8. gastroretentive granules (grgs) 8.1. preparation of...

Gastroretentive granules

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8. Gastroretentive granules (GRGs)

8.1. Preparation of GRGs

Wet granulation technique was used to prepare the gastroretentive granules (GRGs) of

rifampicin. The composition of the granules is given in the Table 8.1. Rifampicin and all

other ingredients were passed through sieve no. 40 to break the lumps before mixing them.

The blends for the granules were prepared by mixing the required quantities of drug, HPMC

K100M, HPMC K4M, POLYOX WSR 301, sodium bicarbonate and superTab11SD for 10

min in double cone blender (Kalweka-VDM-4, Karnavathi Engineering Ltd., Gujarat,

India). Talc and magnesium stearate were added step by step to the above blend and further

mixed for 5 min. The resultant blends were mixed using water with or without binder

(polyvinyl pyrrolidone) which was used as the granulating liquid thoroughly using mortor

and pestle to form a wet coherent mass. This mass was then sieved through sieve no. 12 to

produce wet granules. These wet granules were then dried in tray dryer at 50 oC till constant

weight. Then these dried granules were again sieved to break the agglomerates formed

during the drying process into individual granules (Lachman et al., 2009).

Table 8.1. Composition of GRGs of rifampicin

Ingredients Quantities

Rifampicin 600 mg

HPMC K100M 30-60 mg

HPMC K4M 30-60 mg

POLYOX WSR 301 30-60 mg

Sodium Bicarbonate 80-120 mg

Polyvinyl pyrrolidone 0-5% w/v in granulating liquid

Talc 10 mg

Magnesium stearate 10 mg

SuperTab 11SD 30 mg

8.2. Evaluation of the GRGs

8.2.1 Friability

Granules were randomly selected weighing equal to or more than 6.5 g and placed in the

drum of Roche friability test apparatus. The drum was adjusted to rotate at 25 rpm for 4

min. The granules were removed, de-dusted and accurately weighed. The percentage weight

loss was calculated. The loss of weight should not be more than 1% (IP 2007).

8.2.2. Micromeritic properties

The prepared granules were characterized for angle of repose, Carr’s index and Hausner’s

ratio to confirm the flow properties


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8.2.2.1. Angle of repose

Angle of repose of the granules was determined by fixed funnel method (Lieberman et al.,

1990). The accurately weighed granules were taken in a funnel and were allowed to flow

through the funnel freely to form a heap. The height of the funnel was adjusted in such a

way that the tip of the funnel just touches the apex of the heap of the granules. Then the

procedure was repeated and the height and diameter of the granule cone was measured and

the angle of repose (θ) was calculated using the following formula.

θ= tan-1

(h/r)

Where, θ is angle of repose, h is the height in cm and r is the radius in cm.

8.2.2.2. Bulk density

Known quantity of granules was transferred through a funnel into a 100 ml graduated

cylinder. The volume was then read directly from the cylinder and used to calculate the bulk

density according to the formula mentioned below (Lieberman et al., 1990).

Db= M/Vb

Where, Db is the bulk density, M is the mass of granules and Vb is the bulk volume

8.2.2.3. Tapped density

Known quantity of granules was transferred through a funnel into a 100ml tarred graduated

cylinder. The cylinder was then placed on tap density tester (USP II, ETD-2010, Electrolab,

Mumbai, India) and tapped to attain a constant volume. Then the tapped density was

calculated using the given equation (Lieberman et al., 1990).

Dt= (M/Vt)

Where Dt is the tapped density, M is the mass of granules and Vt is the tapped volume

8.2.2.4. Carr’s index and Hausner’s ratio

The bulk and tapped densities were used to find out the Carr’s index and Hausner’s ratio by

the following equations (Wells and Aulton, 2007).


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8.2.3. Buoyancy

Granules weight equivalent to each formulation was placed in a glass beaker, containing

200 ml of simulated gastric fluid, kept for stirring at 50 rpm using a magnetic stirrer and

maintained at 37±0.5 °C. The floating lag time, the time required for granules to float from

the time of granule introduction and floating time, the time during which granules remain

buoyant in the medium were observed visually and values were noted.

8.2.4. Granule size

Granule size analysis was carried out by vernier caliper. About 20 pellets were randomly

picked up thrice and their size was measured (Sangeetha et al., 2010). Average size was

reported based on this determination.

8.2.5. Usable yield

Usable yield were determined by sieving technique. Sieving is a simple method that is used

for determining the particle size distribution of powder/granules/pellets/beads. It is often the

preferred method of choice for formulators, since it is a straightforward analysis that can be

done during the formulation development process. Sieving is a simple 'go or no go' test,

where in the granule sample is passed over a perforated screen such that the smaller

particles pass through while the larger ones will be retained on the sieve. Thus the granules

get divided into two fractions; one above and the other below a specified size which

corresponds with the size of the sieve opening. The duration for which the sieving is carried

out is of importance, as prolonged sieving will generate some fines due to the attrition of the

coarser particles between each other and against the sieve (Lieberman et al., 1990).

Sieves were cleaned and arranged in the electronic sieve shaker in the descending order

[e.g., sieve no.10, 20, 30, 40......pan] of the sieve opening. Beneath the last sieve pan was

placed. 10 g of the granules were placed on the top sieve and system was closed with a lid.

Then the timer was set for 10 min and the electronic sieve shaker was switched on at a

constant vibratory power of 5. After the run, the sieves were taken out and the weight of the

pellets retained on sieves was collected and weighed. Usable yield was the percentage

weight of pellets passed from the sieve no. 12 and retained on sieve no. 20.

8.2.6. Scanning electron microscopy (SEM)

The surface morphology of granules was studied using scanning electron microscope (Zeiss,

EVO 18, Carl Zeiss SMT Ltd, UK). SEM uses a focussed electron probe to extract

structural and chemical information point-by-point from a region of interest in the sample.


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The samples were mounted on double sided adhesive tape that has been secured on copper

stubs and then analysed. The accelerating voltage applied was 15 kV.

8.2.7. Assay

100 mg weight of granules was taken from each batch and was crushed using a motor and

pestle. It was then transferred into a 100 ml volumetric flask. To this, 50 ml of pH 1.2 HCl

buffer was added and mixed thoroughly. The solution was made up to the 100 ml mark with

pH 1.2 HCl buffer. Then it is filtered, sonicated and suitable dilutions were done with pH

HCl 1.2 buffer. The drug content was estimated by recording absorbance at 335 nm by

using a UV-Visible spectrophotometer. Rifampicin granules should contain not less than

90.0 per cent and not more than 110.0 per cent of the stated amount of rifampicin (IP 2007).

8.2.8. Release at 6 h

Release of the rifampicin at 6 h was considered because from literature it was quite evident

that in in vivo conditions the maximum gastroretention that was attained was 5 h. So the

study was conducted to release the drug in the formulations within 6 h. The dissolution

study was performed using a USP type II (paddle type) dissolution apparatus (TCT- 06P,

Electrolab, Mumbai, India) at 37 ± 0.5 oC and a paddle speed of 50 rpm. The dissolution

testing of optimized formulation was carried out in 900 ml of simulated gastric fluid. At 6 h,

1 ml of sample was withdrawn replacing with fresh medium and the release of rifampicin

analysed at 335 nm using UV- visible spectrophotometer.

8.3. Optimization

8.3.1. Quality target product profile (QTPP) and Critical quality attributes (CQA)

The quality target product profile is a prospective summary of the quality characteristics of

a drug product that ideally will be achieved to ensure the desired quality taking into account

safety and efficacy of the drug product (Table 8.2).

Table 8.2. QTPP for GRGs of rifampicin

QTPP elements Targets

Dosage form Granules

Dosage design Gastroretentive extended release

Route of administration Oral

Dosage strength 600 mg

Dissolution Extended release of drug up to 6 h in gastric conditions

Floating time Up to 6 h in gastric conditions

Usable yield Minimum of 85%

Granule size Target of 1000 µm


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A critical quality attribute (CQA) is “a physical, chemical, biological or microbiological

property or characteristic that should be within an appropriate limit, range or distribution to

ensure the desired product quality (Table 8.3).

Table 8.3. Critical quality attributes of GRGs of rifampicin

CQA Target

Dissolution Target of 100% in 6 h

Floating time Target of 6 h

Usable yield Target of above 85%

Granule size Target of 1000 µm

8.3.2. Risk analysis: Fishbone/Ishikawa representation

An initial risk analysis was performed after identifying QTPPs and CQAs and represented

by fishbone/ishikawa diagram (Fig. 8.1). During the initial studies, it is imperative to

scrutinize the possible product and process variables of the system under study to know

their influence on the quality of the product. The screening study was performed based on

literature and initial experimental trial batches. In the present study, it was observed that the

responses i.e. floating time, release at 6 h were mainly affected by concentrations of the

polymers HPMC K100M, HPMC K4M, POLYOX WSR 301 and the gas generating agent

sodium bicarbonate. Apart from these other responses like granule size and usable yield

were also studied. Usable yield and granule size were mainly affected by the binder

concentration. These variables were identified as critical factors which are to be monitored

for quality product. Based on preliminary experiment, the extreme levels of each factor

were set for experimental design.

Fig. 8.1. Risk and root cause identification: Ishikawa (Fishbone) diagram


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8.3.3. Experimental design

Two level full factorial design is a randomized design which provides information on direct

effects and interaction effects has been widely used for formulation optimization in dosage

forms. It requires a minimum number of experiments to be performed that are necessary to

establish a mathematical model in the experimental design which allows us to determine the

optimum level of experimental factors required for required responses. This design requires

2 levels of each factor. In the present study five independent variables i.e. HPMC K100M,

HPMC K4M, POLYOX WSR 301, sodium bicarbonate and binder concentrations were

studied at two different levels with various constraints as shown in Table 8.4.

Table 8.4. Experimental levels and constraints

Independent variables Levels

-1 +1

X1: HPMC K100M 30 60

X2: HPMC K4M 30 60

X3: POLYOX WSR 301 30 60

X4: Sodium bicarbonate 80 120

X5: Binder concentration 0 5

Dependent variables Constraints

Y1: Release at 6 h Target of 100%

Y2: Floating time Target of 6 h

Y3: Usable yield Target of above 85%

Y4: Granule size Target of 1000 µm

According to the factorial design generated by Design Expert software (v.9.0.3.1, Stat-Ease

Inc., MN), a total of 32 experiments were constructed and performed. The design summary

is shown in Table 8.5 and the values of independent variables in the various experimental

runs are shown in Table 8.6.

Table 8.5. Design summary for the GRGs

File

Version 9.0.3.1 Design Type 2 Level Factorial Runs 32

Factor Name Units Type Subtype Minimum Maximum Mean Std. Dev.

X1 HPMC K100M mg Numeric Continuous 30 60 45 15.24002

X2 HPMC K4M mg Numeric Continuous 30 60 45 15.24002

X3 POLYOX WSR 301 mg Numeric Continuous 30 60 45 15.24002

X4 Sodium bicarbonate mg Numeric Continuous 80 120 100 20.32002

X5 Binder Conc. % Numeric Continuous 0 5 2.5 2.540003

Response Name Units Analysis Model

Y1 Release at 6 h percentage Factorial Main effects

Y2 Floating time hours Factorial Main effects

Y3 Usable yield percentage Factorial Main effects

Y4 Granule size microns Factorial Main effects


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Table 8.6.Presentation of real values of independent variables in the experimental runs

Batch

no. Run

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5

X1:HPMC

K100M

X2:HPMC

K4M

X3:POLYOX

WSR 301

X4:Sodium

bicarbonate X5:Binder Conc.

mg mg mg mg percentage

G1 1 30 30 60 120 5

G2 2 30 60 60 80 5

G3 3 60 30 60 120 0

G4 4 30 60 30 120 5

G5 5 60 30 60 80 0

G6 6 60 60 30 120 5

G7 7 30 60 30 80 5

G8 8 60 60 60 120 0

G9 9 60 60 60 80 0

G10 10 30 60 60 80 0

G11 11 30 30 30 80 0

G12 12 60 60 30 80 0

G13 13 60 30 60 120 5

G14 14 30 30 60 120 0

G15 15 30 30 30 120 5

G16 16 60 30 30 120 0

G17 17 60 60 60 120 5

G18 18 60 30 30 80 0

G19 19 30 30 30 120 0

G20 20 30 30 60 80 0

G21 21 60 30 60 80 5

G22 22 60 30 30 120 5

G23 23 30 30 60 80 5

G24 24 60 60 60 80 5

G25 25 60 30 30 80 5

G26 26 30 60 60 120 5

G27 27 30 60 30 120 0

G28 28 30 60 60 120 0

G29 29 30 60 30 80 0

G30 30 30 30 30 80 5

G31 31 60 60 30 120 0

G32 32 60 60 30 80 5

A numerical optimization technique by design expert software was used to generate

formulations with the desired responses, in which a minimum and maximum level must be

provided for each parameter.

The goals are combined into an overall desirability function. The solutions that meet the

required criteria were reported and ranked based on their desirability values with the highest

desirability solution as the first solution.

8.3.4. Drug-excipient compatibility studies of optimized formulation

8.3.4.1. Fourier Transform Infrared Spectroscopy (FTIR)

Infrared spectroscopy was performed using a Shimadzu FTIR 8300 Spectrophotometer and

the spectrum was recorded in the region of 4000 to 400 cm-1

. In this study, FTIR spectrum


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for the final formulation was obtained. The procedure consisted of dispersing a sample in

Potassium bromide (1:1 ratio) and compressing into discs by applying a pressure of 5 tons

for 5 min in a hydraulic press. The pellet was placed in the light path and the spectrum was

recorded from 4000 to 400 cm-1

.

8.3.4.2. Differential Scanning Calorimetry (DSC)

DSC was performed using DSC-60, Shimadzu, Japan. The instrument comprised of the

calorimeter (DSC 60), flow controller (FCL 60), Thermal analyzer (TA 60) and operating

software TA-60 from Shimadzu Corporation, Japan.

The sample were placed in a sealed aluminium pan, before heating under nitrogen flow (30

ml/min) at a scanning rate of 5 °C/min from 30 °C to 300 °C. Reference was empty

aluminium pan. The heat flow as a function of temperature was recorded for the final

formulation (Lachman et al., 2009).

8.3.5. Validation of optimized formulation

The optimized solution was selected based on the values for the responses meeting all the

constraints and requirements. Satisfying these parameters, the first solution was chosen as

the optimized formulation with the highest desirability.

The obtained optimum formulation was evaluated for all the evaluation parameters. To

validate the elected experimental design, the values of the responses were compared with

the predicted values and the relative error (%) was calculated using the following equation:

% relative error = [(predicted value – experiment value) / predicted value] × 100

8.4. Results and discussion

8.4.1. Evaluation of GRGs

8.4.1.1. Friability

All the formulations have showed friability values well below the limits of <1.0 % which

indicate that these granules have the required strength to bear the wear and tear during the

transport.

8.4.1.2. Micromeritic properties

8.4.1.2.1. Angle of repose

All the formulations have angle of repose values in the range of 25o to 30

o which indicate

that these formulations have good flow properties.


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8.4.1.2.2. Carr’s index and Hausner’s ratio

All the formulations have Carr’s index and Hausner’s ratio values in the range of 11 to 15%

and 1.12 to 1.18 indicating that these granules have good flow properties.

8.4.1.3. Assay

All the formulations have the drug content values in the range of 95 to 105 %. None of them

were either less than 90 % or greater than 110 %.

8.4.1.4. Scanning Electron Microscopy (SEM)

The optimized formulation was subjected to SEM studies and the resulting images are

shown below in Fig 8.2 and 8.3. From these studies it is evident that the granule surface is

relatively rough and not as uniform as that of pellet surface.

Fig. 8.2. SEM image of the optimized formulation at 112X magnification

Fig. 8.3. SEM images of the optimized formulation at 500X magnification


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8.4.2. Statistical analysis of experimental data

Responses obtained from the evaluation study of all the 32 formulations were fed into the

design expert software v.9.0.3.1 for the design of experiments (DoE) using two level full

factorial design and the results and constraints are given in the Tables 8.7 and 8.8. The

results of the experimental design indicated that this system was highly affected by the

amount of the polymers HPMC K100M, HPMC K4M, POLYOX WSR 301 and the gas

generating agent sodium bicarbonate and the concentration of binder polyvinyl pyrrolidone

in the granulating liquid.

Table 8.7. Presentation of measured responses of experimental runs in factorial design

Batch no.

Response Y1 Response Y2 Response Y3 Response Y4

Release at 6 h Floating time Usable yield Granule size

percentage hours percentage Microns

G1 99.4 5.7 82.6 1128

G2 99.5 5.8 81.2 1141

G3 72.6 7.2 86.7 871

G4 99.2 5.6 82.8 1115

G5 73.6 7.1 87.1 880

G6 75.8 7 80.1 1169

G7 99.3 5.5 83.4 1093

G8 62.3 8.4 87.2 891

G9 64.8 7.6 87.5 878

G10 97.5 5.6 87.1 896

G11 100.3 4 90.4 946

G12 76.8 6.6 87.6 889

G13 74.7 7 80.5 1157

G14 99.3 5.5 89.4 914

G15 100.1 4.6 83.5 1097

G16 82.6 6.4 87.4 879

G17 61.9 8.6 79.8 1175

G18 88.6 5.4 90.2 921

G19 100.3 4.4 89.8 906

G20 99.7 5.1 87.9 885

G21 73.1 6.4 81.1 1160

G22 86.7 6.3 83.2 1116

G23 99.6 5.3 84.5 1086

G24 64.9 7.8 80.7 1171

G25 89.2 6.2 83.9 1142

G26 95.9 6.4 83.5 1136

G27 99.7 5.2 89.1 917

G28 96.4 5.9 88.6 905

G29 100.1 4.9 90.1 931

G30 100.4 4.2 85.2 1118

G31 76.3 6.8 87.2 901

G32 78.1 6.2 82.2 1148


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Table 8.8. Summary of the constraints

Name Goal Lower Limit Upper Limit

X1: HPMC K100M (mg) is in range 30 60

X2: HPMC K4M (mg) is in range 30 60

X3: POLYOX WSR 301 (mg) is in range 30 60

X4: Sodium bicarbonate (mg) is in range 80 120

X5: Binder conc. (%) is in range 0 5

Y1: Release at 6 h (%) is target = 100 95 100.4

Y2: Floating time (h) is target = 6 5.5 6.5

Y3: Usable yield (%) is target = 90.4 85 90.4

Y4: Granule size (µm) is target = 1000 900 1100

8.4.2.1. Fraction of design space (FDS)

Fraction of design space plot shows how much of the model prediction variance lies above

(or below) a given value. It summarizes the prediction variance, showing the fractional

design space for all the factors taken together. It displays the area or volume of the design

space having a mean standard error less than or equal to a specified value. It is a great tool

to compare design. Look for lower (less error) and flatter (more uniform) profiles as shown

in the Fig. 8.4.

Fig. 8.4. FDS/Fraction design space graph: GRGs

Design-Expert® Software

Min Std Error Mean: 0.177Avg Std Error Mean: 0.288Max Std Error Mean: 0.433Cuboidalradius = 1Points = 50000t(0.05/2,26) = 2.05553

0.00 0.20 0.40 0.60 0.80 1.00

0.000

0.200

0.400

0.600

0.800

1.000

FDS Graph

Fraction of Design Space

Std

Erro

r M

ea

n


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8.4.2.2. ANOVA of the whole model and that of the model significant terms

A polynomial equation with different number of coefficients to estimate was produced for

the account of the measured responses as a function of the process variables. The

mathematical model was expressed in equation1 as follows

Yi =A0 + A1X1 + A2X2 + A3X3 + A4X4 + A5X5

Where Y is the measured response, A0 is an intercept and A1-A5 are the regression

coefficients and X1 to X5 are the main effects i.e. X1- HPMC K100M, X2- HPMC K4M,

X3- POLYOX WSR 301, X4- sodium bicarbonate and X5- concentration of binder.

This 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 model equation with

the coded factors was generated to fit the data and reflected the influence of process

parameters on different responses Y1 (drug release at 6 h), Y2 (floating time), Y3 (usable

yield) and Y4 (granule size) are represented by the following equations as follows……

Y1 = +87.15 -12.02X1 -2.87X2 -3.70X3 -0.70X4 +0.22X5

Y2 = +6.08 + 0.85X1 + 0.41X2 + 0.50X3 + 0.23X4 + 0.078X5

Y3 = +85.36 – 0.83X1 – 0.48X2 – 0.65X3 – 0.27X4 – 2.97X5

Y4 = +1017.56 + 4.19X1 + 4.69X2 – 0.44X3 – 0.25X4 + 116.94X5

The sign and value of the quantitative effect represent tendency and magnitude of the term’s

influence on the response respectively. A positive value in the regression equation exhibits

an effect that favours the optimization due to synergistic effect, while a negative value

indicates an inverse relationship or antagonistic effect between the factor and the response.

In order to evaluate the significance of the suggested models on the responses and their

quantitative effects, analysis of variance (ANOVA) was carried out. At a 95% confidence

level, a model was considered significant if the p value < 0.05 (Tables 8.9, 8.10, 8.11 and

8.12).

In this case X1, X2 and X3 are significant model terms for Y1; X1, X2, X3 and X4 are

significant model terms for Y2; X1, X2, X3 and X5 are significant model terms for Y3; X5

is significant model term for Y4.


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Table 8.9. ANOVA for factorial model for release at 6 h

Source Sum of

Squares df

Mean

Square

F

Value

p-value

Prob > F

Model 5341.96 5 1068.39 60.74 < 0.0001 significant

A-HPMC K100M 4624.82 1 4624.82 262.95 < 0.0001

B-HPMC K4M 262.78 1 262.78 14.94 0.0007

C-POLYOX 437.34 1 437.34 24.87 < 0.0001

D-NaHCO3 15.54 1 15.54 0.88 0.3559

E-Binder Conc. 1.49 1 1.49 0.085 0.7735

Residual 457.30 26 17.59

Cor Total 5799.26 31

Table 8.10. ANOVA for factorial model for floating time

Source Sum of

Squares df

Mean

Square

F

Value

p-value

Prob > F


A-HPMC K100M 23.29 1 23.29 301.55 < 0.0001

B-HPMC K4M 5.36 1 5.36 69.43 < 0.0001

C-POLYOX 8.10 1 8.10 104.88 < 0.0001

D-NaHCO3 1.67 1 1.67 21.56 < 0.0001

E-Binder Conc. 0.20 1 0.20 2.53 0.1239

Residual 2.01 26 0.077

Cor Total 40.62 31

Table 8.11. ANOVA for factorial model for usable yield

Source Sum of

Squares df

Mean

Square

F

Value

p-value

Prob > F


A-HPMC K100M 22.28 1 22.28 28.22 < 0.0001

B-HPMC K4M 7.32 1 7.32 9.27 0.0053

C-POLYOX 13.39 1 13.39 16.96 0.0003

D-NaHCO3 2.37 1 2.37 3.00 0.0953

E-Binder Conc. 282.63 1 282.63 358.05 < 0.0001

Residual 20.52 26 0.79

Cor Total 348.50 31


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Table 8.12. ANOVA for factorial model for granule size

Source Sum of

Squares df

Mean

Square

F

Value

p-value

Prob > F

Model 4.389E+005 5 87770.50 130.29 < 0.0001 significant

A-HPMC K100M 561.13 1 561.13 0.83 0.3698

B-HPMC K4M 703.13 1 703.13 1.04 0.3164

C-POLYOX 6.13 1 6.13 9.092E-003 0.9248

D-NaHCO3 2.00 1 2.00 2.969E-003 0.9570

E-Binder Conc. 4.376E+005 1 4.376E+005 649.55 < 0.0001

Residual 17515.38 26 673.67

Cor Total 4.564E+005 31

8.4.2.4. Response surface and contour plot

Response surface plot was constructed in three dimensional model graphs for optimization

of gastroretentive granules with desired responses. The three dimensional response surface

and corresponding contour plots for the effect of amount of polymers HPMC K100M,

HPMC K4M and POLYOX WSR 301 on drug release at 6 h and floating time are shown in

the Fig. 8.5, 8.6, 8.7 and 8.8.

a)

Design-Expert® SoftwareFactor Coding: ActualRelease at 6hr (percentage)

100.4

61.9

X1 = A: HPMC K100MX2 = B: HPMC K4M

Actual FactorsC: POLYOX = 45D: NaHCO3 = 100E: Binder Conc. = 2.5

30

36

42

48

54

6030

36

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60

60

70

80

90

100

110

Re

lea

se

at

6h

r (

pe

rc

en

tag

e)

A: HPMC K100M (mg) B: HPMC K4M (mg)


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b)

Fig. 8.5. Influence of the independent variables HPMC K100M and HPMC K4M on

release at 6 h a) 3D surface graph and b) Contour graph

a)


100.4

61.9



30 36 42 48 54 60

30

36

42

48

54

60Release at 6hr (percentage)

A: HPMC K100M (mg)

B:

HP

MC

K4

M (

mg

)

8090

100


100.4

61.9

X1 = C: POLYOXX2 = A: HPMC K100M

Actual FactorsB: HPMC K4M = 45D: NaHCO3 = 100E: Binder Conc. = 2.5

30

36

42

48

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30 36

42 48

54 60

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70

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Re

lea

se

a

t 6

hr (p

erc

en

tag

e)

C: POLYOX (mg)

A: HPMC K100M (mg)


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b)

Fig. 8.6. Influence of the independent variables HPMC K100M and POLYOX WSR

301 on release at 6 h a) 3D surface graph and b) Contour graph

a)


100.4

61.9



30 36 42 48 54 60

30

36

42

48

54

60Release at 6hr (percentage)

C: POLYOX (mg)

A:

HP

MC

K1

00

M (

mg

)

80

90

100

Design-Expert® SoftwareFactor Coding: ActualFloating time (hours)

8.6

4

X1 = B: HPMC K4MX2 = A: HPMC K100M


30

36

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30

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ou

rs

)

B: HPMC K4M (mg)

A: HPMC K100M (mg)


100

b)

Fig. 8.7. Influence of the independent variables HPMC K100M and HPMC K4M on

floating time a) 3D surface graph and b) Contour graph

a)


8.6

4

X1 = B: HPMC K4MX2 = A: HPMC K100M


30 36 42 48 54 60

30

36

42

48

54

60Floating time (hours)

B: HPMC K4M (mg)

A:

HP

MC

K1

00

M (

mg

)

5

5.5

6

6.5

7


8.6

4



30

36

42

48

54

60

30

36

42

48

54

60

4

5

6

7

8

9

Flo

ati

ng

tim

e (

ho

urs

)

C: POLYOX (mg)

A: HPMC K100M (mg)


101

b)

Fig. 8.8. Influence of the independent variables HPMC K100M and POLYOX WSR

301 on floating time a) 3D surface graph and b) Contour graph

The three dimensional response surface and corresponding contour plots for the effect of

amount of polymer HPMC K100M and binder polyvinyl pyrrolidone concentration on

usable yield and granule size are shown below in Fig. 8.9 and 8.10.

a)


8.6

4



30 36 42 48 54 60

30

36

42

48

54

60Floating time (hours)

C: POLYOX (mg)

A:

HP

MC

K1

00

M (

mg

)

5

5.5

6

6.5

7

Design-Expert® SoftwareFactor Coding: ActualUsable yield (percentage)

90.4

79.8

X1 = E: Binder Conc.X2 = A: HPMC K100M

Actual FactorsB: HPMC K4M = 45C: POLYOX = 45D: NaHCO3 = 100

30

36

42

48

54

60

0

1

2

3

4

5

78

80

82

84

86

88

90

92

Us

ab

le y

ield

(p

erc

en

tag

e)

E: Binder Conc. (percentage)

A: HPMC K100M (mg)


102

b)

Fig. 8.9. Influence of the independent variables HPMC K100M and binder

concentration usable yield a) 3D surface graph and b) Contour graph

a)

Design-Expert® SoftwareFactor Coding: ActualUsable yield (percentage)

90.4

79.8



0 1 2 3 4 5

30

36

42

48

54

60Usable yield (percentage)


A:

HP

MC

K1

00

M (

mg

)

82

8486

88

Design-Expert® SoftwareFactor Coding: ActualGranule size (microns)

1175

871



30

36

42

48

54

60

0

1

2

3

4

5

800

900

1000

1100

1200

Gra

nu

le s

ize

(m

icro

ns

)

E: Binder Conc. (percentage)A: HPMC K100M (mg)


103

b)

Fig. 8.10. Influence of the independent variables HPMC K100M and binder

concentration on granule size a) 3D surface graph and b) Contour graph

8.4.2.5. Solutions

The goal of optimization is to determine the necessary process input values to obtain a

desired output. After generating the polynomial equations relating the dependent and

independent variables, optimization process was undertaken with desirable characteristics to

probe the optimal solution which depends on the prescribed criteria of a target of 100%

drug release at 6 h (95 – 100.4%), floating time of 6 h (5.5 – 6.5 h), pellet size with a target

of 1000 µm (900 – 1100 µm) and usable yield above 85% (85-90.4%). The list of solutions

was sorted with the highest desirability first. Solutions that meet the criteria are reported in

the Table 8.13. Desirability for optimization of GRGs of rifampicin is shown in Fig. 8.11.

Table 8.13. Solutions suggested by design expert that meet the criteria for GRGs

Number HPMC

K100M

HPMC

K4M POLYOX NaHCO3

Binder

Conc.

Release

at 6hr

(%)

Floating

time

(hours)

Usable

yield

(%)

Granule

size (µm) Desirability

1 30.000 59.999 39.256 120.000 1.797 96.962 5.654 86.528 985.079 0.410

2 30.000 59.344 39.418 119.975 1.872 97.054 5.644 86.453 988.376 0.410

3 30.000 60.000 38.692 120.000 1.729 97.094 5.633 86.632 981.951 0.409

4 30.000 58.000 40.674 120.000 1.845 96.998 5.649 86.472 986.694 0.408

5 30.000 57.884 40.641 120.000 1.921 97.035 5.647 86.388 990.191 0.408

6 30.000 56.965 41.477 120.000 1.832 96.997 5.647 86.487 985.711 0.407

7 30.000 59.037 39.431 120.000 2.123 97.130 5.644 86.163 1000.046 0.406

8 30.000 56.741 41.855 120.000 1.909 96.953 5.656 86.386 989.240 0.406

9 30.000 56.401 41.865 119.960 1.868 97.014 5.645 86.445 987.236 0.406

10 30.000 59.863 39.063 120.000 1.498 97.009 5.635 86.895 971.075 0.405

Design-Expert® SoftwareFactor Coding: ActualGranule size (microns)

1175

871



0 1 2 3 4 5

30

36

42

48

54

60Granule size (microns)


A:

HP

MC

K1

00

M (

mg

)

950 1000 1050 1100


104

a)

b)

Fig. 8.11. Desirability for optimization of GRGs a) 3D surface graph b) Contour graph

Design-Expert® SoftwareFactor Coding: ActualDesirability

1

0


Actual FactorsC: POLYOX = 39.2557D: NaHCO3 = 120E: Binder Conc. = 1.79661

30

37.5

45

52.5

60

30

37.5

45

52.5

60

0

0.2

0.4

0.6

0.8

1

De

sira

bil

ity

A: HPMC K100M (mg)B: HPMC K4M (mg)

0.409980.40998

Design-Expert® SoftwareFactor Coding: ActualDesirability

1

0


Actual FactorsC: POLYOX = 39.2557D: NaHCO3 = 120E: Binder Conc. = 1.79661

30 37.5 45 52.5 60

30

37.5

45

52.5

60

Desirability

A: HPMC K100M (mg)

B:

HP

MC

K4

M (

mg

)

0

0.1

0.2

0.3

Prediction 0.40998


105

8.4.2.6. Drug-excipient compatibility studies of optimized formulation

From the DSC thermogram (Fig. 8.12) and FTIR spectrum (Fig. 8.13), it is clearly visible

that there is no interaction between the drug and excipients in the optimized formulation.

100.00 200.00 300.00

Temp [C]

-10.00

-5.00

0.00

mW

DSC

66.49 x100COnset

75.24 x100CEndset

71.71 x100CPeak

-7.11 x100J/g

-8.49 x100mcal

Heat

188.04 x100COnset

203.30 x100CEndset

196.04 x100CPeak

-7.76 x100J/g

-9.27 x100mcal

Heat

R10

Fig. 8.12. DSC thermogram of optimized GRGs

Fig. 8.13. FTIR spectrum of optimized GRGs

50075010001250150017502000225025002750300032503500375040001/cm

15

30

45

60

75

90

105

%T

34

40

.16

29

32

.86

17

23

.45

16

46

.30

15

61

.43

14

38

.94

13

78

.18

12

47

.02

11

56

.36

10

92

.71

10

55

.10

97

1.1

9

89

5.0

0

80

6.2

7

76

8.6

6 69

1.5

0

63

6.5

3

R10 (Optimized formulation Granules)


106

8.4.2.7. Validation of optimized formulation of GRGs

The results were found to be close to the predicted values, which confirm the practicability

of the model. The comparison is shown in the Table 8.14.

Table 8.14. Comparison of the predicted and observed responses for the statistically

optimized granule formulation

Release at

6 h (%)

Floating time

(h)

Usable

yield (%)

Granule

size (µm) Desirability

Predicted 96.962 5.654 86.528 985.079 0.410

Observed 97.31 5.6 86.26 962 -

Relative

error (%) -0.35 +0.95 +0.30 +2.34 -

8. gastroretentive granules (grgs) 8.1. preparation of...

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