INTRODUCTION:

For the present study, the aim was to develop controlled release floating dosage form to increase the gastric residence time for the drug which leads to increase the bioavailability of drug. Many literatures were suggested that the most convenient method of controlled delivery of drug is undoubtedly oral, but oral controlled release of the drug for an extended period of time that exhibits more absorption in stomach and upper small intestine, has not been successful with conventional approaches. So, it has been decided to develop the controlled release floating dosage form as a novel approach for the drug delivery and the tablet is well known as most convenient dosage form among all oral drug delivery.

Finally it was decided to develop a controlled release floating tablets (CRFT). [1-4] Repaglinide is a novel antihypertensive agent, widely absorbed from the stomach and upper part of the small intestine. It has shorter elimination half life (0.8hr to 1hr), so necessity to frequent administration and bioavailability can be improved by making the drug completely absorbed in the stomach and upper part of the small intestine. CRFT of Repaglinide was developed using Natural Karaya gum (NKG) and Carbopol 934 P. [5,6]

NKG is a natural gum and it is obtained from gummy extrudes from stem bark of Natural Karaya belongs to the family of sterculiaceae. It is freely soluble in water via hydration and practically insoluble in absolute ethanol. It is used as suspending agent, viscosity enhancer and rate controlling polymer in controlled release dosage. [7-10] Carbopol 934 P is a synthetic high molecular weight cross linked water soluble polymer of acrylic acid, which is known as "Carbomer". It is freely soluble in water and alcohol. It is used as cross linking agent for controlled release matrix as a rate controlling polymer, stabilizing agent in emulsion, thickening and viscosity modifying agent.

Optimization study was done to determine the appropriate concentration of NKG and Carbopol 934 P in combination as a controlled release polymer and aim was to predict individual effect of both polymers (NKG and Carbopol 934 P) at different concentration level. A 3² full factorial design was selected to optimize two independent variable at three different levels by varied response variables. Experimental trials were performed at all nine possible combinations. Two independent variables i.e. amount of NKG (polymer X1) and amount of Carbopol 934 P (polymer X2) were varied at three different levels that was coded for low, medium and high (-1, 0, 1 respectively). The response variables were measured by a multiple factorial regression analysis using the best fitted quadratic model for each trial and it was carried out in MS EXCEL 2007. Various computations required for current study using response surface plot and contour plot were carried out by employing software Design Expert version 8.0.7.1 A statistical model incorporating interactive and polynomial terms was utilized to evaluate the responses. [11,12]

Y = b0 + b1X1 + b2X2 + b12X1X2 + b11X12 + b22X22

Where, Y is the dependent variables, b0 is the arithmetic mean response of the nine runs, and b1 is the estimated coefficient for the factor X1. The main effects (X1 and X2) represent the average result of changing one factor at a time from its low to high value. The interaction terms (X1X2) show how the response changes when two factors are simultaneously changed. The polynomial terms (X12 and X22) are included to investigate non-linearity. [13-17]

MATERIALS:

Repaglinide was obtained as a gift sample from Zydus Cadila Healthcare limited, Ahmedabad. Natural Karaya Gum (NKG) was obtained as a gift sample by Medicinal natural products research laboratory, University Institute of Chemical Technology, Mumbai. PVP K 30 obtained as a gift sample from Alembic limited, Vadodara. Carbopol 934 P was obtained as gift sample from Corel Pharma Chem, Ahmedabad. NaHCO3, Lactose, Talc, Mg. Stearate and IPA used in the present study were provided by.

METHODS:

Preparation of Repaglinide Controlled Release Floating Tablet:

Repaglinide controlled release floating tablets were prepared by wet granulation techniques using different concentrations of various polymers. To prepare tablet, weighed all ingredients except talc and magnesium stearate and shifted through sieve no 40 then blend uniformly in glass mortar with pestle. After sufficient mixing, the blend was wetted by adding sufficient quantity of isopropyl alcohol as a granulating agent. Prepared wet mass was granulated by passing through sieve no 18. Prepared granules were dried at 50 0C – 60 0C for 20 min in hot air oven. After drying, dried granules were lubricated by adding sufficient quantity of magnesium stearate and talc for 5 min. The tablets were compressed using 6 mm punch on 8 station rotary punching machine. Experimental Design [18,19,20]

A central composite design (CCD) was employed for the optimization of Repaglinide controlled release floating tablets. A 3² full factorial design was selected to optimize two independent variables at three different levels by varied response variables. Experimental trials were performed at all nine possible combinations. Two independent variables i.e. amount of NKG (polymer X1) and amount of Carbopol 934 P (polymer X2) were varied at three different levels that was coded for low, medium and high (-1, 0, 1 respectively). The response variables T6 (cumulative % amount of drug released in 6 hr) (Y1), T12 (cumulative % amount of drug released in 12 hr) (Y2), Q50 (time in minutes required to 50 % of drug released) (Y3), FLT (Y4), TFT (Y5), and Swelling Index after 12 hr (Y6) were selected for present study. The experimental design with corresponding formulations is outlined in Table 1. Floating Properties [21].

To measure the floating properties, five tablets from each formulation were selected randomly and placed in beaker containing 250 ml of 0.1 N HCL (pH 1.2). The temperature was maintained at 37 ± 0.5 0C. The time by which the tablet started to float on the surface of medium for FLT and entire duration of time by which the tablet constantly remained on the surface of the medium for TFT was noted. The Floating lag time (FLT) and Total Floating Time (TFT) of tablet of each formulation is shown in Table 2.

Swelling Study [22-24]:

The extent of swelling can be measured in terms of percentage weight gain by the tablet. Five tablets from each formulation were selected randomly for the swelling study. Each tablet individually weighed (W0) and separately placed in beaker containing 100 ml of 0.1N HCL (pH 1.2). The tablet was removed from each beaker after 1 hour of time interval and excess surface solvent from the tablet was wiped out carefully with filter paper. Each swollen tablet was reweighed (Wt) and the swelling index (SI) is calculated using the following formula,

Swelling index (SI) = [(Wt - Wo) / Wo] x 100

Where,

Wt = Final weight of tablet at time t (mg),

Wo = Initial weight of tablet (mg)

The value of swelling index for the tablet of each formulation is given in Table 2.

In Vitro Dissolution Study [25]:

The In-vitro dissolution study for the tablet of each formulation was conducted as per United States Pharmacopoeia type II apparatus. The rotating paddle method was used to study the drug release from the tablets. Dissolution medium 900ml of 0.1 N HCl (pH 1.2) was placed in dissolution vessel. The release was performed at 37 0C±0.5⁰C and at a rotational speed of paddle about 50rpm. Tablets were placed in each dissolution vessel. The 5ml samples were withdrawn at the time interval of one hour for 16 hrs. The collected samples were filtered through Whatman filter paper No. 40 and analyzed for drug content by UV Spectrophotometer. The absorbance for each sample was measured at 207nm and the concentration of drug present was calculated using calibration plot of Repaglinide. Then, the cumulative percentage amount of drug released after each time interval was calculated using the formula,

Cumulative Amount of Drug Release = C × DF × DM

Where,

C = Concentration of drug (µg/ml),

DF = Dilution Factor is 1,

DM = Dissolution Medium (900ml) Statistical analysis [12]

Statistical optimization of Repaglinide tablet was done by design expert software, Version 8.0.7.1. the study type was response surface, 9 runs were applied to the design type central composite and design model was selected as quadratic. The quadratic model is best fitted for the results to determine the effect of independent variable on response variables. There was considerable difference observed in minimum and maximum values of each response variable with respect to the independent variables. By applying two way ANOVA with 95% confidence level, their predicted values were found for each response variables. The value of P < 0.05 was considered to be significant. To demonstrate graphically the influence of each factor on responses, the response surface plots and Contour plots were generated.

RESULTS AND DISCUSSION:

From the preliminary study, it was found that NKG and Carbopol 934 P were efficient polymer to achieve controlled drug releasing property by forming swellable matrix with the drug. Therefore, optimization study was applied to find best possible concentration of both polymer for the present investigation. The formulations were designed by 3² full factorial design which is shown in Table 1. Amount of NKG and amount of Carbopol 934 P were selected as independent variables and it was coded as X1 and X2 respectively. Both variable optimized by varied at three different level.

The matrix tablets of designed formulation were prepared by wet granulation method. Developed tablets were evaluated for various response variables. The response variables T6 (cumulative % amount of drug released in 6 hr) (Y1), T12 (cumulative % amount of drug released in 12 hr) (Y2), Q50 (time in minutes required to 50% of drug released) (Y3), FLT (Y4), TFT (Y5), and Swelling Index after 12 hr (Y6) were selected for present investigation. The results of all response variables are shown in Table 2.

The values of T6 was varied from 30.89% to 43.39%, T12 was varied from 57.93% to 84.19%, Q50 was varied from 408 min to 612 min, FLT was varied from 76 seconds to 95 seconds, TFT varied from 16 hrs to 22 hrs, SWI was varied from 96% to 130%. The quadratic model is best fitted to determine the effect of independent variable on response variables. There was considerable difference observed in minimum and maximum values of each response variable with respect to the independent variables. By applying two way ANOVA with 95% confidence level, their predicted values were found for each response variables and it was shown in Table 3.

Drug release profile from all the developed formulation was applied for model dependent kinetics by providing the kinetic treatment and it was exhibited Super case-II transport drug release kinetics approaching zero-order, as the value of release rate exponent (n) varied between 0.9430 and 1.0133. The kinetic treatment of drug release profile for all the formulation A1 to A9 was shown in Table 4.

Table 1: Selected Factor Combinations as per 3² full factorial design

_Code	Coded level		Actual values (mg)
	X₁	X₂	X₁	X₂
A₁	-1	-1	10	6
A₂	-1	0	10	8
A₃	-1	1	10	10
A₅	0	-1	14	6
A₅	0	0	14	8
A₆	0	1	14	10
A₇	1	-1	18	6
A₈	1	0	18	8
A9	1	1	28	10

Table 2: The results of each response variables as per 3² full factorial design

_Code	_Code	Code	T₆	^T12	^Q50	FLT	TFT	SWI
	X₁	X₂	(%)	(%)	min	Sec	hrs	(%)
A1	-1	-1	43.39	84.19	408	80	16	96.89
A2	-1	0	37.48	73.89	462	79	17	104.2
A3	-1	1	37.68	73.27	474	76	19	113.7
A4	0	-1	40.09	80.12	450	83	18	102.5
A5	0	0	38.41	76.27	456	82	20	110.9
A6	0	1	34.41	69.18	522	79	21	123.7
A7	1	-1	40.88	74.76	453	95	19	106.9
A8	1	0	34.48	66.39	534	90	20	126.7
A9	1	1	30.89	57.93	612	87	22	130.3

Table 3: Significant level and predicted values of each response variables

Response	Name	Units	Obs	Analysis	P-value	Predicted Value
Y₁	T6	%	9	Polynomial	0.0497	39.93
Y₂	T12	%	9	Polynomial	0.0241	78.48
Y₃	Q50	min	9	Polynomial	0.0346	451.33
Y₄	FLT	Sec	9	Polynomial	0.0036	81.56
Y₅	TFT	hr	9	Polynomial	0.0064	18.56
Y₆	SWI	%	9	Polynomial	0.0257	103.43

Table 4: Kinetic treatments to dissolution profile for each formulation A1 to A9

Code	Zero Order		Hixon Crowell		Korsemeyer Peppas			Higuchi Plot
Code	(R²)	K₀	(R²)	K_H	(R²)	n	K_k	(R²)	Kp
A1	0.9996	6.9659	0.9564	0.6225	0.9996	0.967	0.9534	0.9654	0.0342
A2	0.9979	6.2374	0.9592	0.5961	0.9966	1.0133	0.7602	0.9626	0.0381
A3	0.9984	6.1665	0.9576	0.5898	0.9974	0.9987	0.7834	0.964	0.0386
A4	0.9999	6.6667	0.9591	0.6106	0.9993	0.9795	0.8786	0.9628	0.0357
A5	0.9991	6.3956	0.9606	0.602	0.9975	0.9983	0.8014	0.9619	0.0371
A6	0.9992	5.7928	0.9638	0.5748	0.9941	1.006	0.7019	0.9585	0.0409
A7	0.9973	6.2349	0.9509	0.5891	0.9985	0.976	0.8559	0.9696	0.0383
A8	0.9995	5.4715	0.9535	0.5484	0.9978	0.9452	0.7954	0.9675	0.0437
A9	0.9991	4.8097	0.9534	0.5147	0.994	0.943	0.7014	0.9678	0.0497

Response Y1

T6 = + 56.065 + 1.03 * X1 - 3.953 * X2 - 0.143 * X1* X2 -

0.014 * X12+ 0.26 * X22

The regression co efficient was found from the ANOVA study and it was found that the negative effect of X2 coefficient while positive of X1 coefficient at low level on the response variable but at high level opposite results were found. It was ment that the concentration of Carbopol 934 P was not created much impact on drug release rate when it compared with the concentration of NKG at low level in the formulations. Negative coefficient was found in combination of both variables and it was suggested that when the concentration of polymer to drug was increased, the drug release from the dosage was decreased. When the concentration of NKG was increases, the drug release rate was significantly reduced. It was found from the response surface plot and contour plot shown in Figure 1. Response Y2

T12 = +73.068 + 6.17 * X1 - 4.87 * X2 - 0.185 * X1 * X2 -

0.22 * X12+ 0.27 * X22

The regression equation was suggested that the effect of variable X1 and X2 on response Y2. From the Figure 2, it was found that at the low level the effect of variable X1 on the drug release was more considerable than the variable X2. But at high level both are equally significant on the response variable. The negative coefficient was found for the combination of X1 and X2 suggesting that the cumulative percentages of drug release was significantly reduced by increasing the concentration of independent variables in combination.

Figure 1: (a) Response surface plot and (b) Contour plot for response Y1

Figure 2: (a) Response surface plot and (b) Contour plot for response Y2

Figure 3: (a) Response surface plot and (b) Contour plot for response Y3

Figure 4: (a) Response surface plot and (b) Contour plot for response Y4

Figure 5: (a) Response surface plot and (b) Contour plot for response Y5

Figure 6: (a) Response surface plot and (b) Contour plot for response Y6

Response Y3

Q50 = +670.71 - 38.0 * X1 - 25.94 * X2 + 2.91 * X1 * X2 +

0.91 * X12 + 0.6 * X22

The regression equation was suggested that the effect of variable X1 and X2 on response Y3 was found negative. It might be indicated that the effect of selected variable on response (Y3) was not significant individually at low level. But the positive coefficients in the equation were indicated that the significant effect observed when the selected variables were used at high level as well as in combination. From the Figure 3, it was found that at the low level the individual effect of variables X1 and X2 on the response Y3 were not considerable significant. But at high level both are equally significant on the response variable. And in combination also significant response was found. Hence, this results might be reveled that the time required for 50 % drug release was enhanced with the concentration of polymer (NKG and Carbopol 934 P) to drug in the dosage increasing.

Response Y4

FLT = + 90.10 - 3.0 * X1 + 1.75 * X2- 0.13 * X1 * X2 +

0.198 * X12- 0.08 * X22

The regression equation was suggested that the variable X2 was more significant than the variable X1 because negative co efficient was found for variable X1 by ANOVA. It was suggested that FLT was enhanced when the level of NKG in the formulations was enhanced. And, opposite effect was found by X2 variable because the value of its coefficient was positive. It was indicated that the value of FLT was reduced when the level of X2 variable enhance. From the Figure 4, it was reveled that the level of X2 variable more significant because the FLT value lower towards the direction of higher level of X2 variable than X1 variable.

Response Y5

TFT = + 0.76 + 1.83* X1+ 0.083* X2 + 2.67 * X1* X2 -

0.052* X12+ 0.042 * X22

The coefficient for both variables was found to be positive at low level, high level and in combination. It was suggested that there was linear relationship observed on response variable by the selected X1 and X2 variable. From the Figure 5, it was found that gradually rises the value of TFT as the concentration of both polymer NKG and Carbopol 934 P increases. But, it was also indicated that the level of X2 variable was more predominant than the value of X1 variable. Because the response direction move towards the higher level of X2 variable than X1 variable.

Response Y6

SWI = +50.45 - 0.91 * X1 + 8.64 * X2 + 0.21 * X1 * X2 + 0.047 * X12- 0.40 * X22

The coefficient of X2 variable was found to be positive at low level but X1 variable coefficient was negative. It was suggested that X1 variable move towards the predicted value positively with the concentration of Carbopol 934 P while negatively observed with the concentration of NKG at low level but at high level vise versa results were obtained. Both variable might be affecting SWI significantly but the effect of X2 variable was more predominant than X1 variable. From the Figure 6, it was found that the response value was increased by increasing the level of both variable (X1 and X2).

CONCLUSION:

Controlled release floating tablets of Repaglinide with NKG and Carbopol 934 P were prepared and optimized using central composite experimental design (3² Full Factorial Design) and multiple response optimizations. The quantitative effect of these factors on the release rate could be predicted by using polynomial equations. The model was found to be satisfactory for describing the relationships between formulation variables and individual response variables. The experimental values of each response variables obtain from the optimized formulation were very close to the predicted values. The developed tablets were found desirable drug release kinetics and found to be zero order. Formulation A4 was found to be best optimized formulation because of its desirable drug release kinetics and other response variables.

REFERENCES:

1. Formulation and Evaluation of Antidiabetic Herbal Capsules. P. B. Suruse, M. K. Kale, N. J. Duragkar, A. Gundawar. Research J. Pharma. Dosage Forms and Tech. 2011; 4(2): 113-118.

2. Fares Hezam Al-Ostoot, Vidya, R., Zabiulla, Yasser Hussein Eissa Mohammed, Mahima Jyothi, Pallavi. H.M, Shaukath Ara Khanum. Statistical Analysis of Antimicrobial Data of 2-[2-(Aroyl) aroyloxy] methyl-1,3,4 Oxadiazoles analogues Using ANOVA. Asian J. Research Chem. 2018; 11(2):293-297.

3. Malaisse W. Repaglinide, a new oral anti-diabetic agent: a review of recent preclinical studies. Eur J Clin Invest. 1999; 29: 21-29.

4. Landgraf R, Frank M, Bauer C, Dieken M. Prandial glucose regulation with repaglinide: it’s clinical and lifestyle impact in a large cohort of patients with Type II diabetes. Int J Obesity. 2000; 24: S38-S44.

5. Saibaba, S.V.; Kumar, M.S.; Ramu, B. Pharmaceutical impurities and their characterization. European J Pharm Med Res 2016, 3(5), 190-196.

6. S Gopalakrishnan, A Chenthilnathan. Formulation and In Vitro evaluation of Aceclofenac oral floating tablets. Research J. Pharm. and Tech. 4(4): April 2011; Page 642-645.

7. Bandameedi R, Pandiyan PS. Formulation and evaluation of gastro retentive floating bioadhesive tablets of hydrochlorothiazide. Asian J Pharm Clin Res 2017;10: 150-5.

8. Addanki Gopikrishna, B. Ramu, G. Srikanth, Dr. Bigala Rajkamal (2016). Formulation of isoniazide sustained release formulation by using carbopol 934 PP. Int J App Pharm Sci Res. 1(2):60- 69. Doi: 10.21477/ijapsr.v1i2.10177.

9. Lachman L, Lieberman HA, Kanig JL. The Theory and Practice of Industrial Pharmacy. Varghese Publishing House, Bombay. 3^rdedn. 1991, page no: 184.

10. Fares Hezam Al-Ostoot, Vidya, R., Zabiulla, Yasser Hussein Eissa Mohammed, Mahima Jyothi, Pallavi. H.M, Shaukath Ara Khanum. Statistical Analysis of Antimicrobial Data of 2-[2-(Aroyl)aroyloxy]methyl-1,3,4 Oxadiazoles analogues Using ANOVA. Asian J. Research Chem. 2018; 11(2):293-297.

11. Ravi K. Barde, Minal R. Narkhede, V.R. Gudsoorkar, Rahul K. Amrutkar, Prashant S. Walke. Formulation and Evaluation of Sustained Release Floating Matrix Tablets of Labetalol Hydrochloride. Research J. Pharm. and Tech. 4(10): Oct. 2011; Page 1637-1643

12. N. Utharalakshmi, A. Ganesh Kumar, G. Narendrakumar. Optimization of Cellulase Producing Aspergillus flavus SB4 by Solid State Fermentation using Response Surface Methodology (RSM)-CCD. Research J. Pharm. and Tech. 8(4): April, 2015; Page 349-354.

13. Vishnu Vardhana Rao. G, V. Srinivasa Rao, K. Ramakrishna. Quality by Design Approach for the Development and Validation of LC method for the Estimation of in-Vitro release of Raloxifene hydrochloride in Dosage Formulation. Research J. Pharm. and Tech. 8(11): Nov., 2015; Page 1487-1494.

14. P. Pandian, D. Kalpanapriya. Two-factor ANOVA Technique for Fuzzy Data having Membership Grades. Research J. Pharm. and Tech. 2016; 9(12): 2394-2402.

15. Varma MM, Suneetha S, Raju DB. Design and Evaluation of Floating Drug Delivery System of Furosemide. Research J. Pharm. and Tech. 3(2): April- June 2010; Page 452-454.

16. D.Anuradha D. Kalpanapriya. Intuitionistic Fuzzy ANOVA and its Application in Medical Diagnosis. Research J. Pharm. and Tech 2018; 11(2):653-656.

17. Kiran Deshpande, Pranjali Ranaware, A. R. Madgulkar, M.C. Damle. Development and Validation of Stability Indicating HPTLC Method for Determination of Repaglinide. Research J. Pharm. and Tech. 6(2): Feb. 2013; Page 158-161.

18. B. Sumalatha, D. John Babu, A. Venkatanarayana, P. Rajasekhar Reddy, P. Divya Sruthi. Experimental Investigation on Biosorption of Chromium from Aqueous Solution using Citrus limonium peel: Optimization of Process Parameters using Central Composite Design. Research J. Pharm. and Tech 2018; 11(12): 5253-5264.

19. Siddamsetty Upendra, Siddamsetty Mahendra, Poli Sivasankar Rao. Two-way ANOVA Calculations Using MS-Excel. Research J. Science and Tech. 2017; 9(4): 532-536.

20. Hyma Ponnaganti, Abbulu. K. Enhanced Dissolution of Repaglinide: SMEDDS Formulation and In-vitro Evaluation. Research J. Pharm. and Tech. 7(11): Nov. 2014 Page 1246-1252.

21. Sanjay Dey, Snigdha Dutta, Bhaskar Mazumder, “Formulation and evaluation of floating matrix tablet of atenolol for gastro-retentive drug delivery”, Int. J. of Pharm. and Pharm. Sci., 2013;4:433-437.

22. Pramod Patil, Someshwara Rao B, Suresh V Kulkarni, Basavaraj, Chetan Surpur, Anand Ammanage. Formulation and In Vitro Evaluation of Floating Matrix Tablets of Ofloxacin. Asian J. Res. Pharm. Sci. 1(1): Jan.-Mar. 2011; Page 17-22.

23. N. M. Vageesh, Ramya Sri Sura, K Gulijar Begum, B Swathi. Formulation Development and In vitro Evaluation of Floating Tablets of Lafutidine by Employing Effervescent Technology. Asian J. Pharm. Res. 2017; 7(3): 189-197.

24. Grassi M, Grassi G. Mathematical Modelling and Controlled Drug Delivery: Matrix Systems. Current Drug Delivery; 2005; 2:97-116.

25. http://www.anvpublication.org/Universal_Search.aspx?What=CitationList & tmp_PID= 191120201257988354.

Received on 20.11.2019 Modified on 24.01.2020

Research J. Pharm. and Tech. 2021; 14(1):109-114.

DOI: 10.5958/0974-360X.2021.00020.2