Formulation and Development of Capsules Containing Rosuvastatin Calcium Nanoparticles and...

www.iajpr.com

Pag

e22

17

Indo American Journal of Pharmaceutical Research, 2015 ISSN NO: 2231-6876

FORMULATION AND DEVELOPMENT OF CAPSULES CONTAINING ROSUVASTATIN

CALCIUM NANOPARTICLES AND EPIGALLOCATECHIN GALLATE NANOPARTICLES

Ramkumar Ponnuraj1, Janakiraman K

1, Sivaraman Gopalakrishnan

2, Arunkumar Arumugam

2

1Faculty of Engineering and Technology, AnnamalaiUniversity, India.

2Product Development, Apex laboratories, India.

Corresponding author

Ramkumar Ponnuraj

Faculty of Engineering and Technology,

Annamalai University

[email protected]

Copy right © 2015 This is an Open Access article distributed under the terms of the Indo American journal of Pharmaceutical

Research, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

ARTICLE INFO ABSTRACT

Article history

Received 02/06/2015

Available online

30/06/2015

Keywords

Rosuvastatin Calcium,

Epigallocatechin Gallate,

Nanoparticles,

Hard Gelatin Capsule,

Sustained Release.

Rosuvastatin Calcium is a statin used extensively for the treatment of dyslipidemia,

hypercholesterolemia and hypertriglyceridemia. Like all statins, Rosuvastatin can possibly

cause myopathy and rhabdomyolysis. It was reported that statin induced the expression of

atrogin-1, a key gene involved in skeletal muscle atrophy. In this process the function of

mitochondria play a vital role in limiting the atrophy. Epigallocatechin gallate promotes

mitochondrial biogenesis and thereby prevents the atrophy caused by statin. Similarly,

Epigallocatechin gallate can also helps in reducing the LDL cholesterol. Both Rosuvastatin

Calcium and Epigallocatechin gallate has a poor bioavailability and it can be improved by the

use of nanoparticles. Nanoparticles were filled in hard gelatin capsules along with

Microcrystalline Cellulose, Colloidal Silicon Dioxide, Magnesium Trisilicate and Magnesium

Stearate. The ratio of Colloidal Silicon Dioxide, Magnesium Trisilicate and Magnesium

Stearate was studied using two level Factorial design. The final product is stable and provides

a sustained release for 24 hours. The combination proved to be promising with improved

efficacy and reduced side effects.

Please cite this article in press as Ramkumar Ponnuraj et al. Formulation and Development of Capsules Containing Rosuvastatin

Calcium Nanoparticles and Epigallocatechin Gallate Nanoparticles. Indo American Journal of Pharm Research.2015:5(06).

www.iajpr.com

Pag

e22

18

Vol 5, Issue 06, 2015. Ramkumar Ponnuraj et al. ISSN NO: 2231-6876

INTRODUCTION

Rosuvastatin calcium, bis[(E)-7-[4- (4-fluorophenyl)-6-isopropyl-2- [methyl(methylsulfonyl)amino] pyrimidin-5-yl](3R,5S)-

3,5-dihydroxyhept-6-enoic acid] calcium salt is a member of the drug class of statins used to treat high cholesterol and related

conditions and prevent cardiovascular disease. It acts by competitive inhibition of HMG-CoA reductase which is the rate controlling

enzyme of the mevalonate pathway that produces cholesterol [1, 2].

The drug has a poor solubility and poor permeability which leads to low bioavailability (absolute bioavailability 20%) [3].

Since the dissolution and permeation are the rate limiting factors for the low bioavailability, it becomes a requirement to

improve dissolution and permeability of the drug. Absorption and low bioavailability due to limited dissolution rate is the major

impediment allied to the use of many poorly soluble drugs.

Rosuvastatin Nanoparticles were prepared using Chitosan by ionic gelation method to improve the bioavailability [4].

Epigallocatechin gallate, [(2R, 3R)-5,7-dihydroxy-2-(3,4,5-trihydroxyphenyl)chroman-3-yl] 3,4,5-trihydroxybenzoate also

known as epigallocatechin-3-gallate is the ester of epigallocatechin and gallic acid and is a type of catechin. It has been the subject of

a number of studies investigating its potential use as a therapeutic for a broad range of disorders, which includes HIV [5-8], Cancer [9-

12], Chronic fatigue syndrome [13-15], Sjögren's syndrome [16], Endometriosis [17], Spinal muscular atrophy [18],

Neurodegeneration [19-20], Cannabinoid 1 receptor, CB1 receptor Activity [21], Periapical lesions [22], Cerebrovascular insult [23].

Epigallocatechin gallate is unstable in the gastrointestinal tract. It rapidly degrades in both acidic (pH below 2) and neutral

conditions. Encapsulating Epigallocatechin gallate into nanoparticles using chitosan, significantly delayed its degradation in simulated

digestive fluids [24-27].

The present study is aimed to formulate hard gelatin capsule containing Rosuvastatin Calcium nanoparticles and

Epigallocatechin gallate nanoparticles to decrease the side effects attributed to Rosuvastatin and increase the bioavailability. The

nanoparticles were prepared using Chitosan by ionic gelation method [4,28].

Since the nanoparticles tend to aggregate on storage and in biological fluids Zeta Potential plays a major role [29]. Hence in

this formulation Colloidal Silicon dioxide, Magnesium Trisilicate and Magnesium Stearate plays a major role in preventing the

nanoparticle aggregation on storage. This is confirmed by measuring the Zeta Potential. This indirectly helps in uniform

predetermined release of drug similar to that of nanoparticles which is confirmed by the in vitro release studies.

To optimize these excipients and its control in the formulation factorial design was employed. We developed a two factorial

design 23 with two levels (Low concentration and High concentration), three factors (Colloidal Silicon dioxide, Magnesium Trisilicate

and Magnesium Stearate) and measured with two responses (Zeta Potential and in vitro release)

MATERIALS AND METHODS

Materials

Rosuvastatin Calcium (RC) was purchased from Hetero drugs, India. Epigallocatechin gallate (EGCG) was purchased from

Novanat, China. Chitosan (CS) was obtained from Novamatrix, Norway. Dimethyl sulfoxide (DMSO), Acetic acid, Sodium

Tripolyphosphate (TPP), Diethyl ether, Trifluoroacetic acid and Acetonitrile were procured from Sigma-Aldrich, USA. Poloxamer

188 was procured from BASF, Germany. Deionized water was obtained from Millipore filtration system, USA. Hard Gelatin Capsule

(HGC) was procured from Associated Capsules, India. Colloidal Silicon Dioxide (CSD) was procured from Evonik, Singapore.

Microcrystalline Cellulose (MCC) was procured from FMC Biopolymer, US. Magnesium Trisilicate (MTS) and Magnesium Stearate

(MS) was procured from Prachin Chemicals, India.

Method of preparation:

EGCG nanoparticles (EGCG NP) and RC nanoparticles (RC NP) were prepared by ionic gelation method [4, 28].

MCC was passed through 30 mesh screen, CSD, MTS and MS were passed through 30 mesh screen. All the above excipients were

mixed with EGCG NP and RC NP in geometric mixing followed by final mixing in an Octagonal Blender for 5 min.

The resulting granules were then filled in „0‟ size HGC using Hand filling machine.

www.iajpr.com

Pag

e22

19


Optimization of formulations of capsule by 23 factorial designs

The two level factorial design 23 were 3 factors are considered, each at 2 levels as depicted in Table 1.

Table 1: Composite of factorial batches.

Batch Variable level in coded form

X1 X2 X3

ERC1 -1 1 -1

ERC2 1 -1 1

ERC3 1 1 -1

ERC4 -1 -1 1

ERC5 1 1 1

ERC6 -1 1 1

ERC7 -1 -1 -1

ERC8 1 -1 -1

Independent variable

Low (-1) High (+1)

CSD (X1) 2.08% 6.25%

MTS (X2) 3.33% 10.00%

MS (X3) 2.50% 7.50%

Amount of CSD (X1) amount of MTS (X2) and amount of MS (X3) were selected as independent variables. Zeta Potential

and in vitro release of two hours in hydrochloric acid buffer (pH 1.2) followed by ten hours in phosphate buffer (pH 6.8) were selected

as dependent variables. The preparation and evaluation method for capsules and amount of RC NP and EGCG NP were kept constant

for all the trials. The composition of factorial batches ERC1 to ERC8 is shown in Table 2.

Table 2: Formulation of EGCG NP and RC NP capsules with various concentrations of excipients.

Ingredients

(mg)

Formulation Code

ERC1 ERC2 ERC3 ERC4 ERC5 ERC6 ERC7 ERC8

EGCG NP 325.0 325.0 325.0 325.0 325.0 325.0 325.0 325.0

RC NP 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0

MCC 177.5 162.5 152.5 187.5 122.5 147.5 217.5 192.5

CSD 12.5 37.5 37.5 12.5 37.5 12.5 12.5 37.5

MTS 60.0 20.0 60.0 20.0 60.0 60.0 20.0 20.0

MS 15.0 45.0 15.0 45.0 45.0 45.0 15.0 15.0

Total 600.0 600.0 600.0 600.0 600.0 600.0 600.0 600.0

Evaluation of capsules for Zeta potential

The final granules were measured for Zeta potential using Malvern Zetasizer (Malvern Instruments, UK), by dynamic light

scattering and Electrophoretic light scattering principle. The particle size analysis was performed at a scattering angle of 90°C, at

room temperature. The diameter was averaged from three parallel measurements and expressed as mean ± standard deviation.

Evaluation of capsules for in vitro release:

The in vitro release study of the final capsules was carried out using USP dissolution apparatus type I (basket method). Each

capsule was loaded in the basket and using 900 ml of Hydrochloric acid buffer (pH 1.2) for two hours followed by 900 ml of

Phosphate buffer(pH 6.8) for ten hours, at a rotating speed of 50 rpm and 37C ± 0.5C

Samples were collected at specific time intervals. 2 ml of aliquot was collected during each sampling point and it was

replaced with an equal volume of fresh buffer. The amount of drug release was determined by UHPLC method.

The response surface plot by full factorial design

The two level factorial design 23 was plotted and studied using the response surface plot with Design Expert Software (Jandel

Scientific, San Rafael, CA).

Accelerated stability study of the selected batch

The selected batch with the help of Design Expert Software was kept on Accelerated stability condition for a period of one

week at 40C and 75% RH.

www.iajpr.com

Pag

e22

20


RESULTS AND DISCUSSION

Results of factorial batches

Zeta potential of various trials was found to be in the range of +31.0 mV to +44.5 mV. It was found that higher the zeta

potential, lesser will be the particle aggregation, due to electric repulsion. The stability will be high, if the Zeta Potential is high.

Similarly the in vitro release profile is also measured and tabulated in Table 3

Table 3: Results of Factorial batches.

Batch No Zeta Potential In vitro release

ERC1 33.9 85.9

ERC2 41.1 92.94

ERC3 41.9 93.15

ERC4 33.6 86.15

ERC5 44.5 98.54

ERC6 38.4 90.41

ERC7 31.0 81.08

ERC8 36.6 88.15

The response surface plot by full factorial design

The amount of CSD (X1), amount of MTS (X2) and amount of MS (X3) were chosen as independent variables on dependent

variables Zeta Potential and in vitro release in a 23 full factorial design. This is depicted in Figure 1

Figure 1: 23 Full Factorial Design schematic explanation.

Transformation is not required as the response range of Zeta Potential is 1.43548 fold (Figure 2a) and the response range of In vitro

release is 1.21534 fold (Figure 2b), which falls below the ratio of 3, where power transformation have little effect.

Figure 2(a): Ratio of Max to Min response of Zeta

Potential

Figure 2(b): Ratio of Max to Min response of in-vitro

release

The Zeta Potential and in-vitro release for the eight batches (ERC1 to ERC8) showed a wide variation (i.e. 31.0 mV to 44.5

mV and 45.87% to 20.24%). The data clearly indicate that the Zeta potential and percentage drug release after twelve hours are

strongly depending on the selected independent variables.

www.iajpr.com

Pag

e22

21


Factorial design Analysis for Zeta Potential Response:

The absolute value of all effects (plotted as square) on half-normal probability plot (Figure 3) of Zeta Potential. There are

positive and negative effects due to the various trials. It shows that the combination of all the factors shows a negative effect and

the remaining shows positive effects.

To proceed, we must choose which effects to include in the model. All the big effects will be selected by rejecting the

effects from the right corner of the plot till the line of best fit passes close to the origin.

Figure 3: Half-normal probability plot of Zeta Potential.

The magnitude of the above effects can be visualized using Pareto bar chart (Figure 4). The effect decreases in magnitude

from left to right. The effects which we selected from the half normal plot (Figure 3) are highlighted in white. In the below chart the

vertical axis shows the t-value of the absolute effects.

Figure 4: Pareto bar chart, Zeta Potential as a response.

From the above data we do a statistical analysis to check if the model is statistically significant or just random noise in the

data. The ANOVA provided in below Table 4.

Table 4: ANOVA data for Zeta Potential.

Source Sum of

Squares Df Mean Square F Value

p-value

Prob > F

Model 151.305 3 50.435 104.5284974 0.000296189 significant

A – CSD 92.48 1 92.48 191.6683938 0.000157795

B – MTS 33.62 1 33.62 69.67875648 0.001125901

C – MS 25.205 1 25.205 52.23834197 0.001943949

Residual 1.93 4 0.4825

Cor Total 153.235 7

Std. Dev. ±0.694622199 R-Squared 0.987404966

Mean 37.625 Adj R-Squared 0.977958691

C.V. % 1.846171959 Pred R-Squared 0.949619865

PRESS 7.72 Adeq Precision 29.4194254

www.iajpr.com

Pag

e22

22


From the Table 4 the Model F-value of 104.53 implies the model is significant. There is only a 0.03% 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. Generally values

greater than 0.1000 indicate the model terms are not significant. In this case A, B, C are significant model terms.

The "Pred R-Squared" of 0.9496 is in reasonable agreement with the "Adj R-Squared" of 0.9780 i.e. the difference is less

than 0.2.

"Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Our ratio of 29.419 indicates an

adequate signal. This model can be used to navigate the design space.

Table 5: ANOVA data for Zeta Potential with coded factors and actual factors.

Factor Coefficient Estimate df Standard Error 95% CI Low 95% CI High VIF

Intercept 37.625 1 ±0.245586034 36.94314386 38.30685614

A – CSD 3.4 1 ±0.245586034 2.718143859 4.081856141 1

B – MTS 2.05 1 ±0.245586034 1.368143859 2.731856141 1

C – MS 1.775 1 ±0.245586034 1.093143859 2.456856141 1

Final Equation in Terms of Coded Factors and Actual Factors

Coded Factors Actual Factors

Zeta Potential 37.625 Zeta Potential 23.175

3.4 * A 0.272 * CSD

2.05 * B 0.1025 * MTS

1.775 * C 0.118333333 * MS

The equation in terms of coded factors can be used to make predictions about the response for given levels of each factor. By

default, the high levels of the factors are coded as +1 and the low levels of the factors are coded as -1. The coded equation is useful

for identifying the relative impact of the factors by comparing the factor coefficients. The values were captured in Table 5.

The equation in terms of actual factors can be used to make predictions about the response for given levels of each factor.

Here, the levels should be specified in the original units for each factor. This equation should not be used to determine the relative

impact of each factor because the coefficients are scaled to accommodate the units of each factor and the intercept is not at the center

of the design space.

The Normal plot of residuals shown in Figure 5 is the first diagnostic plot that appears. Ideally, the normal plot of residuals

will be a straight line, indicating no abnormalities.

Figure 5: Normal Plot of Residuals, Zeta Potential as a response.

The residuals versus predicted plot Figure 6, plotted for Zeta Potential. The size of the residual should be independent of its

predicted value. In other words, the spread of the residuals (y-axis) should be approximately the same across all levels of the predicted

values (x-axis). In our case the range from left to right is relatively constant and there are no serious patterns like megaphone.

www.iajpr.com

Pag

e22

23


Figure 6: Residuals vs Predicted, plotted for Zeta Potential.

The plot can also be used to check the outliners. If any of the residual points fall outside of the red lines (above or below)

present in the Figure 6, they are considered statistical outliners. In our case, there are no outliners.

The predicted versus actual plot Figure 7, plotted for Zeta Potential. The actual data values (x-axis) were plotted against the

predicted values (y-axis) obtained from the model. A perfect model would fit the 45° line drawn on the plot. However, there is

expected to be some noise, so a perfect model isn‟t what we are looking for.

Figure 7: Predicted vs Actual, plotted for Zeta Potential.

As long as there is an even spread above and below the line, the predictions are unbiased. In our case the predictions seems to

be perfect.

To calculate the best power law transformation Box-Cox plot is used (Figure 8). The recommended transformation in our

case is “None”

Figure 8: Box Cox Plot for Power Transforms with respect to Zeta Potential.

The Cook‟s Distance plot for Zeta Potential is shown in Figure 9. In this plot if there are any values that are highly influential

on the model, they will be above the red line. In our case such problems were not observed and there are no overlay influential values.

www.iajpr.com

Pag

e22

24


Figure 9: The Cook’s Distance plot for Zeta potential.

The numerical values for all the diagnostic statistics reported on a run-by-run basis in standard order shows no discrepant

values.

All the Interaction plots showed that the increase in the concentration of CSD, MS and MTS will increase the Zeta Potential

which is depicted in the below Contour map (Figure 10) and the 3D Surface plot (Figure 11)

Figure 10: Left – The contour map of MTS in comparison to the other two factors, Right – The contour map of MS in

comparison to the other two factors.

Figure 11: Left – The 3D Surface Plot of MTS in comparison to the other two factors, Right – The 3D Surface Plot of MS in

comparison to the other two factors

Factorial design Analysis for Dissolution Response

The absolute value of all effects (plotted as square) on half-normal probability plot (Figure 12) of Dissolution. There are

positive and negative effects due to the various trials. It shows that the combination of all the factors shows a negative ef fect and

the remaining shows positive effects.

To proceed, we must choose which effects to include in the model. All the big effects will be selected by rejecting the

effects from the right corner of the plot till the line of best fit passes close to the origin.

www.iajpr.com

Pag

e22

25


Figure 12: Half-normal probability plot of Dissolution.

The magnitude of the above effects can be visualized using Pareto bar chart (Figure 13). The effect decreases in magnitude

from left to right. The effects which we selected from the half normal plot (Figure 12) are highlighted in white. In the below chart the

vertical axis shows the t-value of the absolute effects.

Figure 13: Pareto bar chart, Dissolution as a response.

From the above data we do a statistical analysis to check if the model is statistically significant or just random noise in the

data. The ANOVA provided in below Table 6.

Table 6: ANOVA data for Dissolution.

Source Sum of Squares df Mean Square F Value p-value

Prob > F

Model 204.0922 3 68.03073 541.8616753 0.000011 significant

A – CSD 106.8722 1 106.8722 851.2321784 0.000008

B – MTS 48.4128 1 48.4128 385.6057348 0.000040

C – MS 48.8072 1 48.8072 388.7471127 0.000039

Residual 0.5022 4 0.12555

Cor Total 204.5944 7

Std. Dev. ±0.354330354 R-Squared 0.997545387

Mean 89.54 Adj R-Squared 0.995704428

C.V. % 0.395722978 Pred R-Squared 0.990181549

PRESS 2.0088 Adeq Precision 68.5294008

From the Table 6 the Model F-value of 541.86 implies the model is significant. Values of "Prob > F" less than 0.0500

indicate model terms are significant. Generally values greater than 0.1000 indicate the model terms are not significant. In this case A,

B, C are significant model terms.

The "Pred R-Squared" of 0.9902 is in reasonable agreement with the "Adj R-Squared" of 0.9957 i.e. the difference is less

than 0.2.

"Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Our ratio of 68.529 indicates an

adequate signal. This model can be used to navigate the design space.

www.iajpr.com

Pag

e22

26


Table 7: ANOVA data for Dissolution with coded factors and actual factors.

Factor Coefficient Estimate df Standard Error 95% CI Low 95% CI High VIF

Intercept 89.54 1 ±0.125274698 89.19218168 89.88781832

A – CSD 3.655 1 ±0.125274698 3.307181677 4.002818323 1

B – MTS 2.46 1 ±0.125274698 2.112181677 2.807818323 1

C – MS 2.47 1 ±0.125274698 2.122181677 2.817818323 1

Final Equation in Terms of Coded Factors and Actual Factors

Coded Factors Actual Factors

Dissolution 89.54 Dissolution 72.37

3.655 * A 0.2924 * CSD

2.46 * B 0.123 * MTS

2.47 * C 0.1647 * MS

The equation in terms of coded factors can be used to make predictions about the response for given levels of each factor. By

default, the high levels of the factors are coded as +1 and the low levels of the factors are coded as -1. The coded equation is useful

for identifying the relative impact of the factors by comparing the factor coefficients. The values were captured in Table 7.

The equation in terms of actual factors can be used to make predictions about the response for given levels of each factor.

Here, the levels should be specified in the original units for each factor. This equation should not be used to determine the relative

impact of each factor because the coefficients are scaled to accommodate the units of each factor and the intercept is not at the center

of the design space.

The Normal plot of residuals shown in Figure 14 is the first diagnostic plot that appears. Ideally, the normal plot of residuals

will be a straight line, indicating no abnormalities.

Figure 14: Normal Plot of Residuals, Dissolution as a response.

The residuals versus predicted plot Figure 15, plotted for Dissolution. The size of the residual should be independent of its

predicted value. In other words, the spread of the residuals (y-axis) should be approximately the same across all levels of the predicted

values (x-axis). In our case the range from left to right is relatively constant and there are no serious patterns like megaphone.

Figure 15: Residuals vs Predicted, plotted for Dissolution.

The plot can also be used to check the outliners. If any of the residual points fall outside of the red lines (above or below)

present in the Figure 15, they are considered statistical outliners. In our case, there are no outliners.

www.iajpr.com

Pag

e22

27


The predicted versus actual plot Figure 16, plotted for Dissolution. The actual data values (x-axis) were plotted against the

predicted values (y-axis) obtained from the model. A perfect model would fit the 45° line drawn on the plot. However, there is

expected to be some noise, so a perfect model isn‟t what we are looking for.

Figure 16: Predicted vs Actual, plotted for Dissolution.

As long as there is an even spread above and below the line, the predictions are unbiased. In our case the predictions seems to

be perfect.

To calculate the best power law transformation Box-Cox plot is used (Figure 17). The recommended transformation in our

case is “None”

Figure 17: Box Cox Plot for Power Transforms with respect to Dissolution.

The Cook‟s Distance plot for Dissolution is shown in Figure 18. In this plot if there are any values that are highly influential

on the model, they will be above the red line. In our case such problems were not observed and there are no overlay influential values.

Figure 18: The Cook’s Distance plot for Dissolution.

www.iajpr.com

Pag

e22

28


The numerical values for all the diagnostic statistics reported on a run-by-run basis in standard order shows no discrepant

values.

All the Interaction plots showed that the increase in the concentration of CSD, MS and MTS will increase the release of Drug

which is depicted in the below Contour map (Figure 19) and the 3D Surface plot (Figure 20)

Figure 19: Left – The contour map of MTS in comparison to the other two factors, Right – The contour map of MS in


Figure 20: Left – The 3D Surface Plot of MTS in comparison to the other two factors, Right – The 3D Surface Plot of MS in


Accelerated stability study of batch ERC5

In order to determine the change in in-vitro release profile on storage, stability study of batch ERC5 was carried out at 40C

in a humidity chamber having 75% RH. Sample were withdrawn after one-week interval and evaluated for change in in-vitro drug

release pattern. The similarity factor (F2) was applied to study the effect of storage on formulation ERC5. The results of accelerated

stability studies are shown in table 8 and Figure 21

Table 8: Results of stability study of batch ERC5.

Time

(hr)

Cumulative %

drug release (Initial)

Cumulative %

drug release

(After storage at 40°C

for 3 months)

0 0 0

1 33.24 31.54

2 41.56 39.24

3 50.18 46.87

4 59.15 56.87

5 63.51 59.24

6 69.48 66.97

7 73.70 70.05

8 80.09 77.54

9 84.08 80.24

10 89.30 86.87

11 92.97 88.20

12 98.54 95.15

www.iajpr.com

Pag

e22

29


Figure 21: Comparative drug release - Initial release profile compared with the release profile of the samples kept in

stability for 3 months.

CONCLUSION

In formulation MCC was incorporated as a diluent. CSD was used for the flow of granules and MTS and MS were used as a

lubricant and antiadherant. A 23 full factorial design was employed for preparation of capsules possessing optimized characteristics

(batches ERC1 to ERC8). The amount of CSD (X1), MTS (X2) and MS (X3) were selected as independent variables. Zeta Potential

and drug release after twelve hours were selected as dependent variable. Based on result, it was concluded that higher Zeta Potential

and better drug release could be obtained when X1, X2 and X3 were kept at higher level. Capsule of batch ERC5 exhibited better and

steady drug dissolution for 12 hours. It was concluded that by adopting a systematic formulation approach, an optimum point could be

reached in the shortest time with minimum efforts. The formulation needs to be further investigated with clinical studies.

ACKNOWLEDGMENTS

This work carried out in the apex laboratories, was part of a project investigating the production of nanoparticle systems

using antilipidemic drugs, for an effective treatment. The instruments and infrastructure used to conduct the above experiment were

facilitated by apex laboratories.

Abbreviation

1. LDL – Low Density Lipoprotein

2. UHPLC – Ultra High Performance Liquid Chromatography

3. mV – millivolts

4. % - Percentage

5. ANOVA – Analysis of Variance

6. Df – Degree of freedom

7. Std. Dev – Standard Deviation

8. PRESS - Predicted Residual Sum of Squares

9. CI – Confidence interval

10. VIF – Variance inflation factor

11. C – degree Centigrade

www.iajpr.com

Pag

e22

30


REFERENCE

1. Mc Taggart F, Buckett L, Davidson R, Holdgate G, McCormick A, Schneck D, et al. Preclinical and clinical pharmacology of

rosuvastatin, a new 3-hydroxy-3-methylglutaryl coenzyme A reducatase inhibitor. Am J Cardiol 2001; 87 (5A): 28B-32B.

2. Smith G, Davidson R, Bloor S, Burns K, Calnam CC, Aulay PMC, et al. Pharmacological properties of ZD4522 – a new

HMGCoA reductase inhibitor. Atherosclerosis Supplements 2000; 151: 39

3. Agarwal, RK; Showkathali, R (June 2013). ―Rosuvastatin calcium in acute coronary syndromes ‖. Expert Opinion on

Pharmacotherapy 14 (9): 1215-1227.

4. Ramkumar Ponnuraj, Janakiraman K, Gopalakrishnan Sivaraman, Henald John Jeyakumar, Vaddiboina Venkateswarlu, Deepak S

Narayanan, Formulation and Characterization of Rosuvastatin Calcium Nanoparticles. IAJPR. 2015; 5(2): 767-779.

5. Williamson MP, McCormick TG, Nance CL, Shearer WT (December 2006). "Epigallocatechin gallate, the main polyphenol in

green tea, binds to the T-cell receptor, CD4: Potential for HIV-1 therapy". The Journal of Allergy and Clinical Immunology 118

(6): 1369–74.

6. Hamza A, Zhan CG (February 2006). "How can (-)-epigallocatechin gallate from green tea prevent HIV-1 infection? Mechanistic

insights from computational modeling and the implication for rational design of anti-HIV-1 entry inhibitors". The Journal of

Physical Chemistry. B 110 (6): 2910–7.

7. Yamaguchi K, Honda M, Ikigai H, Hara Y, Shimamura T (January 2002). "Inhibitory effects of (-)-epigallocatechin gallate on the

life cycle of human immunodeficiency virus type 1 (HIV-1)". Antiviral Research 53 (1): 19–34.

8. Nance CL, Shearer WT (November 2003). "Is green tea good for HIV-1 infection?". The Journal of Allergy and Clinical

Immunology 112 (5): 851–3.

9. Chung MY, et al. Molecular mechanisms of chemopreventive phytochemicals against gastroenterological cancer development

World J Gastroenterol. 2013 Feb 21;19(7):984-93.

10. Connors SK, et al New insights into the mechanisms of green tea catechins in the chemoprevention of prostate cancer Nutr

Cancer. 2012;64

11. Landis-Piwowar K, et al Novel epigallocatechin gallate analogs as potential anticancer agents: a patent review (2009 - present)

Expert Opin Ther Pat. 2013 Feb;23(2):189-202.

12. Chen D, et al. EGCG, green tea polyphenols and their synthetic analogs and prodrugs for human cancer prevention and treatment

Adv Clin Chem. 2011;53:155-77.

13. Sachdeva, A K; Kuhad, A; Chopra, K (2011). "Epigallocatechin gallate ameliorates behavioral and biochemical deficits in rat

model of load-induced chronic fatigue syndrome". Brain Research Bulletin 86 (3–4): 165–72.

14. Sachdeva, A K; Kuhad, A; Tiwari, V; Arora, V; Chopra, K (2010). "Protective effect of epigallocatechin gallate in murine water-

immersion stress model of chronic fatigue syndrome". Basic & Clinical Pharmacology & Toxicology106 (6): 490–96.

15. Sachdeva, A K; Kuhad, A; Tiwari, V; Chopra, K (2009). "Epigallocatechin gallate ameliorates chronic fatigue syndrome in mice:

behavioral and biochemical evidence".Behavioural Brain Research 205 (2): 414–20.

16. Gillespie, K; Kodani, I; Dickinson, DP; Ogbureke, KU; Camba, AM; Wu, M; Looney, S; Chu, TC et al. (2008)."Effects of oral

consumption of the green tea polyphenol EGCG in a murine model for human Sjogren's syndrome, an autoimmune disease". Life

Sciences 83 (17–18): 581–8.

17. Xu, Hui; Becker, Christian M.; Lui, Wai Ting; Chu, Ching Yan; Davis, Tina N.; Kung, Andrew L.; Birsner, Amy E.; d‟Amato,

Robert J. et al. (2011). "Green tea epigallocatechin-3-gallate inhibits angiogenesis and suppresses vascular endothelial growth

factor C/vascular endothelial growth factor receptor 2 expression and signaling in experimental endometriosis in vivo". Fertility

and Sterility 96 (4): 1021–8.

18. Sakla, M. S.; Lorson, C. L. (2007). "Induction of full-length survival motor neuron by polyphenol botanical compounds".Human

Genetics 122 (6): 635–643. doi:10.1007/s00439-007-0441-0.

19. Bai, Yun; Yanyan Wang, Maoquan Li, Xueqing Xu, Min Song, Huansheng Tao, (April 2012). "Green tea epigallocatechin-3-

gallate (EGCG) promotes neural progenitor cell proliferation and sonic hedgehog pathway activation during adult hippocampal

neurogenesis". You have free access to this content Molecular Nutrition & Food Research 56 (8): 1292.

20. Staff, IMM. Utilización de la epigalocatequina galato (EGCG) para modular Dyrk1A y APP y evaluar su impacto sobre el

rendimiento cognitivo en pacientes con síndrome de Down (SD) English translation - Using epigallocatechin gallate (EGCG) to

modulate Dyrk1A and APP and evaluate its impact on cognitive performance in patients with Down syndrome (DS) Page

Accessed June 7, 2013

21. Korte, G.; Dreiseitel, A.; Schreier, P.; Oehme, A.; Locher, S.; Geiger, S.; Heilmann, J.; Sand, P. (2010). "Tea catechins' affinity

for human cannabinoid receptors".Phytomedicine : international journal of phytotherapy and phytopharmacology 17 (1): 19–22.

22. Lee, Y. L.; Hong, C. Y.; Kok, S. H.; Hou, K. L.; Lin, Y. T.; Chen, M. H.; Wang, C. C.; Lin, S. K. (2009). "An Extract of Green

Tea, Epigallocatechin-3-Gallate, Reduces Periapical Lesions by Inhibiting Cysteine-rich 61 Expression in Osteoblasts". Journal of

Endodontics 35 (2): 206–211.

23. Yao C (Jan 2014). "Neuroprotection by (-)-epigallocatechin-3-gallate in a rat model of stroke is mediated through inhibition of

endoplasmic reticulum stress.". J Mol Med Rep 9 (1): 69–76.

24. Zhu, Q. Y.; Zhang, A.; Tsang, D.; Huang, Y.; Chen, Z.-Y., Stability of green tea catechins. Journal of Agricultural and Food

Chemistry 1997, 45, 4624-4628.

25. Onoue, S.; Ochi, M.; Yamada, S., Development of (−)-epigallocatechin-3-gallate (EGCG)-loaded enteric microparticles with

intestinal mucoadhesive property International Journal of Pharmaceutics 2011, 410, 111-113.

www.iajpr.com

Pag

e22

31


26. Sang, S.; Lee, M.-J.; Hou, Z.; Ho, C.-T.; Yang, C. S., Stability of tea polyphenol (−)-epigallocatechin-3-gallate and formation of

dimers and epimers under common experimental conditions. Journal of Agricultural and Food Chemistry 2005, 53, 9478- 9484

27. Kim, M.-R.; Hong, ., Analysis of chemical interactions of (−)-epigallocatechin-3-gallate, a major green tea polyphenol, with

commonly-consumed over-thecounter drugs. Food Science and Biotechnology 2010, 19, 559-564.

28. Ramkumar Ponnuraj, Janakiraman K, Sivaraman Gopalakrishnan, Senthilnathan K, Meghanathan V, Saravanan P. Formulation

and Characterization of Epigallocatechin Gallate Nanoparticles. IAJPR. 2015; 5(1): 387-399.

29. Stefano Lazzari,1 Davide Moscatelli,

2 Fabio Codari,

1 Mario Salmona,

3 Massimo Morbidelli,

1 and Luisa Diomede

3, Colloidal

stability of polymeric nanoparticles in biological fluids. J Nanopart Res. 2012 Jun; 14(6): 920.

54878478451150602

Date post:	29-Nov-2023
Category:	Documents
Upload:	independent
View:	0 times
Download:	0 times

Formulation and Development of Capsules Containing Rosuvastatin Calcium Nanoparticles and...

Documents