Formulation and Optimization of Silymarin Loaded PLGA Nanoparticle for liver targeting

 

Irfan M. Saiyyad¹*, D.S. Bhambere², Dr. Sanjay Kshirsagar³

¹Department of Pharmaceutics, MET’s Institute of Pharmacy, Adgaon, Nashik,

²Department of Pharmaceutics, MET’s Institute of Pharmacy, Adgaon, Nashik, Maharashtra, India.

³Principal of MET’s Institute of Pharmacy, Adgaon, Nashik.

 

ABSTRACT:

The aim of present study was to prepare silymarin loaded PLGA polymeric nanoparticles for liver targeting by solvent evaporation method. Special attention was devoted to targeted drug delivery to liver and then the controlled release of drug from the polymeric nanoparticles. PLGA were employed as a bio-degradable polymer for targeting and controlled release of drug. The particle size of the resultant PN’s was mainly controlled by the agitation speed during the manufacturing process and polymer concentration. PLGA nanoparticles have the stability problem hence is PVA used as Surfactant as well as Stabilizing agent for the production of stable nanoparticles. Following particle size, zeta potential, and DSC and SEM analysis.  The Silymarin nanoparticles were prepared with different ratio of polymer (PLGA), surfactant (PVA) and solvent (acetone) by using solvent evaporation method. The organic phase {drug + polymer (1:1, 1:3, 1:5) + solvent} was added to aqueous phase {water + PVA (1, 2, and 3 %)} and subjected for homogenization with different rpm. The formulation was heated with magnetic stirrer for evaporation of solvent for 2 hrs. After evaporation of solvent, the formulation was centrifuged and supernatant was collected by filtration and dried at room temperature. The formed nanoparticles were evaluated for particle size, entrapment efficiency and in vitro release. The Nanoparticle was obtained having Particle Size in between 422.4-294.3 nm. Entrapment Efficiency in between 74.30-99.8% and % drug release in between 73.53-98.67% which follows the sustained release behavior. From given Data it concludes that Nanoparticle containing PLGA exhibiting excellent sustained release characteristics and Entrapment efficiency and also Good particle Size. Hence After stability studies all formulations were found to be physically and chemically stable.

 

 

1. INTRODUCTION:

There exists a widely held view that silymarin (a.k.a. milk thistle) promotes liver health through anti-oxidant, anti-inflammatory, anti-proliferative, and immunomodulatory effects^(1). In fact, silymarin is one of the top 10 most popular natural products consumed by western society, and is the most commonly consumed botanical medicine reported in patients with chronic hepatitis C ^{(2, 3).}

 

Presently, there is no clear evidence that any of the currently available over-the-counter preparations have efficacy in the treatment of liver disease. While there are compelling in vitro and animal data supporting the hepatoprotective effects of silymarin and inhibition of in vitro HCV infection^[4], clinical data are equivocal, with some studies suggesting a protective effect of silymarin against progression of liver disease in subjects with hepatitis C⁽⁵⁾, while other studies found no such effect^(6,7).Thus, there is clinical controversy around whether silymarin and silymarin-derived compounds protect the liver. It is the intention of this review article to summarize the current state of knowledge on whether and how silymarin (and the mixture of silymarin components known as silibinin) protects the liver and modulates HCV infection, and to make recommendations for areas of further research. Silymarin’s hepatoprotective effects are accomplished via several mechanisms including antioxidation⁽⁸⁾, inhibition of lipid peroxidation⁽⁹⁾, enhanced liver detoxification via inhibition of Phase I detoxification and enhanced glucuronidation^(10,11), and protection of glutathione depletion⁽¹²⁾. Studies have also shown silymarin exhibits several anti-inflammatory effects, including inhibition of leukotriene and prostaglandin synthesis, Kupffer cell inhibition, mast cell stabilization, and inhibition of neutrophil migration^(13-17).In addition, silymarin has been shown to increase hepatocyte protein synthesis, thereby promoting hepatic tissue regeneration⁽¹⁸⁾. Animal studies have also demonstrated silybin reduces the conversion of hepatic stellate cells into my fibroblasts, slowing or even reversing fibrosis.19 Clinical studies conducted in Hungary also demonstrated silymarin to have immunomodulatory effects on the diseased liver ^{(20, 21)}. Nanoparticles are solid colloidal particles with diameters ranging from 1 -1000 nm.  The colloidal carriers based on biodegradable and bio-compatible polymeric systems have largely influenced the controlled and targeted drug delivery concept⁽²²⁾. Many drug candidates face problems like poor absorption, rapid metabolism and elimination, toxicity due to drug Distribution to other tissues, poor drug solubility, unpredictable bioavailability, etc. These problems minimize by Nanoparticles. Unique properties like small size, high surface area, and ease of suspending in liquids, deep access to cells and organelles, variable optical and magnetic properties are offered by nanoparticles as compared to micro or macro particles. Polymeric nanoparticles consist of a polymeric matrix and an incorporated drug. When drug associated with Nano scale carries, their volumes of distribution are reduced and pharmacokinetics improves and drug toxicity reduced. These polymeric nanoparticles are made from copolymers to increase circulation half-life and reduced the MPS (Mononuclear Phagocytic System) uptake and inactivation of Nanoparticles. PLA and PLGA degrade by hydrolysis to Lactic Acid and Glycolic acid via Natural pathway in body. Nanoparticles are sub-nanosize colloidal structures composed of synthetic or semi-synthetic polymers ⁽²²⁾.In case of Conventional Dosage form for the treatment of liver disease it quite difficult to treat Disease because Liver it major organ for metabolism so drug get metabolise by liver(by Phase1 and Phase 2 Metabolism) before reaching to it site of action i.e. liver. So to Developed the targeted Drug Delivery System is Essential. There are two methods for Drug targeting i.e.  a) Active targeting^{(23, 24)}, b) Passive Targeting^{(23, 24)}.

 

2.MATERIAL AND METHODS:

2.1 Material:

The silymarin was obtained as gift sample from Alkem Laboratories Limited, Haryana, and PLGA (50:50) were obtained as gift sample from Cipla, Mumbai and Acetone and Methanol were of Analytical grade and were purchased from SD Fine, Nashik.

 

2.2 Method:

Nanoparticles were prepared by using solvent evaporation technique. The method involves preparation of an organic phase (Acetone) containing drug and polymer PLGA and aqueous phase containing PVA (Polyvinyl alcohol) dissolved in water. The organic phase added drop by drop into the aqueous phase during homogenization. The emulsion was broken down in to Nano droplets by homogenizer (Ultra turrex-T25, IKA laboratories, India), which formed O/W emulsion. Continuously homogenization takes place for specific time periods. Solvent is evaporated by heating the suspension on magnetic stirrer for 2 hours. Filtrate was 2- 3 times centrifuged in cooling centrifuge (Remi equipment’s, India) for 15 mins at 15000 rpm. After washing 2 -3 times using distilled water collect the supernant and add manitol and dry by spray dryer the formed nanoparticle⁽²⁵⁾.

 

2.3 OptimizationDesign Response (design expert for 3²):

In this study, a 3² experimental design was introduced to optimize the formulation of Polymeric nanoparticles (PN). Initial studies were undertaken to decide the excipients and their levels in the experimental design. On the basis of the results obtained in the preliminary screening studies, the Acetone, PLGA, and PVA, were chosen as Solvent, Polymer, surfactant respectively for further study. Among all the methods in experimental design, a 3² experimental design having fewer runs and give an idea about the interactions about the considered factors and levels. A 3² experimental design was used to reduce the number of experiments. Two factors, the Polymer Concentration (X1), Surfactant Concentration (X2) were used in the design and the responses were the average particle size(PS) (Y1) and % Entrapment Efficiency (EE) (Y2). 

 

Table.1 Independent variables and their selected levels for formulation of polymeric nanoparticles

Independent variables	Coded levels			Dependent variables
	-1	0	+1
Polymer Concentration(X1)	100 mg	300mg	500 mg	Particle size (Y1) Entrapment efficiency(Y2) Percentage Release(Y3)

 

Present Drug Release (Y3) these two factors that might affect the designed characteristic of nanoparticle formulation were varied over three levels shown in Table 1. Table show the correlation between independent variables at different tree level to the with dependent variables

 

2.4 Characteristic of NP:

2.4.1 Particle Size:

The average particle size and size distribution are important parameters because they influence the physicochemical properties and biological fate of the Nanoparticles after in vivo administration. Zetasizer (Malvern zeta sizer) was used to determine particle size. Accordingly, the dried samples were suspended in methanol. The obtained homogenous suspension was examined to determine the mean diameter and polydispersity index. Values reported were the mean diameter ± standard deviation for three replicate samples ⁽²⁶⁾.

 

2.4.2 Drug Entrapment Efficiency:

Nanoparticles equivalent to 3 mg of Silymarin were dissolved in 10 ml of methanol. Then sonicate it for 15 min. The solution was filtered and absorbance was measured by a UV spectrophotometer (Shimadzu 1800, Japan) at 287 nm. The % entrapment efficiency was calculated from following formula⁽²⁷⁾.

 

% Entrapment Efficiency

                         = initial weight of drug – free drug× 100

                                         Initial weight of drug

 

2.4.3 Shape and Surface Morphology:

 SEM analysis of the prepared formulations was carried out to understand the morphology of nanoparticles. A pinch of NPs suspension was used as the sample for SEM. A scanning electron microscope was used to analyse the surfaces of the polymeric nanoparticles. The samples were vacuum-dried and sputter-coated with accelerating voltage of8 kV for 90 s^{.  (28)}.

 

2.4.4 In-Vitro Drug Release Study:

The in vitro release of drug from the nanoparticulate formulations was determined using membrane diffusion technique by using 100 μm cellophane membranes. Silymarin loaded PLGA NP (dried product) equivalent to 10 mg of drug from each batch were taken and suspended in 100 ml of buffer acetic acid pH 4.6 solutions. Suspension was placed in donor compartment which was immersed in a beaker containing 250 ml of buffer as diffusion medium (receiver compartment).The whole assembly was stirred on magnetic stirrer maintaining the temperature of 37°C .10 ml of diffusive was withdrawn at various time interval,  1, 2, 4, 8, 24, 28, 32 and 36  hrs. Sink condition was maintained. Solutions were analyzed spectrophotometrically at wavelength 288 nm. The diffusion studies of Silymarin loaded PLGA nanoparticle were career out using membrane diffusion technique. The nanoparticle was transferred into diffusion medium and sample were taken at selected time interval, and analyzed by UV spectrophotometer (Shimadzu 1800) at 288 nm⁽²⁹⁾.

 

2.4.5 Differential Scanning Calorimetric (DSC):

The thermal properties of Silymarin, PLGA, and Silymarin-loaded PLGA nanoparticles were investigated by Differential Scanning Calorimetric (DSC). Samples (3-5 mg) was sealed in aluminum pans with lids and heated in a rate of 10ºC/min using dry nitrogen as carrier gas with a flow rate of 25 ml/min. The heat flow being recorded from 30 to 400°C. Indium was used as the standard reference material to calibrate the temperature and energy scales of the DSC instrument)^{(26, 28)}.

 

2.4.6 Zeta Potential:

The electrophoretic mobility and zeta potential were measured using a zeta potentiometer. To determine the zeta potential, nanoparticles sample was diluted with KCl (0.1 mM) and placed in the electrophoretic cell where an electric field of 15.2 V/ cm was applied. Each sample was analyzed in triplicate ^{(29, 30)}.

 

3. RESULT AND DISCUSSION:

3.1 Drug Excipient compatibility study:

3.1.1 FTIR Analysis; Silymarin:

The IR spectrum of the drug sample was recorded and the functional groups where interpreted as per the structure and where found to be appropriate the structure of the drug. Fig. 1 gives the IR spectra of the pure drug [31, 32],gives the interpretation of the peaks obtained in the IR spectra along with their corresponding functional groups. The given  IR spectra of Silymarin   shows the principle peaks such as >C=C< Aromatic 1508 cm^-1 and -O-H Stretch  1083,1157,1269 cm^-, -COOH Carboxylic acid 1732 cm^-1 indicate that above IR spectra is of Silymarin which is identified and proved.

 

Figure 1:  FT-IR of pure drug Silymarin.

 

3.1.2 FTIR Analysis; PLGA:

Fourier transform infrared (FTIR) spectra of the PLGA were obtained in the range of 400 to 4000 cm^-1 using a FTIR Spectrophotometer (Shimadzu Corp.) by the diffuse reflectance method. The FTIR spectrum of polymer is shown in Fig. 2. Gives the interpretation of the peaks obtained in the IR spectra along with their corresponding functional groups. The given IR spectra of PLGA shows principle peak such as >C=C< Aromatic 1458 cm^-1 and C-H saturated alkanes 2931 cm^-1 which prime group present in structure.

 

Figure 2: FT-IR of PLGA

 

3.1.3 Physical Mixture of Drug and polymer:

The drug polymer compatibility study is done by FTIR. Fig. 3 shows the spectra of drug and polymer mixture. The possible interaction between drug and polymer was studied by FTIR spectroscopy. Gives the interpretation of the peaks obtained in the IR spectra along with their corresponding functional groups. The physical mixture showed that there were no changes in important peaks from the pure drug and polymer IR results show the presence of the above groups in the IR spectra of drug and polymer which conformed that the drug and polymer have not found any compatibility problems. As there no variation in the IR spectrum compared to drug and polymer so there is no interaction between materials. When IR spectra of physical mixture were compared with individual spectra of drug and polymer it was observed that there is no significant change in peaks. Primary functional group of drug and polymer observed in the IR spectra of physical mixture.

 

 

Fig. 3 Infrared spectra of physical mixture

 

3.2 Percentage Practical Yield:

The results of % practical yield studies are shown in Table 4. The present practical yield varied among the formulation due to variation in the composition of formulations and found in the range 85.2-96.87 %. Formulation F-9 shows high yield i.e. 96.87 % and F1 shows low yield i.e. 85.2 %. The % yield increased as the concentration of PLGA increased.

 

3.3 Particle Size:

Particle size of nanoparticles was determined by dynamic light scattering method using the particle size analyser (Malvern Zeta Size). The particle size of nanoparticles varied somewhat among the formulation due to variation in the composition of formulations. The mean particle size of nanoparticles formulation was in the range of nm. Formulation F3 showed relatively large size i.e.422.4 nm and formulation F4 showed relatively small size i.e. 294.3 nm of nanoparticles. The Table 2 shows mean particle size of various batches. Nanoparticles size can be affected by PLGA concentration, pH, ratio of PLGA and Acetone and PVA as surfactant.

 

The use of Acetone gave smaller particle as compared to ethanol because Acetone being better non-solvent for PLGA. Acetone is miscible with water which is essential for the formation of particle nuclei and growth by solvent extraction. Found in F-1 that is 74.3% respectively. Many factors may affect the entrapment efficiency of the drug in nanoparticles. E.g. Nature of the drug, drug-polymer ratio, Stirring speed. Etc.

 

Table2. Mean Particle Size, Entrapment Efficiency and % Yield of 9 Batches of NPs

Sr. No.	Formulation Code	Particle Size (nm)	Entrapment Efficiency (%)	% Yield
1	F1	300.2	98.68	85.2%
2	F2	365.6	76.8	90.32%
3	F3	422.4	89.2	96.1%
4	F4	294.3	87.3	86.5%
5	F5	345	74.3	91.8%
6	F6	407.4	97.3	96.6%
7	F7	295	97.4	86.7%
8	F8	330.6	76.9	91.9%
9	F9	394.4	99.8	96.87%

 

3.5 In-vitro Drug Release:

To determine whether the availability of Silymarin is in controlled manner by formulating the polymeric nanoparticles, in-vitro drug diffusion studies was carried out Acetic Acid buffer pH 4.6 using 0.22 μm Membrane diffusion membranes. The results were tabulated in Table 3 and combine release graph of 9 formulations showed in Fig. 4 Drug release occurs mainly due to diffusion mechanism.

 

Table 3: Percentage Cumulative Drug Release of F1-F9 Batches

TIME (Hrs.)	F1 %	F2 %	F3 %	F4 %	F5 %	F6 %	F7 %	F8 %	F9 %
1	0.1005	2.97	1.53	0.31	4.62	0.37	7.55	6.12	1.06
2	7.532	14.90	9.09	4.72	25.87	6.20	25.60	23.39	3.99
4	14.629	25.09	19.05	14.95	30.74	14.96	34.69	31.81	12.31
8	15.70	31.02	29.07	20.07	45.85	21.33	46.02	44.19	19.52
24	24.79	52.04	43.50	36.49	53.27	30.24	58.46	56.13	29.86
28	44.50	73.20	58.80	53.87	72.30	37.42	73.2	72.80	40.42
32	61.79	88.31	69.70	65.31	82.73	61.75	89.47	84.82	55.16
36	73.53	96.6	84.70	79.46	97.38	74.05	98.67	97.57	74.60

 

 

Figure 4 : Graphical representation of Cumulative % drug release VS Time in Hrs

 

3.6. Design response( design expert for 3²⁾:

3.6.1 Entrapment efficiency response:

The following polynomial equation was obtained from ANOVA for Entrapment variable. in table 4.

 

EE=+73.71+2.12X₁-1.76X₂+7.97X₁X₂+13.96X₁²+3.43X₂²

The following polynomial equation was obtained from ANOVA for Entrapment Efficiency. According to the equation, when X1 factor (polymer concentration) was increased, then Entrapment was increased. When X2 factor (Surfactant Concentration) increases, Entrapment decreases. Positive effect was seen when X1X2 was increased. The Model F-value of 16.85 implies the quadratic model is significant. There is only a 0.011% chance that an F-value this large could occur due to noise. Values of "Probe > F" less than 0.0500 indicate model terms are significant. In this case A, B, B2 are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. The "Pred R-Squared" of 0.5859 is in reasonable agreement with the "Adj R-Squared" of 0.9083; i.e. the difference is less than 0.2. "AdeqPrecision" measures the signal to noise ratio. A ratio greater than 4 is desirable. The ratio of 73.71 indicates an adequate signal. This model can be used to Positive the design space. Fig. 5. Contour plot and two dimensional response surface plots for entrapment efficiency.

 

Table4. ANOVA analysis for Entrapment efficiency

ANOVA for response surface Quadratic Model(Entrapment Efficiency)
Source	Sum of Squares	dt	Mean Square	F Value	p-value prob>f
Model	714.12	5	142.82	16.85	0.0210	Significant
A-polymer Conc.	26.97	1	26.97	3.18	0.1725
B-surfactant conc.	8.66	1	18.66	2.20	0.2345
AB	254.08	1	254.08	29.98	0.0120
A2	390.88	1	390.88	46.12	0.0065
B2	23.53	1	23.53	2.78	0.1942
Residual	25.42	3	8.47
Cor Total	739.54	8
Std.Dev-2.91		R-Squared-0.9656
Mean-85.32		Adj R-Squared -0.9083
C.V % 3.41		Pre R-Squared 0.5859
PRESS-306.27

 

 

 

Figure.5 : Contour plot and two dimensional response surface plots for Entrapment

 

3.6.2 Drug Release Response-

 In Table 7. Show ANOVA analysis for Drug Release.

Note The following polynomial equation was obtained from ANOVA for entrapment efficiency variable.

 

DR=+94.44 – 3.06X₁ + 2.68X₂ – 8.85X₁X₂ – 16.30 X₁²+ 4.05X₂²

 

The following polynomial equation was obtained from ANOVA for Drug Release. According to the equation, when X1 factor (polymer concentration) was increased, then Drug release was decrease. When X2 factor (Surfactant Concentration) increases, drug Release increases. Negative effect was seen when X1X2 was increased. The Model F-value of 17.35 implies the quadratic model is significant. There is only a 0.011% chance that an F-value this large could occur due to noise. Values of "Prob> F" less than 0.0500 indicate model terms are significant. In this case A, B, B2 are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. The "Pred R-Squared" of 0.6793 is in reasonable agreement with the "Adj R-Squared" of 0.9109; i.e. the difference is less than 0.2. "AdeqPrecision" measures the signal to noise ratio. A ratio greater than 4 is desirable. This model can be used to Positive the design space. Fig. 6. Contour plot and two dimensional response surface plots for Drug Release.

 

 

Table5. ANOVA analysis for Drug Release

ANOVA for response surface Quadratic Model(Drug release)
Source	Sum of Squares	dt	Mean Square	F Value	p-value prob>f
Model	976.68	5	195.34	17.53	0.0201	Significant
A-polymer Conc.	56.30	1	56.30	5.00	0.1113
B-surfactant conc.	43.09	1	43.09	3.83	0.1454
AB	312.94	1	312.94	27.80	0.0133
A2	531.60	1	531.60	47.22	0.0063
B2	32.75	1	32.75	2.91	0.1866
Residual	33.78	3	11.26
Cor Total	1010.46	8
Std.Dev-3.36		R-Squared-0.9666
Mean-86.26		Adj R-Squared -0.9109
C.V % 3.89		Pre R-Squared 0.6793
PRESS-324.04		Adeq Precision-10.297

 

 

Figure. : Contour plot and two dimensional response surface plots for drug release.

 

 

 

3.7 Evaluation of optimized batch:

3.7.1.FTIR of Optimized batch:

In the IR spectra of optimized batch (Fig 7) indicates principal peaks as obtained in the spectra of drug and

polymer which indicates that optimized batch contain drug and polymer and there was no physical and chemical interaction between drug and polymer.

 

Figure 7 : FT-IR of optimized Batch

3.7.2. Particle size determination:

The average particle size and size distribution of selected and optimized Silymarin polymeric nanoparticles Fig.8 gives graphical representation of particle size distribution of prepared Silymarin Polymeric nanoparticle. optimized batch of polymeric nanoparticle has shown mean particle size of 376.8nm with polydispersity index as,0.531

 

Figure 8 : Particle Size of Optimized Batch

 

 

3.7.3. Entrapment Efficiency of Optimized Batch:

Entrapment efficiency of optimized batch was found to 99.8% it was observed that the entrapment efficiency increase with the increase in concentration of PLGA and many factor may affects the entrapment efficiency of drug in nanoparticle like Nature of drug, drug-polymer ratio, Stirring speed etc.

 

3.7.4. In-Vitro Release Study of Optimized Batch:

To determine whether the availability of Silymarin is in controlled manner by comparing formulating the polymeric nanoparticles and marketed formulation(hepasil-capsule), in-vitro drug diffusion studies was carried out Acetic Acid buffer pH 4.6 using 0.22 μm Membrane diffusion membranes. The results were tabulated in Table 6 and Table 7 and combine release graph of 9 formulations showed in Fig. 9 Drug release occurs mainly due to diffusion mechanism.

 

 

Table.6 % Drug release of optimized formulation.

Sr.No	Time (Hr)	Absorption	% drug Release
1	1	0.1996	0.3735
2	2	0.2167	2.8454
3	4	0.2729	11.0333
4	8	0.3177	17.9063
5	24	0.3894	28.9017
6	28	0.4509	38.8436
7	32	0.5327	52.0408
8	36	0.6198	66.3712

 

Table.7 % Drug Release of Marketed Formulation Hepasil Capsule:

Sr.No	Time (min)	Absorption	% Drug Release
1	5	0.2705	10.56
2	10	0.2999	15.20
3	15	0.3999	30.16
4	30	0.5779	56.90
5	40	0.6944	75.83
6	50	0.7362	84.69
7	60	0.8136	98.49

 

Fig No 9- Graphical representation of % Cumulative of silymarin nanoparticle and marketed hepasil drug release vs. time in Hrs. 

 

 

3.7.5. Differential scanning calorimetric (DSC) study:

DSC thermograph of Silymarin, PLGA and Silymarin-loaded PLGA nanoparticles were shown in diagram. A physical change gives the endothermic peak and chemical changes give rise exothermic peak. The pure drug Silymarin (Fig.10) gives rise to a sharp endothermic peak that corresponds to melting at 218˚C, indicating its crystalline nature. A broad peak is observed due to the dehydration reaction of the drug. The pure PLGA polymer also gives rise to sharp endothermic peak that corresponds to melting point at 96˚C. (Fig. 11). No distinct melting point was observed because PLGA is amorphous in nature. Pure drug and polymer shows the graph melting point near about the standard ranges and hence, the two peaks at 93.62˚C and 220˚C are related to the thermal decomposition of the polymer and drug. The DSC curves of optimized batch was observed at 24˚C, it showed that the shifting of melting endotherm of Silymarin and PLGA, which could indicate that the amorphous nature of the drug and as loss of crystalline nature, indicates change in melting point of formulation. DSC of optimised formulation was given in fig.12

 

 

Fig10 DSC thermo gram of Silymarin

 

 

 

Figure 11 : DSC thermo gram of PLGA

 

 

Figure 12 : DSC thermo gram of optimized formulation

 

3.7.6. Scanning Electron Microscopy (SEM):

Morphology of the nanoparticles was studied by SEM. The Fig.13 shows image of the optimized batch. It shows the smooth surface with spherical shape, may be due to optimum polymer and PLGA concentration.

 

 

Fig.13 SEM image of optimized formulation

 

3.7.7. Zeta potential:

Zeta potential of Silymarin loaded PLGA nanoparticles for optimized batch was determined and it was found – 7.15 mV, showed in Fig.14 Which indicates moderate stability with no agglomeration.The negative surface charge originates from free carboxylic acid groups at the chain ends of the PLGA. The possible effects of surface charge may affect the in-vivo life span of the natural drug delivery system.

 

 

Figure 14 : Zeta potential graph of optimized formulation

3.7.8. Effect of temperature and humidity:

Effect of temperature and humidity of the prepared nanoparticles were carried out, by storing optimized formulation at 4 ± 2 ^˚C, and at room temperature 45 % RH for 30 days in stability chamber. Two parameters namely entrapment efficiency and in-vitro release studies were carried out. It is found that nanoparticles stored at room temperature are not stable whereas stored at 4^˚C is stable given in Table.8 NPs at room temperature showed decrease in the entrapment efficiency and different release pattern.  After 28 days stability testing the entrapment efficiency and drug release changes is observed within a standard limits.

 

Table.8 Effect of Temperature on Optimized Formulation.

Temperature Condition	Parameters	Days
		0	7	14	28
4 ± 2°C	Entrapment efficiency (%)	73.83	73.7	3.45	73.1
	Release (%)	62.55	62.47 36th  hr.	62.05 in 36th  hr.	62 in 36th hr.
	Entrapment efficiency (%)	73.83	73.5	72.35	72
Room temp.	Release (%)	62.55	62.30 in 36th  hr.	62.29 in 36th  hr.	61 in 36th  hr.

 

4. CONCLUSION:

Particulate drug delivery i.e. nanoparticles have emerge as an efficient means of enhancing the controlled or sustained delivery of many drugs. From the experimental results it can be concluded that: PLGA is an ideal carrier for preparing nanoparticles of Silymarin. PLGA nanoparticles have the stability problem hence is PVA used as Surfactant as well as Stabilizing agent for the production of stable nanoparticles. PVA also affect the entrapment efficiency and release rat, as well as on particle size. High speed homogenizer Speeds have significant effect on the particle size of nanoparticles. The results Shows that Optimized formulation showed smaller particle size (397.7nm), spherical and smooth surface morphology and successful retarding drug release over the test period of 36 hr. in in-vitro studies(73.53%).The experimental data presented here in indicated that a number of parameters such as PLGA concentration and PLGA have more effect on particle size. In contrast, the amount of PVA concentration and agitation speed was shown which have less effect on particle diameter. Particles with high polymer concentration were found to be spherical in shape and have good particle size. Zeta potential value of optimized formulation -7.5 mV shows that the charges on the surface of the nanoparticles are high and which help in minimizing the interaction between the particles, which favor the stability of nanoparticles for long period of time. The in-vitro release studies showed controlled release of Drug. If ideal properties of nanoparticles are considered it mainly involves smaller particle size, good entrapment efficiency of drug and sufficient release of drug for prolong period of time. All above properties play important role while developing a desired nanoparticulate system. According to the studied factors, the obtained nanoparticles corresponded to formulation that was prepared using 100 mg PLGA, 1% PVA, 10 ml of Acetone and 15,000 R.P.M, was selected as optimized formulation because of small particle size, high drug-entrapment, and successful retarding drug release over the 36 hrs. in-vitro studies. Effect of temperature and humidity for optimized formulation  showed slight decrease in drug content but almost identical in vitro release profile for formulation kept at 4°C storage where as it showed significant decrease in the entrapment and also different release pattern at room temp. Thus it can be concluded that 4°C is the most suitable temperature for storage of Silymarin nanoparticles. From the above studies it is revealed that present work was a satisfactory preliminary study to control the release of Silymarin for prolonged period of time and thus reducing the inherent and significant side-effects of the anticancer drug, Silymarin. Based on these findings we can conclude that Silymarin loaded PLGA nanoparticles are promising agents for targeted drug delivery in Liver. The results of our study clearly indicate that there is great potential in delivery of Silymarin to the targeted region as an alternative to the conventional dosage form.

 

5. ACKNOWLEDGEMENT:

The authors are thankful to the Trustees, MET’s Institute of Pharmacy, Bhujbal Knowledge City, Nashik for providing the necessary research facilities.

 

6. REFERENCE:

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Received on 14.07.2017                Accepted on 19.10.2017               

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