INTRODUCTION:

There are instances per 100,000 people of chronic myeloid leukemia (CML), which is shorthand for myeloproliferative neoplasm (MPN)^1,2. One, two Mutations in the hematopoietic system and decreased generation of healthy hematopoietic cells are the hallmarks of this condition. Because these mutations block cell differentiation, the cells either multiply rapidly or build up.

In the United States, typical medical exams and testing can detect around half of the cases of chronic myeloid leukemia (CML) in people who show no symptoms whatsoever.³ In CML, there are three distinct stages: the accelerated phase (AP), the long-term (CP) and short-term (BP) stages.⁴ The development of selective protein kinase inhibitors as a kind of targeted therapy has had a profound influence on the management of a number of human cancers; these drugs cause substantial improvements in patient outcomes while posing far less side effects than traditional cytotoxic chemotherapy.^5,6 Particles on the nanoscale, which is comparable to that of proteins. Particles like this have the potential to interact with specific cells and tissues in order to bring about the desired physiological responses while development and spread of cancer.^7-9 Hesperidin has the ability to change the processes by which tumor cells divide, survive, and die.^10,11 Despite this, hesperidin's low water solubility means it doesn't see much clinical application.¹² The development of suitable hesperidin delivery devices is the primary focus of researchers aiming to overcome this challenge. Solid lipid nanoparticles (SLNs) have a crucial solid lipid core encased in a monolayer surfactant shell. When compared to other nanosystems, SLNs were determined to be the safest choice.¹³ When compared to liposomes, polymeric nanoparticles have several advantages, including increased stability, lower loading capacity, reduced biotoxicity, and no residual organic solvent.¹⁴Scientific evidence suggests that the lipidic components of SLN can dissolve very lipophilic drugs. This makes the SLNs more stable in solution, reduces the amount of surfactants needed, and improves the biopharmaceutical performance after different types of administration.

MATERIALS AND METHODS:

Dr. Reddy's Laboratory Ltd. of Hyderabad, India, supplied the dasatinib, Wuhan Amino Acid Biochemicals of Wuhan, China, minimizing any negative side effects. The precirol ATO and Compritol purchased from Gattetosse of Saint-Priest, France. CDH of New Delhi, India, supplied the poloxamer 188. This research made use of analytically sound (HPLC grade) components.

Development of SLN:

Development of SLN by using high-shear homogenizer technique. It was melted at 50°C (oil phase) with 0.85 and 3% (w/v) Compritol-188, to summarize. For approximately 15 minutes, a magnetic stirrer was used to agitate the oil phase with dasatinib and hesperidin at 600 rpm. To get the necessary nanoscale, the emulsion was homogenized for 15 minutes at 20k rpm in an IKA T 25D homogenizer from Germany. Then, it was injected dropwise into a 100 mL water solution that contained 3% (w/w) poloxamer 188.^15-16.

Experimental Design for SLN Optimization:

Central composite design (CCD) was employed to enhance the dasatinib/hesperidin-loaded SLN using the Design Expert® tool, version 13. The experimental design is the most important part of building the preliminary screening for formulation development. A twenty-run, three-factor, three-level 'CCD' was employed for process optimization in order to reduce the number of runs with three-quarters of the variables.²⁸

Design Expert® for Analyzing Experimental Data:

Utilizing the latest version of Design Expert software, we assessed the experimental data. This analysis yielded great insights and further demonstrated the usefulness of statistical design. may see the results of Compritol in Table 1.

Table 1: CCD variables-actual levels and constraints.

Factors	Coded Levels
Independent variable	Low (-1 )	Medium (0)	High (+1)
X1= Compritol (%)	0.2	0.85	1.5
X2= Poloxamer -188 (%)	1	3	5
X3= Sonication Time (%)	1	5.5	10
Dependent variables	Constrains
Y1= Particle Size (nm)	(100-200)
Y2= Zeta Potential	Maximum
Y3= Entrapment Efficiency	Maximum

Poloxamer 188 and sonication duration affect PDI, particle size, and entrapment. Twenty-nine significant cause-and-effect components were included into polynomial equations using Design-Expert Software's projected statistical characteristics. These parameters include the modified multiple coefficient, expected residual sum of squares, and multiple correlation coefficient. The software's ANOVA feature allowed us to statistically validate the polynomial equation.

Analysis of SLN's Physical Properties:

At a temperature of 25 ± 1°C, appropriate dilutions of the nanodispersion were carried out in water to determine the zeta potential, size, and PDI using Nano ZS.^17-20

Research in Morphology:

Imaging with a transmission electron microscope

Before applying a drop of the optimized SLN solution to the cu-grid, it was diluted ten times with Milli Q water. The sample was then left to dry at room temperature. We took TEM photomicrographs using a Tecnai G2 S-twin equipment from FEI in the Netherlands after staining it with 1% phosphotungstic acid to make it more visible.³¹

Electron microscopy: scanning. For SEM, the optimum sample solution was utilized. images shot under a microscope and recorded at 10Kv.²¹.

Analyzing Physiochemical Parameters:

Analyzed using Fourier transform infrared spectroscopy

In order to study the drug-polymer interactions, a sufficient amount of samples was examined, and the %transmittance was measured in a scanning range of 400 to 4000 cm-1.²² using an FTIR (FTIR, Nicolet iS5) instrument.

The analysis of differential scanning calorimetry (DSC):

An aluminum (Al) DSC pan with a tight seal was used to hold five milligrams (5 mg) of the drug sample. Additionally, a hydraulic press was used to close the pan. The sample was heated to a rate of 10°C per minute while being scanned within a temperature range of 40 to 400°C. Instruments such as the DSC, model DSC6, were used to conduct this investigation.²³

High-performance liquid chromatography:

RP-HPLC was employed utilizing a Zorbax Eclipse XDB C-8 column to ascertain the amounts of hesperidin and dasatinib. 1.0 mL/min flow and 30°C temperature were maintained. A 48:52 ratio of methanol to phosphate buffer (2.75 g KH2PO4 in 1000 mL milli-Q water) was used in the pH 4.5 mobile phase. Conduct scans at 280 and 323 nm following the injection of the 10 µL sample, which required 15 minutes.^24,25

Preparation of Standard:

The concentration of the DMSO solution was 1000 ppm. Additional dilution was accomplished using methanol. This was filtered after undergoing a 10-minute sonication and vortexing process.

HPLC Sample Preparation:

Introduced 1 mL of DMSO to 500 mg of sample into a 2 mL Eppendorf tube. As previously said, vortex and sonicate. Centrifuge for 10 minutes at 14,000 rpm. Subsequently, include an additional 1 mL of DMSO into 1 mL of the supernatant layer. The identical protocols were employed for centrifugation, sonication, and vortexing. Administered the filtered top layer into the HPLC subsequent to its extraction.

Efficient encapsulation:

A 2 mL Eppendorf tube was used to combine 500 mg of the material with 1 mL of DMSO. After a 10-minute centrifugation run, the sample solution should be vortexed and sonicated. After that, I added 1 milliliter of water to the 1-milliliter supernatant layer. Centrifugation, sonication, and vortexing were all performed in the same way. Took the top layer, strained it, and then injected it into the HPLC system for examination.

In vitro drug Release:

We followed the procedure dialysis membrane method outlined by Mühlen et al. (1998) to assess the in vitro drug release after 72 hours of production under sink conditions. At 0, 0.25, 0.5, 1, 2, 4, 6, 8, 12, 24, 36, and 72 hours, HPLC was used to quantify cumulative drug release.²⁵

CONCLUSION AND ANALYSIS:

Method for Preparing SLN via CCD Approach:

The polynomial models used to optimize the formulation were built in 20 experimental runs using three levels of central composite design, as indicated in Table 2.²⁶

Analysis of SLN's Physical Properties:

Yield of the variable with respect to particle size:

Table 2 displays the experimental data showing that the particle size varied between 145 and 265 nanometers. The size was affected by the amount of compritol. The impact of factor levels on particle size may be further explained by the following equation:

The equation is 18.12 A+ 6.89 B - 1.67C+21.89AB+ 4.00AC+0.476BC-9.21A2 -6.41B2 -13.85C2. Now plug those values into the pie chart.

The mean result of adjusting a single variable by one level from Y1 through Y3 is shown in their principal impacts. Size is favorably affected, as indicated by the positive coefficients. When independent variables have negative coefficients, it means they have a negative effect on size. The graph in Figure 1(i) shows the investigation of the given coefficients in the 2nd order polynomial mode indicated before. Table 3 further shows that the linear response surface and quadratic model were statistically significant, with an F-value of 20.69 for complete quadratic nanoparticle size. Figure 1 (a) shows that the expected particle size decreased as Poloxamer 188 was increased, based on the analysis of response surfaces.²⁶

The variable's impact on the polydispersity index (Y2)
Table 2 shows that the particle size value ranges from PDI 0.125 to 0.934, according to the experimental data. For the dispersion media to have a uniform size distribution, the modest PDI value was extremely desirable. How much compritol and how long it was sonicated affected PDI. The link between the factors described above and PDI is shown by the equation below.

A polydispersity index (Y2) plus 0.3575 equals -0.0822A minus 0.1693B minus 0.0040 C plus 0.1524AB plus 0.0894AC plus 0.0004BC minus 0.0581A plus 0.3830B2 minus 0.1594C2. As seen in Table 3, a "Model F-value" of 14.00 indicates that the model is statistically significant. This table's "Model F-value": 2 Formulation development including three components and three levels of CCD a 0.05% probability that something bigger may occur as a result of noise. A variety of factors were shown to affect PDI in Figure 1 (ii). The polydispersity increased as compritol and sonication duration were increased. The presence of poloxamer 188 significantly lowered the PDI.²⁷

Impact on the effectiveness of encapsulation (Y3):

The efficacy of the Compritol formulation in preparation may be seen by measuring its encapsulation efficiency (EE). According to Table 3, there were significant variations in the EE response for the variables X1 (Compritol), X2 (Poloxamer 188), and X3 (Sonication duration) since their p-values were all less than 0.0001. Increasing the concentration of Compritol while decreasing the quantity of cholesterol and stearic acid may decrease encapsulation efficiency. The following equation illustrates the connection between encapsulation efficiency and the aforementioned criteria. A higher encapsulation efficiency (Y3) would be 4.17A-4.51B+5.61C- 4.26AB-6.00AC+1.14BC-8.90A2 -9.67B2 -6.78C2. The "Model F-value" proved that the model was crucial.

In terms of improving entrapment efficiency, the surfactant concentration had no discernible effect. The PDI values ranged from 0.125 to 0.934, while the particle diameters ranged from 136 to 265 nm, as shown in Table 2. The optimized formulation has a particle size range of 136.7 to 0.12 nm, as shown in (Figures 1).

Figure: 1 Particle Size and Zeta Potential

Figure 2: Response surface plots of (i) Particle size, (ii) Polydispersity index, and (iii) Encapsulation efficiency.

Table: 2 Quadratic ANOVA Model

*Response*	*F-value*	*p-value*	*Mean square*	*Adjusted R2*	*Predicted R2*	*Remarks*
Particle size(Y1)	21.68	˂0.0001	2389.66	0.9052	0.9101	Significant
Poly Dispersity Index (Y2)	15.00	<0.0001	0.1678	0.9676	0.8867	Significant
Encapsulation Efficiency(Y3)	23.56	˂0.0001	255.77	0.9340	0.8667	Significant

*Run*	*A: Solid Lipid* *%(w/w)*	*B: Surfactant* *%(w/w)*	*C: Sonication Time (min)*	*Response 1 Particle Size*	*Response 2 PDI*	*Response 3* *%EE*
1	0.2	5	1	161.7	0.125	92
2	0.2	5	10	160.7	0.125	93
3	0.85	3	-2.06807	230	0.631	71
4	0.85	3	5.5	136.7	0.154	90
5	0.85	3	5.5	136.7	0.154	90
6	0.85	3	5.5	143	0.456	91
7	0.2	1	10	151	0.378	92
8	1.5	5	1	161.2	0.125	89
9	0.85	3	13.0681	263	0.561	72
10	0.85	3	5.5	136.7	0.154	90
11	1.94317	3	5.5	152	0.561	85
12	0.85	3	5.5	136.7	0.154	90
13	1.5	5	10	168	0.815	59
14	0.2	1	1	188	0.767	77
15	1.5	1	1	168	0.614	66
16	0.85	3	5.5	136.7	0.154	90
17	0.85	-0.363586	5.5	155	0.843	88
18	0.85	6.36359	5.5	164	0.125	88
19	-0.243165	3	5.5	164	0.125	88
20	1.5	1	10	266	0.934	69

Analyzing Physiochemical Parameters:

Infrared spectroscopy:

Figure shows the Fourier transform infrared spectra of the drugs (dasatinib and hesperidin) and a combination of the drugs. See Figure 5 for further infrared spectra of dasatinib-excipients mixes, these figures depicted all the functional group peaks in the spectra for hesperidin-excipients mixtures, (Figure 3,4).

Figure 3: FTIR spectra of dasatinib and excipients

Figure 4: FTIR spectrum of hesperidin with excipients.

DSC Thermogram Analysis:

Dasatinib exhibited a thermogram at 286.25°C, while Hesperidin showed a peak at 237.0°C in the optimized SLN formulation. The absence of both drug peaks in the thermogram indicates that the drugs, upon incorporation into the SLN formulation, undergo a transformation from a crystalline to an amorphous state. Figure (4,5)

Figure 5: The analysis of differential scanning calorimetry (DSC) of Dasatinib

Figure 6: The analysis of differential scanning calorimetry (DSC) of Formulation

In Figure 6, The endotherm and usual curve of dasatinib, hesperidin, and optimized SLN. The improved SLN formulation does not show dasatinib and hesperidin peaks, which means the drug was loaded into the lipid core.

Drug Release study

The SLN formulation and suspension's release pattern is depicted in Figure 7. Dasatinib and hesperidin were shown to have SLN drug release rates of 30.34% and 30.67% in the first 4 hours, respectively, followed by 70.6% and 65.5% in the last 24 hours. The findings showed that the SLNs' surface adsorbed compounds released their drugs quickly, and then those same substances were released slowly, showing that they were contained in the lipid core. Higuchi square root was used to match the in vitro drug release profile data (R² = 0.6736), and Hixon- Crowell cube root (R² 0.7756), Korsmeyer–Peppas (R² 0.9903) zero-order (R² 0.8962) kinetic models. The Korsmeyer-Peppas model (highest R²) provided a comprehensive explanation for the kinetic modeling of drug release from the improved SLN formulation.^28-30

Figure 7: Release profiles of dasatinib and hesperidin from SNL and drug suspension

RESEARCH IN MORPHOLOGY:

Figure 8 illustrates Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) were employed to examine the morphology of SLN loaded with dasatinib and hesperidin. Showed uniform spherical particle size indicate the stability of SLN.

Figure 8: Transmission Electron Microscopy Analysis

CONCLUSION:

Our results show that high-shear homogenizers are useful for making SLNs with dasatinib and hesperidin for CML, which improves the cytotoxicity and stability of the two drugs while decreasing their side effects. Twenty run experiments revealed that the improved formulation had a small particle size distribution, with a PDI of 0.154 and a range of 136.7 nm. Further evidence that the drugs were encapsulated in the lipid core was their ability to sustainably release dasatinib and hesperidin. The EE for the SLN was 90%. Significant variations in particle size, PDI, and EE% were shown by each of the three variables X1 (Compritol), X2 (Poloxamer 188), and X3 (Sonication duration), all of which had p-values comparable to less than 0.0001. The transmission electron microscopy and scanning electron microanalysis of dasatinib and hesperidin-loaded SLN revealed less than 200 nm-diameter, physically compatible, spherical, and spherical shape. Thus, compared to the drug solution, the dual-targeted dasatinib and hesperidin were more effective at increasing cell sensitivity to the drug trapped in SLN. Patients' prognoses and quality of life may improve as a result of this novel approach to developing sustained-release formulations of anticancer drugs.

ACKNOWLEDGMENTS:

For the opportunity to work at their facilities, the authors would like to thank Maharishi University of Information Technology in Lucknow, India, and Dabur Research Foundation in Ghaziabad, India.

CONFLICT OF INTEREST:

None.

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Received on 23.01.2025 Revised on 14.07.2025

Accepted on 19.11.2025 Published on 13.01.2026

Available online from January 17, 2026

Research J. Pharmacy and Technology. 2026;19(1):119-125.

DOI: 10.52711/0974-360X.2026.00018

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