INTRODUCTION:

The prevalence of cancer is growing day by day at an alarming rate around the globe. This is a very concerning problem that needs immediate attention and action. This disease has significant amount of share in contributing to global mortality.¹

Cancer is the unregulated growth of cells characterised by defective DNA regulation. The malignant cells continuously proliferate, displacing healthy tissue.

The neoplasm can be classified as either benign or malignant: a benign growth is localised to its tissue of origin, whereas a malignant malignancy has the potential to metastasise to other organs. Metastases, or secondary tumours, provide a significant challenge to the treatment of malignant cells. Cancer cells possess the ability to alter their signalling pathways, facilitating cell survival and proliferation. For example, they may stimulate the PI3K/Akt/mTOR pathway, associated with cellular development and metabolism, or the Ras/MAPK pathway, involved in cellular proliferation and survival. Conformist chemotherapeutic agents aggregate in both usual and tumour cells due to their lack of selectivity. The primary objective of cancer treatment is to diminish systemic toxicity and improve quality of life.

Medicinal plants are an essential resource in the exploration and advancement of new pharmacological candidates. Medicinal plants exhibit significant therapeutic effects. Furthermore, they exhibit fewer side effects in comparison to traditional medications⁶

Phytochemicals exhibit potential as therapeutic agents in cancer; however, certain boundaries must be addressed. Many phytochemicals examined in preclinical studies exhibit insufficient understanding of their molecular interactions with various signalling molecules. To discourse issues related to molecular targets and pathways, in silico strategies such as molecular docking should be utilised to elucidate the interactions of phytochemicals within various signalling pathways, which can subsequently be validated through in vitro and in vivo models.⁶ To address such problems, Novel drug delivery systems were used by scientists in order to ensure that high efficacy is achieved. Liquid crystalline cubosomes are self-assembled aqueous mixtures of lipids and surfactants.⁷ These discrete, sub-micron systems exhibit similarities to established vesicular nanostructures, together with niosomes and liposomes. The cubic phases consist of lipophobic, amphiphilic, and hydrophilic components, utilising a rounded, bicontinuous lipid bilayer that contains water channels. Cubosomes are characterised by a honeycomb-like structure.⁸ Methods for characterisation and evaluation encompass UV spectrophotometry, X-ray scattering, transmission electron microscopy, and photon correlation spectroscopy. Cubosomes are frequently employed in the delivery of oral, ophthalmic, transdermal, and chemotherapeutic medications.⁹

MATERIALS AND METHODS:

Materials:

Glyceryl Monooleate was received from Molychem Pharmaceuticals; Compritol HD5 ATO was obtained as a gift sample from Gattefosse India pvt. Ltd. Tween 80 was obtained from Researchlab. Camptothecin was procured from Yarropharm. Captothecin was purchased from Yarrow Chem products, Mumbai.

Methods:

Elements for Quality by Design for formulation:

Quality Target Product Profile (QTPP):

The Quality Target Product Profile (QTPP) is “a prospective summary of the quality characteristics of a drug product that ideally will be achieved to ensure the desired quality, taking into account safety and efficacy of the drug product.” The design for the quality of the product is based on QTPP. To understand the characteristics of the end product is useful to create a robust formulation and formulation that in term results into great performance of the drug product.¹⁰

Critical Quality Attributes (CQAs):

CQA should be within a certain limit or range to ensure good product quality. It is a physical, chemical, microbiological or biological characteristic. The determination of CQA is done from QTPP.¹¹

Determination of Critical Process parameters (CPPs) and Critical material Attributes (CMAs):

CMAs are the factors that influence the Critical Quality Attributes (CQAs). Generally, they are the part of composition of the formulation. E.g., Concentration of excipients and drug, etc. Critical Process Parameters (CPPs) are the factors that are the part of formulation process and that affect the CQAs. E.g., number of cycles, rotations per minute, etc.¹²

Formulation development:

Grounding of Camptothecin encumbered cubosomes: Camptothecin loaded cubosomes were prepared using microfluidization method. Specific amount of Glyceryl Monooleate (GMO) and poloxamer 407 were melted at 60⁰C using water bath. Followed by continuous addition of Camptothecin along with stirring until total dissolution. To this dissolution, milli Q deionised wated was added gradually added along with vortex mixing for about 1 min to achieve complete homogenous solution. This solution was then equilibrated for 24 to 48 hrs at room temperature to obtain a gel like cubic phase which was then further fragmented using probe sonicator to form a crude dispersion. This was subjected to run for 5 cycles in high pressure homogenizer at 20,000 psi and 25⁰C to obtain opalescent dispersion of cubic nanoparticles.¹³

Optimization of prepared camptothecin cubosomes using DOE

Systematic optimization of Camptothecin Cubosomes was carried out using Box Behnken design wherein 13 trial batches were performed. The selected independent factors were as follows-

1) Concentration of GMO

2) Concentration of Poloxamer

3) Number of microfluidizer cycles

Each of these selected critical factors were denoted as +1 and -1 mathematically which shows high and low levels of CMAs respectively to perform experiments.

The parameters such as particle size, Zeta potential, and Entrapment Efficiency were assessed using Design Expert Software (version 9.0, Stat-Ease Inc.), resulting in statistically validated equations for the preparation of optimised batches for subsequent study and scale-up.¹⁴

Characterization of Camptothecin loaded cubosomes

Particle size analysis and Zeta Potential:

Particle size and Zeta potential was determined using Particle size analyser at 25⁰C and backscattered angle. (Table 1)

Determination of Entrapment Efficiency and Drug loading capacity:

To quantify the entrapment efficiency (%EE) and drug loading capacity (DL), a refrigerated centrifuge was employed. The dispersion was subjected to centrifugation at 15,000rpm for 30 minutes at 4°C. Following centrifugation, the supernatant—containing unentrapped or free drug—was carefully separated from the cubosomal pellet. The concentration of free drug in the supernatant was determined spectrophotometrically by measuring absorbance at 325nm using a UV-visible spectrophotometer. This procedure was performed in triplicate to ensure reproducibility and accuracy.¹⁵

%EE= [Total drug− Free drug]/ (Total drug) × 100

%DL= [Initial drug− Free drug]/ (Mixed lipid) × 100

RESULTS AND DISCUSSION:

A Box–Behnken design was employed to investigate the effects of three independent factors on three response variables. The factors examined included: (A) concentration of glyceryl monooleate (GMO), (B) concentration of poloxamer, and (C) the number of microfluidizer cycles. Each factor was studied at three distinct levels. The experimental design facilitated the screening of these variables, resulting in the formulation of 13 distinct experimental runs, as detailed in Table 1. This approach enabled systematic exploration of the factor space to elucidate their influence on the response variables.

Table 1: Box Behneken design to obtain 13 different formulations

Formulations	A: Conc. of GMO (mg)	B: Conc. of poloxamer (mg)	C: Microfluidizer cycles
F1	100	50	5
F2	1000	50	5
F3	100	150	5
F4	1000	150	5
F5	100	100	3
F6	1000	100	3
F7	100	100	7
F8	1000	100	7
F9	550	50	3
F10	550	150	3
F11	550	50	7
F12	550	150	7
F13	550	100	5

Of these 13 runs, five were centre points and eight were axial points. Particle size (Y1), Zeta potential (Y2), and % entrapment efficiency (Y3) was three separate response variables used. Design- Expert® program helped to assess their replies. To ascertain and preserve the quality of formulation, the three chosen response criteria turned out to be the most important ones. Table 2 shows the answers in experimental design modelled in terms.

Table 2: Modelling of responses in experimental design

Formulations	Factor 1.	Factor 2.	Factor 3.	Response 1.	Response 2.	Response 3.
Formulations	A: Conc. of GMO (mg)	B: Conc. of poloxamer (mg)	C: Microfluidizer cycles	Particle size (nm)	Zeta Potential (mV)	Entrapment Efficiency(%)
1	100	50	5	110.7	-23.4	86.2
2	1000	50	5	213.2	-31	76.5
3	100	150	5	108.8	-25.9	90.6
4	1000	150	5	104.4	-33	96.9
5	100	100	3	106.6	-25.3	95.4
6	1000	100	3	226.2	-22.6	70.3
7	100	100	7	108.1	-20.3	94.1
8	1000	100	7	178.8	-33.5	79.3
9	550	50	3	202.5	-28.5	77.5
10	550	150	3	169.5	-26.5	83.5
11	550	50	7	176	-26.7	80.3
12	550	150	7	101.7	-33	99.8
13	550	100	5	223	-29.1	73.2

Effect of the independent variables on particle size

The relationship between the independent variables—GMO concentration, poloxamer concentration, and microfluidizer cycles—and the response variable, the particle size of the prepared cubosomes, was examined through various mathematical models using Design-Expert software version 11. The linear model demonstrated the best fit, evidenced by a significant p-value (<0.05) and an F-value of 4.78, thereby confirming its statistical significance. The proximity of the adjusted R² and predicted R² values, differing by less than 0.2, further substantiates the model's reliability. The particle size measurements for the 13 formulations, as summarised in Table 2, varied from 101.7nm to 226.2 nm. The polynomial equation for particle size is expressed as: Particle size = 152.12 + 36.05A – 27.25B – 17.53C,

whereas A, B, and C denote the concentrations of GMO, poloxamer, and the number of microfluidizer cycles, respectively. The positive coefficient for GMO signifies that an increase in GMO concentration correlates with larger particle sizes, while the negative coefficients for poloxamer and microfluidizer cycles indicate that higher levels of these variables lead to smaller particle sizes. The coefficient for GMO is greater than those of the other two variables, indicating that GMO concentration exerts a more substantial influence on particle size. The relationship was further illustrated using 3D response surface graphs (Fig. 1), which revealed a distinct trend: Particle size significantly increases with higher levels of GMOs and remains minimal at lower concentrations of GMOs and poloxamer, particularly with an increase in microfluidizer cycles. The analysis indicates that GMO concentration primarily affects particle size, with elevated levels resulting in larger particles, whereas increased poloxamer and microfluidizer cycles contribute to a reduction in particle size.

(a)

(b)

(c)

Fig. 1: Response surface Graphs A) particle size, B) Zeta Potential and C) Entrapment Efficiency

Effect of the independent variables on Zeta potential:

The effect on Zeta Potential of CPT cubosomes was found to be in the range of -33mV to -22.5mV. In this case, quadratic model fit was found to be noteworthy with a p-value <0.05. The quadratic equation found for Zeta Potential is given below:

Zeta Potential= -29.10 - 3.15A -1.10B - 1.33C + 0.125AB -3.98AC- 2.08BC + 2.01A²-1.24B²+1.66C²

The equation evidently implies that Zeta potential is inversely proportional to concentration of GMO, concentration of Poloxamer and microfluidizer cycles. Whereas, concentration of GMO has highest influence on zeta potential and concentration of poloxamer being the lowest. The 3D response surface graph in Fig. 2 shows that, zeta potential decreases with the increase in concentration of GMO.

Effect of independent variables on % Entrapment Efficiency:

The effect of independent variables on % entrapment efficiency was found to be in the range of 101.7nm to 226.2nm. In case of % entrapment efficiency, linear equation was found to be with a significant p-value of less than <0.05. The model F-value of 3.97 implies the implication of the model. The polynomial equation obtained for the % entrapment efficiency is given in equation:

% Entrapment Efficiency= +84.89 – 5.41A + 6.29B + 3.35C

From this equation, we can conclude that factors B and C positively impact the % entrapment efficiency. Whereas, factor A has a negative impact on it. The response surface plot Fig.3 for % entrapment efficiency clearly denotes that the increase in concentration of poloxamer and microfluidizer cycles leads to increase in % entrapment efficiency. Whereas, the decrease in concentration of GMO causes increase in % entrapment efficiency.

FTIR studies:

FTIR spectroscopy was performed to investigate possible interactions between camptothecin (CPT) and the cubosomal matrix. The spectra of pure camptothecin and camptothecin-loaded cubosomes are presented in Figures 2 and 3 respectively. FTIR spectra of pure camptothecin (CPT) and CPT-loaded cubosomes revealed notable shifts indicating successful drug encapsulation and potential enhancement of anticancer activity. In the pure CPT spectrum, a distinct peak at 3273.86 cm⁻¹ (O–H/N–H stretching) shifted to 3430.52 cm⁻¹ in the cubosome formulation, suggesting hydrogen bonding with cubosomal components. Similarly, aliphatic C–H stretches at 2917.89 and 2853.12 cm⁻¹ in CPT shifted slightly in the cubosomes, indicating hydrophobic interactions. Importantly, the lactone carbonyl peak at 1731.85 cm⁻¹, crucial for CPT’s anticancer activity, remained intact (shifted to 1706.01 cm⁻¹), suggesting that the active form of the drug is preserved in the formulation. These spectral changes confirm successful incorporation of CPT into cubosomes without chemical degradation. Overall, the FTIR analysis supports that cubosome encapsulation stabilizes CPT’s active structure, which may enhance its bioavailability and therapeutic efficacy against cancer.

Fig. 2: FTIR spectrum of Camptothecin

Fig. 3: FTIR spectrum of Camptothecin Cubosomes

Stability Studies:

The optimized batch of formulation was tested for stability under various conditions like refrigeration, general and accelerated stability studies for a certain period of time. Post these conditions, the formulation was tested for particle size, %EE and Zeta potential to check if there were any physical changes observed.

Cytotoxic Assay:

The MCF-7 human mammary gland breast cancer cell line was obtained from the National Centre for Cell Sciences (NCCS) in Pune and grown in Minimum Essential Medium (MEM) with 10% foetal bovine serum. The cells were seeded at a concentration of 1 × 10^4 cells/mL in culture media and incubated at 37°C with 5% CO₂ for 24hours for the experiment. Each well of a 96-well tissue culture plate received 70 microlitres of cell suspension (1 × 10^4 cells) and 100 microlitres of culture media. To assess cell viability, test substances at concentrations ranging from 10 to 100μg/mL were applied to wells in triplicate. Control wells were treated with DMSO (0.2% in PBS). The plates were incubated for a further 24 hours under the same conditions. After incubation, carefully remove the media and add 20μL of MTT reagent (5mg/mL in PBS) to each well. Plates were incubated for 4hours at 37°C in a CO₂ incubator. Following incubation, the wells were microscopically inspected for the presence of formazan crystals, which indicate live cells. Metabolically active cells turned the yellowish MTT into black formazan. To dissolve the formazan crystals, remove the medium and add 200μL of DMSO. The plates were incubated for 10minutes at 37°C while covered in aluminium foil. The absorbance was measured at 570nm using an ELISA microplate reader (BeneSphera E21). The study found that cubosomes showed dose-dependent cytotoxicity against MCF-7 cells, with an IC₅₀ value of 49.79μg/mL, indicating significant antiproliferative action.

Fig.4. Effects of Sample against MCF-7 cell line by MTT assay

CONCLUSION:

An innovative cubosomal formulation containing Camptothecin was designed and successfully tested in this study with the purpose of improving the treatment effectiveness against cancer. The formulation parameters were optimised to perfection using Design-Expert software and a strict Box-Behnken factorial design, leading to nanoscale vesicles with desirable physicochemical properties. After seven microfluidizer cycles, the optimised formulation (Formulation 12) with 550mg GMO and 150mg Poloxamer displayed outstanding properties, such as a zeta potential of -33 mV, indicating great stability, and a high entrapment efficiency of 99.8 percent. The minimal particle size was 101.7nm. The significant anticancer activity, with an IC₅₀ value of 49.79μg/mL, was demonstrated by in vitro cytotoxicity evaluations against MCF-7 breast cancer cells, highlighting the strong antiproliferative capability of the cubosomal system. The results are impressive, showing that the cubosomes were more effective in fighting cancer. This nanocarrier has great promise as a new way to treat cancer, including hepatocellular carcinoma.

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Received on 27.05.2025 Revised on 22.08.2025

Accepted on 25.10.2025 Published on 08.11.2025

Available online from November 13, 2025

Research J. Pharmacy and Technology. 2025;18(11):5545-5550.

DOI: 10.52711/0974-360X.2025.00799

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.