell viability percentage was calculated according to the following equation:
()
()
2.7. In Silico Modelling of CUR Adsorption on HNTs and Molecular Dynamic (MD) Simulations
The crystal structure of halloysite was retrieved from previous research (Rozza and Ferrante, 2020). Optimizations, such as the removal of solvent molecules or redundant atoms, were performed to prepare the halloysite structure for further simulations. The chemical structure of CUR was downloaded from PubChem and energy-minimized to a stable conformation using the OPLS force field. Furthermore, molecular docking was conducted to predict the binding affinity and interaction between CUR and the halloysite surface. Glide was used to dock the CUR molecule onto the halloysite nanotube. Multiple docking poses were generated, and binding scores were calculated to evaluate the binding affinity of CUR to halloysite. The most favorable docking pose, representing the most stable and energetically favorable CUR–HNT complex, was selected for further analysis.
The selected CUR–HNT complex was subjected to MD simulations to study the stability and behavior of the complex over time. The Desmond package in Schrodinger, along with the OPLS forcefield, was used to perform the MD simulations. The CUR–HNT complex was solvated in water within a periodic boundary box to mimic physiological conditions. Appropriate counterions were added to neutralize the system’s charge. The MD simulations were carried out at constant temperature and pressure (NPT ensemble) to allow the system to equilibrate over 10 ns. The trajectories generated from MD simulations were analyzed using tools in the Schrodinger Maestro package. Structural stability, root mean square deviation (RMSD), root mean square fluctuation (RMSF), hydrogen bonding patterns, and interaction energies were analyzed to investigate the dynamic behavior of the CUR–HNT complex. Thermodynamic parameters, such as temperature, pressure, and potential energy, were monitored during the simulations to assess the stability of the system.
2.8. Data Analysis
Results were expressed as mean and standard deviation from the mean (STDEV) or standard error from the mean (SEM). Continuous data were analyzed using unpaired t-tests for two independent sample means and one-way analysis of variance (ANOVA) for more than two independent sample means. All assays were performed in triplicate, and the statistical significance of differences between groups was analyzed using one-way ANOVA followed by Tukey’s post hoc test, with p < 0.05 considered significant. The in vitro drug release data was fitted to mathematical models including zero-order, first-order, Higuchi, Hixson–Crowell , and Korsmeyer–Peppas to determine the release kinetics and mechanisms based on the best-fit model (highest R2). Molecular docking and MD simulations with Maestro evaluated the binding and interactions between CUR and halloysite. Trajectory analysis determined structural stability, interactions, and thermodynamic parameters. Data visualization and plotting utilized GraphPad Prism and Python programming.
3. Results
3.1. Characteristics of Formulations
The synthesis of CuO resulted in a yield of approximately 40%. CHC-10 gave both the highest encapsulation efficiency at 19.5% and the highest loading efficiency at 3.9%. The properties of the formulations are detailed in Table 2.
Table 2.
Drug encapsulation efficiency and loading efficiency values of the formulations.
Formulation
Loading efficiency (LE) (%)
Encapsulating efficiency (EE) (%)
50% CuO + HNT + curcumin (CHC-50)
2.5
12.5
20% CuO + HNT + curcumin (CHC-20)
3.1
15.5
10% CuO + HNT + curcumin (CHC-10)
3.9
19.5
The UV-Vis spectrum, as shown in Figure 1(a), revealed a maximum absorption peak around 280 nm. This peak is characteristic of CuO NPs, confirming their presence in the sample. The absorption peak at this wavelength is attributed to the charge transfer transitions within the CuO, an indication of successful synthesis.
Spectroscopic characterization of CuO, HNT, and CuO–HNT–CUR nanoparticles. (a) UV-vis absorption spectrum of green-synthesized CuO nanoparticles, showing the wavelength range from 200 to 800 nm. A strong absorption peak is observed around 300 nm, with a gradual decrease in absorbance at higher wavelengths. (b) XRD patterns of CuO (orange), HNT (purple), and CuO–HNT–CUR complex nanoparticles (green). The diffraction peaks are indexed, with major peaks observed at approximately 2θ values of 32°, 35°, and 38°. The CuO–HNT–CUR complex shows a combination of peaks from both CuO and HNT, indicating the successful formation of the composite. (c) FTIR spectrum of the CuO–HNT–CUR complex, displaying transmittance (%) versus wavenumber (cm−1) from 4000 to 0 cm−1. Key absorption bands are labeled, including peaks at 3751, 3629, 1627, 1030, 912, 540, and 430 cm−1, corresponding to various functional groups and molecular interactions within the complex. These complementary spectroscopic techniques provide insights into the optical properties, crystalline structure, and molecular composition of the synthesized nanoparticles and their complexes.
Spectroscopic characterization of CuO, HNT, and CuO–HNT–CUR nanoparticles. (a) UV-vis absorption spectrum of green-synthesized CuO nanoparticles, showing the wavelength range from 200 to 800 nm. A strong absorption peak is observed around 300 nm, with a gradual decrease in absorbance at higher wavelengths. (b) XRD patterns of CuO (orange), HNT (purple), and CuO–HNT–CUR complex nanoparticles (green). The diffraction peaks are indexed, with major peaks observed at approximately 2θ values of 32°, 35°, and 38°. The CuO–HNT–CUR complex shows a combination of peaks from both CuO and HNT, indicating the successful formation of the composite. (c) FTIR spectrum of the CuO–HNT–CUR complex, displaying transmittance (%) versus wavenumber (cm−1) from 4000 to 0 cm−1. Key absorption bands are labeled, including peaks at 3751, 3629, 1627, 1030, 912, 540, and 430 cm−1, corresponding to various functional groups and molecular interactions within the complex. These complementary spectroscopic techniques provide insights into the optical properties, crystalline structure, and molecular composition of the synthesized nanoparticles and their complexes.
Spectroscopic characterization of CuO, HNT, and CuO–HNT–CUR nanoparticles. (a) UV-vis absorption spectrum of green-synthesized CuO nanoparticles, showing the wavelength range from 200 to 800 nm. A strong absorption peak is observed around 300 nm, with a gradual decrease in absorbance at higher wavelengths. (b) XRD patterns of CuO (orange), HNT (purple), and CuO–HNT–CUR complex nanoparticles (green). The diffraction peaks are indexed, with major peaks observed at approximately 2θ values of 32°, 35°, and 38°. The CuO–HNT–CUR complex shows a combination of peaks from both CuO and HNT, indicating the successful formation of the composite. (c) FTIR spectrum of the CuO–HNT–CUR complex, displaying transmittance (%) versus wavenumber (cm−1) from 4000 to 0 cm−1. Key absorption bands are labeled, including peaks at 3751, 3629, 1627, 1030, 912, 540, and 430 cm−1, corresponding to various functional groups and molecular interactions within the complex. These complementary spectroscopic techniques provide insights into the optical properties, crystalline structure, and molecular composition of the synthesized nanoparticles and their complexes.
The SEM images distinctly illustrated the tubular structure of HNTs and the spherical form of CuO. In the case of CuO–HNT, the SEM images revealed a high degree of agglomeration. Interestingly, the complexing of CUR to CuO–HNT did not result in any noticeable morphological changes; the sample continued to exhibit substantial agglomeration. The SEM–EDX analysis of the CUR-loaded CuO–HNT identified the presence of copper (Cu), aluminum (Al), silicon (Si), oxygen (O), and carbon (C), thereby confirming the existence of CuO, HNTs, and CUR within the sample [41–44] (Figure 2).
Microscopic characterization of CuO nanostructures (a, b) SEM micrographs showing the morphology of CuO nanostructures. (a) High-magnification image of CuO nanoparticles with a granular surface structure. (b) CuO nanoparticles in combination with halloysite nanotubes (HNTs), demonstrating the composite nature of the material. Scale bars: 5 μm. (c, d) TEM–EDX images of the CuO structures at lower magnification. Designated areas (Spectrum 1–5, Table S1) indicate regions selected for elemental analysis. Scale bars: 100 μm. Images (a, b) were obtained using a scanning electron microscope, while (c, d) were captured using transmission electron microscopy with energy-dispersive X-ray spectroscopy capabilities.
In the EDX spectrum, five spectra consistently indicated the presence of carbon (C), oxygen (O), aluminum (Al), silicon (Si), chloride (Cl), and copper (Cu). The dominant elements were oxygen (45.38%) and carbon (21.7%), Figure 3. The presence of copper, averaging 17.43%, can be linked to the CuO component. The aluminum and silicon signatures, with averages around 7.31% and 7.36%, respectively, can be attributed to the HNT fraction. Trace amounts of chloride were observed, averaging at 0.82%, which likely stems from the CuCl2 used during the synthesis of CuO. Carbon displayed the most significant variability (standard deviation of 8.42%), whereas chloride exhibited the least (0.17%).
Thermogravimetric Analysis (TGA) of CuO, CuO + HNT, and CuO + HNT + CUR nanocomposites. Derivative thermogravimetric (DTG) curves showing the rate of weight loss as a function of temperature for different nanocomposite formulations: (a) CuO nanoparticles, exhibiting two main weight loss events around 100°C and 200°C. (b) CuO + HNT composite, showing a complex thermal decomposition pattern with multiple weight loss events, including a significant peak around 450°C. (c) CuO + HNT + CUR complex, displaying a prominent weight loss event centered at approximately 500°C. (d) Overlay of all formulations, allowing for direct comparison of thermal behaviors. The CuO + HNT and CuO + HNT + CUR samples show similar patterns but with distinct differences in peak intensities and positions compared to pure CuO. All graphs display the temperature range from 0°C to 800°C on the x-axis and the derivative of weight loss percentage on the y-axis. This analysis provides insights into the thermal stability and decomposition characteristics of the nanocomposites, revealing how the addition of HNT and curcumin (CUR) affects the thermal properties of CuO nanoparticles.
Thermogravimetric Analysis (TGA) of CuO, CuO + HNT, and CuO + HNT + CUR nanocomposites. Derivative thermogravimetric (DTG) curves showing the rate of weight loss as a function of temperature for different nanocomposite formulations: (a) CuO nanoparticles, exhibiting two main weight loss events around 100°C and 200°C. (b) CuO + HNT composite, showing a complex thermal decomposition pattern with multiple weight loss events, including a significant peak around 450°C. (c) CuO + HNT + CUR complex, displaying a prominent weight loss event centered at approximately 500°C. (d) Overlay of all formulations, allowing for direct comparison of thermal behaviors. The CuO + HNT and CuO + HNT + CUR samples show similar patterns but with distinct differences in peak intensities and positions compared to pure CuO. All graphs display the temperature range from 0°C to 800°C on the x-axis and the derivative of weight loss percentage on the y-axis. This analysis provides insights into the thermal stability and decomposition characteristics of the nanocomposites, revealing how the addition of HNT and curcumin (CUR) affects the thermal properties of CuO nanoparticles.
Thermogravimetric Analysis (TGA) of CuO, CuO + HNT, and CuO + HNT + CUR nanocomposites. Derivative thermogravimetric (DTG) curves showing the rate of weight loss as a function of temperature for different nanocomposite formulations: (a) CuO nanoparticles, exhibiting two main weight loss events around 100°C and 200°C. (b) CuO + HNT composite, showing a complex thermal decomposition pattern with multiple weight loss events, including a significant peak around 450°C. (c) CuO + HNT + CUR complex, displaying a prominent weight loss event centered at approximately 500°C. (d) Overlay of all formulations, allowing for direct comparison of thermal behaviors. The CuO + HNT and CuO + HNT + CUR samples show similar patterns but with distinct differences in peak intensities and positions compared to pure CuO. All graphs display the temperature range from 0°C to 800°C on the x-axis and the derivative of weight loss percentage on the y-axis. This analysis provides insights into the thermal stability and decomposition characteristics of the nanocomposites, revealing how the addition of HNT and curcumin (CUR) affects the thermal properties of CuO nanoparticles.
Thermogravimetric Analysis (TGA) of CuO, CuO + HNT, and CuO + HNT + CUR nanocomposites. Derivative thermogravimetric (DTG) curves showing the rate of weight loss as a function of temperature for different nanocomposite formulations: (a) CuO nanoparticles, exhibiting two main weight loss events around 100°C and 200°C. (b) CuO + HNT composite, showing a complex thermal decomposition pattern with multiple weight loss events, including a significant peak around 450°C. (c) CuO + HNT + CUR complex, displaying a prominent weight loss event centered at approximately 500°C. (d) Overlay of all formulations, allowing for direct comparison of thermal behaviors. The CuO + HNT and CuO + HNT + CUR samples show similar patterns but with distinct differences in peak intensities and positions compared to pure CuO. All graphs display the temperature range from 0°C to 800°C on the x-axis and the derivative of weight loss percentage on the y-axis. This analysis provides insights into the thermal stability and decomposition characteristics of the nanocomposites, revealing how the addition of HNT and curcumin (CUR) affects the thermal properties of CuO nanoparticles.
The XRD pattern for CuO showed that it had both cubic and monoclinic phases [41, 43, 44]. Identification of these phases was based on the 2 theta positions of approximately 33, 35.5, 39, 49, and 62, which corresponded to specific crystallographic planes. Notably, the (110), (002), (111), (020), and (202) planes displayed distinct X-ray reflections, thereby confirming the cubic crystallinity of the NPs. The average crystallite size, calculated using the Debye–Scherrer equation, was determined to be 252.69 nm. This XRD analysis provided evidence for the crystalline nature of the CuO NPs (Figure 1(b)).
The following are specific wavenumbers and their associated functional groups, derived from the resulting FTIR spectra: At 3751 and 3629 cm−1, peaks are observed in the FTIR spectrum of CuO, which are associated with the O–H stretching vibration. The stretching vibrations of carbonyl (C=O) and alkene (C=C) groups in CUR are apparent at 1627 cm−1 (Figure 1(c)). The peak observed at 1030 cm−1 in the HNTs’ spectrum is linked to the Si–O network vibration modes. An absorption peak at 912 cm−1 is indicative of the hydroxy bending vibration. Additional absorption bands seen around 580, 480, and 430 cm−1 can be associated with the stretching and bending vibrations of metal–oxygen (Cu–O) bonds in CuO, further confirming the presence of CuO in the synthesized nanocomposite.
Cytotoxicity profiles of curcumin (CUR) and its formulations (CHC-50, CHC-20, and CHC-10) on HepG2, MCF-7, and HMVII cell lines. (a–c) HepG2 cells: dose-response curves without (a) and with (b) UV exposure, and corresponding IC50 values (c). (d–f) MCF-7 cells: dose-response curves without (d) and with (e) UV exposure, and corresponding IC50 values (f). (g–i) HMVII cells: dose-response curves without (g) and with (h) UV exposure, and corresponding IC50 values (i). Dose-response curves show the mean cell viability (%) plotted against the log concentration of the treatments. Bar graphs display IC50 values (μg/mL) for each formulation before (UV−) and after (UV+) UV exposure. Error bars represent standard deviation. Statistical significance is indicated by asterisks: ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001. Across all cell lines, CUR generally showed the highest potency. CHC-10 demonstrated significant UV-responsive behavior in HepG2 and HMVII cells, with marked decreases in IC50 post-UV exposure. The effects varied among cell lines, highlighting the importance of formulation optimization for targeted therapy.
Cytotoxicity profiles of curcumin (CUR) and its formulations (CHC-50, CHC-20, and CHC-10) on HepG2, MCF-7, and HMVII cell lines. (a–c) HepG2 cells: dose-response curves without (a) and with (b) UV exposure, and corresponding IC50 values (c). (d–f) MCF-7 cells: dose-response curves without (d) and with (e) UV exposure, and corresponding IC50 values (f). (g–i) HMVII cells: dose-response curves without (g) and with (h) UV exposure, and corresponding IC50 values (i). Dose-response curves show the mean cell viability (%) plotted against the log concentration of the treatments. Bar graphs display IC50 values (μg/mL) for each formulation before (UV−) and after (UV+) UV exposure. Error bars represent standard deviation. Statistical significance is indicated by asterisks: ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001. Across all cell lines, CUR generally showed the highest potency. CHC-10 demonstrated significant UV-responsive behavior in HepG2 and HMVII cells, with marked decreases in IC50 post-UV exposure. The effects varied among cell lines, highlighting the importance of formulation optimization for targeted therapy.
Cytotoxicity profiles of curcumin (CUR) and its formulations (CHC-50, CHC-20, and CHC-10) on HepG2, MCF-7, and HMVII cell lines. (a–c) HepG2 cells: dose-response curves without (a) and with (b) UV exposure, and corresponding IC50 values (c). (d–f) MCF-7 cells: dose-response curves without (d) and with (e) UV exposure, and corresponding IC50 values (f). (g–i) HMVII cells: dose-response curves without (g) and with (h) UV exposure, and corresponding IC50 values (i). Dose-response curves show the mean cell viability (%) plotted against the log concentration of the treatments. Bar graphs display IC50 values (μg/mL) for each formulation before (UV−) and after (UV+) UV exposure. Error bars represent standard deviation. Statistical significance is indicated by asterisks: ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001. Across all cell lines, CUR generally showed the highest potency. CHC-10 demonstrated significant UV-responsive behavior in HepG2 and HMVII cells, with marked decreases in IC50 post-UV exposure. The effects varied among cell lines, highlighting the importance of formulation optimization for targeted therapy.
Cytotoxicity profiles of curcumin (CUR) and its formulations (CHC-50, CHC-20, and CHC-10) on HepG2, MCF-7, and HMVII cell lines. (a–c) HepG2 cells: dose-response curves without (a) and with (b) UV exposure, and corresponding IC50 values (c). (d–f) MCF-7 cells: dose-response curves without (d) and with (e) UV exposure, and corresponding IC50 values (f). (g–i) HMVII cells: dose-response curves without (g) and with (h) UV exposure, and corresponding IC50 values (i). Dose-response curves show the mean cell viability (%) plotted against the log concentration of the treatments. Bar graphs display IC50 values (μg/mL) for each formulation before (UV−) and after (UV+) UV exposure. Error bars represent standard deviation. Statistical significance is indicated by asterisks: ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001. Across all cell lines, CUR generally showed the highest potency. CHC-10 demonstrated significant UV-responsive behavior in HepG2 and HMVII cells, with marked decreases in IC50 post-UV exposure. The effects varied among cell lines, highlighting the importance of formulation optimization for targeted therapy.
Cytotoxicity profiles of curcumin (CUR) and its formulations (CHC-50, CHC-20, and CHC-10) on HepG2, MCF-7, and HMVII cell lines. (a–c) HepG2 cells: dose-response curves without (a) and with (b) UV exposure, and corresponding IC50 values (c). (d–f) MCF-7 cells: dose-response curves without (d) and with (e) UV exposure, and corresponding IC50 values (f). (g–i) HMVII cells: dose-response curves without (g) and with (h) UV exposure, and corresponding IC50 values (i). Dose-response curves show the mean cell viability (%) plotted against the log concentration of the treatments. Bar graphs display IC50 values (μg/mL) for each formulation before (UV−) and after (UV+) UV exposure. Error bars represent standard deviation. Statistical significance is indicated by asterisks: ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001. Across all cell lines, CUR generally showed the highest potency. CHC-10 demonstrated significant UV-responsive behavior in HepG2 and HMVII cells, with marked decreases in IC50 post-UV exposure. The effects varied among cell lines, highlighting the importance of formulation optimization for targeted therapy.
Cytotoxicity profiles of curcumin (CUR) and its formulations (CHC-50, CHC-20, and CHC-10) on HepG2, MCF-7, and HMVII cell lines. (a–c) HepG2 cells: dose-response curves without (a) and with (b) UV exposure, and corresponding IC50 values (c). (d–f) MCF-7 cells: dose-response curves without (d) and with (e) UV exposure, and corresponding IC50 values (f). (g–i) HMVII cells: dose-response curves without (g) and with (h) UV exposure, and corresponding IC50 values (i). Dose-response curves show the mean cell viability (%) plotted against the log concentration of the treatments. Bar graphs display IC50 values (μg/mL) for each formulation before (UV−) and after (UV+) UV exposure. Error bars represent standard deviation. Statistical significance is indicated by asterisks: ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001. Across all cell lines, CUR generally showed the highest potency. CHC-10 demonstrated significant UV-responsive behavior in HepG2 and HMVII cells, with marked decreases in IC50 post-UV exposure. The effects varied among cell lines, highlighting the importance of formulation optimization for targeted therapy.
Cytotoxicity profiles of curcumin (CUR) and its formulations (CHC-50, CHC-20, and CHC-10) on HepG2, MCF-7, and HMVII cell lines. (a–c) HepG2 cells: dose-response curves without (a) and with (b) UV exposure, and corresponding IC50 values (c). (d–f) MCF-7 cells: dose-response curves without (d) and with (e) UV exposure, and corresponding IC50 values (f). (g–i) HMVII cells: dose-response curves without (g) and with (h) UV exposure, and corresponding IC50 values (i). Dose-response curves show the mean cell viability (%) plotted against the log concentration of the treatments. Bar graphs display IC50 values (μg/mL) for each formulation before (UV−) and after (UV+) UV exposure. Error bars represent standard deviation. Statistical significance is indicated by asterisks: ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001. Across all cell lines, CUR generally showed the highest potency. CHC-10 demonstrated significant UV-responsive behavior in HepG2 and HMVII cells, with marked decreases in IC50 post-UV exposure. The effects varied among cell lines, highlighting the importance of formulation optimization for targeted therapy.
Cytotoxicity profiles of curcumin (CUR) and its formulations (CHC-50, CHC-20, and CHC-10) on HepG2, MCF-7, and HMVII cell lines. (a–c) HepG2 cells: dose-response curves without (a) and with (b) UV exposure, and corresponding IC50 values (c). (d–f) MCF-7 cells: dose-response curves without (d) and with (e) UV exposure, and corresponding IC50 values (f). (g–i) HMVII cells: dose-response curves without (g) and with (h) UV exposure, and corresponding IC50 values (i). Dose-response curves show the mean cell viability (%) plotted against the log concentration of the treatments. Bar graphs display IC50 values (μg/mL) for each formulation before (UV−) and after (UV+) UV exposure. Error bars represent standard deviation. Statistical significance is indicated by asterisks: ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001. Across all cell lines, CUR generally showed the highest potency. CHC-10 demonstrated significant UV-responsive behavior in HepG2 and HMVII cells, with marked decreases in IC50 post-UV exposure. The effects varied among cell lines, highlighting the importance of formulation optimization for targeted therapy.
Cytotoxicity profiles of curcumin (CUR) and its formulations (CHC-50, CHC-20, and CHC-10) on HepG2, MCF-7, and HMVII cell lines. (a–c) HepG2 cells: dose-response curves without (a) and with (b) UV exposure, and corresponding IC50 values (c). (d–f) MCF-7 cells: dose-response curves without (d) and with (e) UV exposure, and corresponding IC50 values (f). (g–i) HMVII cells: dose-response curves without (g) and with (h) UV exposure, and corresponding IC50 values (i). Dose-response curves show the mean cell viability (%) plotted against the log concentration of the treatments. Bar graphs display IC50 values (μg/mL) for each formulation before (UV−) and after (UV+) UV exposure. Error bars represent standard deviation. Statistical significance is indicated by asterisks: ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001. Across all cell lines, CUR generally showed the highest potency. CHC-10 demonstrated significant UV-responsive behavior in HepG2 and HMVII cells, with marked decreases in IC50 post-UV exposure. The effects varied among cell lines, highlighting the importance of formulation optimization for targeted therapy.
The TGA plots illustrating derivative weight loss versus temperature (Figures 3(a), 3(b), 3(c), and 3(d)) reveal the specific temperatures at which major weight changes took place. For CuO, the weight stabilized after heating to 220°C, indicating the removal of volatile components or adsorbed water, and demonstrating thermal stability beyond this point. With CuO–HNT, the weight leveled off after reaching 580°C, suggesting significant weight loss due to the decomposition of organic components or structural changes in the halloysite nanotubes, thus showing enhanced thermal stability compared to CuO alone. For CuO–HNT–CUR, the weight became constant after heating to 570°C, indicating that the addition of CUR does not significantly alter the thermal stability compared to CuO–HNT, with the slight difference in stabilization temperature possibly due to the decomposition of CUR or its interaction with the composite material. Overall, the TGA analysis highlights the thermal stability of the materials and the impact of adding HNTs and CUR to CuO, with increased stabilization temperatures for the composites suggesting improved thermal properties beneficial for applications requiring high thermal stability.
3.2. Drug Release Profile
In the drug release studies, the HNT–based complexes (CHC) exhibited controlled and sustained release of CUR over a 24 h period, as shown in Figure 5. To determine the release mechanism, the data were fitted to five kinetic models: zero-order, first-order, Higuchi, Hixson–Crowell, and Korsmeyer–Peppas (Figures 5(a), 5(b), 5(c), 5(d), and 5(e)), with model fit assessed using the coefficient of determination (R2).
Mathematical modeling of drug release kinetics for CHC-50, CHC-20, CHC-10, and CUR formulations. Drug release data were fitted to five different models to determine the best fit for each formulation. Among the models tested, the Hixson–Crowell model (d) provided the best overall fit for the CHC formulations, with R2 values of 0.9897, 0.9900, and 0.8486 for CHC-50, CHC-20, and CHC-10, respectively, suggesting that changes in surface area during dissolution play a significant role in their release mechanisms. In contrast, the Korsmeyer–Peppas model (e) also showed a strong fit for CHC-50 (R2 = 0.9526) and CHC-20 (R2 = 0.9697), indicating a diffusion-controlled release. Free curcumin (CUR) exhibited poor fits across all models due to its rapid release profile. These results suggest that the Hixson–Crowell model best describes the release behavior of the CHC formulations. (a) Zero order. (b) First order. (c) Higuchi model. (d) Hixson–Crowell. (e) Korsmeyer–Peppas.
Mathematical modeling of drug release kinetics for CHC-50, CHC-20, CHC-10, and CUR formulations. Drug release data were fitted to five different models to determine the best fit for each formulation. Among the models tested, the Hixson–Crowell model (d) provided the best overall fit for the CHC formulations, with R2 values of 0.9897, 0.9900, and 0.8486 for CHC-50, CHC-20, and CHC-10, respectively, suggesting that changes in surface area during dissolution play a significant role in their release mechanisms. In contrast, the Korsmeyer–Peppas model (e) also showed a strong fit for CHC-50 (R2 = 0.9526) and CHC-20 (R2 = 0.9697), indicating a diffusion-controlled release. Free curcumin (CUR) exhibited poor fits across all models due to its rapid release profile. These results suggest that the Hixson–Crowell model best describes the release behavior of the CHC formulations. (a) Zero order. (b) First order. (c) Higuchi model. (d) Hixson–Crowell. (e) Korsmeyer–Peppas.
Mathematical modeling of drug release kinetics for CHC-50, CHC-20, CHC-10, and CUR formulations. Drug release data were fitted to five different models to determine the best fit for each formulation. Among the models tested, the Hixson–Crowell model (d) provided the best overall fit for the CHC formulations, with R2 values of 0.9897, 0.9900, and 0.8486 for CHC-50, CHC-20, and CHC-10, respectively, suggesting that changes in surface area during dissolution play a significant role in their release mechanisms. In contrast, the Korsmeyer–Peppas model (e) also showed a strong fit for CHC-50 (R2 = 0.9526) and CHC-20 (R2 = 0.9697), indicating a diffusion-controlled release. Free curcumin (CUR) exhibited poor fits across all models due to its rapid release profile. These results suggest that the Hixson–Crowell model best describes the release behavior of the CHC formulations. (a) Zero order. (b) First order. (c) Higuchi model. (d) Hixson–Crowell. (e) Korsmeyer–Peppas.
Mathematical modeling of drug release kinetics for CHC-50, CHC-20, CHC-10, and CUR formulations. Drug release data were fitted to five different models to determine the best fit for each formulation. Among the models tested, the Hixson–Crowell model (d) provided the best overall fit for the CHC formulations, with R2 values of 0.9897, 0.9900, and 0.8486 for CHC-50, CHC-20, and CHC-10, respectively, suggesting that changes in surface area during dissolution play a significant role in their release mechanisms. In contrast, the Korsmeyer–Peppas model (e) also showed a strong fit for CHC-50 (R2 = 0.9526) and CHC-20 (R2 = 0.9697), indicating a diffusion-controlled release. Free curcumin (CUR) exhibited poor fits across all models due to its rapid release profile. These results suggest that the Hixson–Crowell model best describes the release behavior of the CHC formulations. (a) Zero order. (b) First order. (c) Higuchi model. (d) Hixson–Crowell. (e) Korsmeyer–Peppas.
Mathematical modeling of drug release kinetics for CHC-50, CHC-20, CHC-10, and CUR formulations. Drug release data were fitted to five different models to determine the best fit for each formulation. Among the models tested, the Hixson–Crowell model (d) provided the best overall fit for the CHC formulations, with R2 values of 0.9897, 0.9900, and 0.8486 for CHC-50, CHC-20, and CHC-10, respectively, suggesting that changes in surface area during dissolution play a significant role in their release mechanisms. In contrast, the Korsmeyer–Peppas model (e) also showed a strong fit for CHC-50 (R2 = 0.9526) and CHC-20 (R2 = 0.9697), indicating a diffusion-controlled release. Free curcumin (CUR) exhibited poor fits across all models due to its rapid release profile. These results suggest that the Hixson–Crowell model best describes the release behavior of the CHC formulations. (a) Zero order. (b) First order. (c) Higuchi model. (d) Hixson–Crowell. (e) Korsmeyer–Peppas.
The Hixson–Crowell model provided the best fit for the CHC-50 and CHC-20 formulations, with high R2 values of 0.9897 and 0.9900, respectively (Table 3), suggesting that surface area and particle diameter changes during dissolution played a major role in the release mechanism. For CHC-10, the Higuchi model offered the best fit (R2 = 0.8838), indicating a diffusion-driven release mechanism, while free CUR fit the Korsmeyer–Peppas model (R2 = 0.9212), reflecting an immediate release behavior due to the rapid dissolution of CUR.
Table 3.
R2 and rate constant values for the different kinetic models.
Release models
CHC-50
CHC-20
CHC-10
Curcumin
Zero
R2
0.9026
0.6652
0.6103
0.5653
K0
0.03062
0.06393
0.03386
0.03297
First
R2
0.7800
0.7286
0.5300
0.5965
K1
0.0453
0.0636
0.04018
0.02410
Higuchi
R2
0.9198
0.6097
0.8838
0.6400
KH
0.02010
0.0333
0.0155
0.01471
Hixcon–Crowell
R2
0.9897
0.9900
0.8486
0.9212
KHC
0.04067
0.04159
0.03871
0.03779
Korsmeyer–Peppas model
R2
0.9526
0.9697
0.6986
0.9219
Kkp
0.5031
1.870
0.1026
0.4534
Unlike pure CUR, which showed near-instantaneous release (approaching 100% at early time points), the CHC formulations exhibited more sustained and controlled release profiles. CHC-50 demonstrated the most prolonged release, reflected in its consistently higher R2 values across all kinetic models. The sustained release profile of our formulations indicates that even with current loading efficiencies, fewer doses may be required compared to conventional CUR formulations that exhibit rapid release.
Based on our experimental results, we identified several potential strategies to enhance the drug loading and encapsulation efficiency of the HNT system. These include surface modification of the nanotubes with silane coupling agents, optimization of vacuum loading cycles, and exploration of alternative solvent systems to improve CUR solubility and loading. In addition, investigating cross-linking methods could potentially improve drug retention within the carrier system.
3.3. Cytotoxicity Assays
The cytotoxic effects of CUR and its formulations (CHC-50, CHC-20, and CHC-10) were evaluated on MCF-7, HMVII, and HepG2 cell lines, both with and without UV exposure and a summary of the results is presented in Tables 4, 5, and 6, respectively. IC50 values were used to quantify and compare the potency of each formulation.
Table 4.
IC50 values of various formulations before and after UV irradiation on MCF-7 cell lines.
Formulation
IC50 ± SD (µg/mL) before UV exposure
IC50 ± SD (µg/mL) after UV exposure
p value
CUR
9.348 ± 0.8977
13.52 ± 0.8469
0.0045 ∗∗
CHC-50
14.39 ± 0.9388
14.77 ± 1.3163
0.7048
CHC-20
22.45 ± 2.6505
24.53 ± 2.9770
0.4172
CHC-10
20.53 ± 2.3852
19 ± 0.8444
0.3541
Note: All data are expressed as means ± SD (n = 3); CHC-50 = 50% CuO + halloysite + curcumin; CHC-20 = 20% CuO + halloysite + curcumin; CHC-10 = 10% CuO + halloysite + curcumin.
Abbreviation: CUR, curcumin.
∗∗p < 0.01 paired t-test for values before and after UV exposure.
Table 5.
IC50 values of various formulations before and after UV irradiation on HMVII cell lines.
Formulation
IC50 ± SD (µg/mL) before UV exposure
IC50 ± SD (µg/mL) after UV exposure
p value
CUR
5.490 ± 0.4467
5.004 ± 0.7531
0.3908
CHC-50
29.06 ± 3.4592
23.43 ± 1.2194
0.0565
CHC-20
32.44 ± 5.2015
32 ± 1.5944
0.8954
CHC-10
23.74 ± 1.3138
12.91 ± 0.9234
0.0003 ∗∗∗
Note: All data are expressed as means ± SD (n = 3); CHC-50 = 50% CuO + halloysite + curcumin; CHC-20 = 20% CuO + halloysite + curcumin; CHC-10 = 10% CuO + halloysite + curcumin.
Abbreviation: CUR, curcumin.
∗∗∗p < 0.001 paired t-test for values before and after UV exposure.
Table 6.
IC50 values of various formulations before and after UV irradiation on HepG2 cell lines.
Formulation
IC50 ± SD (µg/mL) before UV exposure
IC50 ± SD (µg/mL) after UV exposure
p value
CUR
11.70 ± 0.8341
10.54 ± 0.4806
0.1052
CHC-50
19.39 ± 3.5842
18.83 ± 1.413
0.8136
CHC-20
23.27 ± 5.8776
26.11 ± 4.0969
0.5301
CHC-10
13.72 ± 1.8721
10.43 ± 0.7094
0.0431 ∗
Note: All data are expressed as means ± SD (n = 3); CHC-50 = 50% CuO + halloysite + curcumin; CHC-20 = 20% CuO + halloysite + curcumin; CHC-10 = 10% CuO + halloysite + curcumin.
Abbreviation: CUR, curcumin.
∗p < 0.05 paired t-test for values before and after UV exposure.
In HepG2 cells, CUR maintained high potency (IC50: 11.59 μg/mL for UV− and 10.5 μg/mL for UV+). The CHC-10 formulation again demonstrated UV-responsive behavior, with a significant decrease in IC50 post-UV exposure (from 13.74 ± 2.0411 μg/mL to 10.33 ± 0.7638 μg/mL, p < 0.05). CHC-50 and CHC-20 formulations showed minimal changes in IC50 values after UV exposure, indicating stability in their cytotoxic effects (Figures 4(a), 4(b), and 4(c) and Table 6).
In MCF-7 cells, unmodified CUR demonstrated the highest potency, with the lowest IC50 values (9.434 μg/mL for UV- and 13.62 μg/mL for UV+). A statistically significant increase in IC50 was observed for CUR after UV exposure (p < 0.01), suggesting a potential photodegradation effect. The formulations generally exhibited higher IC50 values compared to CUR, indicating lower immediate cytotoxicity in this cell line (Figures 4(d), 4(e), and 4(f) and Table 4).
For HMVII cells, CUR again showed the highest potency (IC50: 5.482 μg/mL for UV- and 4.713 μg/mL for UV+). Notably, the CHC-10 formulation displayed a significant decrease in IC50 after UV exposure (from 23.74 ± 1.3138 μg/mL to 12.91 ± 0.9234 μg/mL, p = 0.0003), suggesting UV-enhanced cytotoxicity. Other formulations (CHC-50 and CHC-20) maintained relatively stable IC50 values regardless of UV exposure (Figures 4(g), 4(h), and 4(i) and Table 5).
Across all cell lines, unmodified CUR consistently exhibited the highest potency. However, the CHC-10 formulation demonstrated the most pronounced UV-responsive behavior, with significant decreases in IC50 after UV exposure in both HMVII and HepG2 cell lines. In contrast, formulations with higher CuO content (CHC-50 and CHC-20) generally showed higher IC50 values and less UV responsiveness, possibly due to stronger cytotoxicity of CuO at higher concentrations.
These results highlight the complex interplay between formulation composition, UV exposure, and cell-specific responses, underscoring the potential of UV-responsive formulations such as CHC-10 for targeted cancer therapy. The consistent UV-responsive behavior of CHC-10 across multiple cell lines, particularly its significant decrease in IC50 values after UV exposure, suggests that this formulation could serve as a promising basis for future optimization studies. Further investigation into the mechanisms underlying its UV-triggered release and enhanced cytotoxicity could lead to the development of more effective and targeted nanocarrier systems for CUR delivery in cancer treatment. These results are shown in Figure 4.
3.4. In Silico Studies
Docking scores indicated that CUR could interact with the inner surface of HNTs, although the scores were not high. These interactions likely contribute to slowing the release of CUR from the nanotube (refer to Table 7).
Table 7.
Docking scores of the top 3 poses.
Top poses
Docking scores (kcal/mol)
Pose 1
−3.803
Pose 2
−3.430
Pose 3
−2.570
MD simulations conducted over a 10 ns timeframe revealed that the CUR–HNT complex is stable, exhibiting well-defined interactions (refer to Figure 6). The ligand RMSF analysis (Figure 6(a)) showed fluctuations below 3 Å for the majority of atoms in CUR, indicating minimal instability within the HNT. The RMSD remained below 3 Å throughout the simulation, further demonstrating the structural stability of the CUR–HNT complex (Figure 6(c)).
Molecular dynamics simulation analysis of curcumin bound to halloysite nanotubes (HNTs). (a) Ligand root mean square fluctuation (RMSF) plot illustrating the flexibility of different atoms in the curcumin molecule when bound to HNT. The 2D structure of curcumin with atom numbering is provided for reference. (b) Ligand torsion profile depicting the rotational behavior of various bonds in the HNT–bound curcumin molecule. The 2D structure highlights the analyzed torsion angles, and circular histograms show the distribution of torsion angles throughout the simulation. (c) Ligand properties over the course of a 10 nanosecond simulation of curcumin bound to HNT. From top to bottom, the plots show the root mean square deviation (RMSD) of the ligand structure, radius of gyration (Rg) indicating the compactness of the molecule, molecular surface area (MolSA), solvent-accessible surface area (SASA), and polar surface area (PSA). Each property is plotted as a time series (left) with corresponding histogram distributions (right) to show the range and frequency of values observed during the simulation. This comprehensive analysis provides insights into the conformational dynamics, flexibility, and physicochemical properties of curcumin when bound to HNT in a simulated environment. These findings are crucial for understanding the behavior of the curcumin–HNT complex in biological systems and its potential interactions with target molecules, which may influence its therapeutic efficacy and delivery characteristics.
Molecular dynamics simulation analysis of curcumin bound to halloysite nanotubes (HNTs). (a) Ligand root mean square fluctuation (RMSF) plot illustrating the flexibility of different atoms in the curcumin molecule when bound to HNT. The 2D structure of curcumin with atom numbering is provided for reference. (b) Ligand torsion profile depicting the rotational behavior of various bonds in the HNT–bound curcumin molecule. The 2D structure highlights the analyzed torsion angles, and circular histograms show the distribution of torsion angles throughout the simulation. (c) Ligand properties over the course of a 10 nanosecond simulation of curcumin bound to HNT. From top to bottom, the plots show the root mean square deviation (RMSD) of the ligand structure, radius of gyration (Rg) indicating the compactness of the molecule, molecular surface area (MolSA), solvent-accessible surface area (SASA), and polar surface area (PSA). Each property is plotted as a time series (left) with corresponding histogram distributions (right) to show the range and frequency of values observed during the simulation. This comprehensive analysis provides insights into the conformational dynamics, flexibility, and physicochemical properties of curcumin when bound to HNT in a simulated environment. These findings are crucial for understanding the behavior of the curcumin–HNT complex in biological systems and its potential interactions with target molecules, which may influence its therapeutic efficacy and delivery characteristics.
Molecular dynamics simulation analysis of curcumin bound to halloysite nanotubes (HNTs). (a) Ligand root mean square fluctuation (RMSF) plot illustrating the flexibility of different atoms in the curcumin molecule when bound to HNT. The 2D structure of curcumin with atom numbering is provided for reference. (b) Ligand torsion profile depicting the rotational behavior of various bonds in the HNT–bound curcumin molecule. The 2D structure highlights the analyzed torsion angles, and circular histograms show the distribution of torsion angles throughout the simulation. (c) Ligand properties over the course of a 10 nanosecond simulation of curcumin bound to HNT. From top to bottom, the plots show the root mean square deviation (RMSD) of the ligand structure, radius of gyration (Rg) indicating the compactness of the molecule, molecular surface area (MolSA), solvent-accessible surface area (SASA), and polar surface area (PSA). Each property is plotted as a time series (left) with corresponding histogram distributions (right) to show the range and frequency of values observed during the simulation. This comprehensive analysis provides insights into the conformational dynamics, flexibility, and physicochemical properties of curcumin when bound to HNT in a simulated environment. These findings are crucial for understanding the behavior of the curcumin–HNT complex in biological systems and its potential interactions with target molecules, which may influence its therapeutic efficacy and delivery characteristics.
The radius of gyration (Rg) for the ligand ranged between 5 and 6.5 Å, confirming a compact and stable conformation of the complex. The solvent-accessible surface area (SASA) varied between 300 and 700 Å2, showing dynamic interactions with the solvent environment during the simulation. Furthermore, polar surface area (PSA) and molecular surface area (molSA) analyses indicated significant exposure of polar and nonpolar regions, suggesting potential interactions with surrounding biomolecules (Figure 6(c)).
4. Discussion
In this study, we developed an innovative nanocomposite combining green-synthesized copper oxide (CuO) NPs with CUR encapsulated in HNTs. The CuO–HNT carrier system not only effectively encapsulated CUR but also exhibited controlled drug release properties, potentially enabling targeted delivery of active agents to cancer cells and optimizing therapeutic outcomes [45]; Iurciuc [14, 46]. This novel formulation also demonstrated promising synergistic therapeutic effects and photodynamic potential, addressing critical challenges in cancer therapy. By integrating multiple therapeutic modalities within a single nanocarrier, this approach offers a sophisticated strategy to enhance drug delivery efficiency and combat drug resistance, two persistent obstacles in current cancer treatments.
Developments in current anticancer therapy with the use of existing drug delivery systems present with limitations such as lack of specificity, selectivity in the mechanism of action, and the tendency of resistance. The CuO–HNT carrier system is an eco-friendly, innovative solution in targeted and controlled release systems in circumventing these challenges in drug delivery. The Cuo–HNT composite, of natural material origin, with the inclusion of copper for enhanced functionality and efficiency, is a promising candidate in anticancer drug delivery [45].
We successfully synthesized CuO and encapsulated both CuO and CUR within HNTs, achieving loading efficiencies of 2%–4%. While these efficiencies may appear modest, they are significant given the poor solubility and bioavailability of CUR [47, 48]. Our results align with studies of Nyankson et al. [42], who reported similar loading efficiency (∼4%) with silver and titanium dioxide HNTs (Ag–TiO2–HNTs) [42]. It is worth noting that Nyankson and colleagues achieved a higher encapsulating efficiency of 30% in their study, suggesting potential for further optimization of our system [42]. The encapsulation of CUR within HNTs in our study enhances drug stability and controls its release and bioavailability, factors that are crucial for improving therapeutic efficacy in patients [49]. These findings collectively demonstrate the potential of CuO–HNT–CUR nanocomposites as a promising platform for drug delivery. Through enhanced release properties, this delivery system demonstrates the potential to improve curcumin’s bioavailability and reduce dosing frequency, key factors that could enhance patient compliance while maintaining therapeutic concentrations necessary to prevent drug resistance.
Our computational analysis provided deeper insights into the molecular mechanisms underlying CUR loading and controlled release from HNTs, helping to interpret our experimental findings. The binding of CUR to the inner walls of HNTs with a moderate free energy of −3.803 kcal/mol suggests an optimal balance between retention and mobility, which likely contributes to the sustained release observed experimentally. This interaction remained stable over a 10 ns simulation, with CUR exhibiting dynamic behavior through conformational changes around 2 ns and RMSD stabilization at 2.4 Å. The Rg fell within 5–6.5 Å, revealing a compact and well-folded complex. The gradual increase in SASA from 300 to 700 Å2 indicates CUR repositioning within the lumen, potentially facilitating the controlled release observed over 24 h.
Despite the valuable insights provided by our computational approach, there are limitations that must be acknowledged. The 10 ns simulation timescale, while adequate for capturing short-term conformational changes, may not fully reflect the long-term behavior of CUR within the nanotube in a physiological context. In addition, while we modeled the CUR–HNT interaction in detail, the inability to incorporate the role of CuO NPs using density functional theory (DFT) limits our understanding of the potential chemical reactions that may occur over time.
The erosion-controlled release mechanism further supports these findings, as indicated by the strong fit of the Hixson–Crowell model to our release data, with R2 values ranging from 0.8486 to 0.9900. This suggests that the breakdown of the HNT structure, rather than simple diffusion, plays a key role in CUR release, aligning with similar studies, such as Wang et al. [50] on zein fibers [50]. The moderate binding energy observed in our simulations corroborates this mechanism, indicating that while CUR is sufficiently retained within the nanotube, it is also able to be released in a controlled manner. The presence of CuO NPs may further modulate this erosion process, contributing to the prolonged release observed in vitro. Compared to traditional diffusion-based systems such as polymeric NPs, our HNT–based system offers a more predictable and controlled release profile, potentially maintaining therapeutic levels of CUR for extended periods in cancer therapy [51–53].
In the in vitro experiments to assess the anticancer potential of the CuO–HNT–CUR complex, three cell lines were used: HMVII, HepG2, and MCF-7. The rationale for selecting these cell lines lies in their relevance to cancers that could potentially be treated using photodynamic therapy (PDT), especially given the promise of combining CuO with CUR in a photosensitive delivery system [29, 39, 54]. MCF-7 and HMVII cell lines were chosen to target breast and skin cancers, respectively, which have been shown to respond well to noninvasive PDT using light wavelengths between 600 and 800 nm [29, 55, 56]. In addition, the HepG2 cell line allowed for the exploration of liver cancer treatments and the effects of green-synthesized CuO NPs, which have been found to accumulate in liver cells [30, 57]. This strategic selection of cell lines facilitated the evaluation of the efficacy of the nanocomposite across a range of cancer types and highlighted its potential for PDT–based cancer treatment.
Our data demonstrated that the combination of CuO and CUR within HNTs exhibited notable anticancer effects. Although pure CUR showed significantly lower IC50 values (5.490–11.70 μg/mL) compared to the new formulations, the anticancer efficacy observed across all three cell lines for our nanocomposites is promising. This serves as a basis for future optimization, particularly as a potential combination therapy with improved pharmacokinetics. The controlled release of CUR from our HNT–based system offers additional benefits over the immediate release, potentially reducing dosing frequency and enhancing patient compliance, critical factors in long-term cancer treatment regimens [51, 52, 58]. Mechanistically, CUR alone is known to exert anticancer effects by modulating apoptosis and tumor suppressor genes such as p53 [59, 60], while CuO demonstrates cytotoxicity through ROS generation and cell cycle arrest [30, 61, 62]. By combining CUR and CuO in a single, controlled release system, we can target cancer through multiple mechanisms simultaneously. This multipronged approach may help overcome resistance to any single pathway, offering a more comprehensive and potentially more effective anticancer strategy. The synergistic effect of these components, coupled with the controlled release properties of our nanocomposite, presents a promising avenue for developing more efficient and patient-friendly cancer therapies.
A significant photodynamic effect was observed in the CHC-10 formulation for HMVII and HepG2 cell lines, underscoring the potential of PDT to enhance the cytotoxicity of nanocomposites such as CuO–HNT–CUR. While CuO exhibits anticancer properties independently of UV activation [63, 64], optimizing irradiation parameters could further amplify its therapeutic efficacy [65]. UV exposure for 1 h did not significantly alter cytotoxicity compared to unexposed formulations, indicating that refining UV exposure parameters might be necessary [55, 66, 67]. Notably, PDT effectiveness can be significantly influenced by light wavelength and intensity. Visible light, particularly blue (450–495 nm), green (500–550 nm), and red (600–700 nm) wavelengths, are commonly used due to their effective activation of many photosensitizers and better tissue penetration compared to UV light [55, 66–68]. Near-infrared light (700–800 nm) offers deeper tissue penetration and could be explored for optimizing PDT [65, 68]. In addition, varying light intensities can impact treatment efficacy and safety [55]. Future research should focus on these parameters to fully exploit the photodynamic potential of the CuO–HNT–CUR formulation, potentially leading to more effective cancer treatments.
Overall, this study serves as a proof of concept that green-synthesized CuO NPs, combined with CUR and delivered through HNTs, could provide a novel approach to cancer therapy with improved delivery mechanisms and sustained release profiles. While the moderate anticancer activity observed in vitro suggests the need for further optimization, the promising release kinetics and photodynamic potential of the CuO–HNT–CUR complex highlight its potential as a candidate for future drug delivery systems. Moving forward, further studies should aim to optimize the release profile, investigate different UV exposure parameters to maximize PDT efficacy, and expand the range of cancer models to fully validate the therapeutic potential of this nanocomposite. By addressing these limitations, our approach has the potential to make significant contributions to the fields of nanomedicine and cancer therapy.
5. Conclusions
In conclusion, our nanocomposite of halloysite nanotubes, green-synthesized CuO NPs, and CUR demonstrated potent anticancer properties against multiple cell lines (IC50: 10–24 μg/mL), with CHC-10 showing significant photodynamic potential. The erosion-controlled release mechanism (Hixson–Crowell model, R2 = 0.8486–0.9900) and moderate CUR–HNT binding energy (−3.803 kcal/mol) suggest extended therapeutic benefits. This study highlights the promise of CuO–HNT–CUR nanocomposites for sustained drug delivery in cancer treatment, addressing challenges in drug resistance and controlled delivery. Our findings lay the groundwork for developing targeted cancer therapeutics, with future research focusing on in vivo studies to validate the in vitro findings and assess biocompatibility, toxicity, and overall therapeutic efficacy. Furthermore, statistical comparisons between pure CUR and the nanocomposite formulations across different cell lines are recommended in future studies [69].
Conflicts of Interest
The authors declare no conflicts of interest.
Author Contributions
I.A., O.A.-D., E.N. and S.K.A. conceived the study. I.A., O.A.-D., R.A.-O., E.O.-A., A.A., E.K.O., E.N., and S.K.A. designed the methodology. I.A. performed the experiments under the supervision of O.A.-D., R.A.-O., E.N., and S.K.A. I.A., E.O.-A., A.A., and E.K.O. summarized the data. I.A., O.A.-D., R.A.-O., E.O.-A., A.A., E.K.O., E.N., and S.K.A. were involved in the analysis of data and writing of the manuscript. All authors read and approved the final manuscript.
Funding
No funding was received for this research.
Acknowledgments
The authors thank Clement Sasu and Samuel Otinkorang of School of Pharmacy, University of Ghana. Thanks also go to Gloria Manu, Grace Arkorful, Safoa, and Pearline Newlands of School of Engineering Sciences, University of Ghana, for their invaluable technical support.
Supporting Information
The EDX data and spectrum for the green-synthesized CuO are presented in the Supporting file.
Supporting Information Additional supporting information can be found online in the Supporting Information section.
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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