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Development and Statistical Optimization of Solid Lipid Nanoparticle Formulations of Fluticasone Propionate

INTRODUCTION: The aim of this study was to develop Fluticasone propionate (FP)-loaded solid lipid nanoparticle (SLN) formulations using the factorial design approach. METHODS: Tristearin percentages (X1) (1%, 2% and 4%) and homogenization cycles (X2) (2, 4, 8 cycles) were selected as independent variables in the factorial design. SLN formulations were optimized by multiple regression analysis (MLR) to evaluate the influence of the selected process and formulation independent variables on SLNs characteristics namely as encapsulation efficiency (Q1) and particle size (Q2). Polydispersity index and surface charge of the SLNs were also evaluated in this research. Besides, transmission electron microscopy, Differential scanning calorimetry and in vitro drug relase studies were carried out on the optimum SLN formulation. RESULTS: MLR analysis indicated that as homogenization cycle (X2) increased in the production process, the mean particle size was decreased. DISCUSSION AND CONCLUSION: This research displayed that FP-encapsulated SLNs with desired characteristics can be produced by varied the production and content variables of the formulations.


INTRODUCTION
Topical corticosteroids are the most commonly used drugs in the practice of dermatology especially for the treatment of inflammatory skin diseases. However, their long-term application is restricted due to their local and systemic adverse effects.
Several researches have been performed to enhance the anti-inflammatory efficiency of these active substances and to reduce their side effects [1][2][3] .
Fluticasone propionate (FP) is a synthetic trifluorinated topical corticosteroid and has been classified as a potent anti-inflammatory, immunosuppressive and antiproliferative drug for the therapy of skin disorders such as atopic dermatitis and psoriasis 4,5 . It is a highly lipophilic substance and is characterized by high glucocorticoid receptor binding and activation 2 . FP is available in 0.005% ointment and 0.05% cream formulations for the treatment of the inflammatory skin disorders which are responsive to corticosteroids 5,6 .
Dermal drug delivery means that the targeting of drugs to the different layers of skin with minimum systemic absorption. The accumulation of the drug in skin is an important issue for the therapy of the diseases such as atopic dermatitis, psoriasis, skin cancer etc 7,8 . In other words, drugs should reach the skin layers at an appropriate concentration and stay there for a certain time of period. However, the barrier function of the stratum corneum, which is the uppermost layer of epidermis, considerably restricts the penetration of drugs into skin 9 . Nanosized drug delivery systems receive a great deal of interest for dermal application since they offer several advantages.
These advantages can be summarized as enhancing the skin penetration and reducing the side effects of active substances, achieving the site-specific drug targeting into skin, providing the sustained and/or controlled release of drugs and increasing the chemical stability of molecules [9][10][11] . Dermal drug delivery by liposomes 12 ; niosomes 13 ; nanoemulsions 14 ; polymeric nanoparticles 15 and lipid nanoparticles [16][17][18] has been extensively researched by several groups.
Solid lipid nanoparticles (SLNs) were presented in the early 1990s with intent to extinguish the drawbacks of pre-existing colloidal drug delivery systems such as nanoemulsions, liposomes and polymeric nanoparticles 19,20 . SLNs are produced by physiologically tolerated lipids or the mixture of lipids which are in solid state at body u n c o r r e c t e d p r o o f and room temperature. SLNs have several advantages like biocompatibility, protection of drugs against degradation, modification of drug release rate and possibility of the large scale production without using organic solvents. Moreover, the structural similarity and interactions between the epidermal lipids and the lipid matrix of SLNs could enhance the skin permeation of the encapsulated drugs. The nanosize, narrow size distribution and the greater surface area of SLNs also facilitate the drug penetration into skin [21][22][23] . The controlled drug release can be achieved by using solid lipids because the mobility of drug is significantly lower in solid lipid matrix than in an oil droplet. Several types of solid lipids including fatty acids, triglycerides, partial glycerides, waxes and steroids can be used as the main ingredients of SLNs. The most frequently used surfactants are poloxamers, polysorbates, lecithins, polyvinyl alcohol and bile salts for providing the stabilization of nanodispersion 24-25 . There are various methods for production of SLNs as high pressure homogenization, microemulsion, high shear homogenization and/or ultrasonication, solvent emulsification-evaporation, solvent emulsification-diffusion, electrospraying, solvent injection, membrane contactor and supercritical fluid (SFC). High pressure homogenization is the most preferred method for manufacturing SLN dispersions since it exhibits several advantages compared to other techniques such as suitability for large scale industrial production, possibility of production without using an organic solvent and the quite short production time [26][27] .
Factorial design is a statistical approach and conducts systematic scientific researches to determine the impact of the independent variables on the responses of the dependent variables. The main objective in the factorial design approach is to obtain the maximum information between the minimum sample size and the cause-effect relationship for optimization of the formulation 28 . For this purpose, factorial design approach makes controlled changes in input variables. The factorial design helps to scale the replies of the dependent variables based on the defined goals. Response surface methodology is also performed by applying factorial design 29,30 . The present investigation was aimed to develop FP-loaded SLNs using the factorial design approach. Optimization of various physico-chemical characteristics of SLNs was carried out using the 3 2 factorial design through Design Expert 6.0.8 software. Two formulation parameters, tristearin percentages (1%, 2% and 4%) and homogenization u n c o r r e c t e d p r o o f cycles (2, 4, 8 cycles) were selected as independent variables. The response variables investigated to characterize the particles were the encapsulation efficiency percentage (Q1) and particle size (Q2) of the SLNs.

Materials
FP was kindly supplied as a gift from Deva Drug Company (Istanbul, Turkey). Tristearin was obtained from Sigma Aldrich (USA). Tween 80 was purchased from Fluka (USA).
All other reagents and chemicals were of analytical reagent grade.

Analytical Validation of the HPLC Method
The analytical validation of the HPLC method for the determination of FP was performed. The linearity, accuracy, precision, limit of detection and limit of quantification (LOD and LOQ) values of the HPLC method were calculated.

Optimization by 3 2 Factorial Design
Multiple linear regression (MLR) analysis was performed to investigate the factors affecting the final properties of SLNs31. Nine SLN formulations were prepared as per 32 factorial design to determine the impact of two independent variables; tristearin percentages (X1) and homogenization cycle (X2) on the two responses; entrapment efficiency percentage (Q1), and mean particle size (Q2) of the FP-loaded SLNs. Three levels (-1, 0 and 1) were designated for the testing of each factor. The regression equation of the fitted model for the responses was calculated using Eq.1.  Table 1. Table 1. Actual values and variable leves designed through 3 2 factorial design of FPloaded SLNs.

Preparation of SLN Formulations
FP loaded SLN formulations were prepared by high pressure homogenization technique. Tristearin was melted at a temperature which is 10 o C above its melting point and 50 mg of FP was added into the melted lipid. Aqueous Tween 80 (1%) solution was also heated to the same temperature. Lipid phase and aqueous phase were mixed to obtain a pre-emulsion by Ultraturrax T25 (IKA, Germany) at 13500 rpm for 3 minutes.

Particle Size and Zeta Potential Analysis
The mean particle size and polydispersity index of FP loaded SLNs were determined using a dynamic light scattering analyzer (Nano ZS, Malvern, UK). The zeta potential values were determined from the electrophoretic mobility (Zetasizer Nano ZS, Malvern, UK). Before analysis, all samples were diluted 1000 times with ultrapure water.

Determination of Encapsulation Efficiency
For the determination of the encapsulation efficiency of SLN formulations, 10 mg of SLN was dissolved in methanol at 75 o C. The solution was stirred using a magnetic stirrer in a tightly sealed vial. After that, the solution was ultrasonicated with 50% power for 5 minutes (Bandelin Sonoplus HD 2070, Germany) and then cooled to room temperature. It was centrifuged at 26000 rpm for 20 minutes at 4 o C (Sigma 3K30, Germany) and then the supernatant was filtered from 0.22 µm cellulose acetate u n c o r r e c t e d p r o o f membrane filter. The amount of FP was determined using an HPLC system (Agilent 1260 Infinity). Separation was carried out using NovaPak® C18 column (4 µm, 150 x 3.9 mm) (Waters, Ireland). The column temperature was set at 35 o C. The mobile phase composition was acetonitrile:water (60:40 v/v) and the flow rate was 1 ml/min. 10 µl of each sample was injected into the system and the samples were analyzed at a wavelength of 236 nm.

In vitro drug release study
The dialysis bag method was used in order to determine the in vitro release profile of

Transmission Electron Microscopy (TEM) Analysis
The morphological characterization of the FP-loaded SLNs was performed using FEI Tecnai Spirit transmission electron microscope (Osaka, Japan). Lipid nanoparticles were diluted with ultrapure water and then dispersion was placed on a copper grid.
The sample was imaged at an accelerated voltage of 120 kV.

Differential Scanning Calorimetry (DSC) Analysis
Thermal analysis and crystallinity of pure FP, bulk lipid and FP-loaded SLNs were carried out by a differential scanning calorimeter (Shimadzu DSC-60, Japan). Five mg of sample was placed in hermetically sealed aluminum pans and heated from 20°C to 300°C at a rate of 5°C/min. Inndium used as a reference for calibration.

Analytical Validation of the HPLC Method Linearity
Each of the eight different concentration of FP was analyzed by HPLC for six times.
The average peak areas were plotted vs concentrations and a linear relationship between peak area and concentration was observed. Excellent linearity was obtained between concentrations of 0.25 and 10 μg/ml in methanol with r 2 = 0.9996.

Accuracy
Solutions of FP in methanol at concentrations of 0.5 μg/ml, 4 μg/ml and 10 μg/ml were injected 6 times as a test sample. The concentrations of FP were calculated using the detector responses. The accuracy was determined in terms of the variation coefficient (relative standard deviation -RSD) of the percent recovery values. Since the RSD values obtained were less than or around 2%, the method was assumed to be accurate (Table 2).

Precision
The repeatability (intra-day precision) and intermediate (inter-day) precision of the method were evaluated. The repeatability of the method was determined by the analysis of 6 replicate injections of FP-methanol solutions and was expressed as the RSD of measured concentrations. As seen in Table 2, the RSD values were less than 2%. The intermediate precision of the HPLC method was defined by the RSD value of 12 injections performed on two different days and the RSD values were found to be less than 2% (Table 2). On the other hand, there is no statistically significant difference between the means of the measured concentrations obtained on two different days for each FP solution (p 0.05).

LOD and LOQ values
The LOD and LOQ values were calculated with respect to the following equations using the standard deviation (s) of the response and the slope (m) of the calibration curve.
While LOD value was found to be 0.09 μg/ml, LOQ value was 0.28 μg/ml.

Formulation Optimization by 3 2 Factorial Design
Tristearin percentages were varied from 1%, 2%, and 4%. These three different ratios were tested at three different homogenization cycle of 2, 4 and 8. In this way, nine formulations were prepared as per 3 2 factorial design. The magnitude and sign of the main influence indicate the relative effect of each factor on the response by means of polynomial equations. Table 3 gives the predicted and the observed values of responses (Q1, Q2). The predicted values were derived from the equations and the observed values were determined from experimental results. Table 3. Observed and predicted responses of FP-loaded SLNs. Table 4 shows the results of model co-efficients estimated by MLR and the ANOVA of the investigated model for all responses. The quality of the model developed was evaluated based on the regression coefficient values. The determination co-efficient (r 2 value) for the response Q2 was nearer to 1 indicating that there was a good agreement between the observed and the predicted measures from the model. The Negative sign in front of the co-efficients is indicated that the response of the nanoparticles increases when the independent factor was decreased, and the positive sign for the co-efficients showed the positive effect of the independent factors on the observed replies. The Model F-value of 3.87 for Q1 response implied there was a 5.30% probability that a "Model F-Value" of this magnitude could be caused by noise.
On the other hand, the Model F-value of 57.71 for Q2 response indicated that the model was statistically meaningful. The possibility of such a large "Model F-Value" due to noise is only 0.01% 28,31,32 .   Figure 1 shows the linearity plots between the Q1 and Q2 values. The correlation graphs that show linearity between actual and predicted response variables indicated that the fit to the model was at an excellent level for Q2 (p<0.05) whereas the linear correlation plots showed a low compliance to the model for Q1 (p>0.05) (Figure 1). This situation is also evidenced by the calculated F value for Q1 model. The F value of the Q1 model (F=3.87) is smaller than the tabulated F value (Ftab=4.46), indicating that the model is not statistically significant (Figure 1, and also the P values in Table   4). As seen from Table 3, drug entrapment efficiency of all factorial formulations was produced with a broad range of 27.07-94.65%. Drug entrapment efficiency was not affected significantly by both the level of X1 and X2 (p>0.05). Generally, as seen in P values that indicated the significance of the co-efficients (Table 4), both of the independent factors -X1 and X2 -had not a strong effect on the drug entrapment efficiency (Q1) (p>0.05).
When the average particle size of the SLNs was investigated depending on the variation of homogenization cycle (X2) at each tristearin percentages (X1), it was observed that as X2 increased from 2 to 8, the mean particle size decreased significantly (p<0.05). The average particle size of SLNs ranged from 130.9±3.30 to 352.9±10.93 nm. Generally, from the P values of the co-efficients that presented in Table 4, it was concluded that both of the investigated variables -X1 and X2 -had a major influence on the response Q2 (p<0.05). The biggest mean particle size was observed in the lowest level of X1 (1%) and the lowest level of X2 (2 cycle) in factorial formulation SLN1.
PDI, which is the indicator of homogeneity of particle size distribution in nano-sized drug delivery systems, is generally expressed as smaller than 0.3 for narrow size distribution 28,32 . PDI values of the all factorial formulations were found between 0.181 u n c o r r e c t e d p r o o f and 0.497 (Figure 2). It was observed that the factorial formulations which contain tristearin with a percentage of 1 or 2, showed a wide size distribution (PDI>0.2) based on the homogenization cycles investigated except at the formulations that contained percentage of tristearin of 2% at homogenization cycle of 8 (SLN6 coded formulation).
As tristearin percentage increased from 1% or 2% to 4%, the PDI values were found less than 0.3 indicating a uniform size distribution.

Differential Scanning Calorimetry (DSC) Analysis
When the DSC thermograms given in Figure 7 were examined, it was observed that pure FP is decomposed by a small exothermic peak at 271.72°C. This result is in agreement with a previous study by El-Gendy et al. and also Dai et al. 33,34 .The peak of the active agent thus observed also indicated that the FP was a crystal structure.
When the DSC thermogram of pure form of Tristearin was evaluated, it was seen that Tristearin produced a small exothermic shoulder peak at 49.89°C at first and then it gave a large endothermic peak at 60.73°C, which indicated the presence of a crystal structure of the Tristearin 35 . When the DSC thermogram of the optimum formulation was examined, it was detected that the exothermic peak of FP disappeared. This situation was interpreted as the crystal structure of FP was returned to amorphous nature within the SLN matrix structure. When the thermogram of the optimum formulation was examined for Tristearin peaks, it was seen that the exothermic shoulder peak of Tristearin disappeared where the main endothermic main peak at