# Ranitidine Loaded Biopolymer Floats: Designing, Characterization, and Evaluation

Authors: Abdul Karim; Muhammad Ashraf Shaheen; Tahir Mehmood; Abdul Rauf Raza; Musadiq Aziz; Badar Din
Journal: Journal of Chemistry
 (2017)

---

## Abstract

The float formulation is a strategy to improve the bioavailability of drugs by gastroretentive drug delivery system (GRDDS). A drug delivery model based on swellable and reswellable low density biopolymers has been designed to evaluate its drug release profile using ranitidine (RNT) as a model drug and formulations have been prepared utilizing 32 factorial designs. The drug release (DR) data has been subjected to various kinetic models to investigate the DR mechanism. A reduction in rate has been observed by expanding the amounts of PSG and LSG parts, while an expansion has been noted by increasing the concentration of tragacanth (TG) and citric acid (CA) with an increment in floating time. The stearic acid (SA) has been used to decrease the lag time because a decrease in density of system was observed. The kinetic analysis showed that the optimized formulation (S4F3) followed zero-order kinetics and power law was found to be best fitted due to its minimum lag time and maximum floating ability. The resemblance of observed and predicted values indicated the validity of derived equations for evaluating the effect of independent variables while kinetic study demonstrated that the applied models are feasible for evaluating and developing float for RNT.


---

## Body

## 1. Introduction

The control drug delivery has been a critical zone of current research to upgrade the bioavailability of the drug. With phenomenal progress in medical engineering and material design, novel materials have been exploited for the improvement of drug delivery systems (DDS). Both natural and engineered macromolecules have been extensively utilized as a part of controlled drug delivery to amplify the bioadequacy of the drug and to enhance the patient compliance. A few strategies are being utilized for effective plan of a GRDDS to improve the GRT for most extreme assimilation in upper part of gastric system such as floating DDS, low density system (LDS), bioadhesive, superporous hydrogel, and magnetic system. The floating drug delivery is most broadly utilized among these methods [1–3]. The float formulation is an efficient approach to extend the residence time in stomach to increase the absorption of less soluble or less stable drugs in digestive system. This system is hydrodynamically well-adjusted having lesser bulk density than GI (gastrointestinal) fluid. As a result system remains floating in the stomach without influencing other gastric functions and hence drug discharges out from float gradually at the coveted rate with least variance in blood plasma. The controlled drug delivery system (CDDS) has significant advantage, for example, patient compliance, simple administration, and flexible dosage forms [4, 5]. Psyllium husk and dried seed layer ofPlantago ovata have several pharmaceutical and therapeutic applications. Psyllium husk forms a clear transparent gel on absorbing water which is used as a food supplement [6]. Psyllium hydrogel (HG) comprises both dissolvable and insoluble reswellable polysaccharides. Psyllium husk forms three-dimensional structures in aqueous medium because of its gel forming ability [7, 8]. The HG was utilized for float formation due to absence of toxic impurities, potential biocompatibility and ease of extraction, biodegradability, and aversion of gastric bothering by forming a coat around the gastric aggravation. In addition, it has many pharmaceutical applications and has been used in the treatment of numerous chronic infections [9–11]. Ranitidine is used for the treatment of Zollinger-Ellison Syndrome, gastroesophageal reflux, duodenal ulcers, gastric ulcer, and duodenal ulcer. The suggested dose of ranitidine for gastroesophageal reflux illness is 150 mg twice a day; however, for erosive esophagitis same dose is four times a day or much more [12]. Studies have demonstrated that patient’s compliances are antagonistically influenced by increase in number of doses. In this way sustained release would be more profitable in decreasing its frequency for compelling results [13, 14]. The gastric residence time is essential for the drugs which are primarily absorbed through stomach or unstable at higher pH. In this way, the controlled drug release in particular area of gastric tract offers different advantages like enhanced bioavailability and remedial viability [15].The objective of this study was to prepare ranitidine floats of controlled drug delivery because of its low bioavailability (half) and short organic half-life (2-3 h) [16]. In the present premises, the RNT floats were composed of fractions disengaged from Psyllium husk gel and studied for their gastroretentive profile. The regression analysis was utilized to optimize the formulations; similarity (f2) and difference factors (f1) were used to look at the drug release profiles of all husk fractions with standard tablet accessible in the market. Distinctive kinetic models were connected, for example, zero order, first order, Higuchi model, Power law, and H-Crowell cube root law, to study the drug release rate and all these results demonstrate that the applied methods were sufficient for designing sustained release floats of RNT having better therapeutic and clinical outcomes.


## 2. Materials and Methods

Ranitidine (RNT) was provided by Ferozsons Laboratories Limited, Pakistan, as a gift. All other chemicals and solvents were purchased from Sigma Aldrich, Pakistan. All chemicals were of AR grade and used without further purification.


### 2.1. Isolation of Hydrogel

Psyllium husk (10 g) was immersed in excess distilled water (2 L) and kept overnight. After complete swelling, NaOH (0.4 M) solution was added to swelled husk with continuous stirring. The solution was filtered with muslin cloth to separate insoluble impurities. The glacial acetic acid was added gradually with stirring to filtered solution. The gel was decanted off and dried after complete dialysis by distilled water. For isolation of different fractions, the dried gel was again swelled in distilled water and treated with different solvents, for example, acetone, methanol, ethanol, and chloroform. Different fractions in different concentrations were again coagulated using the said solvents. After separation, these fractions were dialyzed thoroughly with distilled water to remove separating solvents. The purified gels were dried and ground to fine powder. The powdered gel was dissolved in drug solution. After absorbing the drug, whole mass was dried again. The dried mass was ground, mixed with excipients, and sprayed with isopropyl alcohol (iPrOH) and then compressed to form floats. The separation of different fractions is shown in Figure 1.Figure 1
Separation of different fractions of husk gel.


### 2.2. Characterization of ENa

#### 2.2.1. Elemental Analysis

CHNS analyzer was used to find the amount of N and C in given sample. The elemental analysis for each sample was performed in triplicate.


#### 2.2.2. Thermal Analysis (TA)

TA of isolated samples was carried out under N2 atmosphere at multiple heating rate (10, 15, and 20°C) using Broido’s method and activation energy (Ea) was calculated from the slope of line by least square methods:(1)log⁡1y=-EaR1T+constant,where T is the temperature in Kelvin and R is general gas constant having value 8.31 JK–1 mol–1, y is ratio of difference in mass before and after heating = (mt-mf)/(m0-mf), where mt is weight of polymer at temperature T, m0 is the mass at initial temperature, and mf is the mass at the final temperature of particular decomposition stage, so Broido’s equation can be represented as Ea = − (slope × 8.314). The data was analyzed (TA Instruments, USA) using Universal Analysis 2000 software, version 4.2E, and MS Excel® 2007.


#### 2.2.3. Gel Permeation Chromatography (GPC)

GPC analysis was carried out with Agilent 1200 Series GPC-SEC System using pullulan and dextran as standards. The system was equipped with 2 columns and dimethyl sulphide was used as eluent with flow rate of 0.5 mL min–1 at 70°C using injections about 50 μL.


#### 2.2.4. Fourier Transform-Infrared Spectroscopy

The absorption bands in FT-IR spectra of ENa were noted to compare with earlier work for same type of materials [17].


#### 2.2.5. Scanning Electron Microscopy (SEM)

Pellets of ENa fraction were prepared at 3000 psi pressure using hydraulic press. The pellets were mounted with the help of silver paint on aluminum stubs and subsequently were coated with gold. The SEM images were taken at different magnification using JEOL, JSM-6480 LV, Japan, at 10 kV.


#### 2.2.6. Determination of Water Retention Value (WRV)

The water retention was calculated by centrifugation method. About 1.0 g dried gel was placed in a beaker containing 100 mL distilled water at 30°C for 3 h for complete swelling. The swelled material was filled in 4 fritted glass tubes containing pores at the bottom. The tubes were centrifuged at 2500 rpm for 40 min to remove the entrapped water. After centrifugation, sample material was dried in an oven at 60°C for complete dehydration. WRV% was calculated as follows:(2)WRV%=mw-mdmd×100,where mw is mass of sample (wet state) and md is mass of sample (dry state).The drug content, friability, and stability of floats were measured as per set criteria by taking 10 floats and after grinding concentration of solution was noted by dissolving in water. The friability was checked by the formula(3)Friability=Initial  mass  of  sampleFinal  mass  of  sample×100.


### 2.3. Preparation of Floats

For each float, a specific quantity of hydrogel along with excipients as per factorial design was taken and mixed geometrically in plastic bag. The mixture was passed through sieve of mesh size 16 and then poured into drug solution. The drug loaded mixture was separated, dried, and ground to fine powder and then sprayed withiPrOH (~2 mL) with rapid geometric mixing. The floats were prepared by punch machine having punch size 10 × 1.5 mm by direct compression technique with small amount of Mg-stearate as lubricant.


### 2.4. Preparation of Factorial Formulations

Floats were prepared as per factorial model (32) as described in Table 1. Four different batches were designed by varying the concentrations of ENa and SA, ENa, LSG, and PSG as low, medium, and high as shown in Table 1.Table 1
Formulations design for RNT floats (drug 120 mg).
Batches
Materials (mg)
Low (−1)
Medium (0)
High (+1)
Constant∗
1st
ENa
100
150
200
NaHCO3 = 10
SA
30
60
90
CA = 10
2nd
ENa
100
150
200
NaHCO3 = 10
Tcanth
30
60
90
CA = 10
3rd
ENa
100
150
200
NaHCO3 = 10
LSG
30
60
90
CA = 10
4th
ENa
100
150
200
NaHCO3 = 15
PSG
30
60
90
CA = 10
LSG = linseed gel; PSG = psyllium seed gel;∗CHCl3 = iPrOH = 2 mL; TG = tragacanth gel; SA = stearic acid; CA = citric acid; HPMC: hydroxy propyl methyl cellulose; Mg stearate = 5 g to each float.


### 2.5. Dissolution Study

All DR profiles were studied using USP paddle dissolution apparatus II at 37°C ± 0.1°C and 50 rpm in buffer solutions of pH 1.2 (900 mL in each beaker). About 5 mL sample was taken with syringe. After suitable dilution, sample was analyzed atλmax 314 nm. After removal of sample, same amount of dissolution medium at same temperature was added to keep the total volume 900 mL. Accumulative DR percentage was calculated from a standard curve. A calibration graph for RNT was plotted against its concentration (ppm) in buffer solution (pH 1.2) and was regressed into linear line.


### 2.6. In Vitro Buoyancy

The floating lag time (LT) and the total floating time (TFT) were determined for each float in USP dissolution apparatus II containing simulated gastric fluid at pH 1.2 at37±0.5°C. The lag time and TFT for each dosage form were also calculated.


### 2.7. Similarity and Difference Factors

According to Moore and Flanner, similarity factor (f2) is, actually, measurement of similarity in the percent dissolution of drug of sample profile and control profile. (4)f2=50×log⁡1+1n∑Rt2-Tt21/2×100.The dissolution profiles will be similar to control (standard) profile whenf2 value is >50 wheref1 measures percentage difference between test and control dissolution profile. Its standard values are 0 to 15 for no difference.


### 2.8. Kinetic Study

In vitro DR data was studied by using different kinetic models to find the mechanism and drug release rate.


### 2.9. Statistical Analysis

The drug release percentage at 2 h, 5 h, and 10 h was statistically analyzed by using the DESIGN EXPERT 7.1.5.0 (STAT-EASE) demo version software. The linear regression analysis (LRA) was applied to all formulations of each batch and an equation was derived for fixed time interval to find the effect of independent variables.


## 2.1. Isolation of Hydrogel

Psyllium husk (10 g) was immersed in excess distilled water (2 L) and kept overnight. After complete swelling, NaOH (0.4 M) solution was added to swelled husk with continuous stirring. The solution was filtered with muslin cloth to separate insoluble impurities. The glacial acetic acid was added gradually with stirring to filtered solution. The gel was decanted off and dried after complete dialysis by distilled water. For isolation of different fractions, the dried gel was again swelled in distilled water and treated with different solvents, for example, acetone, methanol, ethanol, and chloroform. Different fractions in different concentrations were again coagulated using the said solvents. After separation, these fractions were dialyzed thoroughly with distilled water to remove separating solvents. The purified gels were dried and ground to fine powder. The powdered gel was dissolved in drug solution. After absorbing the drug, whole mass was dried again. The dried mass was ground, mixed with excipients, and sprayed with isopropyl alcohol (iPrOH) and then compressed to form floats. The separation of different fractions is shown in Figure 1.Figure 1
Separation of different fractions of husk gel.


## 2.2. Characterization of ENa

### 2.2.1. Elemental Analysis

CHNS analyzer was used to find the amount of N and C in given sample. The elemental analysis for each sample was performed in triplicate.


### 2.2.2. Thermal Analysis (TA)

TA of isolated samples was carried out under N2 atmosphere at multiple heating rate (10, 15, and 20°C) using Broido’s method and activation energy (Ea) was calculated from the slope of line by least square methods:(1)log⁡1y=-EaR1T+constant,where T is the temperature in Kelvin and R is general gas constant having value 8.31 JK–1 mol–1, y is ratio of difference in mass before and after heating = (mt-mf)/(m0-mf), where mt is weight of polymer at temperature T, m0 is the mass at initial temperature, and mf is the mass at the final temperature of particular decomposition stage, so Broido’s equation can be represented as Ea = − (slope × 8.314). The data was analyzed (TA Instruments, USA) using Universal Analysis 2000 software, version 4.2E, and MS Excel® 2007.


### 2.2.3. Gel Permeation Chromatography (GPC)

GPC analysis was carried out with Agilent 1200 Series GPC-SEC System using pullulan and dextran as standards. The system was equipped with 2 columns and dimethyl sulphide was used as eluent with flow rate of 0.5 mL min–1 at 70°C using injections about 50 μL.


### 2.2.4. Fourier Transform-Infrared Spectroscopy

The absorption bands in FT-IR spectra of ENa were noted to compare with earlier work for same type of materials [17].


### 2.2.5. Scanning Electron Microscopy (SEM)

Pellets of ENa fraction were prepared at 3000 psi pressure using hydraulic press. The pellets were mounted with the help of silver paint on aluminum stubs and subsequently were coated with gold. The SEM images were taken at different magnification using JEOL, JSM-6480 LV, Japan, at 10 kV.


### 2.2.6. Determination of Water Retention Value (WRV)

The water retention was calculated by centrifugation method. About 1.0 g dried gel was placed in a beaker containing 100 mL distilled water at 30°C for 3 h for complete swelling. The swelled material was filled in 4 fritted glass tubes containing pores at the bottom. The tubes were centrifuged at 2500 rpm for 40 min to remove the entrapped water. After centrifugation, sample material was dried in an oven at 60°C for complete dehydration. WRV% was calculated as follows:(2)WRV%=mw-mdmd×100,where mw is mass of sample (wet state) and md is mass of sample (dry state).The drug content, friability, and stability of floats were measured as per set criteria by taking 10 floats and after grinding concentration of solution was noted by dissolving in water. The friability was checked by the formula(3)Friability=Initial  mass  of  sampleFinal  mass  of  sample×100.


## 2.2.1. Elemental Analysis

CHNS analyzer was used to find the amount of N and C in given sample. The elemental analysis for each sample was performed in triplicate.


## 2.2.2. Thermal Analysis (TA)

TA of isolated samples was carried out under N2 atmosphere at multiple heating rate (10, 15, and 20°C) using Broido’s method and activation energy (Ea) was calculated from the slope of line by least square methods:(1)log⁡1y=-EaR1T+constant,where T is the temperature in Kelvin and R is general gas constant having value 8.31 JK–1 mol–1, y is ratio of difference in mass before and after heating = (mt-mf)/(m0-mf), where mt is weight of polymer at temperature T, m0 is the mass at initial temperature, and mf is the mass at the final temperature of particular decomposition stage, so Broido’s equation can be represented as Ea = − (slope × 8.314). The data was analyzed (TA Instruments, USA) using Universal Analysis 2000 software, version 4.2E, and MS Excel® 2007.


## 2.2.3. Gel Permeation Chromatography (GPC)

GPC analysis was carried out with Agilent 1200 Series GPC-SEC System using pullulan and dextran as standards. The system was equipped with 2 columns and dimethyl sulphide was used as eluent with flow rate of 0.5 mL min–1 at 70°C using injections about 50 μL.


## 2.2.4. Fourier Transform-Infrared Spectroscopy

The absorption bands in FT-IR spectra of ENa were noted to compare with earlier work for same type of materials [17].


## 2.2.5. Scanning Electron Microscopy (SEM)

Pellets of ENa fraction were prepared at 3000 psi pressure using hydraulic press. The pellets were mounted with the help of silver paint on aluminum stubs and subsequently were coated with gold. The SEM images were taken at different magnification using JEOL, JSM-6480 LV, Japan, at 10 kV.


## 2.2.6. Determination of Water Retention Value (WRV)

The water retention was calculated by centrifugation method. About 1.0 g dried gel was placed in a beaker containing 100 mL distilled water at 30°C for 3 h for complete swelling. The swelled material was filled in 4 fritted glass tubes containing pores at the bottom. The tubes were centrifuged at 2500 rpm for 40 min to remove the entrapped water. After centrifugation, sample material was dried in an oven at 60°C for complete dehydration. WRV% was calculated as follows:(2)WRV%=mw-mdmd×100,where mw is mass of sample (wet state) and md is mass of sample (dry state).The drug content, friability, and stability of floats were measured as per set criteria by taking 10 floats and after grinding concentration of solution was noted by dissolving in water. The friability was checked by the formula(3)Friability=Initial  mass  of  sampleFinal  mass  of  sample×100.


## 2.3. Preparation of Floats

For each float, a specific quantity of hydrogel along with excipients as per factorial design was taken and mixed geometrically in plastic bag. The mixture was passed through sieve of mesh size 16 and then poured into drug solution. The drug loaded mixture was separated, dried, and ground to fine powder and then sprayed withiPrOH (~2 mL) with rapid geometric mixing. The floats were prepared by punch machine having punch size 10 × 1.5 mm by direct compression technique with small amount of Mg-stearate as lubricant.


## 2.4. Preparation of Factorial Formulations

Floats were prepared as per factorial model (32) as described in Table 1. Four different batches were designed by varying the concentrations of ENa and SA, ENa, LSG, and PSG as low, medium, and high as shown in Table 1.Table 1
Formulations design for RNT floats (drug 120 mg).
Batches
Materials (mg)
Low (−1)
Medium (0)
High (+1)
Constant∗
1st
ENa
100
150
200
NaHCO3 = 10
SA
30
60
90
CA = 10
2nd
ENa
100
150
200
NaHCO3 = 10
Tcanth
30
60
90
CA = 10
3rd
ENa
100
150
200
NaHCO3 = 10
LSG
30
60
90
CA = 10
4th
ENa
100
150
200
NaHCO3 = 15
PSG
30
60
90
CA = 10
LSG = linseed gel; PSG = psyllium seed gel;∗CHCl3 = iPrOH = 2 mL; TG = tragacanth gel; SA = stearic acid; CA = citric acid; HPMC: hydroxy propyl methyl cellulose; Mg stearate = 5 g to each float.


## 2.5. Dissolution Study

All DR profiles were studied using USP paddle dissolution apparatus II at 37°C ± 0.1°C and 50 rpm in buffer solutions of pH 1.2 (900 mL in each beaker). About 5 mL sample was taken with syringe. After suitable dilution, sample was analyzed atλmax 314 nm. After removal of sample, same amount of dissolution medium at same temperature was added to keep the total volume 900 mL. Accumulative DR percentage was calculated from a standard curve. A calibration graph for RNT was plotted against its concentration (ppm) in buffer solution (pH 1.2) and was regressed into linear line.


## 2.6. In Vitro Buoyancy

The floating lag time (LT) and the total floating time (TFT) were determined for each float in USP dissolution apparatus II containing simulated gastric fluid at pH 1.2 at37±0.5°C. The lag time and TFT for each dosage form were also calculated.


## 2.7. Similarity and Difference Factors

According to Moore and Flanner, similarity factor (f2) is, actually, measurement of similarity in the percent dissolution of drug of sample profile and control profile. (4)f2=50×log⁡1+1n∑Rt2-Tt21/2×100.The dissolution profiles will be similar to control (standard) profile whenf2 value is >50 wheref1 measures percentage difference between test and control dissolution profile. Its standard values are 0 to 15 for no difference.


## 2.8. Kinetic Study

In vitro DR data was studied by using different kinetic models to find the mechanism and drug release rate.


## 2.9. Statistical Analysis

The drug release percentage at 2 h, 5 h, and 10 h was statistically analyzed by using the DESIGN EXPERT 7.1.5.0 (STAT-EASE) demo version software. The linear regression analysis (LRA) was applied to all formulations of each batch and an equation was derived for fixed time interval to find the effect of independent variables.


## 3. Results and Discussion

### 3.1. Extraction of Hydrogel

The ENa fraction extracted by alkali-ethanol method was found to be most suitable due to its minimum lag time as compared to other fractions as matrix. In present study, NaOH solution was used for dissolution of gel. Although this is a chemical method it is more economical for production of pure gel. The ion dipoles interaction between NaOH and hydroxyl (-OH) groups results in cleavage of molecular H-bonding among the hydrogel layers and decreases the viscosity of solution; hence filtration becomes easier. The separated gels were dialyzed with distilled water and washed until the pH was restored to 7. The purified transparent gel was dried and its yield was found to be 30% relative to weight of husk. The extracted material was found to be good DR retardant and suitable for floats due to its low density.


### 3.2. Characterization of ENa

#### 3.2.1. Elemental Analysis

The elemental analysis shows that ENa is free of any nitrogenous matter (proteins). Even with performing the analysis in triplicate no evidence of nitrogen was found in all samples. The percentage of C and H was found to be 40.2% and 6.5% against 45.0% and 6.0%, respectively, as observed in other natural polysaccharides (pullulan, etc.) [17].


#### 3.2.2. Thermal Analysis

The ENa was studied by TGA and DSC from ambient temperature to 600°C. The TGA showed an endothermic weight loss about 10% up to 200°C, which was attributed to the loss of absorbed moisture. The weight loss of ~45% was observed in range 226–326°C, which was due to degradation of ENa structure. This step was exothermic in nature. The second weight loss of ~24% was observed at temperature 452–563°C due to complete degradation of ENa leaving behind a carbon rich residue. Glass transition temperature(Tg) could not be observed in the experimental range due to presence of carbon. The average Ea value calculated by Broido’s method was 134 kJ mol–1 using 10°C min–1 and thermogram was comparable to those of other commercially available polysaccharides [18].An overall thermal stability of ENa along with other fractions was assessed by integral procedural decomposition temperature (IPDT) and comprehensive index of thermal stability (ITS) values by Doyle’s method. The IPDT and ITS value were found to be 289°C and 0.46, respectively. These values indicate that ENa has good thermal stability [19, 20]. The apparent Ea values for major stage of decomposition were also calculated using Flynn-Wall-Ozawa (FWO) method. The Ea values varied greatly with α indicating a multistep degradation. The average activation energies by FWO method are given in Table 2 and Figure 2.Table 2
Thermal parameters of ENa gel fractions.
Samplecode
Temp.range(°C)
Massloss(%)
E
a kJ mol−1
ΔH∗ kJ mol−1
ΔS∗ JK−1 mol−1
ΔG∗ kJ mol−1
ln
⁡
A
IPDT(°C)
ITS
Mass at600°C(%)
ENa
223–315
49.24
146.68
139.76
−51.47
169.66
22.82
289.42
0.46
0.983Figure 2
Thermogravimetric curves of ENa (L) and representativeα-T curve and log⁡ß (R) for ENa (R).


#### 3.2.3. Gel Permeation Chromatography (GPC)

GPC was used to determine molar mass distributionMn (number-average), Mw (weight-average), Mz (average molar masses), and polydispersity index (PDI) which is the ratio of Mw/Mn. The PDI value of ENa was to be found as 3.724 which indicates that ENa is polydispersed in nature Table 3.Table 3
GPC parameters of ENa.
Parameters
ENa
M
n (g mol−1)
3.443 × 103
M
w (g mol−1)
1.344 × 104
M
z (g mol−1)
2.967 × 104
M
p (g mol−1)
8.273 × 103
V
p (cm3)
7.497
PDI
3.724


#### 3.2.4. Fourier Transform-Infrared Spectroscopy (FT-IR)

The absorption bands in FT-IR spectra (Figure3) were assigned taking two fractions for comparison. The absorbance at 3323.35 cm–1 was due to -OH stretching (broad band). The absorbances at 1251.80–1328.86 cm–1 were due to deformation caused by absorbed water. A sharp band at 1012–1047.5 cm–1 was due to beta-glycosidic bond. The absorbances at 651–690.62 and 528–549 cm−1 were observed due to polymer backbone.Figure 3
Representative FT-IR graphs of ENa and AANa (comparison).


#### 3.2.5. Surface Morphology of ENa

Scanning electron microscopy (SEM) of ENa (Figure4) indicates that its surface is highly rough, which could provide more surface area to adhere with drug for adsorption reported in Iqbal et al. [21].Figure 4
Scanning electron microscopy of ENa.


#### 3.2.6. Water Retention Value (WRV)

The WRV was found to be 7000% to 7500% to its original volume. The H2O holding capacity decreased on exposure to atmosphere with the passage of time; therefore WRV is not permanent. Being reswellable, it can be reused after drying. Being highly hydrophilic, it is considered as hydrogel. All formulations were found to be uniform and their drug content ranged from 97.37 to 100.34.


#### 3.2.7. Dissolution Study and Float Formulations

The concentration of drug was calculated using the equationy = 0.002 × concentration + 0.003.From above equation and Figure5 it was found that DR% from all fractions ranged from 83.5 to 90.2% where the DR by husk and marketed tablets (Cstd) was found to be 93.3 and 79.8%, respectively. On studying the DR% of different formulations, it was found that the DR was retarded notably by increasing the amount of husk gel but DR data indicates that the use of HG by itself as sustained release matrix is insufficient to achieve the CR (controlled release) profile, Figure 5. Therefore different excipients were used to accommodate balance in DR rates due to difference in swelling from Psyllium polysaccharide. ENa was selected as the main excipient (matrix) due to its better DR retarding ability. The DR% of various batches was calculated at different time intervals and given in Table 4. The rate of swelling depends upon the amount of H2O taken up by the polymer which depends upon nature of added excipients. The hardness of all floats formulations was in the range 6.0–8.0 kg/cm2. The percentage friability of all the floats was not found to be more than 0.7%. To increase the wetting ability and decrease the lag time, NaHCO3 was added in the matrix, which produced CO2 on contact with simulated gastric fluid which was entrapped within the polymer and decreased the density of system. The drug release % of all formulations for 10 h are shown in Tables 6–9, which indicates that drug release rate depends upon nature and concentration of adding excipients and their effect was studied by regression analysis. By addition of TG, DR rate increased due to having soluble component. TG gum is actually a mixture of soluble component tragacanthin (30–40%) and insoluble component bassorin (60–70%). On addition of LSG, the DR rate was decreased due to its greater retarding ability and it was further decreased by addition of PSG. In order to increase the DR rate, concentration of NaHCO3 was increased in 4th batch. So by the adjustment of variables, as recommended by regression analysis and formulation, S4F3 was found to better due to its minimum lag time and maximum floating ability with better sustainability (Figure 6).Table 4
Lag time (LT) of all batches.
RNT
MNa
ENa
AANa
ClfNa
ANa
Husk
C
s
t
d
LT (min)
22
19
27
30
26
24
12
Batch-S1
F1
F2
F3
F4
F5
F6
F7
F8
F9
LT (sec)
120
100
45
165
145
130
80
70
60
Batch-S2
F1
F2
F3
F4
F5
F6
F7
F8
F9
LT (sec)
80
125
150
180
200
256
300
325
375
Batch-S3
F1
F2
F3
F4
F5
F6
F7
F8
F9
LT (sec)
100
150
195
170
225
245
260
285
295
Batch-S4
F1
F2
F3
F4
F4
F6
F7
F8
F9
LT (min)
07
05
03
08
06
08
11
14
19Figure 5
Drug release profiles of RNT from isolated fractions.Figure 6
Drug release profiles of four batches (S1 to S4 each containing nine formulations).TA data indicates that ENa is equally stable as other hydrogel, Table2. The DR% of all fractions and designed formulations are shown in Table 6 and similarity and difference factors are shown in Table 7, which are major indicators to understand the formulation design. The kinetic study of some selected batches, Tables 6–9, indicates that as the n value is greater than 0.45, the DR behavior is trending towards non-Fickian diffusion. In case of optimized formulation S4F3, the value of n is 0.31 indicating that release mechanism is totally Fickian diffusion [22].


#### 3.2.8. In Vitro Buoyancy

The total floating and lag time of all formulation were calculated, given in Table4, and minimum lag time and maximum floating time were detected in formulation S4F3. So this formulation was selected and found to be best one for floating DDS. The target of this study was to choose the formulations having maximum GRT with best bioavailability of drug. Among all the fractions, ENa has minimum lag time and maximum floating ability; therefore this fraction was chosen for designing other formulations.


#### 3.2.9. Similarity and Difference Factors

Similarity and difference factors of all the formulations, using ENa reference matrix tablet as standard, (Table5) indicate that all formulations which havef2 factor greater than 50 resemble standard tablets with respect to the release rate but the formulation having lesserf2 than 50 deviate from resemblance. Similarly the formulations havingf1 0–15 resemble Cstd but deviation from these values indicates that their release pattern is somewhat different from reference formulation.Table 5
Similarity (f2) and difference factor (f1) for all batches.
RNT
F1
F2
F3
F4
F5
F6
F7
F8
F9
S1
f1
28.50
13.71
13.02
18.80
19.83
26.45
23.61
23.62
25.18
f2
50.76
65.49
64.92
59.58
57.76
50.97
54.84
55.56
53.73
C
s
t
d
f1
13.02
13.67
20.77
23.33
29.98
36.26
32.51
32.52
34.21
f2
62.93
64.22
55.95
52.51
44.82
40.19
45.33
43.95
42.10
S2
f1
20.71
9.27
9.47
22.67
15.51
17.01
17.15
18.66
21.28
f2
59.10
72.03
74.01
56.30
62.44
62.87
61.62
59.78
56.04
C
s
t
d
f1
15.27
16.62
20.61
18.53
24.92
26.80
26.37
28.97
31.61
f2
60.52
58.40
52.50
57.21
50.56
48.20
49.46
46.40
43.60
S3
f1
13.37
14.58
18.28
18.46
23.60
31.02
24.05
30.49
37.87
f2
62.12
59.09
54.58
54.54
49.12
43.48
47.69
43.14
39.04
C
s
t
d
f1
22.82
21.35
21.45
23.52
23.75
29.28
27.30
30.49
35.60
f2
52.63
54.08
53.64
52.17
49.91
45.03
47.37
43.89
40.36
S4
f1
44.14
17.58
10.30
33.38
23.43
8.92
14.07
07.08
07.81
f2
44.25
62.44
71.55
50.21
57.92
74.99
65.19
75.54
76.34
C
s
t
d
f1
18.34
15.79
20.19
14.87
15.45
25.78
22.84
24.25
27.39
f2
55.76
60.88
55.18
60.65
60.57
49.01
52.45
50.38
47.30Table 6
Kinetic study of DR from polymer matrix.
RNT
S
10 h
Zero order
First order
Higuchi
H-Crowell
Power law
k
o
R
2
k
1
R
2
k
H
R
2
k
H
C
R
2
k
P
R
2
n
MNa
86.8
0.133
0.918
−0.004
0.985
3.922
0.968
−0.042
0.907
0.024
0.976
0.577
ENa
84.5
0.123
0.862
−0.004
0.957
3.659
0.962
−0.039
0.773
0.021
0.951
0.561
AANa
91.2
0.095
0.852
−0.004
0.967
3.061
0.942
−0.032
0.851
0.071
0.922
0.389
ANa
82.7
0.133
0.973
−0.004
0.977
3.775
0.958
−0.043
0.982
0.022
0.972
0.575
ClfNa
84.1
0.128
0.982
−0.004
0.965
4.231
0.981
−0.045
0.987
0.006
0.977
0.789
H
95.3
0.118
0.918
−0.004
0.828
3.272
0.885
−0.033
0.924
0.062
0.887
0.423
C
s
t
d
82.8
0.113
0.952
−0.004
0.918
3.241
0.985
−0.035
0.942
0.091
0.972
0.357Table 7
DR study for 1st batch matrix tablets.
RNT (S1)
S
10 h
Zero order
First order
Higuchi
H-Crowell
Power law
k
o
R
2
k
1
R
2
k
H
R
2
k
H
C
R
2
k
P
R
2
n
F1
83.5
0.134
0.989
−0.002
0.958
4.091
0.969
−0.044
0.989
0.003
0.978
0.832
F2
78.4
0.132
0.882
−0.003
0.949
4.295
0.953
−0.044
0.992
0.005
0.962
0.829
F3
74.4
0.132
0.857
−0.003
0.946
4.278
0.944
−0.044
0.857
0.003
0.953
0.877
F4
81.8
0.128
0.988
−0.002
0.987
3.946
0.989
−0.042
0.988
0.003
0.995
0.852
F5
76.8
0.124
0.994
−0.002
0.96
3.777
0.963
−0.041
0.994
0.003
0.989
0.869
F6
74.3
0.122
0.986
−0.002
0.938
3.652
0.928
−0.042
0.986
0.004
0.978
0.858
F7
71.7
0.129
0.968
−0.002
0.932
3.864
0.912
−0.043
0.968
0.002
0.956
0.912
F8
68.4
0.118
0.962
−0.002
0.88
3.492
0.879
−0.039
0.962
0.003
0.956
0.849
F9
66.0
0.111
0.941
−0.001
0.841
3.252
0.852
−0.037
0.941
0.004
0.941
0.865Table 8
DR study for 2nd batch matrix tablets.
RNT (S2)
S
10 h
Zero order
First order
Higuchi
H-Crowell
Power law
k
o
R
2
k
1
R
2
k
H
R
2
k
H
C
R
2
k
P
R
2
n
F1
83.5
0.135
0.955
−0.0025
0.982
3.913
0.982
−0.043
0.945
0.022
0.985
0.554
F2
85.2
0.107
0.969
−0.0027
0.975
3.351
0.995
−0.035
0.969
0.043
0.985
0.470
F3
87.6
0.103
0.904
−0.002
0.933
3.269
0.969
−0.032
0.903
0.033
0.973
0.482
F4
78.4
0.086
0.755
−0.002
0.897
2.849
0.882
−0.027
0.766
0.482
0.876
0.482
F5
81.8
0.102
0.951
−0.002
0.975
3.176
0.988
−0.036
0.944
0.033
0.962
0.512
F6
83.4
0.091
0.853
−0.002
0.927
2.913
0.927
−0.034
0.842
0.043
0.888
0.450
F7
74.7
0.088
0.793
−0.002
0.823
2.853
0.878
−0.027
0.793
0.044
0.965
0.464
F8
79.2
0.104
0.881
−0.003
0.985
3.326
0.964
−0.036
0.872
0.054
0.966
0.446
F9
80.1
0.095
0.736
−0.003
0.932
3.154
0.866
−0.034
0.744
0.056
0.824
0.457Table 9
DR study for 4th batch matrix tablets.
RNT (S4)
S
10 h
Zero order
First order
Higuchi
H-Crowell
Power law
k
o
R
2
k
1
R
2
k
H
R
2
k
H
C
R
2
k
P
R
2
n
F1
73.7
0.100
0.855
−0.002
0.956
3.265
0.952
−0.033
0.855
0.033
0.948
0.516
F2
81.6
0.105
0.935
−0.002
0.983
3.315
0.981
−0.035
0.935
0.041
0.984
0.462
F3
85.5
0.084
0.962
−0.002
0.989
2.517
0.981
−0.028
0.962
0.051
0.975
0.313
F4
74.2
0.080
0.811
−0.001
0.921
2.612
0.903
−0.026
0.811
0.045
0.895
0.446
F5
77.5
0.097
0.924
−0.002
0.928
3.091
0.974
−0.032
0.924
0.038
0.969
0.479
F6
82.2
0.075
0.771
−0.001
0.826
2.404
0.834
−0.025
0.771
0.071
0.839
0.340
F7
74.2
0.089
0.958
−0.001
0.972
2.783
0.991
−0.029
0.959
0.051
0.976
0.410
F8
75.1
0.096
0.951
−0.002
0.886
2.811
0.869
−0.032
0.951
0.041
0.807
0.393
F9
79.4
0.092
0.986
−0.002
0.935
2.763
0.937
−0.031
0.986
0.051
0.891
0.377


#### 3.2.10. Kinetic Study

The DR data was evaluated by using different kinetic models. By applying Korsmeyer and Peppas equation to DR data of different fractions of Psyllium husk, the diffusion coefficient(n) was found in the range from 0.356 to 0.799±0.01 with DR% from 81.8 to 94.3 in 10 h. These results indicate that the release mechanism was non-Fickian diffusion as compared to Cstd, Table 5. Therefore new formulations were designed to find the desired formulation, Tables 6–9.


#### 3.2.11. Regression Analysis

The equations (see (5)–(16)) derived by SRA indicate that each factor has either positive or negative sign. The negative sign indicates that its concentration must be decreased while having positive sign indicates that concentration must be increased to achieve desired DR profile. The following notations were used for abbreviation for different variables X1=A, X2=B, X1X2=C, X12=D, X22=E.


#### 3.2.12. 1st Batch

(5)
S
1
=
0.0972
9
t
+
0.028367
A
-
0.8673
8
B
+
0.00185
3
C
-
0.0004
7
D
+
0.01076
7
E
-
15.65378
C
R
C
=
0.903129
,
R
2
=
0.813737
,
Adj.
R
2
=
0.757978
,
n
=
27
(6)
S
1
5 h
=
0.10078
9
t
-
0.0201
8
A
-
1.0632
2
B
+
0.00331
9
C
-
0.0002
7
A
+
0.01240
4
B
+
24.3492
C
R
C
=
0.969284
,
R
2
=
0.939511
,
Adj.
R
2
=
0.931789
,
n
=
99
(7)
S
1
10 h
=
0.12567
2
t
-
0.064
8
A
-
0.8473
1
B
+
0.00274
7
C
-
0.0003
8
A
+
0.00424
9
B
+
22.84727
C
R
C
=
0.992215
,
R
2
=
0.983514
,
Adj.
R
2
=
0.982528
,
n
=
99
.In study, the SRA shows that effect ofB and C is more significant while effect of D is least effective (see (5)). After study it is found that effect of B is most significant while effect of A is negligible (see (6)) and after 10 h it was found that effect of B and C2 is significant while effect of D, A, and E is negligible (see (7)).The abbreviated kinetic study of 1st, 2nd, and 4th batch with nine formulations each has been discussed to identify the optimized formulation.


#### 3.2.13. 2nd Batch

(8)
S
2
2 h
=
0.221171
t
+
0.182557
A
+
0.178385
B
+
0.000876
C
-
0.00118
D
-
0.00112
E
+
4.82514
C
R
C
=
0.942855
,
R
2
=
0.887506
,
Adj.
R
2
=
0.855448
,
n
=
27
(9)
S
2
5 h
=
0.154039
t
+
0.006524
A
+
0.210632
B
+
0.0015
C
-
0.00157
D
-
0.00082
E
+
10.87422
C
R
C
=
0.971366
,
R
2
=
0.944552
,
A
d
j
.
R
2
=
0.946346
,
n
=
54
.The SRA shows that the effect ofA, B, and C is positive while effect of D and E is negative. (10)S210 h=0.114934t+0.098263A+0.429285B-0.00038C-0.00055D-0.00226E+8.20543CRC=0.969798,R2=0.942314,Adj.R2=0.936522,n=99.By applying the LRA to DR, it was found that impact ofD is most critical while impact of E is minimally noteworthy (see (8)) and then impact of D and B is most huge while impact of A is irrelevant (see (9)). Also after 10 h, impact of A, C, and D was observed to be most critical while impact of X22 is most slightly noteworthy. The relapse examination was again led on DR after 10 h and it was found that impact of X2 and X12 is most critical while impact ofC is insignificant (see (10)).


#### 3.2.14. 3rd Batch

(11)
S
3
=
0.256874
t
-
0.26814
A
-
0.16514
B
+
0.000262
C
+
0.000548
D
+
0.006923
E
+
35.92275
C
R
C
=
0.976225
,
R
2
=
0.957119
,
Adj.
R
2
=
0.944255
,
n
=
27
(12)
S
3
=
0.162851
t
-
0.31975
A
-
0.00574
B
+
0.00088
C
+
0.00053
D
+
0.002823
E
+
44.42843
C
R
C
=
0.976629
,
R
2
=
0.961753
,
Adj.
R
2
=
0.954716
,
n
=
54
(13)
S
3
10 h
=
0.09369
3
t
-
0.2486
2
A
+
0.19663
1
B
-
0.0002
4
C
+
0.00065
4
D
+
0.00037
6
E
+
46.55
C
R
C
=
0.951827
,
R
2
=
0.906334
,
Adj.
R
2
=
0.901202
,
n
=
99
.After drug release study, SRA shows that effect of A and B is more significant while effect of C is negligible (see (11)). The effect of A is most significant but effect of E and B is negligible (see (12)). For 10 h study, effect of A and D is significant while effect of B and C and E is negligible (see (13)).


#### 3.2.15. 4th Batch

(14)
S
4
=
0.179377
t
-
0.06247
A
-
0.41753
B
+
0.001268
C
-
5.5
E
-
05
D
+
0.001364
E
+
24.487
C
R
C
=
0.911831
,
R
2
=
0.818584
,
Adj.
R
2
=
0.778545
,
n
=
27
(15)
S
4
=
0.145528
t
-
0.01115
A
-
0.46955
B
+
0.000945
C
-
0.00043
D
+
0.002388
E
+
27.266
C
R
C
=
0.985076
,
R
2
=
0.952796
,
Adj.
R
2
=
0.945592
,
n
=
99
(16)
S
4
10 h
=
0.12160
4
t
-
0.0789
6
A
-
0.4524
7
B
+
0.00167
2
C
-
0.0002
5
D
+
0.00267
8
E
+
37.27
C
R
C
=
0.977618
,
R
2
=
0.954764
,
Adj.
R
2
=
0.953648
,
n
=
99
.In drug release study for 2 h, the effects of factorsB and C are negligible but, in case of drug release study for 5 h, the effects of the factors B and C become significant (see (14)). Similarly in drug release study for 5 h, the effects of B and D become prominently significant, while, in studying the drug release for 10 h, effects of E and A are negligible (see (15)). The effects of all variables are mentioned in Table 10.Table 10
Standard regression analysis (SRA) and effects of variables on DR profiles.
Batches
Time
Effects
1st
10 h
B (0.96305) > D2 (0.60631) > C (0.511874) > E (0.467321) > A (0.15455)
B (0.48382) > C (0.264335) >D (0.2517) > E (0.238427) > A (0.05542)
B (0.95425) > D2 (0.60641) > C (0.510984) > E (0.468321) > A (0.15445)
2nd
10 h
D (0.78219) > A (0.437285) > C (0.319727) > B (0.257393) > E (0.19455)
D (0.23438) > B (0.208289) > C (0.147678) > E (0.10895) > A (0.010716)
B (0.322212) > D (0.24913) > E (0.20344) > A (0.122779) > C (0.07176)
3rd
10 h
A (0.68074) > D (0.476) > E (0.316554) > B (0.12522) > C (0.055576)
A (0.51543) > D (0.320761) > C (0.112551) > E (0.084883) > B (0.00452)
A (0.36251) > D (0.280217) > B (0.085294) > C (0.03699) > E (0.017337)
4th
10 h
B (0.37577) > C (0.2633) > A (0.18647) > D (0.04554) > E (0.064829)
B (0.24738) > D (0.18553) > C (0.120534) > E (0.075439) > A (0.01652)
B (0.17275) > A (0.10162) > E (0.056415) > C (0.054636) > A (0.04857)


## 3.1. Extraction of Hydrogel

The ENa fraction extracted by alkali-ethanol method was found to be most suitable due to its minimum lag time as compared to other fractions as matrix. In present study, NaOH solution was used for dissolution of gel. Although this is a chemical method it is more economical for production of pure gel. The ion dipoles interaction between NaOH and hydroxyl (-OH) groups results in cleavage of molecular H-bonding among the hydrogel layers and decreases the viscosity of solution; hence filtration becomes easier. The separated gels were dialyzed with distilled water and washed until the pH was restored to 7. The purified transparent gel was dried and its yield was found to be 30% relative to weight of husk. The extracted material was found to be good DR retardant and suitable for floats due to its low density.


## 3.2. Characterization of ENa

### 3.2.1. Elemental Analysis

The elemental analysis shows that ENa is free of any nitrogenous matter (proteins). Even with performing the analysis in triplicate no evidence of nitrogen was found in all samples. The percentage of C and H was found to be 40.2% and 6.5% against 45.0% and 6.0%, respectively, as observed in other natural polysaccharides (pullulan, etc.) [17].


### 3.2.2. Thermal Analysis

The ENa was studied by TGA and DSC from ambient temperature to 600°C. The TGA showed an endothermic weight loss about 10% up to 200°C, which was attributed to the loss of absorbed moisture. The weight loss of ~45% was observed in range 226–326°C, which was due to degradation of ENa structure. This step was exothermic in nature. The second weight loss of ~24% was observed at temperature 452–563°C due to complete degradation of ENa leaving behind a carbon rich residue. Glass transition temperature(Tg) could not be observed in the experimental range due to presence of carbon. The average Ea value calculated by Broido’s method was 134 kJ mol–1 using 10°C min–1 and thermogram was comparable to those of other commercially available polysaccharides [18].An overall thermal stability of ENa along with other fractions was assessed by integral procedural decomposition temperature (IPDT) and comprehensive index of thermal stability (ITS) values by Doyle’s method. The IPDT and ITS value were found to be 289°C and 0.46, respectively. These values indicate that ENa has good thermal stability [19, 20]. The apparent Ea values for major stage of decomposition were also calculated using Flynn-Wall-Ozawa (FWO) method. The Ea values varied greatly with α indicating a multistep degradation. The average activation energies by FWO method are given in Table 2 and Figure 2.Table 2
Thermal parameters of ENa gel fractions.
Samplecode
Temp.range(°C)
Massloss(%)
E
a kJ mol−1
ΔH∗ kJ mol−1
ΔS∗ JK−1 mol−1
ΔG∗ kJ mol−1
ln
⁡
A
IPDT(°C)
ITS
Mass at600°C(%)
ENa
223–315
49.24
146.68
139.76
−51.47
169.66
22.82
289.42
0.46
0.983Figure 2
Thermogravimetric curves of ENa (L) and representativeα-T curve and log⁡ß (R) for ENa (R).


### 3.2.3. Gel Permeation Chromatography (GPC)

GPC was used to determine molar mass distributionMn (number-average), Mw (weight-average), Mz (average molar masses), and polydispersity index (PDI) which is the ratio of Mw/Mn. The PDI value of ENa was to be found as 3.724 which indicates that ENa is polydispersed in nature Table 3.Table 3
GPC parameters of ENa.
Parameters
ENa
M
n (g mol−1)
3.443 × 103
M
w (g mol−1)
1.344 × 104
M
z (g mol−1)
2.967 × 104
M
p (g mol−1)
8.273 × 103
V
p (cm3)
7.497
PDI
3.724


### 3.2.4. Fourier Transform-Infrared Spectroscopy (FT-IR)

The absorption bands in FT-IR spectra (Figure3) were assigned taking two fractions for comparison. The absorbance at 3323.35 cm–1 was due to -OH stretching (broad band). The absorbances at 1251.80–1328.86 cm–1 were due to deformation caused by absorbed water. A sharp band at 1012–1047.5 cm–1 was due to beta-glycosidic bond. The absorbances at 651–690.62 and 528–549 cm−1 were observed due to polymer backbone.Figure 3
Representative FT-IR graphs of ENa and AANa (comparison).


### 3.2.5. Surface Morphology of ENa

Scanning electron microscopy (SEM) of ENa (Figure4) indicates that its surface is highly rough, which could provide more surface area to adhere with drug for adsorption reported in Iqbal et al. [21].Figure 4
Scanning electron microscopy of ENa.


### 3.2.6. Water Retention Value (WRV)

The WRV was found to be 7000% to 7500% to its original volume. The H2O holding capacity decreased on exposure to atmosphere with the passage of time; therefore WRV is not permanent. Being reswellable, it can be reused after drying. Being highly hydrophilic, it is considered as hydrogel. All formulations were found to be uniform and their drug content ranged from 97.37 to 100.34.


### 3.2.7. Dissolution Study and Float Formulations

The concentration of drug was calculated using the equationy = 0.002 × concentration + 0.003.From above equation and Figure5 it was found that DR% from all fractions ranged from 83.5 to 90.2% where the DR by husk and marketed tablets (Cstd) was found to be 93.3 and 79.8%, respectively. On studying the DR% of different formulations, it was found that the DR was retarded notably by increasing the amount of husk gel but DR data indicates that the use of HG by itself as sustained release matrix is insufficient to achieve the CR (controlled release) profile, Figure 5. Therefore different excipients were used to accommodate balance in DR rates due to difference in swelling from Psyllium polysaccharide. ENa was selected as the main excipient (matrix) due to its better DR retarding ability. The DR% of various batches was calculated at different time intervals and given in Table 4. The rate of swelling depends upon the amount of H2O taken up by the polymer which depends upon nature of added excipients. The hardness of all floats formulations was in the range 6.0–8.0 kg/cm2. The percentage friability of all the floats was not found to be more than 0.7%. To increase the wetting ability and decrease the lag time, NaHCO3 was added in the matrix, which produced CO2 on contact with simulated gastric fluid which was entrapped within the polymer and decreased the density of system. The drug release % of all formulations for 10 h are shown in Tables 6–9, which indicates that drug release rate depends upon nature and concentration of adding excipients and their effect was studied by regression analysis. By addition of TG, DR rate increased due to having soluble component. TG gum is actually a mixture of soluble component tragacanthin (30–40%) and insoluble component bassorin (60–70%). On addition of LSG, the DR rate was decreased due to its greater retarding ability and it was further decreased by addition of PSG. In order to increase the DR rate, concentration of NaHCO3 was increased in 4th batch. So by the adjustment of variables, as recommended by regression analysis and formulation, S4F3 was found to better due to its minimum lag time and maximum floating ability with better sustainability (Figure 6).Table 4
Lag time (LT) of all batches.
RNT
MNa
ENa
AANa
ClfNa
ANa
Husk
C
s
t
d
LT (min)
22
19
27
30
26
24
12
Batch-S1
F1
F2
F3
F4
F5
F6
F7
F8
F9
LT (sec)
120
100
45
165
145
130
80
70
60
Batch-S2
F1
F2
F3
F4
F5
F6
F7
F8
F9
LT (sec)
80
125
150
180
200
256
300
325
375
Batch-S3
F1
F2
F3
F4
F5
F6
F7
F8
F9
LT (sec)
100
150
195
170
225
245
260
285
295
Batch-S4
F1
F2
F3
F4
F4
F6
F7
F8
F9
LT (min)
07
05
03
08
06
08
11
14
19Figure 5
Drug release profiles of RNT from isolated fractions.Figure 6
Drug release profiles of four batches (S1 to S4 each containing nine formulations).TA data indicates that ENa is equally stable as other hydrogel, Table2. The DR% of all fractions and designed formulations are shown in Table 6 and similarity and difference factors are shown in Table 7, which are major indicators to understand the formulation design. The kinetic study of some selected batches, Tables 6–9, indicates that as the n value is greater than 0.45, the DR behavior is trending towards non-Fickian diffusion. In case of optimized formulation S4F3, the value of n is 0.31 indicating that release mechanism is totally Fickian diffusion [22].


### 3.2.8. In Vitro Buoyancy

The total floating and lag time of all formulation were calculated, given in Table4, and minimum lag time and maximum floating time were detected in formulation S4F3. So this formulation was selected and found to be best one for floating DDS. The target of this study was to choose the formulations having maximum GRT with best bioavailability of drug. Among all the fractions, ENa has minimum lag time and maximum floating ability; therefore this fraction was chosen for designing other formulations.


### 3.2.9. Similarity and Difference Factors

Similarity and difference factors of all the formulations, using ENa reference matrix tablet as standard, (Table5) indicate that all formulations which havef2 factor greater than 50 resemble standard tablets with respect to the release rate but the formulation having lesserf2 than 50 deviate from resemblance. Similarly the formulations havingf1 0–15 resemble Cstd but deviation from these values indicates that their release pattern is somewhat different from reference formulation.Table 5
Similarity (f2) and difference factor (f1) for all batches.
RNT
F1
F2
F3
F4
F5
F6
F7
F8
F9
S1
f1
28.50
13.71
13.02
18.80
19.83
26.45
23.61
23.62
25.18
f2
50.76
65.49
64.92
59.58
57.76
50.97
54.84
55.56
53.73
C
s
t
d
f1
13.02
13.67
20.77
23.33
29.98
36.26
32.51
32.52
34.21
f2
62.93
64.22
55.95
52.51
44.82
40.19
45.33
43.95
42.10
S2
f1
20.71
9.27
9.47
22.67
15.51
17.01
17.15
18.66
21.28
f2
59.10
72.03
74.01
56.30
62.44
62.87
61.62
59.78
56.04
C
s
t
d
f1
15.27
16.62
20.61
18.53
24.92
26.80
26.37
28.97
31.61
f2
60.52
58.40
52.50
57.21
50.56
48.20
49.46
46.40
43.60
S3
f1
13.37
14.58
18.28
18.46
23.60
31.02
24.05
30.49
37.87
f2
62.12
59.09
54.58
54.54
49.12
43.48
47.69
43.14
39.04
C
s
t
d
f1
22.82
21.35
21.45
23.52
23.75
29.28
27.30
30.49
35.60
f2
52.63
54.08
53.64
52.17
49.91
45.03
47.37
43.89
40.36
S4
f1
44.14
17.58
10.30
33.38
23.43
8.92
14.07
07.08
07.81
f2
44.25
62.44
71.55
50.21
57.92
74.99
65.19
75.54
76.34
C
s
t
d
f1
18.34
15.79
20.19
14.87
15.45
25.78
22.84
24.25
27.39
f2
55.76
60.88
55.18
60.65
60.57
49.01
52.45
50.38
47.30Table 6
Kinetic study of DR from polymer matrix.
RNT
S
10 h
Zero order
First order
Higuchi
H-Crowell
Power law
k
o
R
2
k
1
R
2
k
H
R
2
k
H
C
R
2
k
P
R
2
n
MNa
86.8
0.133
0.918
−0.004
0.985
3.922
0.968
−0.042
0.907
0.024
0.976
0.577
ENa
84.5
0.123
0.862
−0.004
0.957
3.659
0.962
−0.039
0.773
0.021
0.951
0.561
AANa
91.2
0.095
0.852
−0.004
0.967
3.061
0.942
−0.032
0.851
0.071
0.922
0.389
ANa
82.7
0.133
0.973
−0.004
0.977
3.775
0.958
−0.043
0.982
0.022
0.972
0.575
ClfNa
84.1
0.128
0.982
−0.004
0.965
4.231
0.981
−0.045
0.987
0.006
0.977
0.789
H
95.3
0.118
0.918
−0.004
0.828
3.272
0.885
−0.033
0.924
0.062
0.887
0.423
C
s
t
d
82.8
0.113
0.952
−0.004
0.918
3.241
0.985
−0.035
0.942
0.091
0.972
0.357Table 7
DR study for 1st batch matrix tablets.
RNT (S1)
S
10 h
Zero order
First order
Higuchi
H-Crowell
Power law
k
o
R
2
k
1
R
2
k
H
R
2
k
H
C
R
2
k
P
R
2
n
F1
83.5
0.134
0.989
−0.002
0.958
4.091
0.969
−0.044
0.989
0.003
0.978
0.832
F2
78.4
0.132
0.882
−0.003
0.949
4.295
0.953
−0.044
0.992
0.005
0.962
0.829
F3
74.4
0.132
0.857
−0.003
0.946
4.278
0.944
−0.044
0.857
0.003
0.953
0.877
F4
81.8
0.128
0.988
−0.002
0.987
3.946
0.989
−0.042
0.988
0.003
0.995
0.852
F5
76.8
0.124
0.994
−0.002
0.96
3.777
0.963
−0.041
0.994
0.003
0.989
0.869
F6
74.3
0.122
0.986
−0.002
0.938
3.652
0.928
−0.042
0.986
0.004
0.978
0.858
F7
71.7
0.129
0.968
−0.002
0.932
3.864
0.912
−0.043
0.968
0.002
0.956
0.912
F8
68.4
0.118
0.962
−0.002
0.88
3.492
0.879
−0.039
0.962
0.003
0.956
0.849
F9
66.0
0.111
0.941
−0.001
0.841
3.252
0.852
−0.037
0.941
0.004
0.941
0.865Table 8
DR study for 2nd batch matrix tablets.
RNT (S2)
S
10 h
Zero order
First order
Higuchi
H-Crowell
Power law
k
o
R
2
k
1
R
2
k
H
R
2
k
H
C
R
2
k
P
R
2
n
F1
83.5
0.135
0.955
−0.0025
0.982
3.913
0.982
−0.043
0.945
0.022
0.985
0.554
F2
85.2
0.107
0.969
−0.0027
0.975
3.351
0.995
−0.035
0.969
0.043
0.985
0.470
F3
87.6
0.103
0.904
−0.002
0.933
3.269
0.969
−0.032
0.903
0.033
0.973
0.482
F4
78.4
0.086
0.755
−0.002
0.897
2.849
0.882
−0.027
0.766
0.482
0.876
0.482
F5
81.8
0.102
0.951
−0.002
0.975
3.176
0.988
−0.036
0.944
0.033
0.962
0.512
F6
83.4
0.091
0.853
−0.002
0.927
2.913
0.927
−0.034
0.842
0.043
0.888
0.450
F7
74.7
0.088
0.793
−0.002
0.823
2.853
0.878
−0.027
0.793
0.044
0.965
0.464
F8
79.2
0.104
0.881
−0.003
0.985
3.326
0.964
−0.036
0.872
0.054
0.966
0.446
F9
80.1
0.095
0.736
−0.003
0.932
3.154
0.866
−0.034
0.744
0.056
0.824
0.457Table 9
DR study for 4th batch matrix tablets.
RNT (S4)
S
10 h
Zero order
First order
Higuchi
H-Crowell
Power law
k
o
R
2
k
1
R
2
k
H
R
2
k
H
C
R
2
k
P
R
2
n
F1
73.7
0.100
0.855
−0.002
0.956
3.265
0.952
−0.033
0.855
0.033
0.948
0.516
F2
81.6
0.105
0.935
−0.002
0.983
3.315
0.981
−0.035
0.935
0.041
0.984
0.462
F3
85.5
0.084
0.962
−0.002
0.989
2.517
0.981
−0.028
0.962
0.051
0.975
0.313
F4
74.2
0.080
0.811
−0.001
0.921
2.612
0.903
−0.026
0.811
0.045
0.895
0.446
F5
77.5
0.097
0.924
−0.002
0.928
3.091
0.974
−0.032
0.924
0.038
0.969
0.479
F6
82.2
0.075
0.771
−0.001
0.826
2.404
0.834
−0.025
0.771
0.071
0.839
0.340
F7
74.2
0.089
0.958
−0.001
0.972
2.783
0.991
−0.029
0.959
0.051
0.976
0.410
F8
75.1
0.096
0.951
−0.002
0.886
2.811
0.869
−0.032
0.951
0.041
0.807
0.393
F9
79.4
0.092
0.986
−0.002
0.935
2.763
0.937
−0.031
0.986
0.051
0.891
0.377


### 3.2.10. Kinetic Study

The DR data was evaluated by using different kinetic models. By applying Korsmeyer and Peppas equation to DR data of different fractions of Psyllium husk, the diffusion coefficient(n) was found in the range from 0.356 to 0.799±0.01 with DR% from 81.8 to 94.3 in 10 h. These results indicate that the release mechanism was non-Fickian diffusion as compared to Cstd, Table 5. Therefore new formulations were designed to find the desired formulation, Tables 6–9.


### 3.2.11. Regression Analysis

The equations (see (5)–(16)) derived by SRA indicate that each factor has either positive or negative sign. The negative sign indicates that its concentration must be decreased while having positive sign indicates that concentration must be increased to achieve desired DR profile. The following notations were used for abbreviation for different variables X1=A, X2=B, X1X2=C, X12=D, X22=E.


### 3.2.12. 1st Batch

(5)
S
1
=
0.0972
9
t
+
0.028367
A
-
0.8673
8
B
+
0.00185
3
C
-
0.0004
7
D
+
0.01076
7
E
-
15.65378
C
R
C
=
0.903129
,
R
2
=
0.813737
,
Adj.
R
2
=
0.757978
,
n
=
27
(6)
S
1
5 h
=
0.10078
9
t
-
0.0201
8
A
-
1.0632
2
B
+
0.00331
9
C
-
0.0002
7
A
+
0.01240
4
B
+
24.3492
C
R
C
=
0.969284
,
R
2
=
0.939511
,
Adj.
R
2
=
0.931789
,
n
=
99
(7)
S
1
10 h
=
0.12567
2
t
-
0.064
8
A
-
0.8473
1
B
+
0.00274
7
C
-
0.0003
8
A
+
0.00424
9
B
+
22.84727
C
R
C
=
0.992215
,
R
2
=
0.983514
,
Adj.
R
2
=
0.982528
,
n
=
99
.In study, the SRA shows that effect ofB and C is more significant while effect of D is least effective (see (5)). After study it is found that effect of B is most significant while effect of A is negligible (see (6)) and after 10 h it was found that effect of B and C2 is significant while effect of D, A, and E is negligible (see (7)).The abbreviated kinetic study of 1st, 2nd, and 4th batch with nine formulations each has been discussed to identify the optimized formulation.


### 3.2.13. 2nd Batch

(8)
S
2
2 h
=
0.221171
t
+
0.182557
A
+
0.178385
B
+
0.000876
C
-
0.00118
D
-
0.00112
E
+
4.82514
C
R
C
=
0.942855
,
R
2
=
0.887506
,
Adj.
R
2
=
0.855448
,
n
=
27
(9)
S
2
5 h
=
0.154039
t
+
0.006524
A
+
0.210632
B
+
0.0015
C
-
0.00157
D
-
0.00082
E
+
10.87422
C
R
C
=
0.971366
,
R
2
=
0.944552
,
A
d
j
.
R
2
=
0.946346
,
n
=
54
.The SRA shows that the effect ofA, B, and C is positive while effect of D and E is negative. (10)S210 h=0.114934t+0.098263A+0.429285B-0.00038C-0.00055D-0.00226E+8.20543CRC=0.969798,R2=0.942314,Adj.R2=0.936522,n=99.By applying the LRA to DR, it was found that impact ofD is most critical while impact of E is minimally noteworthy (see (8)) and then impact of D and B is most huge while impact of A is irrelevant (see (9)). Also after 10 h, impact of A, C, and D was observed to be most critical while impact of X22 is most slightly noteworthy. The relapse examination was again led on DR after 10 h and it was found that impact of X2 and X12 is most critical while impact ofC is insignificant (see (10)).


### 3.2.14. 3rd Batch

(11)
S
3
=
0.256874
t
-
0.26814
A
-
0.16514
B
+
0.000262
C
+
0.000548
D
+
0.006923
E
+
35.92275
C
R
C
=
0.976225
,
R
2
=
0.957119
,
Adj.
R
2
=
0.944255
,
n
=
27
(12)
S
3
=
0.162851
t
-
0.31975
A
-
0.00574
B
+
0.00088
C
+
0.00053
D
+
0.002823
E
+
44.42843
C
R
C
=
0.976629
,
R
2
=
0.961753
,
Adj.
R
2
=
0.954716
,
n
=
54
(13)
S
3
10 h
=
0.09369
3
t
-
0.2486
2
A
+
0.19663
1
B
-
0.0002
4
C
+
0.00065
4
D
+
0.00037
6
E
+
46.55
C
R
C
=
0.951827
,
R
2
=
0.906334
,
Adj.
R
2
=
0.901202
,
n
=
99
.After drug release study, SRA shows that effect of A and B is more significant while effect of C is negligible (see (11)). The effect of A is most significant but effect of E and B is negligible (see (12)). For 10 h study, effect of A and D is significant while effect of B and C and E is negligible (see (13)).


### 3.2.15. 4th Batch

(14)
S
4
=
0.179377
t
-
0.06247
A
-
0.41753
B
+
0.001268
C
-
5.5
E
-
05
D
+
0.001364
E
+
24.487
C
R
C
=
0.911831
,
R
2
=
0.818584
,
Adj.
R
2
=
0.778545
,
n
=
27
(15)
S
4
=
0.145528
t
-
0.01115
A
-
0.46955
B
+
0.000945
C
-
0.00043
D
+
0.002388
E
+
27.266
C
R
C
=
0.985076
,
R
2
=
0.952796
,
Adj.
R
2
=
0.945592
,
n
=
99
(16)
S
4
10 h
=
0.12160
4
t
-
0.0789
6
A
-
0.4524
7
B
+
0.00167
2
C
-
0.0002
5
D
+
0.00267
8
E
+
37.27
C
R
C
=
0.977618
,
R
2
=
0.954764
,
Adj.
R
2
=
0.953648
,
n
=
99
.In drug release study for 2 h, the effects of factorsB and C are negligible but, in case of drug release study for 5 h, the effects of the factors B and C become significant (see (14)). Similarly in drug release study for 5 h, the effects of B and D become prominently significant, while, in studying the drug release for 10 h, effects of E and A are negligible (see (15)). The effects of all variables are mentioned in Table 10.Table 10
Standard regression analysis (SRA) and effects of variables on DR profiles.
Batches
Time
Effects
1st
10 h
B (0.96305) > D2 (0.60631) > C (0.511874) > E (0.467321) > A (0.15455)
B (0.48382) > C (0.264335) >D (0.2517) > E (0.238427) > A (0.05542)
B (0.95425) > D2 (0.60641) > C (0.510984) > E (0.468321) > A (0.15445)
2nd
10 h
D (0.78219) > A (0.437285) > C (0.319727) > B (0.257393) > E (0.19455)
D (0.23438) > B (0.208289) > C (0.147678) > E (0.10895) > A (0.010716)
B (0.322212) > D (0.24913) > E (0.20344) > A (0.122779) > C (0.07176)
3rd
10 h
A (0.68074) > D (0.476) > E (0.316554) > B (0.12522) > C (0.055576)
A (0.51543) > D (0.320761) > C (0.112551) > E (0.084883) > B (0.00452)
A (0.36251) > D (0.280217) > B (0.085294) > C (0.03699) > E (0.017337)
4th
10 h
B (0.37577) > C (0.2633) > A (0.18647) > D (0.04554) > E (0.064829)
B (0.24738) > D (0.18553) > C (0.120534) > E (0.075439) > A (0.01652)
B (0.17275) > A (0.10162) > E (0.056415) > C (0.054636) > A (0.04857)


## 3.2.1. Elemental Analysis

The elemental analysis shows that ENa is free of any nitrogenous matter (proteins). Even with performing the analysis in triplicate no evidence of nitrogen was found in all samples. The percentage of C and H was found to be 40.2% and 6.5% against 45.0% and 6.0%, respectively, as observed in other natural polysaccharides (pullulan, etc.) [17].


## 3.2.2. Thermal Analysis

The ENa was studied by TGA and DSC from ambient temperature to 600°C. The TGA showed an endothermic weight loss about 10% up to 200°C, which was attributed to the loss of absorbed moisture. The weight loss of ~45% was observed in range 226–326°C, which was due to degradation of ENa structure. This step was exothermic in nature. The second weight loss of ~24% was observed at temperature 452–563°C due to complete degradation of ENa leaving behind a carbon rich residue. Glass transition temperature(Tg) could not be observed in the experimental range due to presence of carbon. The average Ea value calculated by Broido’s method was 134 kJ mol–1 using 10°C min–1 and thermogram was comparable to those of other commercially available polysaccharides [18].An overall thermal stability of ENa along with other fractions was assessed by integral procedural decomposition temperature (IPDT) and comprehensive index of thermal stability (ITS) values by Doyle’s method. The IPDT and ITS value were found to be 289°C and 0.46, respectively. These values indicate that ENa has good thermal stability [19, 20]. The apparent Ea values for major stage of decomposition were also calculated using Flynn-Wall-Ozawa (FWO) method. The Ea values varied greatly with α indicating a multistep degradation. The average activation energies by FWO method are given in Table 2 and Figure 2.Table 2
Thermal parameters of ENa gel fractions.
Samplecode
Temp.range(°C)
Massloss(%)
E
a kJ mol−1
ΔH∗ kJ mol−1
ΔS∗ JK−1 mol−1
ΔG∗ kJ mol−1
ln
⁡
A
IPDT(°C)
ITS
Mass at600°C(%)
ENa
223–315
49.24
146.68
139.76
−51.47
169.66
22.82
289.42
0.46
0.983Figure 2
Thermogravimetric curves of ENa (L) and representativeα-T curve and log⁡ß (R) for ENa (R).


## 3.2.3. Gel Permeation Chromatography (GPC)

GPC was used to determine molar mass distributionMn (number-average), Mw (weight-average), Mz (average molar masses), and polydispersity index (PDI) which is the ratio of Mw/Mn. The PDI value of ENa was to be found as 3.724 which indicates that ENa is polydispersed in nature Table 3.Table 3
GPC parameters of ENa.
Parameters
ENa
M
n (g mol−1)
3.443 × 103
M
w (g mol−1)
1.344 × 104
M
z (g mol−1)
2.967 × 104
M
p (g mol−1)
8.273 × 103
V
p (cm3)
7.497
PDI
3.724


## 3.2.4. Fourier Transform-Infrared Spectroscopy (FT-IR)

The absorption bands in FT-IR spectra (Figure3) were assigned taking two fractions for comparison. The absorbance at 3323.35 cm–1 was due to -OH stretching (broad band). The absorbances at 1251.80–1328.86 cm–1 were due to deformation caused by absorbed water. A sharp band at 1012–1047.5 cm–1 was due to beta-glycosidic bond. The absorbances at 651–690.62 and 528–549 cm−1 were observed due to polymer backbone.Figure 3
Representative FT-IR graphs of ENa and AANa (comparison).


## 3.2.5. Surface Morphology of ENa

Scanning electron microscopy (SEM) of ENa (Figure4) indicates that its surface is highly rough, which could provide more surface area to adhere with drug for adsorption reported in Iqbal et al. [21].Figure 4
Scanning electron microscopy of ENa.


## 3.2.6. Water Retention Value (WRV)

The WRV was found to be 7000% to 7500% to its original volume. The H2O holding capacity decreased on exposure to atmosphere with the passage of time; therefore WRV is not permanent. Being reswellable, it can be reused after drying. Being highly hydrophilic, it is considered as hydrogel. All formulations were found to be uniform and their drug content ranged from 97.37 to 100.34.


## 3.2.7. Dissolution Study and Float Formulations

The concentration of drug was calculated using the equationy = 0.002 × concentration + 0.003.From above equation and Figure5 it was found that DR% from all fractions ranged from 83.5 to 90.2% where the DR by husk and marketed tablets (Cstd) was found to be 93.3 and 79.8%, respectively. On studying the DR% of different formulations, it was found that the DR was retarded notably by increasing the amount of husk gel but DR data indicates that the use of HG by itself as sustained release matrix is insufficient to achieve the CR (controlled release) profile, Figure 5. Therefore different excipients were used to accommodate balance in DR rates due to difference in swelling from Psyllium polysaccharide. ENa was selected as the main excipient (matrix) due to its better DR retarding ability. The DR% of various batches was calculated at different time intervals and given in Table 4. The rate of swelling depends upon the amount of H2O taken up by the polymer which depends upon nature of added excipients. The hardness of all floats formulations was in the range 6.0–8.0 kg/cm2. The percentage friability of all the floats was not found to be more than 0.7%. To increase the wetting ability and decrease the lag time, NaHCO3 was added in the matrix, which produced CO2 on contact with simulated gastric fluid which was entrapped within the polymer and decreased the density of system. The drug release % of all formulations for 10 h are shown in Tables 6–9, which indicates that drug release rate depends upon nature and concentration of adding excipients and their effect was studied by regression analysis. By addition of TG, DR rate increased due to having soluble component. TG gum is actually a mixture of soluble component tragacanthin (30–40%) and insoluble component bassorin (60–70%). On addition of LSG, the DR rate was decreased due to its greater retarding ability and it was further decreased by addition of PSG. In order to increase the DR rate, concentration of NaHCO3 was increased in 4th batch. So by the adjustment of variables, as recommended by regression analysis and formulation, S4F3 was found to better due to its minimum lag time and maximum floating ability with better sustainability (Figure 6).Table 4
Lag time (LT) of all batches.
RNT
MNa
ENa
AANa
ClfNa
ANa
Husk
C
s
t
d
LT (min)
22
19
27
30
26
24
12
Batch-S1
F1
F2
F3
F4
F5
F6
F7
F8
F9
LT (sec)
120
100
45
165
145
130
80
70
60
Batch-S2
F1
F2
F3
F4
F5
F6
F7
F8
F9
LT (sec)
80
125
150
180
200
256
300
325
375
Batch-S3
F1
F2
F3
F4
F5
F6
F7
F8
F9
LT (sec)
100
150
195
170
225
245
260
285
295
Batch-S4
F1
F2
F3
F4
F4
F6
F7
F8
F9
LT (min)
07
05
03
08
06
08
11
14
19Figure 5
Drug release profiles of RNT from isolated fractions.Figure 6
Drug release profiles of four batches (S1 to S4 each containing nine formulations).TA data indicates that ENa is equally stable as other hydrogel, Table2. The DR% of all fractions and designed formulations are shown in Table 6 and similarity and difference factors are shown in Table 7, which are major indicators to understand the formulation design. The kinetic study of some selected batches, Tables 6–9, indicates that as the n value is greater than 0.45, the DR behavior is trending towards non-Fickian diffusion. In case of optimized formulation S4F3, the value of n is 0.31 indicating that release mechanism is totally Fickian diffusion [22].


## 3.2.8. In Vitro Buoyancy

The total floating and lag time of all formulation were calculated, given in Table4, and minimum lag time and maximum floating time were detected in formulation S4F3. So this formulation was selected and found to be best one for floating DDS. The target of this study was to choose the formulations having maximum GRT with best bioavailability of drug. Among all the fractions, ENa has minimum lag time and maximum floating ability; therefore this fraction was chosen for designing other formulations.


## 3.2.9. Similarity and Difference Factors

Similarity and difference factors of all the formulations, using ENa reference matrix tablet as standard, (Table5) indicate that all formulations which havef2 factor greater than 50 resemble standard tablets with respect to the release rate but the formulation having lesserf2 than 50 deviate from resemblance. Similarly the formulations havingf1 0–15 resemble Cstd but deviation from these values indicates that their release pattern is somewhat different from reference formulation.Table 5
Similarity (f2) and difference factor (f1) for all batches.
RNT
F1
F2
F3
F4
F5
F6
F7
F8
F9
S1
f1
28.50
13.71
13.02
18.80
19.83
26.45
23.61
23.62
25.18
f2
50.76
65.49
64.92
59.58
57.76
50.97
54.84
55.56
53.73
C
s
t
d
f1
13.02
13.67
20.77
23.33
29.98
36.26
32.51
32.52
34.21
f2
62.93
64.22
55.95
52.51
44.82
40.19
45.33
43.95
42.10
S2
f1
20.71
9.27
9.47
22.67
15.51
17.01
17.15
18.66
21.28
f2
59.10
72.03
74.01
56.30
62.44
62.87
61.62
59.78
56.04
C
s
t
d
f1
15.27
16.62
20.61
18.53
24.92
26.80
26.37
28.97
31.61
f2
60.52
58.40
52.50
57.21
50.56
48.20
49.46
46.40
43.60
S3
f1
13.37
14.58
18.28
18.46
23.60
31.02
24.05
30.49
37.87
f2
62.12
59.09
54.58
54.54
49.12
43.48
47.69
43.14
39.04
C
s
t
d
f1
22.82
21.35
21.45
23.52
23.75
29.28
27.30
30.49
35.60
f2
52.63
54.08
53.64
52.17
49.91
45.03
47.37
43.89
40.36
S4
f1
44.14
17.58
10.30
33.38
23.43
8.92
14.07
07.08
07.81
f2
44.25
62.44
71.55
50.21
57.92
74.99
65.19
75.54
76.34
C
s
t
d
f1
18.34
15.79
20.19
14.87
15.45
25.78
22.84
24.25
27.39
f2
55.76
60.88
55.18
60.65
60.57
49.01
52.45
50.38
47.30Table 6
Kinetic study of DR from polymer matrix.
RNT
S
10 h
Zero order
First order
Higuchi
H-Crowell
Power law
k
o
R
2
k
1
R
2
k
H
R
2
k
H
C
R
2
k
P
R
2
n
MNa
86.8
0.133
0.918
−0.004
0.985
3.922
0.968
−0.042
0.907
0.024
0.976
0.577
ENa
84.5
0.123
0.862
−0.004
0.957
3.659
0.962
−0.039
0.773
0.021
0.951
0.561
AANa
91.2
0.095
0.852
−0.004
0.967
3.061
0.942
−0.032
0.851
0.071
0.922
0.389
ANa
82.7
0.133
0.973
−0.004
0.977
3.775
0.958
−0.043
0.982
0.022
0.972
0.575
ClfNa
84.1
0.128
0.982
−0.004
0.965
4.231
0.981
−0.045
0.987
0.006
0.977
0.789
H
95.3
0.118
0.918
−0.004
0.828
3.272
0.885
−0.033
0.924
0.062
0.887
0.423
C
s
t
d
82.8
0.113
0.952
−0.004
0.918
3.241
0.985
−0.035
0.942
0.091
0.972
0.357Table 7
DR study for 1st batch matrix tablets.
RNT (S1)
S
10 h
Zero order
First order
Higuchi
H-Crowell
Power law
k
o
R
2
k
1
R
2
k
H
R
2
k
H
C
R
2
k
P
R
2
n
F1
83.5
0.134
0.989
−0.002
0.958
4.091
0.969
−0.044
0.989
0.003
0.978
0.832
F2
78.4
0.132
0.882
−0.003
0.949
4.295
0.953
−0.044
0.992
0.005
0.962
0.829
F3
74.4
0.132
0.857
−0.003
0.946
4.278
0.944
−0.044
0.857
0.003
0.953
0.877
F4
81.8
0.128
0.988
−0.002
0.987
3.946
0.989
−0.042
0.988
0.003
0.995
0.852
F5
76.8
0.124
0.994
−0.002
0.96
3.777
0.963
−0.041
0.994
0.003
0.989
0.869
F6
74.3
0.122
0.986
−0.002
0.938
3.652
0.928
−0.042
0.986
0.004
0.978
0.858
F7
71.7
0.129
0.968
−0.002
0.932
3.864
0.912
−0.043
0.968
0.002
0.956
0.912
F8
68.4
0.118
0.962
−0.002
0.88
3.492
0.879
−0.039
0.962
0.003
0.956
0.849
F9
66.0
0.111
0.941
−0.001
0.841
3.252
0.852
−0.037
0.941
0.004
0.941
0.865Table 8
DR study for 2nd batch matrix tablets.
RNT (S2)
S
10 h
Zero order
First order
Higuchi
H-Crowell
Power law
k
o
R
2
k
1
R
2
k
H
R
2
k
H
C
R
2
k
P
R
2
n
F1
83.5
0.135
0.955
−0.0025
0.982
3.913
0.982
−0.043
0.945
0.022
0.985
0.554
F2
85.2
0.107
0.969
−0.0027
0.975
3.351
0.995
−0.035
0.969
0.043
0.985
0.470
F3
87.6
0.103
0.904
−0.002
0.933
3.269
0.969
−0.032
0.903
0.033
0.973
0.482
F4
78.4
0.086
0.755
−0.002
0.897
2.849
0.882
−0.027
0.766
0.482
0.876
0.482
F5
81.8
0.102
0.951
−0.002
0.975
3.176
0.988
−0.036
0.944
0.033
0.962
0.512
F6
83.4
0.091
0.853
−0.002
0.927
2.913
0.927
−0.034
0.842
0.043
0.888
0.450
F7
74.7
0.088
0.793
−0.002
0.823
2.853
0.878
−0.027
0.793
0.044
0.965
0.464
F8
79.2
0.104
0.881
−0.003
0.985
3.326
0.964
−0.036
0.872
0.054
0.966
0.446
F9
80.1
0.095
0.736
−0.003
0.932
3.154
0.866
−0.034
0.744
0.056
0.824
0.457Table 9
DR study for 4th batch matrix tablets.
RNT (S4)
S
10 h
Zero order
First order
Higuchi
H-Crowell
Power law
k
o
R
2
k
1
R
2
k
H
R
2
k
H
C
R
2
k
P
R
2
n
F1
73.7
0.100
0.855
−0.002
0.956
3.265
0.952
−0.033
0.855
0.033
0.948
0.516
F2
81.6
0.105
0.935
−0.002
0.983
3.315
0.981
−0.035
0.935
0.041
0.984
0.462
F3
85.5
0.084
0.962
−0.002
0.989
2.517
0.981
−0.028
0.962
0.051
0.975
0.313
F4
74.2
0.080
0.811
−0.001
0.921
2.612
0.903
−0.026
0.811
0.045
0.895
0.446
F5
77.5
0.097
0.924
−0.002
0.928
3.091
0.974
−0.032
0.924
0.038
0.969
0.479
F6
82.2
0.075
0.771
−0.001
0.826
2.404
0.834
−0.025
0.771
0.071
0.839
0.340
F7
74.2
0.089
0.958
−0.001
0.972
2.783
0.991
−0.029
0.959
0.051
0.976
0.410
F8
75.1
0.096
0.951
−0.002
0.886
2.811
0.869
−0.032
0.951
0.041
0.807
0.393
F9
79.4
0.092
0.986
−0.002
0.935
2.763
0.937
−0.031
0.986
0.051
0.891
0.377


## 3.2.10. Kinetic Study

The DR data was evaluated by using different kinetic models. By applying Korsmeyer and Peppas equation to DR data of different fractions of Psyllium husk, the diffusion coefficient(n) was found in the range from 0.356 to 0.799±0.01 with DR% from 81.8 to 94.3 in 10 h. These results indicate that the release mechanism was non-Fickian diffusion as compared to Cstd, Table 5. Therefore new formulations were designed to find the desired formulation, Tables 6–9.


## 3.2.11. Regression Analysis

The equations (see (5)–(16)) derived by SRA indicate that each factor has either positive or negative sign. The negative sign indicates that its concentration must be decreased while having positive sign indicates that concentration must be increased to achieve desired DR profile. The following notations were used for abbreviation for different variables X1=A, X2=B, X1X2=C, X12=D, X22=E.


## 3.2.12. 1st Batch

(5)
S
1
=
0.0972
9
t
+
0.028367
A
-
0.8673
8
B
+
0.00185
3
C
-
0.0004
7
D
+
0.01076
7
E
-
15.65378
C
R
C
=
0.903129
,
R
2
=
0.813737
,
Adj.
R
2
=
0.757978
,
n
=
27
(6)
S
1
5 h
=
0.10078
9
t
-
0.0201
8
A
-
1.0632
2
B
+
0.00331
9
C
-
0.0002
7
A
+
0.01240
4
B
+
24.3492
C
R
C
=
0.969284
,
R
2
=
0.939511
,
Adj.
R
2
=
0.931789
,
n
=
99
(7)
S
1
10 h
=
0.12567
2
t
-
0.064
8
A
-
0.8473
1
B
+
0.00274
7
C
-
0.0003
8
A
+
0.00424
9
B
+
22.84727
C
R
C
=
0.992215
,
R
2
=
0.983514
,
Adj.
R
2
=
0.982528
,
n
=
99
.In study, the SRA shows that effect ofB and C is more significant while effect of D is least effective (see (5)). After study it is found that effect of B is most significant while effect of A is negligible (see (6)) and after 10 h it was found that effect of B and C2 is significant while effect of D, A, and E is negligible (see (7)).The abbreviated kinetic study of 1st, 2nd, and 4th batch with nine formulations each has been discussed to identify the optimized formulation.


## 3.2.13. 2nd Batch

(8)
S
2
2 h
=
0.221171
t
+
0.182557
A
+
0.178385
B
+
0.000876
C
-
0.00118
D
-
0.00112
E
+
4.82514
C
R
C
=
0.942855
,
R
2
=
0.887506
,
Adj.
R
2
=
0.855448
,
n
=
27
(9)
S
2
5 h
=
0.154039
t
+
0.006524
A
+
0.210632
B
+
0.0015
C
-
0.00157
D
-
0.00082
E
+
10.87422
C
R
C
=
0.971366
,
R
2
=
0.944552
,
A
d
j
.
R
2
=
0.946346
,
n
=
54
.The SRA shows that the effect ofA, B, and C is positive while effect of D and E is negative. (10)S210 h=0.114934t+0.098263A+0.429285B-0.00038C-0.00055D-0.00226E+8.20543CRC=0.969798,R2=0.942314,Adj.R2=0.936522,n=99.By applying the LRA to DR, it was found that impact ofD is most critical while impact of E is minimally noteworthy (see (8)) and then impact of D and B is most huge while impact of A is irrelevant (see (9)). Also after 10 h, impact of A, C, and D was observed to be most critical while impact of X22 is most slightly noteworthy. The relapse examination was again led on DR after 10 h and it was found that impact of X2 and X12 is most critical while impact ofC is insignificant (see (10)).


## 3.2.14. 3rd Batch

(11)
S
3
=
0.256874
t
-
0.26814
A
-
0.16514
B
+
0.000262
C
+
0.000548
D
+
0.006923
E
+
35.92275
C
R
C
=
0.976225
,
R
2
=
0.957119
,
Adj.
R
2
=
0.944255
,
n
=
27
(12)
S
3
=
0.162851
t
-
0.31975
A
-
0.00574
B
+
0.00088
C
+
0.00053
D
+
0.002823
E
+
44.42843
C
R
C
=
0.976629
,
R
2
=
0.961753
,
Adj.
R
2
=
0.954716
,
n
=
54
(13)
S
3
10 h
=
0.09369
3
t
-
0.2486
2
A
+
0.19663
1
B
-
0.0002
4
C
+
0.00065
4
D
+
0.00037
6
E
+
46.55
C
R
C
=
0.951827
,
R
2
=
0.906334
,
Adj.
R
2
=
0.901202
,
n
=
99
.After drug release study, SRA shows that effect of A and B is more significant while effect of C is negligible (see (11)). The effect of A is most significant but effect of E and B is negligible (see (12)). For 10 h study, effect of A and D is significant while effect of B and C and E is negligible (see (13)).


## 3.2.15. 4th Batch

(14)
S
4
=
0.179377
t
-
0.06247
A
-
0.41753
B
+
0.001268
C
-
5.5
E
-
05
D
+
0.001364
E
+
24.487
C
R
C
=
0.911831
,
R
2
=
0.818584
,
Adj.
R
2
=
0.778545
,
n
=
27
(15)
S
4
=
0.145528
t
-
0.01115
A
-
0.46955
B
+
0.000945
C
-
0.00043
D
+
0.002388
E
+
27.266
C
R
C
=
0.985076
,
R
2
=
0.952796
,
Adj.
R
2
=
0.945592
,
n
=
99
(16)
S
4
10 h
=
0.12160
4
t
-
0.0789
6
A
-
0.4524
7
B
+
0.00167
2
C
-
0.0002
5
D
+
0.00267
8
E
+
37.27
C
R
C
=
0.977618
,
R
2
=
0.954764
,
Adj.
R
2
=
0.953648
,
n
=
99
.In drug release study for 2 h, the effects of factorsB and C are negligible but, in case of drug release study for 5 h, the effects of the factors B and C become significant (see (14)). Similarly in drug release study for 5 h, the effects of B and D become prominently significant, while, in studying the drug release for 10 h, effects of E and A are negligible (see (15)). The effects of all variables are mentioned in Table 10.Table 10
Standard regression analysis (SRA) and effects of variables on DR profiles.
Batches
Time
Effects
1st
10 h
B (0.96305) > D2 (0.60631) > C (0.511874) > E (0.467321) > A (0.15455)
B (0.48382) > C (0.264335) >D (0.2517) > E (0.238427) > A (0.05542)
B (0.95425) > D2 (0.60641) > C (0.510984) > E (0.468321) > A (0.15445)
2nd
10 h
D (0.78219) > A (0.437285) > C (0.319727) > B (0.257393) > E (0.19455)
D (0.23438) > B (0.208289) > C (0.147678) > E (0.10895) > A (0.010716)
B (0.322212) > D (0.24913) > E (0.20344) > A (0.122779) > C (0.07176)
3rd
10 h
A (0.68074) > D (0.476) > E (0.316554) > B (0.12522) > C (0.055576)
A (0.51543) > D (0.320761) > C (0.112551) > E (0.084883) > B (0.00452)
A (0.36251) > D (0.280217) > B (0.085294) > C (0.03699) > E (0.017337)
4th
10 h
B (0.37577) > C (0.2633) > A (0.18647) > D (0.04554) > E (0.064829)
B (0.24738) > D (0.18553) > C (0.120534) > E (0.075439) > A (0.01652)
B (0.17275) > A (0.10162) > E (0.056415) > C (0.054636) > A (0.04857)


## 4. Conclusion

The Psyllium husk is now an established herbal medicine for colon malignancy and heart ailments. The husk and its fractions are suitable as matrix for designing the floats due to having desirable pharmaceutical properties for designing controlled drug delivery system. The 32 full factorial designs reveal that every independent variable of each formulation affects drug release rate of floats. The SRA (standard regression analysis) indicates that the level of parameters A and B, in regression equations, plays vital role in procuring the coveted outcomes. The sign of variables in equation might be negative (decrease in the value of that variable) or positive (increase in the value of that variable) so their sums are adjustable to achieve the desired drug release profile. Excipients used in this work are mostly herbal in nature which are enlisted in GRAS (Generally Recognized as Safe) list and found to be biocompatible. The thermal study divulges that all polymer fractions are thermally stable and can withstand the environmental changes. The GPC (gel permeation chromatography) analysis indicates that polydispersity index (PDI) values of the different fractions have values between 2 and 3.75 but this difference has no significant effects on the drug release rate or disintegration of floats. Due to having tunable drug release profile and therapeutic significance, Psyllium husk and its fractions have potential utility in biomedical fields and food industries including designing of capsule shells and tablet matrix.



---

*Source: 6924601-2017-12-14.xml*