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9. Gastroretentive beads (GRBs)

9.1. Preparation of GRBs

Beads were prepared by ionotropic gelation method using sodium alginate and calcium

chloride as per previously reported (Dhalleine et al., 2011; Tekade and Gattani, 2010).

These ingredients react with each other forming calcium alginate beads. Calcium alginate

beads by ionotropic gelation have been prepared by dropping the drug-sodium alginate

dispersion in calcium chloride solution (Tekade and Gattani, 2010). The inert environment

within the polymer network of alginates allows for the entrapment of a wide range of

bioactive substances and drug molecules with minor interactions between them and the

biopolymer. The release of the drug from the gel matrix of calcium alginate beads depends

upon the diffusion and the swelling of the beads (Tekade and Gattani, 2010). The release

pattern of loaded drug substance could be modified by incorporating polymers along with

sodium alginate.

Accurately weighed quantities of rifampicin and sodium alginate were dissolved in distilled

water under stirring. Required quantities of HPMC K100M and POLYOX WSR 301 were

added into above solution and stirred for 30 min using a mechanical stirrer to achieve

uniform mixing. The dispersion was dropped into the calcium chloride solution using (5 ml

Discardit syringe without needle) and then allowed to cross link. After cross linking for pre-

determined times, the cross linked beads were separated by filtration, washed with 100 ml

of double distilled water and finally dried in tray dryer at 50 oC till constant weight.

Based on the literature, in the present study two concentrations of sodium alginate were

used with the lower concentration of 2% w/v and higher concentration of 4% w/v was used.

For calcium chloride 1% w/v concentration was considered as lower limit and 2% w/v

concentration was considered as upper limit. The compositions of cross linked beads are

given in Table 9.1.

Table 9.1. Composition of GRBs

Ingredients Quantities

Rifampicin 600 mg

HPMC K100M 30-60 mg

POLYOX WSR 301 30-60 mg

Sodium Bicarbonate 80-120 mg

Sodium alginate 2-4%

Calcium chloride 1-2%

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9.2. Evaluation of the GRBs

9.2.1. Friability

Beads 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

beads 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).

9.2.2. Micromeritic properties

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

to confirm the flow properties

9.2.2.1. Angle of repose

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

1990). The accurately weighed beads 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 beads. Then the

procedure was repeated and the height and diameter of the bead 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.

9.2.2.2. Bulk density

Known quantity of beads 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 beads and Vb is the bulk volume

9.2.2.3. Tapped density

Known quantity of beads 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)

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

9.2.2.4. 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).

9.2.3. Buoyancy

Beads 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 time which is the time during which beads remain

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

9.2.5. Bead size

Bead size analysis was carried out by vernier caliper. About 20 beads were randomly picked

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

based on this determination.

9.2.6. 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 pellet 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 beads 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, 12, 20, 30, 40......pan] of the sieve opening. Beneath the last sieve pan

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

Gastroretentive beads

110

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 beads

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

beads passed from the sieve no. 12 and retained on sieve no. 20.

9.2.7. Scanning Electron Microscopy (SEM)

The surface morphology of beads 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.

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.

9.2.8. Drug entrapment efficiency

100 mg weight of beads 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

1.2 HCl buffer. The drug content was estimated by recording absorbance at 336 nm by

using a UV-Visible spectrophotometer (Eldeen et al., 2006).

Drug entrapment efficiency = [(practical yield of drug/theoretical yield of drug)] × 100

9.2.9. 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,

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

analysed at 336 nm using UV- visible spectrophotometer.

9.3. Optimization

9.3.1. 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 9.2).

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Table 9.2. QTPP for GRBs of rifampicin

QTPP elements Targets

Dosage form Beads

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

Drug entrapment efficiency Above 65%

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 9.3).

Table 9.3. Critical quality attributes of GRBs of rifampicin

CQA Target

Dissolution Target of 100% in 6 h

Floating time Target of 6 h

Drug entrapment efficiency Target of above 65%

9.3.2. Risk analysis: Fishbone/Ishikawa representation

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

by fishbone/ishikawa diagram. (Fig. 9.1) 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, POLYOX WSR 301, sodium alginate and the gas generating

agent sodium bicarbonate. Apart from drug entrapment efficiency was also studied. Drug

entrapment efficiency was mainly affected by the concentration of sodium alginate and

cross linker calcium chloride. 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.

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Fig. 9.1. Risk and root cause identification: Ishikawa (Fishbone) diagram

9.3.3. 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

two levels of each factor. In the present study five independent variables i.e. HPMC

K100M, POLYOX WSR 301, sodium bicarbonate, sodium alginate and calcium chloride

concentrations were studied at two different levels along with various constraints as shown

in Table 9.4.

Table 9.4. Experimental levels and constraints

Independent variables Levels

-1 +1

X1: HPMC K100M 30 60

X3: POLYOX 30 60

X3: Sodium bicarbonate 80 120

X4: Calcium chloride concentration 1 2

X5: Sodium alginate concentration 2 4

Dependent variables Constraints

Y1: Release at 6 h Target of 100%

Y2: Floating time Target of 6 h

Y3: Drug entrapment efficiency Target of above 65%

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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 as shown in Table 9.5

and the design summary is shown in the Table 9.6.

Table 9.5. 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:POLYOX

WSR 301

X3:Sodium

bicarbonate

X4:Calcium

chloride

X5:Sodium

alginate

mg mg mg percentage percentage

B1 1 60 60 120 1 4

B2 2 60 30 120 2 4

B3 3 30 60 120 1 4

B4 4 60 30 80 2 4

B5 5 60 30 80 1 4

B6 6 60 30 120 2 2

B7 7 30 60 80 1 2

B8 8 60 60 120 2 4

B9 9 60 30 80 1 2

B10 10 30 30 80 1 2

B11 11 60 30 80 2 2

B12 12 30 30 120 1 4

B13 13 60 60 120 2 2

B14 14 60 60 80 2 2

B15 15 60 60 80 2 4

B16 16 60 60 120 1 2

B17 17 30 30 120 1 2

B18 18 30 60 120 2 4

B19 19 30 60 80 2 2

B20 20 30 30 120 2 4

B21 21 30 60 80 2 4

B22 22 60 60 80 1 2

B23 23 30 60 80 1 4

B24 24 30 30 80 1 4

B25 25 30 60 120 2 2

B26 26 60 30 120 1 2

B27 27 30 30 80 2 2

B28 28 30 30 80 2 4

B29 29 30 30 120 2 2

B30 30 30 60 120 1 2

B31 31 60 30 120 1 4

B32 32 60 60 80 1 4

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.

Gastroretentive beads

114

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.

Table 9.6. Design summary for the GRBs

File

Version 9.0.3.1 Design Type 2 Level Factorial Runs 32

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

A HPMC K100M mg Numeric Continuous 30 60 45 15.24002

B POLYOX WSR 301 mg Numeric Continuous 30 60 45 15.24002

C Sodium bicarbonate mg Numeric Continuous 80 120 100 20.32002

D Calcium chloride percentage Numeric Continuous 1 2 1.5 0.508001

E Sodium alginate percentage Numeric Continuous 2 4 3 1.016001

Response Name Units Analysis Model

R1 Release at 6 h percentage Factorial Main effects

R2 Floating time hours Factorial Main effects

R3 Drug entrapment efficiency percentage Factorial Main effects

9.3.4. Drug-excipient compatibility studies of optimized formulation

9.3.4.1. 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

for the final formulation was obtained (Lachman et al., 2009). 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

.

9.3.4.2. 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. Empty aluminium pan was used as reference. The heat flow

as a function of temperature was recorded for the final formulation (Lachman et al., 2009).

9.3.5. 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,

Gastroretentive beads

115

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

9.4. Results and discussion

9.4.1. Evaluation of GRBs

9.4.1.1. Friability

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

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

transport.

9.4.1.2. Micromeritic properties

9.4.1.2.1. Angle of repose

All the formulations have angle of repose values in the range of 25o to 32

o which indicate

that these formulations have good flow properties.

9.4.1.2.2. 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 17%

and 1.12 to 1.20 indicating that these beads have good flow properties.

Bead size

The average bead size along with the standard deviation for the 20 beads was found to be

1065±23.61 µm.

Usable yield

Percentage of beads passed through sieve no. 12 and retained on sieve no. 20 was

considered as usable yield and all the formulations have shown usable yield values of above

80%.

9.4.1.3. Scanning Electron Microscopy (SEM)

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

shown below in Fig. 9.2 and 9.3. From these studies it is evident that the bead surface is not

that smooth and uniform when compared to pellets but they are spherical in shape as pellets.

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Fig. 9.2. SEM image of the optimized formulation at 95X magnification

Fig. 9.3. SEM image of the optimized formulation at 500X magnification

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9.4.2. 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 9.7 and 9.8. The

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

amount of the polymers HPMC K100M, POLYOX WSR 301, sodium alginate, gas

generating agent sodium bicarbonate and concentration of cross-linker calcium chloride.

Table 9.7. Presentation of measured responses of experimental runs

Batch No.

Response Y1 Response Y2 Response Y3

Release at 6 h Floating time Drug entrapment

efficiency

percentage hours percentage

B1 61.2 8.8 60.4

B2 72.9 7.5 70.6

B3 97.6 6 55.2

B4 75.3 7.2 72.4

B5 78.8 7 61.3

B6 82 6.9 59

B7 99.7 5.2 51.2

B8 60.2 9 71.2

B9 74.1 7.3 52.4

B10 100.3 4.1 53.4

B11 83 6.8 55

B12 99.4 5.4 59.6

B13 65.7 8.5 57.2

B14 73.4 7.6 58.5

B15 71.2 7.8 72.9

B16 69.1 8.4 53.9

B17 100.2 4.5 52.1

B18 99.2 5.5 71.2

B19 100 4.8 61.8

B20 100.1 4.6 71.6

B21 99.2 5.8 72

B22 72.9 7.5 53

B23 99.8 5.1 56.9

B24 100.2 4.3 59.1

B25 99.4 5.6 62.7

B26 79.4 7.1 51.9

B27 100.3 4.3 64.7

B28 100.3 4.5 71.9

B29 100.2 4.9 62

B30 99.8 5.3 54.2

B31 75.8 7.4 60.6

B32 77.1 7.2 58.2

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Table 9.8. Summary of the constraints

Name Goal Lower

Limit

Upper

Limit

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

X2: POLYOX WSR 301(mg) is in range 30 60

X3: Sodium bicarbonate (mg) is in range 80 120

X4: Calcium chloride (%) is in range 1 2

X5: Sodium alginate (%) is in range 2 4

Y1: Release at 6 h (%) is target = 100 95 100.3

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

Y3: Drug entrapment efficiency (%) target of above 65% 65% 73%

9.4.2.1. 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. 9.4.

Fig. 9.4. FDS/Fraction design space graph: GRBs

Design-Expert® Software

Min Std Error Mean: 0.177Avg Std Error Mean: 0.289Max 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

Gastroretentive beads

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9.4.2.2. 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- POLYOX WSR

301, X3- sodium bicarbonate, X4- concentration of calcium chloride and X5- concentration

of sodium alginate.

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 6h), Y2 (floating time) and Y3 (drug entrapment efficiency) are represented by

the following equations as follows……

Y1 = +86.49 -13.24X1 -2.40X2 -1.36X3 -0.094X4 -0.97X5

Y2 = + 6.31 + 1.32X1 + 0.45X2 + 0.28X3 + 0.022X4 + 0.13X5

Y3 = + 60.88 – 0.35X1 -0.22X2 – 0.041X3 + 5.04X4 + 4.44X5

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 9.9, 9.10 and 9.11).

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

model terms for Y2; X4 and X5 are significant model terms for Y3.

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Table 9.9. ANOVA for factorial model for release at 6 h

Source Sum of

Squares df

Mean

Square

F

Value

p-value

Prob > F

Model 5881.29 5 1176.26 77.62 < 0.0001 significant

A-HPMC K100M 5607.41 1 5607.41 370.04 < 0.0001

B-POLYOX 184.32 1 184.32 12.16 0.0018

C-Sodium bicarbonate 58.86 1 58.86 3.88 0.0595

D-Calcium chloride 0.28 1 0.28 0.019 0.8927

E-Sodium alginate 30.42 1 30.42 2.01 0.1684

Residual 393.99 26 15.15

Cor Total 6275.28 31

Table 9.10. ANOVA for factorial model for floating time

Source Sum of

Squares df

Mean

Square

F

Value

p-value

Prob > F

Model 64.85 5 12.97 125.79 < 0.0001 significant

A-HPMC K100M 55.39 1 55.39 537.22 < 0.0001

B-POLYOX 6.39 1 6.39 61.98 < 0.0001

C-Sodium bicarbonate 2.48 1 2.48 24.01 < 0.0001

D-Calcium chloride 0.015 1 0.015 0.15 0.7031

E-Sodium alginate 0.58 1 0.58 5.60 0.0256

Residual 2.68 26 0.10

Cor Total 67.53 31

Table 9.11. ANOVA for factorial model for drug entrapment efficiency

Source Sum of

Squares df

Mean

Square

F

Value

p-value

Prob > F

Model 1449.54 5 289.91 44.93 < 0.0001 significant

A-HPMC K100M 3.85 1 3.85 0.60 0.4468

B-POLYOX 1.58 1 1.58 0.24 0.6254

C-Sodium bicarbonate 0.053 1 0.053 8.185E-003 0.9286

D-Calcium chloride 813.05 1 813.05 126.00 < 0.0001

E-Sodium alginate 631.01 1 631.01 97.79 < 0.0001

Residual 167.77 26 6.45

Cor Total 1617.31 31

9.4.2.4. Response surface and contour plot

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

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

Gastroretentive beads

121

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

POLYOX WSR 301 on drug release at 6 h and floating time are shown in the Fig. 9.5 and

9.6.

a)

b)

Fig. 9.5. Influence of the independent variables HPMC K100M and POLYOX WSR

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

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

100.3

60.2

X1 = A: HPMC K100MX2 = B: POLYOX

Actual FactorsC: Sodium bicarbonate = 100D: Calcium chloride = 1.5E: Sodium alginate = 3

30

36

42

48

54

60

30

36

42

48

54

60

60

70

80

90

100

110

Re

lea

se

at

6h

r (p

erc

en

tag

e)

A: HPMC K100M (mg)

B: POLYOX (mg)

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

100.3

60.2

X1 = A: HPMC K100MX2 = B: POLYOX

Actual FactorsC: Sodium bicarbonate = 100D: Calcium chloride = 1.5E: Sodium alginate = 3

30 36 42 48 54 60

30

36

42

48

54

60Release at 6hr (percentage)

A: HPMC K100M (mg)

B:

PO

LY

OX

(m

g)

8090

100

Gastroretentive beads

122

a)

b)

Fig. 9.6. Influence of the independent variables HPMC K100M and POLYOX WSR

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

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

9

4.1

X1 = A: HPMC K100MX2 = B: POLYOX

Actual FactorsC: Sodium bicarbonate = 100D: Calcium chloride = 1.5E: Sodium alginate = 3

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

)

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

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

9

4.1

X1 = A: HPMC K100MX2 = B: POLYOX

Actual FactorsC: Sodium bicarbonate = 100D: Calcium chloride = 1.5E: Sodium alginate = 3

30 36 42 48 54 60

30

36

42

48

54

60Floating time (hours)

A: HPMC K100M (mg)

B:

PO

LY

OX

(m

g)

5

6 7

Gastroretentive beads

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

amount of HPMC K100M, sodium alginate and calcium chloride on drug entrapment

efficiency is shown in the Fig. 9.7 and 9.8.

a)

b)

Fig. 9.7. Influence of the independent variables HPMC K100M and sodium alginate

on drug entrapment efficiency a) 3D surface graph and b) Contour graph

Design-Expert® SoftwareFactor Coding: ActualDrug entrapment efficiency (percentage)

72.9

51.2

X1 = E: Sodium alginateX2 = A: HPMC K100M

Actual FactorsB: POLYOX = 45C: Sodium bicarbonate = 100D: Calcium chloride = 1.5

30

36

42

48

54

60

2

2.5

3

3.5

4

50

55

60

65

70

75

Dru

g e

ntr

ap

me

nt

eff

icie

nc

y (

pe

rc

en

tag

e)

E: Sodium alginate (percentage)

A: HPMC K100M (mg)

Design-Expert® SoftwareFactor Coding: ActualDrug entrapment efficiency (percentage)

72.9

51.2

X1 = E: Sodium alginateX2 = A: HPMC K100M

Actual FactorsB: POLYOX = 45C: Sodium bicarbonate = 100D: Calcium chloride = 1.5

2 2.5 3 3.5 4

30

36

42

48

54

60Drug entrapment efficiency (percentage)

E: Sodium alginate (percentage)

A:

HP

MC

K1

00

M (

mg

)

58 60 62 64

Gastroretentive beads

124

a)

b)

Fig. 9.8. Influence of the independent variables HPMC K100M and calcium chloride

on drug entrapment efficiency a) 3D surface graph and b) Contour graph

Design-Expert® SoftwareFactor Coding: ActualDrug entrapment efficiency (percentage)

72.9

51.2

X1 = D: Calcium chlorideX2 = A: HPMC K100M

Actual FactorsB: POLYOX = 45C: Sodium bicarbonate = 100E: Sodium alginate = 3

30

36

42

48

54

60

1

1.2

1.4

1.6

1.8

2

50

55

60

65

70

75

Dru

g e

ntr

ap

me

nt

eff

icie

nc

y (

pe

rc

en

tag

e)

D: Calcium chloride (percentage)

A: HPMC K100M (mg)

Design-Expert® SoftwareFactor Coding: ActualDrug entrapment efficiency (percentage)

72.9

51.2

X1 = D: Calcium chlorideX2 = A: HPMC K100M

Actual FactorsB: POLYOX = 45C: Sodium bicarbonate = 100E: Sodium alginate = 3

1 1.2 1.4 1.6 1.8 2

30

36

42

48

54

60Drug entrapment efficiency (percentage)

D: Calcium chloride (percentage)

A:

HP

MC

K1

00

M (

mg

)

56

58 60 62 64

66

Gastroretentive beads

125

9.4.2.5. 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 – 100.3%), floating time of 6 h (5.5 – 6.5 h) and drug entrapment

efficiency above 65%. The list of solutions was sorted with the highest desirability first.

Solutions that meet the criteria are reported in the Table 9.12. Desirability for optimization

of GRBs of rifampicin is shown in Fig. 9.9.

Table 9.12. Solutions suggested by design expert that meet the criteria for GRBs

Number HPMC

K100M POLYOX

Sodium

bicarbonate

Calcium

chloride

Sodium

alginate

Release

at 6hr

Floating

time

Drug

entrapment

efficiency

Desirability

1 30.000 51.248 119.935 2.000 4.000 96.311 5.613 70.573 0.369

2 30.000 52.585 118.741 1.999 3.967 96.210 5.632 70.401 0.366

3 30.000 51.966 120.000 1.986 3.994 96.200 5.634 70.393 0.366

4 30.000 52.208 120.000 2.000 3.948 96.204 5.636 70.328 0.366

5 30.002 53.292 115.833 2.000 4.000 96.261 5.617 70.550 0.366

6 30.000 52.069 119.999 1.978 4.000 96.179 5.638 70.343 0.365

7 30.000 54.909 114.144 2.000 4.000 96.118 5.642 70.531 0.365

8 30.000 53.333 116.910 1.986 4.000 96.185 5.633 70.405 0.364

9 30.000 54.352 114.370 1.995 4.000 96.193 5.628 70.485 0.364

10 30.000 51.013 120.000 2.000 3.966 96.377 5.603 70.425 0.364

a)

Design-Expert® SoftwareFactor Coding: ActualDesirability

1

0

X1 = A: HPMC K100MX2 = B: POLYOX

Actual FactorsC: Sodium bicarbonate = 119.935D: Calcium chloride = 2E: Sodium alginate = 4

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

sir

ab

ilit

y

A: HPMC K100M (mg)

B: POLYOX (mg)

0.3692210.369221

Gastroretentive beads

126

b)

Fig. 9.9. Desirability value for GRBs a) 3D surface graph and b) Contour graph

9.4.2.6. Drug-excipient compatibility studies of optimized formulation

From the DSC thermogram (Fig. 9.10) and FTIR spectrum (Fig. 9.11), 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

-8.00

-6.00

-4.00

-2.00

mW

DSC

65.97 x100COnset

74.25 x100CEndset

70.60 x100CPeak

-4.70 x100J/g

-5.62 x100mcal

Heat

185.78 x100COnset

202.75 x100CEndset

194.97 x100CPeak

-5.71 x100J/g

-6.82 x100mcal

Heat

R9

Fig. 9.10. DSC thermogram of the optimized GRBs

Design-Expert® SoftwareFactor Coding: ActualDesirability

1

0

X1 = A: HPMC K100MX2 = B: POLYOX

Actual FactorsC: Sodium bicarbonate = 119.935D: Calcium chloride = 2E: Sodium alginate = 4

30 37.5 45 52.5 60

30

37.5

45

52.5

60

Desirability

A: HPMC K100M (mg)

B:

PO

LY

OX

(m

g)

0

0

0

0

0.1

0.2

0.3

Prediction 0.369221

Gastroretentive beads

127

Fig. 9.11. FTIR spectrum of optimized GRBs

9.4.2.7. Validation of optimized formulation of GRBs

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 9.13.

Table 9.13. Comparison of the predicted and observed responses for the statistically

optimized bead formulation

50075010001250150017502000225025002750300032503500375040001/cm

50

55

60

65

70

75

80

85

90

95

%T

37

25

.63

34

42

.09

29

33

.83

23

79

.27

23

08

.87

17

19

.60

16

56

.91

15

31

.53

14

30

.26

13

78

.18 13

35

.75

12

46

.06

11

53

.47

10

93

.67

10

54

.13

97

4.0

8

89

6.9

3

80

9.1

7

69

2.4

7

64

2.3

2

R9 (Optimized formulation Beads)

Release at

6 h (%)

Floating time

(h)

Drug loading capacity

(%) Desirability

Predicted 96.311 5.613 70.573 0.369

Observed 98.26 5.4 71.45 -

Relative

error (%) -2.02 +3.79 -1.24 -