Formulation Optimization of Rosuvastatin Calcium-Loaded Solid Lipid Nanoparticles by 32 Full-Factorial Design

The present investigation was aimed at developing Rosuvastatin Calcium loaded solid lipid nanoparticles (SLNs). The SLNs were prepared using high pressure homogenization technique. Glyceryl monostearate (GMS) and Poloxamer 188 were employed as lipid carrier and surfactant respectively. A two factor, three level (32) full factorial design was applied to study the effect of independent variables i.e. amount of GMS (X1) and amount of Poloxamer 188 (X2) on dependent variables i.e. Particle size (Y1) and % entrapment efficiency (Y2). Particles size, % entrapment efficiency (%EE), zeta potential, drug content, in vitro drug release and particles morphology were evaluated for SLNs. Contour plots and response surface plots showed visual representation of relationship between the experimental responses (dependent variables) and the set of input (independent) variables. The adequacy of the regression model was verified by a check point analysis. The optimized batch (B10) contained 2.2 gm of GMS and 1% of Poloxamer 188. Batch B 10 exhibited mean particle size of 529.6 nm ± 6.36 nm; polydispersity index (PDI) of 0.306 ± 0.042; zeta potential of -31.88 mV ± (-2.50) mV and %EE of 48.90% ± 1.72%. The drug release experiments exhibited an initial rapid release followed by sustained release extended upto 36 h. Differential scanning calorimetry (DSC) studies showed that there was no chemical interaction between drug (Rosuvastatin Calcium) and lipid (GMS) whereas scanning electron microscopy (SEM) studies indicated that Rosuvastatin Calcium loaded SLNs are spherical, discrete and homogenous. Accelerated stability studies showed that there was no significant change occurring in the responses after storage for a total period of 3 months.


Introduction
Rosuvastatin Calcium is one of the most potent statins, and is approved for reducing circulating low-density lipoprotein cholesterol (LDL-C) levels.Rosuvastatin Calcium is a fully synthetic 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitor and has dose-linear pharmacokinetics.Rosuvastatin Calcium is BCS class-II drug (low solubility and high permeability).
The absolute oral bioavailability of Rosuvastatin Calcium is approximately 20%.
Rosuvastatin undergoes first pass metabolism, and only a small proportion of Rosuvastatin (about 10%) is recovered as metabolites, mainly N-desmethyl Rosuvastatin, which has approximately one-sixth to one-half the HMG-CoA reductase inhibitory activity of Rosuvastatin.Cytochrome P450 (CYP)2C9 is the principal isoenzyme responsible for the metabolism of Rosuvastatin, with a Formulation Optimization of Rosuvastatin Calcium-Loaded Solid Lipid Nanoparticles by 3 2 Full-Factorial Design Shah et al.

Preparation of Rosuvastatin calcium SLNs
The design matrix was built by the statistical software package, Design-Expert (version 8.0.7.1) and Table 1 shows the factors and their respective levels.Table 2 indicates the quantitative formula of the batches.SLNs were prepared using high pressure homogenization technique.GMS was melted at 70 °C and Rosuvastatin Calcium was dissolved in the melted lipid phase.Poloxamer 188 was dissolved in 60 ml of distilled water heated at 80 °C.The lipid phase maintained at a temperature of 70 °C, was added drop wise to the hot aqueous surfactant solution under stirring at 500 rpm for 30 min using a mechanical stirrer.This resulted into formation of an emulsion which was then subsequently homogenized in a high pressure homogenizer (ATS CE nanohomogenize machine, AH 100D) for 4 homogenization cycles at a pressure of 300 bar, 4 homogenization cycles at a pressure of 500 bar, 2 homogenization cycles at a pressure of 720 bar and 12 homogenization cycles at a pressure of 800 bar.Later, the mixture was cooled to room temperature which resulted in SLNs.The SLN dispersion was finally lyophilized using a HetoPowerDry® LL1500 Freeze Dryer (Thermo Electron Corporation, Denmark) using the flowing procedure: 2% w/v Mannitol solution was added to the nanoparticle dispersion and then manually filled in 2 ml glass vials.The vials were partially stoppered using lyo rubber stoppers.First, the shelf temperature was reduced from 5 to −70 °C at a rate of 1 °C/ min.The pressure was 60 mT ( = 0.08 mbar).These parameters were held for 6 h.For primary drying, the temperature was increased from −70 to −25 °C at 0.5 °C/min, whereas pressure remained unchanged.With termination of the primary drying, the secondary drying was performed by increasing the temperature at a rate of 0.2 °C/min to 25 °C.This temperature was held for 6 h at a pressure of 60 mT ( = 0.08 mbar).

Optimization by factorial design
A full factorial design for two factors at three levels each was selected to optimize the response of the variables.Two minimal effect from CYP2C19 [1].
SLNs are attractive submicron colloidal carriers (10-1000 nm) for hydrophilic as well as lipophillic drugs.The drugs are entrapped in biocompatible lipid core and surfactant at the outer shell [2].SLNs can be employed to improve the bioavailability and to obtain sustained release of the drug.They provide advantages like lack of acute and chronic toxicity of the carrier, good tolerability & biodegradability as well as scalability to large scale production.In addition, the process can be modulated for obtaining desired drug release and the entrapped drug can be protected against chemical/enzymatic degradation.Hence, SLNs are considered to be better alternative than polymeric nanoparticles, liposomes, microemulsions, nanoemulsions and self-emulsifying drug delivery systems [2][3][4][5][6][7][8][9][10].
The aim of the present research work was to prepare Rosuvastatin Calcium loaded SLNs by high pressure homogenization technique.To optimize the preparation of these SLNs, an experimental design methodology was employed.Factorial design allows determination of influence of the individual factors and their interactions on the responses by performing reduced number of experiments.In a factorial design, all combinations of the levels of the factors are investigated.Moreover, the design gives equation of the responses as a function of the parameters investigated [11][12][13][14][15][16].
In this study, the SLN formulation was optimized by using 2-factor, 3-level 3 2 full factorial design.After selecting the critical variables (independent variables), namely amount of lipid (GMS) and amount of surfactant (Poloxamer 188) affecting particle size and entrapment efficiency, the response surface methodology of the 3 2 full factorial design (version 8.0.7.1, Stat-Ease, Inc., Minneapolis, Minnesota, USA), was employed to optimize the level of dependent variablesparticle size and entrapment efficiency.The 3 2 full factorial designs are one of the most efficient designs to study the effect of formulation components on responses for exploring quadratic response surfaces and the second-order polynomial model.The optimized formulation was evaluated in terms of parameters like particle size, PDI, zeta potential, %EE, drug content, drug release and surface morphology [11][12][13][14][15][16].

Materials and Methods
Rosuvastatin Calcium was obtained as a gift sample from Cipla Ltd., Mumbai, India.GMS and Poloxamer 188 (Pluronic F 68) were purchased from Gattefosse, Mumbai, India and Balaji Drugs, Surat, India respectively.All the other reagents and solvents used were of analytical grade.Design Expert 8.0.7.1 software was used to optimize the formulation.

Preliminary trials
Preliminary trials were conducted to screen lipid and surfactant as well as to fix the stirring speed and stirring time.The effect on these factors on particle size and entrapment efficiency was used as criteria for screening.GMS and Poloxamer 188 were selected as lipid and surfactant respectively whereas stirring speed and stirring time were fixed at 500 rpm and 30 min respectively.factors, amount of GMS (X 1 ) and amount of Poloxamer 188 (X 2 ) used were varied, and their levels-low, medium and high were suitably coded as -1, 0 and +1 respectively.The particle size (nm) (Y 1 ) and %EE (Y 2 ) were taken as the response variables.In this design, experimental trials were performed at all 9 possible combinations.All other formulation variables and processing variables were kept invariant throughout the study.

Independent variables
The regression equation for the response is mentioned below.
In equation 1, Y is the dependent variables, namely, particle size (nm) (Y 1 ) and %EE (Y 2 ).Y indicates the quantitative effect of the independent variables X 1 and X 2 ; b 0 is the arithmetic mean response of the 9 runs; b 1 -b 5 are the coefficients of the term X.The main effects (X 1 and X 2 ) represent the average result of changing one factor at a time from its low to high value.The interaction term (X 1 X 2 ) shows how the response changes when two factors are simultaneously changed.The polynomial terms (X 1 2 and X 2 2 ) are included to investigate non-linearity.The simplified models were then utilized to produce three-dimensional response surface plots and contour plots to analyze the influence of independent variables.

Drug content
1 ml SLNs dispersion was taken into 10 ml volumetric flask and volume was made up with methanol.It was sonicated for 5 min in bath sonicator.Solution was filtered through cellulose Whatman filter paper (0.45 µ) and filtrate was analyzed spectrophotometrically at λ max 243 nm using UV spectrophotometer (UV-1800, Shimadzu, Japan).

Particle size, polydispersity index and Zeta potential
Freeze dried nanoparticles were dispersed in double distilled water.Particle size and Zeta potential was measured using a Malvern Zetasizer 3000 (Malvern Instruments, UK).The measurement of particle size was based on photon correlation spectroscopy.Polydispersity index was studied to determine the narrowness of the particle size distribution.Zeta potential was studied to determine the surface charge of SLNs.The zeta potential was determined using electrophoretic light scattering (ELS) at 25 °C with electric field strength of 23 V/cm using Zetasizer nano ZS.All the measurements were carried out in triplicate.

Percentage entrapment efficiency (%EE)
Entrapment efficiency is defined as the ratio of amount of entrapped drug to the amount of total drug used for preparation of nanoparticles. 2 ml of the SLNs dispersion was placed in the eppendorf tubes.The tubes were centrifuged using cooling centrifuge (Remi Instrument Ltd., Mumbai, India) at 10,000 rpm for 30 min at 4 °C.Supernatant was suitably diluted with methanol and analyzed spectrophotometrically at 243 nm.The entrapment efficiency was calculated by equation:

Contour plots and surface response plots
Contour plots and surface response plots are diagrammatic representation of the values of the response.They are helpful in explaining the relationship between independent and dependent variables.Response surface methodology (RSM) shows how an experimental response and a set of input variables are related.RSM sets a mathematical trend in the experimental design for determining the optimum level of experimental factors required for a given response.The reduced models were used to plot two dimension contour plots and three dimension RSM using STATISTICA software at the values of X 1 and X 2 between -1 and +1 at predetermined value of particle size and %EE.

In vitro drug release study
The dialysis membrane (Himedia, molecular cut off 12,000 to 14,000 D) technique was used to characterize the in vitro release of the prepared nanosuspension using Keshary-Chein glass diffusion cell (donor phase surface area 1.13 cm 2 and receptor phase volume 20 ml).Dispersion of nanoparticles corresponding to 40 mg of Rosuvastatin Calcium was placed in the donor compartment whereas 50 ml of phosphate buffer pH 6.8 was used as receptor medium.The entire system was kept at 37° ± 1 °C with continuous magnetic stirring.Sample of 5 ml were withdrawn at predetermined time intervals (1, 2, 4, 8, 12, 20, 24, 32, 36 h) and replaced by an equal volume of buffer.The amount of drug released was determined at 243 nm.Each test was carried out in triplicate and cumulative percentage drug release was calculated.The data was statistically analyzed using the software Sigmastat (Sigma Stat, USA).

Drug release kinetics
Data obtained from in vitro release studies of the optimized batch was fitted to various kinetic equations to understand the mechanism of drug release.The kinetic models used were zero order, first order, Higuchi, and Peppas equation.The following graphs were plotted: Qt vs. t (zero order kinetic model); log (Q0-Qt) vs. t (first order kinetic model,) and Qt vs. square root of t (Higuchi model) and log Qt/Q∞ = nlog t + k (Peppas equation), where Qt is the amount of drug released at time t and Q0 is the initial amount of drug present.Qt/Q∞ is the fraction of drug released after time t with respect to amount of drug released at infinite time, k is the release rate constant and n is the diffusional exponent which characterizes the transport mechanism.

Differential scanning calorimetry (DSC)
The thermograms of Rosuvastatin Calcium, GMS and nanoparticles were obtained using a Shimadzu DSC-60 (Shimadzu Instruments, Japan) differential scanning calorimeter.10 mg samples were placed in aluminum pans and heated from 25 °C to 300 °C at a scanning rate of 10 °C/min under nitrogen flow rate of 20 ml/min.An empty aluminum pan was used as reference.The instrument was calibrated with an Indium standard.

X-Ray diffraction studies (XRD)
Crystallinity of the Rosuvastatin Calcium, GMS and lyophilized nanoparticles of optimized batch (B10) was determined using X-Ray Diffractometer (Bruker, Germany).

Scanning electron microscopy
Surface morphology of the lyophilized particles was examined by scanning electron microscope-JSM-6390 LV ( JEOL Ltd, Tokyo, Japan).The particles were mounted directly on to the bras stub using double sided adhesive tape and coated with platinum film.The stub was fixed into a sample holder and placed in the vacuum chamber.Scanning electron photographs were taken at an accelerating voltage of 10kV with chamber pressure of 0.6 mm of Hg.

Accelerated stability studies
The optimized formulations were studied for their stability and their potential to withstand atmospheric/environmental changes.The freeze dried samples and aqueous dispersion (without freeze drying) were stored at 4 °C; 25 ± 2 °C/60 ± 5% RH and 40 ± 2 °C/75 ± 5% RH.Samples were withdrawn at 1, 2 and 3 months' time interval and analyzed for mean particle size, and drug content.The study was performed in triplicate.

Formulation optimization of Rosuvastatin Calcium loaded SLNs
The present work was focused on the formulation development of Rosuvastatin Calcium loaded SLNs for oral delivery.Based on the preliminary batches, GMS and Poloxamer 188 were selected as lipid and surfactant respectively whereas stirring speed and stirring time were fixed at 500 rpm and 30 min respectively.Preliminary studies decided the levels at which factors will be studied.Rosuvastatin Calcium loaded nanoparticles were prepared using high pressure homogenization technique.The prepared nanoparticles were then lyophilized using mannitol as cryoprotectant.The effect of formulation variables namely amount of GMS (X 1 ) and Poloxamer 188 (X 2 ) was studied using 3 2 factorial design.The particle size and %EE for the 9 batches (B1 to B9) showed a wide variation and were found in the range of 75-750 nm and 20-50% respectively (Table 3).The data clearly indicated the dependence of response variables on the selected independent variables.

Drug content
The drug content of all batches of SLNs is tabulated in Table 3.The drug content was found to be in the range of 100 ± 5% indicating that the Rosuvastatin Calcium was uniformly distributed in nanoparticle dispersion and there was no loss of the material during the preparation.

Data analysis of Y 1 (particle size)
The mean particle size and PDI results of all the nine batches of Rosuvastatin Calcium loaded SLNs are tabulated in Table 3.The mean particle sizes of batches B1-B9 were found in the range of 75 to 750 nm and the PDI was in the range of 0.1 to 0.5.The results indicated a profound effect of amount of GMS and Poloxamer 188 on the particle size.The response (Y 1 ) obtained at various levels of two independent variables were subjected to multiple regression to give a quadratic polynomial equation.
- -------(3) The above equation shows wide range of coefficient value.The model coefficients estimated by multiple linear regression for particle size are shown in Table 4.The regression coefficients having P value < 0.05 are highly significant.The terms X 1 2 & X 2 2 having P value > 0.05 were insignificant in contributing to prediction of particle size.The reduced equation can now be written as The two independent variables X 1 (amount of GMS) & X 2 (amount of Poloxamer 188) as well as the interaction term (X 1 X 2 ) were found to be significant (P < 0.05) in affecting Y 1.The positive co-efficient value for independent variable X 1 (+203.28)indicated positive effect on dependent variable Y 1 .While negative coefficient for independent variable X 2 (-172.59)and interaction term X 1 X 2 (-102.43)indicated negative effect on dependent variable Y 1 .
From the ANOVA data, the F cal value was found to be 2568.37,which is more than the F tab value of 2.51, indicating that the model was significant.The P value is < 0.05 for all   the response factors indicating that the models are significant.Batches B1 to B3 contained increasing amounts of GMS whereas amount of surfactant was constant (0.5%).The particle sizes of the batches B1, B2 & B3 were 229.43 nm, 537.37 nm and 751.20 nm respectively.An increase in particle size (Table 3) was observed on increasing the amount of lipid from 1 gm to 2.2 gm.This could probably be explained by the increase in aggregation of particles as the amount of GMS is increased.A similar trend of particle size was observed in batches B4 to B6 and batches B7 to B9.The amount of lipid was in increasing order whereas the surfactant concentration was constant at 1.0% and 1.5% respectively.
All SLN batches (B1, B2 and B3) containing 0.5% of Poloxamer 188 had higher particle sizes because of the lesser concentration of Poloxamer 188, which was probably not sufficient enough to form a protective coating on each particle.With increments in surfactant concentration (Poloxamer 188-1% & 1.5%), the particle size was significantly reduced due to the ability of surfactant to cover the individual particles thereby presenting the particles in a non-aggregated form.Our results are in agreement with findings of Yasir et al. [10].Amongst all the batches, batch B7 containing lowest amount of lipid (1 gm) and highest amount of surfactant (1.5%), exhibited smallest particle size (76.77nm).This can be explained by the surfactant-induced reduction in surface tension between aqueous phase and organic phase and stabilization of the newly generated surfaces which prevents particle aggregation.
The PDI is an important parameter that governs the physical stability of SLNs dispersion and should be as low as possible for the long term stability of SLNs dispersion.The PDI defined as dispersion homogeneity, has the range of 0 to 1. Values close to 1 indicate heterogeneity and those less than 0.5 indicate homogeneity.The PDI value of all formulation was found in the range of 0.077 to 0.499 which was less than 0.5, indicating their homogeneity [17][18].
Zeta potential provides information related to the storage stability of colloidal dispersions.In general, the greater the zeta potential value of a nanoparticulate system, the better the colloidal suspension stability due to repulsion effect between charged nanoparticles.The zeta potential values ranged between -24 to -40 mV.The surfactant concentration affected the charge on the particle.It was seen that as the surfactant concentration was increased from 0.5 to 1.5%, there was a decrease in the zeta potential value.This is because the surfactant is non-ionic and increasing its concentration lowers the total charge on the particle.The optimized batch of Rosuvastatin Calcium loaded SLNs (Batch B10) was found to have Zeta potential of -31.88 ± (-2.50) mV.Zeta potential values in the ± 15 mV to ± 50 mV are common for well stabilized nanoparticles [19][20].Hence, it was concluded that he nanoparticles would remain stable.

Data analysis of Y 2 (%EE)
%EE of SLNs was determined using ultracentrifugation method.The %EE varied from 22.03% to 51.20%.The results clearly indicated that Y 2 is strongly affected by the amount of lipid and concentration of surfactant selected (Table 3) for the study.The response (Y 2 ) obtained at various levels of two independent variables were subjected to multiple regression to give a quadratic polynomial equation.

----(5)
The above equation shows wide range of coefficient values.The model coefficients estimated by multiple linear regression for %EE are shown in Table 4.The regression coefficients having P value < 0.05 are highly significant.The terms X 1 X 2; X 1 2 & X 2 2 having P value > 0.05 were insignificant in contributing to prediction of particle size.The reduced equation can now be written as The two independent variables X 1 (amount of GMS) & X 2 (amount of Poloxamer 188) were found to be significant (P < 0.05) in affecting Y 1.The positive co-efficient value for independent variable X 1 (+12.80)indicated positive effect on dependent variable Y 2 .While negative coefficient for independent variable X 2 (-2.37) indicated negative effect on dependent variable Y 2 .From the ANOVA data, the F cal value was found to be 58.56,which is more than the F tab value of 2.51, indicating that the model was significant.
Batches B1, B2 and B3 contained 1.0, 1.6 and 2.2 gm of GMS respectively whereas amount of surfactant was constant (0.5%).The %EE of the batches B1, B2 & B3 were 25.40%, 39.43% and 51.20 % respectively.Increase in amount of lipid led to increase in %EE, which could be explained by more amount of the lipid available for Rosuvastatin Calcium to dissolve.Similar trend was observed in batches-B4 to B6 and B7 to B9.Our results are in agreement with findings of Sudhakar et al. [21], and Yadav and Sawant [22].

Contour plots and response surface analysis
Two-dimensional contour plots and 3-D response surface plots for variables Y 1 (particle size) are shown in Figures 1 & 2 respectively.Similarly, two-dimensional contour plots and 3-D response surface plots for variables Y 2 (%EE) are shown in Figures 3 & 4 respectively.They are very useful to study the interaction effects, main effects and quadratic effects of the factors (independent variables) on the responses (dependent variables).These types of plots are helpful in depicting the study of the effects of two factors on the response at a time.The desirability and counter plots were constructed and the optimized formulae were predicted using the constraints on the dependent variables.The desirability and overlay plots are shown in Figures 5 & 6 respectively.The desirability function was found to be near to 1 for the optimized formula indicating the suitability of the formulations.The composition of the optimized formulations was matching with batch B6.However, the experiment was repeated to reconfirm the results (Batch B10).The Batch B10 was analyzed for particle size and %EE.The % relative error for particle size and %EE within the experimental and predicted values for batch B10 was found to be 3.7% and 2.8% respectively (Table 5).However, the values were found to be < 5% and hence it confirmed the suitability of experimental design followed for this study.

In vitro drug release study
In vitro drug release of Rosuvastatin Calcium loaded SLNs was studied using modified dialysis method.The results are tabulated in Table 6.All batches showed the initial rapid drug release of around 25-40% within two hours followed by sustained drug release of the remaining 60-75% upto 36 h and beyond.The initial rapid drug release may be attributed to the diffusion of dissolved drug initially deposited inside the pores of the nanoparticles; presence of free drug in the external phase and on the surface of the nanoparticles.The lipophillic nature of Rosuvastatin could be the reason for sustained release of the drug from internal lipidic phase after initial burst release.Particle size also was found to influence the rate of drug release.Smaller nanoparticles lead to a shorter average diffusion path of the Rosuvastatin entrapped and lead to faster rate of release of the entrapped drug compared to bigger size NP.The larger nanoparticles could sustain the release of the drug upto 36 h  and beyond.The results obtained are in accordance with the findings of several authors who claimed that the particle size differences are a significant factor for drug release rate kinetics in nanoparticulate drug delivery systems [23][24][25][26].
The concentration of lipid and surfactant were among the other factors affecting the rate of drug release.When the lipid concentration was increased, the drug release rate decreased; this may be due to the higher concentration of drug presence in the inner core.As the surfactant concentration increased, the drug release rate increased due to the increased solubility of drug in external phase.
The data obtained from the drug release of optimized batch of Rosuvastatin Calcium loaded SLNs was fitted to different kinetic models to understand the drug release mechanism and kinetics.When the data was fitted to Higuchi model, R 2 value was found to be 0.989.This indicated that the release from the batch B10 followed Higuchi diffusion.The release was fitted to both the zero order and first order models.R 2 values for zero order and first order models were found to be 0.923 and 0.95 respectively, indicating that the release followed first order release kinetics.The drug release data was fitted to Korsmeyer-Peppas model to determine the value of diffusion exponent (n).The value of n for a spherical system is < 0.5 for Fickian release; 0.5 < n < 1 indicates non-Fickian release; n > 1 indicates super case II release.The n value for Batch B10 was less than 0.5 therefore the release mechanism is said to follow Fickian diffusion kinetics.It can be concluded that the release of Rosuvastatin Calcium from the SLNs follows first order kinetics and mechanism of drug release is Fickian.

XRD studies
XRD patterns of Rosuvastatin Calcium, lipid (GMS), physical mixture of the Rosuvastatin Calcium and GMS and Rosuvastatin Calcium SLNs (lyophilized) are shown in the Figures 7, 8, 9 and 10 respectively.XRD pattern of Rosuvastatin Calcium was showing sharp peaks at 2θ-scattered angles at 4.5, 9.7 and 14.5 indicating the highly crystalline nature of the drug.The XRD pattern of GMS was showing peak at about 20 indicating the crystalline nature of the lipid.In the physical mixture of the drug Rosuvastatin Calcium with the lipid GMS, decreased peak intensities were observed for Rosuvastatin Calcium showing that the degree of crystallinity reduced in physical mixture samples.In the XRD pattern of solid lipid nanoparticles, it was found that the drug lost its crystalline nature, however the lipid retained its crystalline nature.Our results are in agreement with findings of Yadav and Sawant [22].

DSC studies
DSC studies were performed for the assessment of the drug excipients interactions [27][28].The melting point of Rosuvastatin Calcium and GMS is 147-156 °C and 58 °C respectively.The DSC thermograms of Rosuvastatin Calcium (Figure 11) and GMS (Figure 12) showed sharp endothermic peaks at 144.67 °C and 58.96 °C respectively.The DSC thermogram (Figure 13) of physical mixture of Rosuvastatin Calcium and GMS (1:1) exhibited no significant shift in the position of individual endothermic peaks, indicating absence of any chemical interaction between Rosuvastatin Calcium and GMS.DSC thermogram of Rosuvastatin Calcium loaded SLNs (Figure 14) exhibited an endothermic peak at 55.22 °C for GMS, but Rosuvastatin Calcium peak was not found.This result suggests that Rosuvastatin Calcium got entrapped in SLNs and existed in the amorphous state.

Scanning Electron Microscopy studies
Scanning Electron Microscopy of the optimized batch (Batch B10) was performed to study the surface morphology.The electron micrographs showed spherical, discrete and homogenous particles in the nanometer size range (Figure 15).

Accelerated stability studies
Stability studies were carried out for the optimized formulation (Batch B10) in aqueous dispersion and freeze dried state to evaluate the change in mean particle size, % entrapment efficiency and drug content over a period of 3 months at 4 °C, 25 °C ± 2 °C/60% ± 5% RH and      There was no significant change (P > 0.05) in the mean particle size of SLNs in freeze dried state at 4 °C upto 3 months and in the aqueous dispersion state for 2 months.The mean particle size of SLNs in aqueous dispersion increased from initial 458.09nm to 495.38 nm at the end of 3 M.
At 25 °C ± 2 °C/60% ± 5% RH, there was no significant change (P > 0.05) in the mean particle size upto 2 months in the freeze dried state and upto 1 month in the aqueous dispersion state.At the end of 3 months, the particle size of nanoparticles in freeze dried state was increased from 529.60 nm to 554.77 nm whereas at the end of 2 and 3 months in aqueous dispersion state, the particle size changed from 458.09 nm to 479.26 nm and 500.07nm at the end of 2 and 3 months respectively.At 40 °C ± 5 °C/75% ± 2% RH, there was no significant change (P > 0.05) in the mean particle size upto 1 months in the freeze dried state whereas the Rosuvastatin calcium loaded solid lipid nanoparticles were not stable in the aqueous dispersion state.At the end of 2 and 3 months, the particle size of nanoparticles in freeze dried state was increased from 529.6 nm to 564.93 nm and 587.85 nm respectively.
It has been reported that the mean particle size of nanoparticles increases over a period of time due to aggregation.At higher temperature, the NPs were not stable as a large increase in particle size was observed.The results are in conformation with findings of Gasper et al. [29] and Dunne et al. [30].
There was no significant change (P > 0.05) in the drug content for Rosuvastatin Calcium loaded SLNs in the freeze dried state at 2-8 °C upto 3 months and for 1 month in aqueous dispersion state.The drug content decreased from 98.62% to 94.51% and 91.77% respectively at the end of 2 and 3 months in aqueous dispersion state.At 25 °C ± 5 °C/60% ± 2% RH, batch B10 was stable in freeze dried state upto 2 months after which the drug content was reduced to 92% in the third month.In the aqueous dispersion state, as time increased, the drug content decreased from 98.62% to 93.16%, 89.68% and 81.45% at the end of 1, 2 and 3 months respectively.At 40 °C ± 5 °C/75% ± 2% RH, batch B10 was unstable and a significant change was observed in the drug content for both freeze dried and aqueous dispersion state.Drug content in the third month was reduced to 80% and 73% for in freeze dried and aqueous dispersion state respectively.The nanoparticles were not stable at 40 °C, probably due to polymer degradation at higher temperatures.It was concluded that it was best to store nanoparticle formulations in the freeze dried state at 4 °C where they remained stable in terms of both mean particle size and drug content.

Conclusion
The SLNs of Rosuvastatin Calcium were successfully formulated using GMS as carrier lipid and Poloxamer 188 (surfactant) as surfactant using High pressure homogenization technique.The optimization of amount of lipid and % surfactant in the SLNs formulation was carried out using 3 2 full factorial designs.The developed SLNs exhibited controlled drug release upto a period of 36 h.The developed formulation was found to be stable with no significant change in particle size, and drug content.The SLNs due to their size and lipophillic characteristics may be useful in avoiding the first pass metabolism.The Rosuvastatin Calcium SLNs may provide a better bioavailability, reduction in dose, dosing frequency, dose related side effects and better control of the disease.

Figures 1 &
Figures 1 & 2 reveal a decline in particle size with an increase in concentration of surfactant.Increase in amount of lipid led to an increase in particle size.The lowest particle size

Figures 3 & 4
Figures 3 & 4 reveal an increase in entrapment efficiency with an increase in concentration of lipid.The highest entrapment efficiency was obtained with the highest amount of lipid.

Figure 9 :
Figure 9: XRD pattern of physical mixture of Rosuvastatin Calcium and GMS.

Figure 13 :
Figure 13: DSC thermogram of physical mixture of drug and GMS.

Figure 16 :
Figure 16: Effect of storage conditions on mean particle size of Rosuvastatin Calcium loaded SLNs in Freeze Dried state.The values are mean of three batches with ± S.D.

Figure 17 :
Figure 17: Effect of storage conditions on mean particle size of Rosuvastatin Calcium loaded SLNs in aqueous dispersion state.The values are mean of three batches with ± S.D.

Table 1 :
Coding of actual values of variables.

Table 2 :
Translation of coded values in actual units.

Formulation Optimization of Rosuvastatin Calcium-Loaded Solid Lipid Nanoparticles by 3 2 Full-Factorial Design Shah et al.
XRD patterns were recorded using Cu Kα radiation (40 kV, 40 mA) and scanned over a 2θ range of 10-70°.The scanning was carried out at 1.2 °C/min and at 25 °C.Obtained X-ray diffractograms were analyzed with DIFFRAC plus EVA (version 9.0) diffraction software.
1) X100 Wt.of drug used in formulation-Wt. of unbound drug in supernatent Wt.of drug used in formulation (%EE) =

Table 4 :
Model coefficients estimated by multiple linear regression for response Y 1 &Y 2 .

Table 5 :
Response variables of optimized batch.