The aim of this investigation was to develop and optimize bisoprolol fumarate matrix tablets for sustained release application by response surface methodology based on 2^{3} factorial design. The effects of the amounts of calcium alginate, HPMC K4M, and Carbopol 943 in bisoprolol fumarate matrix tablets on the properties of bisoprolol fumarate sustained release matrix tablets like drug release and hardness were analyzed and optimized. The observed responses were coincided well with the predicted values by the experimental design. The optimized bisoprolol fumarate matrix tablets showed prolonged sustained release of bisoprolol fumarate over 6 hours. These matrix tablets followed the firstorder model with anomalous (nonFickian) diffusion mechanism.
Matrix tablets offer a great potential as oral drug delivery system due to their simplicity, cost effectiveness, reduced risk of systemic toxicity, and minimal chance of dose dumping [
Bisoprolol fumarate, chemically 1[4(2isopropoxyethoxymethyl)phenoxy]Nisopropyl3aminopropan2ol fumarate, is a cardioselective
Chemical structure of bisoprolol fumarate.
In the development of any pharmaceutical formulation like matrix tablet for sustained release ability, an important issue is to design a formulation with optimized quality in a short time period and minimum number of trials. The response surface methodology has been commonly used for designing and optimization of different pharmaceutical formulations, which requires minimum experimentation [
Bisoprolol fumarate, microcrystalline cellulose (PH 101), and lactose were obtained from B. S. Traders Pvt. Ltd., India. Calcium alginate, hydroxypropyl methylcellulose (HPMC K4M), and Carbopol 943 were purchased from Merck Specialties Pvt. Ltd., India. All other chemicals and reagents used were of analytical grade.
Bisoprolol fumarate matrix tablets were prepared by the direct compression method after proper mixing of suitable ratios of various hydrophilic polymers as release modifiers with others excipients. The drug, polymers, and other excipients were first passed through sieve #80. Then drug and all the materials were uniformly mixed and compressed on single punch tablet machine (Cadmach Machinery Co. Pvt. Ltd., India) using 6 mm round and flat punches (for batch size 100 tablets).
2^{3} (threefactor and twolevel) factorial design was employed for the optimization of bisoprolol fumarate matrix tablet. The amount of hydrophilic polymers in polymerblend, namely, amount of calcium alginate (
The formulation chart for all proposed trial formulations of bisoprolol fumarate matrix tablets.
Formulation codes  Drug 
Calcium alginate (mg) 
HPMC K4M (mg) 
Carbopol 943 (mg) 
Lactose 
MCC 
Mgstearate 

F1  20  15 (−1)  0 (−1)  0 (−1)  70  20  10 
F2  20  30 (+1)  20 (+1)  20 (+1)  70  20  10 
F3  20  30 (+1)  0 (−1)  0 (−1)  70  20  10 
F4  20  30 (+1)  20 (+1)  0 (−1)  70  20  10 
F5  20  15 (−1)  20 (+1)  20 (+1)  70  20  10 
F6  20  15 (−1)  0 (−1)  20 (+1)  70  20  10 
F7  20  30 (+1)  0 (−1)  20 (+1)  70  20  10 
F8  20  15 (−1)  20 (+1)  0 (−1)  70  20  10 
^{
a}MCC: Microcrystalline cellulose;
2^{3} factorial designs and their observed response values with drug contents in bisoprolol fumarate matrix tablets.
Formulation codes  Calcium alginate (mg) 
HPMC K4M (mg) 
Carbopol 943 (mg) 
Responses  


Hardness (kg/cm^{2})^{b}  
F1  15 (−1)  0 (−1)  0 (−1)  74.62 ± 2.36  3.19 ± 0.25 
F2  30 (+1)  20 (+1)  20 (+1)  46.15 ± 2.12  4.18 ± 0.15 
F3  30 (+1)  0 (−1)  0 (−1)  65.07 ± 3.54  3.52 ± 0.20 
F4  30 (+1)  20 (+1)  0 (−1)  51.32 ± 3.25  3.84 ± 0.15 
F5  15 (−1)  20 (+1)  20 (+1)  50.39 ± 2.16  4.10 ± 0.10 
F6  15 (−1)  0 (−1)  20 (+1)  62.33 ± 1.92  3.90 ± 0.30 
F7  30 (+1)  0 (−1)  20 (+1)  55.85 ± 2.22  3.93 ± 0.10 
F8  15 (−1)  20 (+1)  0 (−1)  59.17 ± 1.82  3.41 ± 0.25 
^{
a}
For optimization, the effects of independent variables upon the responses were modeled using the following firstorder polynomial equations involving independent variables and their interactions for various measured responses, studied in this investigation. For optimization, effects of various independent variables upon measured responses were modeled using following mathematical model equation involving independent variables and their interactions for various measured responses generated by 2^{3} factorial design is as follows:
20 tablets from each formulation batch were weighted and powdered. The powder equivalent to 20 mg of bisoprolol fumarate was taken and transferred to 100 mL of volumetric flask. Then, the volume was made up to 100 mL with 0.1 N HCl. Vigorous shaking was done to dissolve the powdered material in 0.1 N HCl. Samples were filtered using filter paper no. 40. After proper dilution, absorbance values were measured at the maximum wavelength
Tewenty tablets from each batch were sampled and accurately weighed using an electronic analytical balance. The weight variation (%) was calculated as
Pfizer hardness tester was used to determine the hardness of prepared bisoprolol matrix tablets. The tablets were first kept in between two jaws after adjusting the tester to zero, a force was applied until the tablet breaks into fragments, and the reading was noted from the scale, which indicated the pressure required in kg.
To analyze the mechanism of drug release from these bisoprolol fumarate matrix tablets, the
Zeroorder model:
Firstorder model:
Higuchi model:
KorsmeyerPeppas Model:
Again, the KorsmeyerPeppas model has been employed in the
Statistical optimization was performed using DesignExpert 8.0.6.1 software (StatEase Inc., USA). All other data were analyzed with simple statistics.
Traditionally, pharmaceutical formulators develop various formulations by changing one variable at a time, and the method is timeconsuming. However, many experiments, not succeed in their purpose because they are not properly thought out and designed, and even the best data analysis cannot compensate lack of planning. Therefore, it is essential to understand the influence of formulation variables on the quality of formulations with a minimal number of experimental trials and subsequent selection of formulation variables to develop an optimized formulation using established statistical tools for optimization [
Summary of ANOVA for response parameters.
Source  Sum of square  d.f.^{a}  Mean square 



For 

Model  593.77  6  98.96  2714.99  0.0147 (S) 

98.84  1  98.84  2711.71  0.0122 (S) 

323.09  1  323.09  8863.87  0.0068 (S) 

157.18  1  157.18  4312.11  0.0097 (S) 

1.94  1  1.94  53.24  0.0867 (NS) 

5.58  1  5.58  153.08  0.0514 (NS) 

7.14  1  7.14  196.00  0.0454 (S) 
 
For hardness (kg/cm^{2})  
Model  0.85  6  0.14  454.17  0.0359 (S) 

0.10  1  0.10  302.76  0.0365 (S) 

0.12  1  0.12  392.04  0.0321 (S) 

0.58  1  0.58  1849.00  0.0148 (S) 

2.81 × 10^{−3}  1  2.81 × 10^{−3}  9.00  0.2048 (NS) 

0.05  1  0.05  169.00  0.0489 (S) 

1.01 × 10^{−3}  1  1.01 × 10^{−3}  3.24  0.3228 (NS) 
^{
a}d.f. indicates degree of freedom; ^{b}
Model simplification was carried out by eliminating nonsignificant terms (
Each response coefficient was studied for its statistical significance by Pareto charts as shown in Figures
Pareto chart relating
Pareto chart relating hardness (kg/cm^{2}).
Effect of amounts of calcium alginate and HPMC K4M on
Effect of amounts of calcium alginate and Carbopol 943 on
Effect of amounts of HPMC K4M and Carbopol 943 on
Effect of amounts of calcium alginate and HPMC K4M on hardness (kg/cm^{2}) presented by response surface plot (a), and contour plot (b).
Effect of amounts of calcium alginate and Carbopol 943 on hardness (kg/cm^{2}) presented by response surface plot (a), and contour plot (b).
Effect of amounts of HPMC K4M and Carbopol 943 on hardness (kg/cm^{2}) presented by response surface plot (a), and contour plot (b).
A numerical optimization technique based on the desirability approaches was adopted to achieve new optimized formulation with desired responses. The desirable range of these responses was restricted to
Results of experiments to assure optimization capability.
Code  Calcium alginate (mg) 
HPMC K4M (mg) 
Carbopol 943 (mg) 
Responses  


Hardness (kg/cm^{2})  
FO  15.28  32.12  30.31  Actual values^{b}  
41.61 ± 1.97  4.65 ± 0.07  
Predicted values  
40.00  4.54  
 
% Error^{c}  4.03  2.42 
^{
a}
All the bisoprolol fumarate matrix tablets contained bisoprolol fumarate within
Drug content and weight variation of bisoprolol fumarate matrix tablets.
Formulation codes  Drug content 
Weight variation 

F1  99.03 ± 0.59  2.12 ± 0.18 
F2  98.42 ± 0.72  2.25 ± 0.24 
F3  98.14 ± 0.65  3.04 ± 0.28 
F4  97.99 ± 0.85  3.07 ± 0.19 
F5  99.11 ± 1.22  2.31 ± 0.15 
F6  98.62 ± 0.52  1.90 ± 0.12 
F7  98.92 ± 0.78  2.02 ± 0.14 
F8  98.66 ± 0.55  1.92 ± 0.13 
FO  98.93 ± 0.72  1.88 ± 0.08 
^{
a}Mean ± S.D.,
The hardness test for bisoprolol fumarate matrix tablets was done to assess the ability of tablets to withstand handling without fracturing or chipping. A force of about 4 kg/cm^{2} is the minimum requirement for a satisfactory hardness of tablets [
The
Results of curve fitting of the
Formulation code  Correlation coefficient ( 
Release exponent ( 


Zeroorder  Firstorder  Higuchi  KorsmeyerPeppas  
F1  0.9744  0.9908  0.8279  0.9410  0.60 
F2  0.9698  0.9929  0.7067  0.9415  0.73 
F3  0.9467  0.9951  0.6842  0.8979  0.69 
F4  0.9710  0.9948  0.7286  0.9317  0.70 
F5  0.9632  0.9941  0.7060  0.9083  0.70 
F6  0.9566  0.9906  0.6924  0.9047  0.71 
F7  0.9673  0.9953  0.7058  0.9092  0.71 
F8  0.9650  0.9944  0.7212  0.9099  0.69 
FO  0.9733  0.9910  0.7111  0.9460  0.74 
Bisoprolol fumarate matrix tablets for sustained release application were successfully developed by response surface methodology based on 2^{3} factorial design. The amounts of calcium alginate, HPMC K4M, and Carbopol 943 in bisoprolol fumarate matrix tablets on the properties of bisoprolol fumarate sustained release matrix tablet like drug release and hardness were analyzed and optimized. The threedimensional response surface plots and corresponding contour plots relating
All authors report no conflict of interests.