Assessing the Impact of Formulation Variables on Dissolution Profile of Sustained Release Tablet of Metformin Hydrochloride by Quality by Design Approach

 

Amit Kaushal, Sandeep Arora, Sukhbir Singh, Neelam Sharma

Chitkara College of Pharmacy, Chitkara University, Punjab, India

 

ABSTRACT:

 

 

1. INTRODUCTION

Pharmaceutical preparations may be as old as mankind and regulation to achieve the craved quality profile has been developed regularly with time [1]. The eventual goal of drug regulation is to warrant that drug product must be of good quality, safe and effective. Traditionally quality by testing (QbT) approach is applied in pharmaceutical industry to ensure quality of pharmaceutical product. Rather than built and guarantee the quality, only the quality of the product is measured by the testing in the QbT approach [2-4]. In 2006, International Conference on Harmonization (ICH) Q8 guidelines firstly introduced Quality by Design (QbD) concept to the pharmaceutical industry.

 

In these guidelines, QbD is delineated as “a systematic approach to development that starts with predefined objectives, emphasizes product and process understanding and process control, based on sound science and quality risk management.” The goal is “Quality must be built into the product” and bring down the costs of pharmaceutical product for the patient. The shortcomings of  QbT is frequent testing, no flexibility in manufacturing processes, not establishing relation between specification and process variables and  no concluding for quality failures. In contrast, QbD provides a design space within this; changes in process variables are acceptable and verification of the quality of the finished product is no longer necessary. When QbD approach is employed at the formulation development step, the understandings of processes are improved. It intends that the chances of variability reduce and failure of quality reduces [5].

 

Basically QbD based product development involves numerous steps. Primary step is selection of quality target product profile (QTTP) that are vital from the safety and efficacy perspective and ICH Q8 (R2) delineates the Quality Target Product Profile (QTPP) as “a prospective précis of the quality characteristics of a drug product that ideally will be attained to ensure the desired quality, taking into account safety and efficacy of the drug product [6]. In second step, identify critical quality attributes (CQAs) which have significant impact on the QTPPs. Secondly assists in showing the relationship between process/material parameters and critical quality attributes (CQAs) to systematically deliver a quality drug product to the patient [7-9]. Afterward, Risk assessment assists to increase quality of method or process. From risk assessment one can recognize highly influencing material attributes and process attributes for drug product quality attributes [10]. Risk estimation matrix (REM) is the simplest approach for risk assessment of various factors, where the probable risks arising due to factors are placed /arranged and their impact on quality attributes of product is assessed. Then design of experiment (DOE) is performed to propose a design space. Graphical representation of interaction among various material and process parameters that have impact on CQAs can be defined as design space. Various regulatory bodies allow change of variables in this design space [11,12].    

 

Metformin hydrochloride is well established antihyperglycemic drug for treatment of type 2 diabetes mellitus (T2DM). Metformin is used for T2DM from last six decades as a single or in combination with other anti-diabetic drugs [13]. Extended release metformin was get approval in 2000 and research revealed that it has comparable safety and efficacy to conventional immediate release formulation with advantage of reduce dose administration. In recent years, metformin has been shown beneficial effect on poly cystic ovarian syndrome (PCOS) in woman and used as alone or component of combination therapies for ovulation induction. Metformin have showed osteogenic effect and it used for bone disorder in current studies. Several studies have been published on development of extended release formulation of metformin. But literature review indicated that there was no any study utilizing the QbD approach in detail. A few studies use Artificial Neural Network (ANN) [14], box-behnken response surface design [15-17], factorial [18-22], placket-burman and D-optimal mixture design [23] for optimization only. The numbers of studies applying detailed QbD approach are scanty. Therefore the main aim of current work is assessing effect of critical factors on quality of sustained release metformin formulation by using quality by design approach.

 

2. MATERIALS AND METHOD:

2.1 Materials:

Metformin hydrochloride was obtained as gift from Cipla limited (Baddi, India). Hydroxy propyl methyl cellulose (HPMC K4M), Glyceryl dibehenate (Compritol 888 ATO) and poly vinyl pyrollidone (PVP K 30) were procured from Gattefosse India pvt.ltd (Mumbai, India). Microcrystalline cellulose (MCC), Magnesium stearate (MS) and talc were purchased from S D Fine chemical ltd. (Ambala, India). Rest reagents and chemicals were of analytical reagent (AR) grade.

 

2.2 Method:

2.2.1 Defining the Quality Target Product Profile (QTPP) and Determination of Critical Quality Attributes (CQAs):

Quality target product profile (QTPP) is a first step for QbD-oriented development of metformin hydrochloride sustained release tablet.  The QTPP is delineated as a compendious of the quality parameters of the drug product that will be accomplish to guarantee the desired quality. Critical quality attributes (CQAs) were selected such as cumulative drug release at 2 hr, drug release at 5 hr and drug release at 8 hr, in order to meet the QTPP. These elements of QTPP are indispensable for the effective development of modified drug release product.

 

2.2.2 Risk Estimation Matrix:

The initial risk assessment study was carried out to choose critical material attributes (CMAs) and critical process parameters (CPPs) for sustained release tablet. Risk estimation matrix (REM) was used for the identification of high risk factors. In this matrix each material attribute (MA) and process parameter (PP) was assigned low, medium and high-risks levels.

 

2.2.3 Preparation of Sustained Release Tablet:

Matrix tablets of metformin hydrochloride were formulated employing various level of material and process parameters (table 3 and 4). The required amount of metformin hydrochloride, polymers (HPMC/compritol) and diluents (MCC) were passed through mesh no. 80. All materials were blended in a mortar by geometric dilution technique. The binder (PVP solution) was added and mixed properly to shape dough mass. The wet mass was passed through mesh No. 12 and then dried for 4-8 hr at 60⁰C. The lubricants (MCC) and glidant (talc) were screened through mesh No. 100 on to prepared dry granules and mixed for 3-5 minutes. The prepared granules were compressed in to tablet using rotary tablet punching machine (M/s Cadmach Machinery Co. Pvt. Ltd., Mumbai). The tablets were evaluated for drug content (assay), friability, weight variation, hardness and thickness [24]. Prior to compression, flow and compressibility properties of granules were evaluated.

 

2.2.4 Factor Screening:

The Taguchi design was employed to screen critical factors out of studied seven factors: amount of HPMC, amount of compritol, amount of PVP, amount of MS, blending time, compression force and lubrication time [25]. According to design, total eight formulations were prepared and evaluated for drug release profile. Model was fitted by linear polynomial equations, and coefficients for each variable were estimated corresponding to the CQAs of drug product by eliminating the interaction term(s). Pareto charts were applied for quantitative identification of the effect of each CMAs/CPPs on the selected CQAs for screening [26-28].

 

2.2.5 Formulation Optimization and Establishment of Design Space:

Box-Behnken Design (BBD) employed for optimization of critical factors screened by Taguchi design. Critical factors; amount of HPMC (X1), amount of compritol (X2) and compression force (X3) were varied at three different equidistant level i.e; +1 (high), 0 (intermediate) and -1 (low). A total 17 experimental runs were given by design including center point (0, 0, 0) in quintuplicate. All the 17 formulations were prepared and evaluated for drug release at 2 hr (CDR 2) (%), at 5 hr (CDR 5) (%) and at 10 hr (CDR 10) (%) as the critical quality attributes (CQAs). The quadratic polynomial model was chose as mathematical model to analyse studied responses [29]. The 2 D – contour and 3 D response plots were constructed to find out impact of parameters on studied responses. The optimize formulation was selected by using numerical optimization approach based on desirability function [29,30].

 

2.2.6 Validation of Model:

Including optimized formulation eight check points were selected to validate model. The linear residual and correlation plots were generated and predication error (%) was also estimated [31].

 

2.2.7 In-Vitro Drug Release Study:

Dissolution studies were performed in triplicate, using the USP XXIII dissolution apparatus type 2 (paddle method) at 100 rpm and 37±0.5°C containing phosphate buffer pH 6.8 (1 L) as the dissolution medium. A 5 ml of sample was withdrawn periodically at each hour for 12 hr and the volume was replaced with an equivalent volume of pre warmed (37±0.5°C) fresh dissolution medium. Samples were analyzed by UV-spectrophotometer at 232nm [24, 32-34].

 

3. RESULT AND DISCUSSION:

3.1 Define QTPP and Risk Assessment:

The study aimed at developing a sustained-release and pro-long effect formulation containing metformin hydrochloride, based on QbD approach. As the first step of QbD approach, QTPP defined and listed in table 1. Cumulative drug release at 2 hr (CDR2), 5 hr (CDR5) and 8 hr (CDR8) were selected as CQAs to achieve QTPP.

 

Table 1. Quality Target Product Profile for Sustained Release Metformin Hydrochloride tablet.

QTPP	Target	Justification
Therapeutic Indication	Anti-hyperglycemic	Metformin, a well known medication for treatment of type 2 diabetes
Target Population	Adult	Type 2 diabetes is an age related disease and age of onset for type 2 diabetes is 45 year.
Dose strength	500 mg	Safe and effective dose strength.
Route of administration	oral	Better patient compliance
Dissolution profile	Sustained release	Sustained release metformin hydrochloride is desirable to maintain plasma conc. within therapeutic window for prolong time and to reduce the number of daily dose, thus bettering patient compliance.

 

After defining QTPP and selecting CQAs the risk estimation matrix was constructed to find possible process parameters and material attributes that influence CQAs. REM (Table 2) recommended that the amount of HPMC, amount of compritol, amount of magnesium stearate, and lubrication time were colligated with high risk levels as highlighted with red colour. Amount of PVP, mixing time and compression force was colligated with medium risk highlighted with yellow colour while amount of talc, drug particle size, drug solubility was associated with low risk represented with green colour.

 

Table 2. Initial Risk Assessment for Sustained Release Tablet Containing Metformin Hydrochloride

CQAs	Drug particle size	Drug solubility	HPMC amount	Compritol amount	PVP amount	MS amount	Talc amount	Mixing time	Lubrication time	Compression force
CDR 2	Low	Low	High	High	Medium	High	Low	Medium	High	Medium
CDR 5	Low	Low	High	High	Medium	High	Low	Medium	High	Medium
CDR 5	Low	Low	High	High	Medium	High	Low	Medium	High	Medium

 

3.2 Factor Screening:

Factor screening studies was performed on the factors, short-listed using REM analysis to identify critical CMAs/CPPs using Taguchi orthogonal array design. This design is a unique screening approach that allows factor screening with fewer numbers of experiments. The vantages of Taguchi design over the other design are that several factors can be simultaneously analysed and critical factors can be selected from minimum experimental trials. Table 3 shows levels of factors and observed values of CQAs as per the Taguchi design matrix. As ascertained from table 3, CDR 2; CDR5 and CDR8 varied from 19.28% to 35.67%; from 50.83% to 73.96% and from 76.84% to 96.97% respectively for different experimental runs.Figure 1 revealed that amount of HPMC (A); amount of compritol (B) and compression force (G) strikingly influenced (cross t value limit) cumulative drug release at 2 hr (CDR2), amount of HPMC and amount of compritol were critical factor for cumulative drug release at 5 hr (CDR5) while cumulative drug release at 8 hr (CDR 8) was significantly affected by amount of HPMC and amount of compritol. 

 

Table 3. Taguchi Design Matrix Showing Different Levels of Factors and Result

Run	Amount of HPMC (%)	Amount of compritol (%)	Amount of PVP (%)	Amount of MCC (%)	Blending time (Min)	Lubrication time (Min)	Comp. force (kN)	Results
								CDR2 (%)	CDR5 (%)	CDR8 (%)
1	30	10	2.5	15	5	3	15	22.32± 0.82	53.74± 1.39	80.86± 0.92
2	30	10	2.5	5	15	5	5	19.28± 0.53	50.83± 1.28	76.84± 1.51
3	20	10	5	15	15	3	5	25.42± 0.39	56.43± 1.41	83.52± 1.18
4	20	2.5	2.5	15	15	5	15	35.67± 0.84	73.96± 0.82	96.97± 1.31
5	20	10	5	5	5	5	15	28.92± 0.52	60.47± 1.05	88.46± 0.93
6	20	2.5	2.5	5	5	3	5	32.13± 1.05	64.78± 0.46	93.13± 0.73
7	30	2.5	5	5	15	3	15	29.53± 0.64	63.94± 1.23	86.12± 1.39
8	30	2.5	5	15	5	5	5	28.46± 0.26	64.92± 0.75	92.03± 0.91

CDR2 = Cumulative drug release at 2 hr; CDR 5= Cumulative drug release at 5 hr; CDR 8= Cumulative drug release at 8 hr

 

Figure 1. Pareto chart: For different CQAs; CDR 2 (A), CDR 5(B) and CDR 8 (C).

 

Based on the outcome of the Taguchi design, it is indicated that the CQAs were influenced significantly by amount of HPMC, amount of compritol and compression force. These three factors were further analyzed for their interactions and their impact on CQAs using the Box Behnken design.

 

3.3 Optimisation and Statistical Analysis:

A three factor three level Box-Behnken design was used to determine optimum level and interaction between critical factors (amount of HPMC, amount of compritol and compression force). Table 3 and figure 2 represented level of critical factors and observed value of CQAs. A total seventeen formulations were formulated for statistical optimization.

Table 4. Box-Behnken Design (BBD) Matrix

Run	X1 (Amount of HPMC %)	X2 (Amount of compritol %)	X3 (Compression force kN)	Y1 (CDR 2 %) ± SD	Y2 (CDR 5 %)	Y3 (CDR 8%)
1	20.00	10.00	10.00	15.93±0.38	48.2±1.75	86.47±0.92
2	25.00	5.00	10.00	17.43±0.62	47.74±2.03	86.82±1.72
3	25.00	10.00	5.00	19.26±0.42	45.94±1.63	82.76±1.62
4	25.00	5.00	10.00	16.92±0.36	47.04±1.39	86.73±1.53
5	20.00	5.00	5.00	21.54±0.72	53.78±0.83	88.45±2.06
6	30.00	10.00	10.00	14.48±0.81	39.63±0.94	79.2±2.86
7	30.00	0.00	10.00	16.84±0.63	46.51±1.06	84.32±1.69
8	30.00	5.00	15.00	15.72±0.91	41.46±0.78	80.03±1.73
9	25.00	10.00	15.00	14.23±0.73	41.72±0.82	81.62±0.74
10	30.00	5.00	5.00	18.92±0.84	42.82±1.27	82.43±1.64
11	25.00	0.00	5.00	20.12±1.03	52.46±1.51	87.12±2.18
12	25.00	0.00	15.00	16.53±0.63	49.25±1.36	86.65±0.96
13	25.00	5.00	10.00	17.87±0.58	47.93±1.06	85.75±1.35
14	25.00	5.00	10.00	17.62±0.65	46.54±0.81	85.32±1.57
15	20.00	5.00	15.00	15.94±0.72	47.36±0.68	89.62±1.84
16	20.00	0.00	10.00	18.95±0.96	56.1±1.51	92.4±1.47
17	25.00	5.00	10.00	17.32±0.85	48.1± 1.49	86.28±2.31

 

As shown in table 5, quadratic model had significant model term (p<0.05), insignificant lack of fit (p>0.05), adjusted R square closer to one and smaller PRESS value for response Y1, Y2 and Y3. Therefore quadratic model was chosen as an adequate model for all three studied responses.

 

 

Table 5. Selection of Adequate Model: Model Analysis, Lack of Fit and R Square Analysis

Source	Y1		Y2		Y3
	Sum of Squares	P>F	Sum of Squares	P>F	Sum of Squares	P>F
Model Analysis
Mean vs Total	5140.66		37890.27		1.240E+005
Linear vs Mean	52.17	<0.0001	286.11	<0.0001	173.05	<0.0001
2FI vs Linear	2.07	0.3590	6.92	0.0457	3.46	0.3099
Quadratic vs 2FI	4.55	0.0086	3.97	0.0447	6.46	0.0138
Cubic vs Quadratic	0.68	0.2835	0.29	0.8742	0.37	0.8246
Residual	0.50		1.73		1.65
Total	5200.63		38189.30		1.242E+005
Lack of fit
Linear	7.31	0.0439	11.18	0.1611	10.29	0.1689
2FI	5.24	0.0407	4.27	0.3284	6.83	0.1721
Quadratic	0.68	0.2835	0.29	0.8742	0.37	0.8246
Cubic	0.000		0.000		0.000
Pure Error	0.50		1.73		1.65
R-square analysis
	Adj R Squar	PRESS	Adj R Squar	PRESS	Adj R Squar	PRESS
Linear	0.8398	15.85	0.9469	25.04	0.9206	20.54
2FI	0.8469	27.45	0.9679	22.52	0.9267	25.06
Quadratic	0.9548	11.74	0.9845	7.40	0.9751	8.51
Cubic	0.9665	ND	0.9768	ND	0.9644	ND

 

Table 6. Coefficient Estimate and P Value of Factor for Responses Y1, Y2 and Y3.

Factor	Y1		Y2		Y3
	Coefficient Estimate	P- value Prob>F	Coefficient Estimate	P- value Prob>F	Coefficient Estimate	P- value Prob>F
X1	-0.80	0.0009	-4.38	< 0.0001	-3.87	< 0.0001
X2	-1.07	0.0002	-3.60	< 0.0001	-2.56	< 0.0001
X3	-2.18	< 0.0001	-1.90	< 0.0001	-0.36	0.1036
X1X2	0.16	0.4492	0.26	0.3745	0.20	0.4752
X1X3	0.60	0.0225	1.27	0.0022	-0.89	0.0127
X2X3	-0.36	0.1238	-0.25	0.3790	-0.17	0.5524
X12	-0.19	0.3670	-0.42	0.1499	6.250E-003	0.9816
X22	-0.69	0.0110	0.56	0.0685	-0.59	0.0592
X32	0.79	0.0056	-0.69	0.0335	-1.05	0.0050

 

Figure 2. In vitro drug release profile experimental run.

 

3.3.1 Effect on Response Y1: Cumulative Drug Release at 2 hrs (CDR2):

The drug release at 2 hr of the formulations was found in from 14.23±0.73% to 21.54±0.72 %. Final Quadratic polynomial equation employ to assess the effect on the drug release at 2 hr (CDR2) is,

 

CDR 2 = +17.43 -0.80* X1 -1.07* X2 - 2.18* X3+0.16 X1X2+0.60X1 X3 - 0.36X2 X3- 0.19 * X1² -0.69* X2² + 0.79 * X3²

The effect of variables (amount of HPMC (%), amount of compritol (%) and compression force (Kn) on the drug release has been represented via 3 D response surface and contour plots (Figure 4). As presented in table 6, a negative coefficients for the variables X_1, X2 and X3 indicated a decrease in drug release at 2 hr with the increasing the amount of HPMC, amount of compritol and compression force. Similar effect has been illustrated via repose surface and contour plots (Figure 3). As shown in figure 4, a steeper slope in factor X3(C) for response Y1 indicated that compression force was found to be the dominating factor for drug release at 2 hr. The higher coefficient value for factor X3 also indicated the same. As shown in table 6, there was a significant interaction between the amount of HPMC and the compression force with respect to an increase in the Y2 (P < 0.05). No significant interaction between amount of HPMC and amount of compritol; amount of compritol and compression force for response Y1 (P > 0.05).

 

Figure 3. Effect of Variables on the Drug Release at 2 hour Response Surface Plots: (A) Amount of HPMC (%) and Amount of Compritol (%), (B) Amount of HPMC (%) and Compression Force (Kn); Contour Plots: (C) Amount of HPMC (%) and Amount of Compritol (%), (D) Amount Of HPMC (%) and Compression Force (Kn)

 

 

Figure 4. Effect of Variables on the Drug Release at 2 Hr. Perturbation Plots for A: Response Y1, B: Response Y2 and C: Response Y3. (In This Graph Factor X1=A, X2=B and X3=C)

 

Figure 5. Effect of Variables on Drug Release at 5 hours. Response Surface Plots: (A) Amount of HPMC (%) and Amount of Compritol (%), (B) Amount of HPMC (%) And Compression Force (Kn); Contour Plots: (C) Amount of HPMC (%) and Amount of Compritol (%), (D) Amount Of HPMC (%) And Compression Force (Kn)

 

3.3.2 Effect on Response Y2: Cumulative Drug Release at 5 hrs (CDR5):

The drug release at 5 hr of the developed formulations was found in the range of 39.63±0.94 to 56.1±1.51 %. Final Quadratic polynomial equation use to evaluate the effect of variables on the drug release at 5 hr is,

 

CDR 5 = +47.21 -4.38 X1 -3.60 X2-1.90 X3+1.27 X1X3

 

The effect of variables (amount of HPMC (%), amount of compritol (%) and compression force (Kn) on the CDR 5 (%) has been represented via response surface and contour plots (Figure 5).As per the quadratic polynomial equation, a negative coefficients for the variables X1 indicated an decrease in % CDR5 with the increasing the amount of HPMC. Similar effect has been illustrated via repose surface and contour plots (Figure 5). Similar negative effect have been seen in case of amount of compritol (X₂)  which also lead to decrease in % CDR 5 (Figure 5 (A, C).The quadratic polynomial equation an table 6 also reviled a negative effect of third variable compression force on % CDR 5.. The effect of amount of HPMC was predominant over other variables, as shown in figure 4 steeper curvature for factor X1 (A) for response Y1. For response Y1, significant interaction (p< 0.05) was observed between factor X1(amount of HPMC) and factor X3 (compression force).

 

3.3.3 Effect on Response Y3: Cumulative Drug Release at 8 hrs (CDR8):

The drug release at 8 hr of the formulations was found in from 79.2±2.86 % to 92.4±1.47%. As shown in table 6, amount of HPMC (X1) and amount of compritol was significant (p<0.05) factor for response Y3 (cumulative drug release at 8 hr). Final Quadratic polynomial equation use to study the effect on the drug release at 8 hr (CDR8) is,

 

CDR 8 = +86.18 -3.87 * X1 -2.56 * X2 – 0.36 * X3 -0.89X1 X3 -0.59 * X2² -1.05* X3²

 

The effect of variables (amount of HPMC (%), amount of compritol (%) and compression force (Kn) on the drug release has been represented via 3 D response surface and contour plots (Figure 6). As listed in table 6 , a low negative coefficients for the variables X_{1 ,} X2 and X3 indicated a decrease in drug release at 8 hr with the increasing the amount of HPMC, amount of compritol and compression force. As presented in figure 4, the amount of HPMC (X1) was found to be most influencing factor for response Y3 because it had the steeper slop. The significant interaction was found between factor X1 and factor X3 for response Y3.

 

 

Figure 6. Effect of Variables on Drug Release at 8 hours. Response Surface Plots: (A) Amount of HPMC (%) and Amount of Compritol (%), (B) Amount of HPMC (%) and Compression Force (Kn); Contour Plots: (C) Amount of HPMC (%) and Amount of Compritol (%), (D) Amount of HPMC (%) and Compression Force (Kn)

 

3.3.4 Model Diagnosis for Response Y1, Y2 and Y3:

The goodness of fit of the proposed model for response Y1, Y2 and Y3 was investigated by plotting diagnostic plots (Figure 7). The normal probability plot (Figure 7A-7C) of externally studentized residuals indicated that most of the coloured points depicting values of responses were located around the normal probability line. The normality assumption for residuals was less satisfied since the residuals are plotted less close to straight line. The predicted vs. actual values plot revealed that the experimentally observed values of responses were in close agreement with the predicted values (Figure 7D-7F).

 

Figure 7. Diagnostic Plots to Investigate Goodness of Fit of Proposed Model: Response Y1 (A  D), Response Y2 (B  E) and Response Y3 (C  F).

 

3.3.5 Overlay and Desirability Plot:

The overlay and desirability plots were constructed in order to select the optimized conditions that will result sustained release tablet with desired dissolution profile (Figure 8(A-D). The overlay plots showed that the effect of variables on the responses.

 

Figure 8. Overlay and Desirability Plots for Optimized Formulation having Desired Dissolution Profile.

 

An optimized set of variable, such as a amount of HPMC (24.9%), amount of compritol (6.93%) and compression force (7.09 KN) will result into a tablet with a cumulative drug release 18.53%, 47.10% and 85%  at 2, 5 and 8 hr respectively   (Figure 8 (C, D)). For a given set of variables, desirability was evaluated. Desirability close to one is usually preferred in order to get an optimized formulation with desired properties. The desirability plots showed desirability value of one which indicates that the chosen set of variable values is perfect to get optimized formulation (Figure 8A and B).

 

4. CONCLUSION:

In our study, we have developed sustained release tablet of metformin hydrochloride using QbD approach. Factor screening was performed by Taguchi orthogonal array design. The amounts of HPMC, amount of compritol and compression force were selected as critical factors. Further formulation was optimized using Box-Behnken design and the optimized formulation was formulated with amount of HPMC (24.9%), amount of compritol (6.93%) and compression force (7.09 KN).

 

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Accepted on 07.01.2020           © RJPT All right reserved

Research J. Pharm. and Tech 2020; 13(5): 2350-2358.