CHAPTER.6 FORMULATION AND EVALUATION OF FAST DISSOLVING TABLET OF ONDANSETRON HCl

Transcription

1 CHAPTER.6 FORMULATION AND EVALUATION OF FAST DISSOLVING TABLET OF ONDANSETRON HCl 6.1 Taste making of ondansetron HCl Development and characterization of taste masked granules of ondansetron HCl Preparation of drug polymer complex The drug polymer complex (DPC) was prepared by using different ratio (1:1, 1:3, 1:5) of ondansetron HCl and Eudragit EPO. A gel containing ondansetron HCl and Eudragit EPO was prepared by gradual addition of 10 % ethanol using a mechanical stirrer in a glass beaker. The gel was manually extruded through a syringe. The ethanol was evaporated by keeping the extrudates overnight at room temperature. The solidified gel in the shape of string was crushed and sieved through sieve sized 280 μm to make the granules Characterization of drug polymer complex In-Vitro taste evaluation The drug polymer complex (DPC) containing 10 mg of ondansetron HCl were mixed with 10 ml of phosphate buffer (ph 6.8)in a 10 ml syringe by revolving the syringe end to end for 60 seconds. Thereafter solution of ondansetron HCl was filtered and amount of drug release was determined spectrophotometrically at 249 nm. Drug content DPC equivalent to 10 mg of drug was stirred by using magnetic stirrer with 100 ml of 0.1 N HCl for 60 minutes, till the entire drug leached out from complex, than the solution was filter through whatman filter paper. Further solution was diluted with 0.1 N HCl and the drug content was determined spectrophotometrically at 249 nm. Thermal analysis DSC analysis was performed using Netzsch DSC 204, Tokyo, Japan. The samples were heated in a sealed aluminium pans at a rate of 100 C per min in a 30 to 3000 C temperature under nitrogen flow of 40 ml/min. K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 170

2 Fourier Transform Infrared (FTIR) Spectroscopy FTIR spectra were obtained on Shimadzu FTIR Model 8400-S spectrometer. The spectra was recorded as a dispersion of the sample in potassium bromide in IR disk (2 mg sample in 200 mg KBr) with the scanning range of 400 to 4000 cm-1 and the resolution was 1 cm -1. X-ray Diffraction (XRD) studies X-ray Diffraction analysis was carried out to evaluate the degree of crystallinity. The pure ondansetron HCl, pure Eudragit EPO, and drug polymer complex (1:5) were subjected topowder XRD (P.W. 1729, X-Ray Generator, Philips, Netherland) at 2θ angles between 20 0 and 38 0 in increments of K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 171

3 6.2 Design and optimization of using different ratio of mcc and lactose Preparation of tablet Fast dissolving tablets of ondansetron HCl were prepared by direct compression method. All the raw materials were passed through a # 60 sieve prior to mixing. Drug polymer complex (1:5), containing amount equivalent to 10 mg of ondansetron HCl, was mixed with the other excipients. The powder blend was lubricated with magnesium stearate and compressed on a 10 station mini press tablet machine (CPMD 3-10, Chamunda Pharma Machinery Pvt. Ltd., Ahmedabad, India.) equipped with 9 mm concave punch. Composition of tablets is shown Table 6.1. Table 6.1 Composition of fast dissolving tablet Ingredient Value (%) DPC 24 Ac-Di-Sol 2-6 MCC 0-70 Lactose Mag. Stearate 1.5 Saccharine sodium 0.6 Tablet weight=250 mg Optimization of formulation Optimization technique based on response surface methodology was utilized. Response surface methodology can be defined as a statistical method that uses quantitative data from appropriate experiments to determine and simultaneously solve multivariate equations. It is generally used to determine the optimum combination of factors that yield a desired response and describes the response near the optimum. This methodology was used in the present study to optimize the variables affecting the formulation. K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 172

4 Statistical design A randomized 3 level full factorial design using two factors was adopted to systematically study the formulation of. A total of 12 experimental run with 3 centre points were performed at all possible combination. The independent variable, were selected on the basis of trials taken during preliminary batches. The disintegration time and hardness were selected as dependent variable. Different variables used in full factorial design are shown in Table 6.2. Matrix design for different experimental run is shown in Table 6.3. Analysis of response Response were analysed by Analysis of variance (ANOVA), to identify the insignificant factors, which were then removed from the full model to generate the reduced model. Independent variables-factor Table 6.2 Variables in 3 level full factorial design Low (-1) Levels (%) Middle (0) X 1 = MCC in MCC- Lactose combination X 2 = Ac-Di-Sol Concentration Dependent variable- Response Y1= Disintegration time (seconds) Y2= Hardness (kg/cm 2 ) High (+1) K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 173

5 Batch Table 6.3 Layout of full factorial design X 1 : %MCC in Lactose/MCC:Lactose combination (%) X 2 : Ac-Di-Sol(%) OH OH OH OH4 0 0 OH5 0-1 OH OH7 0 0 OH8 1 1 OH9-1 1 OH OH OH Validation of statistical model Levels of factors were selected at different points and responses predicted by the statistical models were calculated. Tablets were prepared using these levels and responses were measured practically. The predicted responses were compared against observed responses and closeness between them was checked. Response surface plots Response surface plots were generated for each response to study the effect of both factors on each response. Different constraints were applied (Table 6.4) and on the basis of confirmation report (Two-sided, prepared. Confidence = 95%, n = 1) as shown in Table 6.5, tablets were K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 174

6 Name Table 6.4 Constraints Goal Lower Limit X 1 :MCC in Lactose/MCC:Lactose comination is in range -1 1 Upper Limit X 2 :Ac-Di-Sol is in range -1 1 DT Target= Hardness Target= Table 6.5 Confirmation Report (Two-sided, Confidence = 95%, n = 1) Factor Name Level X 1 MCC in Lactose/MCC:Lactose comination Low Level High Level Std. Dev. Coding Actual X 2 Ac-Di-Sol Actual Preparation of optimized batch (OFDT1) Optimized batch was prepared as method discussed earlier. The formula for optimized batch (DFDT2) shown in Table 6.6. Table 6.6 Composition of optimized Batch Ingredients Quantity (%) Quantity (mg) DPC Ac-Di-Sol MCC:lactose 66: :58.9 Mag. Stearate Saccharine sodium Tablet weight= 250 mg K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 175

7 6.3 Design and optimization of fast dissolving tablets of ondansetron HCl using vacuum-drying approach Preparation of tablets Composition of fast dissolving tablets is mentioned in Table 6.7. All the raw materials were passed through a 60 # sieve prior to mixing. Drug polymer complex (1:5), containing amount equivalent to 10 mg of ondansetron HCl, was mixed with the other excipients. The powder blend was lubricated with magnesium stearate and compressed on a 10 station mini press tablet machine (CPMD 3-10, Chamunda Pharma Machinery Pvt. Ltd., Ahmedabad, India.) equipped with 9 mm concave punch. The tablets were dried in a vacuum oven for 4 h at a temperature of 60 0 C and at a pressure of 300 mm Hg. Table 6.7 Composition of fast dissolving tablet Ingredients Quantity (%) DPC 24 Camphor 0-40 Mannitol Mag. Stearate 1.5 Lactose q. s. to 250 Tablet weight=250 mg Optimization of formulation Optimization technique based on response surface methodology was utilized. Response surface methodology can be defined as a statistical method that uses quantitative data from appropriate experiments to determine and simultaneously solve multivariate equations. It is generally used to determine the optimum combination of factors that yield a desired response and describes the response near the optimum. This methodology was used in the present study to optimize the variables affecting the formulation. Statistical Design A randomized 3 level full factorial design using two factors was adopted to systematically study the formulation of. A total of 12 experimental K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 176

8 run with 3 centre points were performed at all possible combination. The independent variable, were selected on the basis of trials taken during preliminary batches. The disintegration time and hardness were selected as dependent variable. Different variables used in full factorial design are shown in Table 6.8. Matrix design for different experimental run is shown in Table 6.9. Independent variables-factor Table 6.8 Variables in 3 level full factorial design Low (-1) Levels (%) Middle (0) High (+1) X 1 = Mannitol X 2 = Camphor Dependent variable- Response Y 1 = Disintegration time (seconds) Y 2 = Hardness (kg/cm 2 ) Table 6.9 Layout for full factorial design Run X 1 : Mannitol (%) X 2 : Camphor (%) OV OV OV OV4 0-1 OV5 1 0 OV6 1 1 OV7 0 0 OV8 0 1 OV9-1 1 OV OV OV K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 177

9 Analysis of response Response were analysed by Analysis of variance (ANOVA), to identify the insignificant factors, which were then removed from the full model to generate the reduced model. Validation of statistical model Levels of factors were selected at different points and responses predicted by the statistical models were calculated. Tablets were prepared using these levels and responses were measured practically. The predicted responses were compared against observed responses and closeness between them was checked. Response surface plots Response surface plots were generated for each response to study the effect of both factors on each response. Different constraints were applied (Table 6.10) and on the basis of confirmation report (Two-sided, prepared. Confidence = 95%, n = 1) as shown in Table 6.11, tablets were Table Constraints Name Goal Lower Limit Upper Limit X 1 :Mannitol In range -1 1 X 2 :Camphor In range -1 1 Hardness Target= DT Target = Table Confirmation Report (Two-sided, Confidence = 95%, n = 1) Factor Name Level Low Level High Level Std. Dev. Coding X 1 Mannitol Actual X 2 Camphor Actual K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 178

10 6.3.3 Preparation of optimized batch (OFDT2) Optimized batch was prepared as method discussed earlier. The formula for optimized batch (DFDT2) shown in Table Table 6.12 Composition of optimized batch (OFDT2) Ingredients Quantity (%) Quantity (mg) DPC Camphor Mannitol Mag stearate Lactose Qs to 100 % Qs to 100 % 6.4 Evaluation of fast dissolving tablets of ondansetron HCl Pre-compression characterization The quality of tablet was generally dictated by the quality of physicochemical properties of blends. There were many formulations and process variables involved in mixing steps all these can affect the characteristic of blend produced. The characterization parameters for evaluating the flow property of mixed blends includes bulk density, tapped density, Hausner s ratio, compressibility index and angle of repose. Bulk density Apparent bulk density (ρ b ) was determined by pouring the blend in to a graduated cylinder. The bulk volume (V b ) and weight of powder (M) was determined [ ]. The bulk density was calculated using the formula:- Tapped density The measuring cylinder containing a known amount of tablet blend was tapped 100 times using density apparatus. The constant minimum volume (V t ) occupied in the cylinder K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 179

11 after tapping and the weight (M) of the blend was measured [ ]. The tapped density (ρ t ) was calculated using formula Compressibility index Compressibility is the simplest way for the measurement of powder flow property. It is an indication of ease with which a material can be induced to flow [ ]. it is expressed as compressibility index (I), which can be calculated as follows:- Where, ρ t = Tapped density; ρ b = bulk density Limits for compressibity index are shown in Table Table 6.13 Compressibility index as an indication of powder flow properties Compressibility Index (%) Type of flow >12 Excellent Good Fair to passable Poor Very poor >40 Extremely poor Hausner s ratio Hausner s ratio (HR) is an indirect index of ease of powder flow. It was calculated by the following formula:- Where, ρ t = Tapped density; ρ b = bulk density Lower Hausner s ratio (<1.25) indicates better flow properties than higher ones [160] K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 180

12 Angle of repose Angle of repose was determined using the funnel method. The blend was poured through a funnel that can be raised vertically until a specified cone height (h) was obtained. Radius was measured and angle of repose was determined using the formula [ ]. Therefore, ( ) Where, θ is angle of repose; h is the height of cone; r is radius of cone. Limits of angle of repose are shown in Table Table 6.14 Angle of repose as an indication of powder flow properties Angle of repose(θ) Type of flow <25 Excellent Good Passable >40 Very poor Post compression characterization After compression, the prepared tablets were evaluated for organoleptic characteristics like color, taste, odor, diameter, thickness and physical characteristics like hardness, friability, disintegration time, wetting time. General appearance The general appearance of a tablet, its visual identification and over all elegance is essential for consumer acceptance. This include tablet s size, shape, odor, color, taste, surface texture etc [167] K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 181

13 Tablet thickness Tablet thickness is an important characteristic in reproducing appearance and also in counting by suing filling equipment. Some filling equipment utilizes the uniform thickness of the tablets as a counting mechanism. Thickness of tablets was recorded using micrometer (Mityato, Japan). Weight variation The weight variation test would be satisfactory method of determining the drug content uniformity. As per USP [168], twenty tablets were taken and weighted individually. Average weight was calculated and compared the individual weight to average weight. Weight variation limits are given in Table Table 6.15 Weight variation limit for tablets as per USP Average weight of tablet (mg) Maximum % difference allowed 130 or less More than Hardness Hardness of the tablet is defined as the force applied across the diameter of the tablet in order to break the tablet. The resistance of the tablet to chipping, abrasion or breakage under condition of storage transformation and handling before usage depends on its hardness. Hardness of the tablet of each formulation was determined using Pfizer Hardness taster [ ]. Friability Friability of tablets was determined using Roche friabilator apparatus. This device subjects the tablet to the combined effect of abrasion and shock in a chamber, revolving at 25 rpm and dropping the tablet at the height of 6 inch in each revolution. Pre-weighed sample of tablets was placed in the friabilator and were subjected to 100 revolutions. Tablets were dedusted using a soft muslin cloth and reweighed. The friability (F %) was determined by the formula K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 182

14 Where, W 0 is the initial weight of the tablets before the test and W is the weight of the tablet after test [167,170] Drug content The test is obligatory for tablets containing less than 10 mg or less than 10 % w/w of active ingredient. This test was performed as per Indian Pharmacopoeia, A tablet was crushed and dissolved 1 ml of dilute hydrochloric acid and 30 ml of distilled water. This solution was shaken for 15 min. the volume of this solution was made up to 50 ml with distilled water and centrifuged. Five milliliters of the clear supernatant was mixed with 10 ml of 0.1 N HCl, and made up to 100 ml with distilled water. The absorption of the solution was determined spectrophotometrically at 249 nm. The same procedure was followed for another nine tablets. Disintegration time The disintegration time was measured using a modified disintegration method. According to this method, a petri dish of 10-cm diameter was filled with 10 ml of phosphate buffer ph 6.8, the tablet was carefully placed at the center of the petri dish, and the time necessary for the complete disintegration of the tablet into fine particles was noted as disintegration time [171]. Wetting time A piece of tissue paper folded twice was kept in a culture dish (internal diameter 5.5 cm) containing 6 ml of purified water. A tablet having a small amount of amaranth powder on the upper surface was placed on the tissue paper. The time required to develop a red colour on the upper surface of the tablet was recorded as the wetting time [172]. Dissolution studies Tablet test condition for the dissolution rate studies were used according USP specification using USP 24, type II apparatus. The dissolution medium was 900 ml of 0.1 N HCl (ph 1.2). The temperature of the dissolution medium and the rate of agitation were maintained at 37± 0.50 C and 50 rpm respectively. Aliquots of 10 ml of dissolution medium were withdrawn at specific time interval and the volume replaced by fresh dissolution K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 183

15 medium, pre warmed to 37± 0.50 C. The drug concentration was determined spectrophotometrically at 249nm using UV spectrophotometer (Shimadzu S 1800, Japan). Scanning electron microscopy (SEM) The optimized tablet was also observed by scanning electron microscope (ESEM TMP with EDAX, Philips, Holland). Pictures were taken at an excitation voltage of 30 kv and a magnification of 120 X. 6.5 In vivo study for optimized ondansetron HCl fast dissolving tablet Pharmacokinetic Studies Sample preparation In a 10 ml capacity glass tube, 1 ml plasma was mixed with 50 µl of saturated sodium carbonate solution and 5 ml of dichloromethane and mixture was stirred by rotary mixer for 15 min at room temperature. The mixture was centrifuged for 5 min at 5000 rpm and 4.5 ml of the organic phase was transferred into another test tube and evaporated to dryness at 40 C under a stream of nitrogen. The residue was reconstituted in 100 µl of mobile phase and a volume of 20 µl was injected into the HPLC for analysis [204]. Seven-week-old male wistar rats were used in the present experiment. Their mean weight was ± 8.96 g in the range of g. Animals were housed in a room maintained on a 12 hrs light/dark cycle at 23±2 C with free access to food and water.all the animal experiments were performed according to the guideline of local animal ethical committee (Ref no- BU/BT/185/11-12). The Test (OFDT 1) formulation and Reference (Ondem MD) were administered to the rats by gastric intubation method after calculating the animal dose [174]. Blood samples were withdrawn after 0, 0.50, 1, 2, 4 and 6 hrs. from different animals at each time (n=3). Pharmacokinetics and statistical analyses The following pharmacokinetics parameters were calculated using noncompartmental methods: area under the plasma concentration time curve from zero to the last measurable Ondansetron concentration sample time (AUC 0-t ), area under the plasma concentration time from zero extrapolated to infinite time AUC 0-, maximum plasmatic drug concentration (C max ) and time to reach C max (t max ), terminal rate constant (K el ) and terminal half-life (t 1/2 ). C max and t max were obtained directly from the concentration time K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 184

16 curve. AUC 0 t was calculated using the linear trapezoidal method. K el was calculated by applying a log-linear regression analysis to at least the last three quantifiable concentrations of ondansetron; t 1/2 was calculated as 0.693/K el [175]. For the purpose of bioequivalence analysis AUC 0 t, AUC 0 and C max were considered as primary variables. Bioequivalence between the products was determined by calculating 90% confidence intervals (90% CI) for the ratio of C max, AUC 0 t and AUC 0 values for the test and reference products, using logarithmic trans-formed data. Analysis of variance (ANOVA) was used to assess product, group and period effects. The products were considered bioequivalent if the 90%CI for AUC 0 t andc max fell within % Pharmacodynamic Study Method As discussed earlier General procedure Behavioral testing was always conducted during the same period of the day. The procedure was performed in three consecutive phases. Pre-conditioning phase This phase consisted of three consecutive days. Animals were subjected individually to the apparatus in untreated condition with the guillotine doors open for 15 min per trial. After the third baseline trial the preference for one of the two compartments was calculated by taking the mean time spent in the compartments over the three baseline trials. Conditioning phase The mice were assigned randomly to the treatment groups (vehicle, lithium sulphate (160 mg/kg), Zofer MD (10 mg/kg), Ondem MD (10 mg/kg) and OFDT1 (10 mg/kg) with lithium sulphate, in a volume of 5 ml/kg body weight). The mice were treated with the conditioning drug on one day and the vehicle on the alternate day. Each mice was exposed to an equal number of drug pairings with both the compartments. The treatment lasted for 8 days (four pairings). K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 185

17 Post-conditioning phase On the day following the conditioning phase, drugs were not administered to animals and they were placed in the apparatus with the doors open and the time spent in the preferred compartment was recorded during the 15 min test session. Experimental set up for the study is shown in Table Table 6.16 Experimental setup for ondansetron HCl Group Group I Group II Group III Group IV Group V Treatment Treated with vehicle Treated with Lithium sulphate Treated with Comp I (Zofer- MD, Sun Pharmaceutical Ltd) + Lithium sulphate Treated with Comp II (Ondem-MD 8, Alkem Laboratories Ltd.) + Lithium sulphate Treated with Comp III (OFDT 1) + Lithium sulphate 6.6 Stability study Selection of fast dissolving tablets The results of tablet characterizations of different batches were compared and optimized batch OFDT1 and OFDT2 were selected for stability studies. The optimized fast dissolving tablets were packed in wide mouth air tight glass container. Stability studies were carried out according to ICH and WHO guidelines as shown in Table (6.17). Table 6.17 Conditions as Per ICH Protocol Time (Month) Conditions C ± 2 0 C and 60 ± 5% RH 40 0 C ± 2 0 C and 75 ± 5% RH 6 K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 186

18 6.6.2 Physical and chemical stability The tablets are withdrawn after end of period and analysed for physical characterization and drug content. The drug content data obtained was fitted in to first order equation to determine the kinetics of degradation. Accelerated stability data were plotted according to Arrhenius equation to determine the shelf life at 25 0 C.[ ] K= Ae -Ea/RT T 10% = 0.104/ K Where, K is specific reaction constant; A is Arrhenius factor; T is absolute temperature; R is Gas constant; Ea is energy of activation Comparison of dissolution profile In recent years, FDA has placed more emphasis on a dissolution profile comparison in the area of post-approval changes biowaivers. A dissolution profile comparison between pre-change and post-change product or with different strength, helps assure similarity in product performance and signal bioequivalence. Among several methods investigated for dissolution profile comparison, f2 is the simplest one. f2= 50* log {*1 + (1/n) t=1 n (R t - T t )2] -0.5 * 100} Where R t and T t are the cumulative percentage drug dissolved at each of the selected n time points of the reference (before storage) and test (after storage) product respectively. When the two profile are identical, f2 = 100. An average difference of 10% at all measured time point s results in f2 value 50. FDA sets a standard of f2 value in between 50 to 100; indicate similarity between two dissolution profiles. [ ] K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 187

19 RESULT and DISCUSSION 6.7 Characterization of taste masked granules of ondansetron HCl In vitro taste evaluation In vitro taste evaluation of different ratios of taste masked drug polymer complex (DPC) was determined in phosphate buffer (ph 6.8) and in 0.1 N HCl (ph 1.2). Results are shown in Table Table 6.18 In-Vitro taste evaluation Drug Polymer Ratio in DPC % Drug Dissolve in Phosphate Buffer (ph 6.8) % Drug Content in 0.1 N HCl (ph 1.2) 1:1 2.0± ±0.25 1:3 0.82± ±0.41 1:5 0.41± ±0.08 Results are the mean of 3 observations ± SD Percentage drug content of drug polymer complex in 0.1 N HCl (ph 1.2) was found to be to The drug release in phosphate buffer (ph 6.8) was found least with drug polymer complex ratio (1:5). It showed that appreciable amount of drug was not released as the drug particles were coated by the polymer. Thus complete taste masking was achieved. DPC (1:5) was selected as an optimized ratio for the development of formulation. Thermal analysis Figure ( ) represented DSC Thermogram of ondansetron HCl, Eudragit EPO, drug-polymer physical mixture and drug polymer complex. K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 188

20 Figure 6.1 DSC Thermogram of ondansetron HCl Figure 6.2 DSC Thermogram of Eudragit EPO K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 189

21 Figure 6.3 DSC Thermogram of physical mixture Figure 6.4 DSC Thermogram of drug polymer complex K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 190

22 Thermal profile of pure product exhibited a single endothermic effect corresponding to the melting of ondansetron HCl (T fus C, H fus J/g) while amorphous nature of polymer. The DSC curve of physical mixture showed progressive broadening and lowering of drug melting temperature and concomitant reduction of its enthalpy. In DSC curve of DPC total disappearance of drug melting temperature. These finding suggest the formation of new solid phase with lower degree of crystallinity. FT-IR spectroscopy Figure ( ) represented FT-IR spectroscopy of ondansetron HCl, Eudragit EPO, drug-polymer physical mixture and drug polymer complex. Interpretation of FT-IR is shown in Table %T Ondansetron HCl /cm Figure 6.5 FT-IR spectra of ondansetron HCl K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 191

23 Figure 6.6 FT-IR spectra of Eudragit EPO Figure 6.7 FT-IR spectra of physical mixture K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 192

24 Figure 6.8 FT-IR spectra of DPC The FTIR spectrum of drug and polymer showed no significant shift or reduction in intensity of peaks of ondansetron HCl. However, the FT-IR spectrum of DPC was found to exhibit some significant difference in the characteristic peaks of ondansetron HCl, revealing modification of drug environment. As shown in Figure 6.5, a broad band of bonded OH of ondansetron HCl was observed from 3412 to cm -1. DPC showed the absence of peak at 3412 to cm -1 suggest the formation of complexation of drug with polymer [205]. Table 6.19 Interpretation of FT-IR spectra Functional group Band width OH stretching - C=O stretching cm -1 C-C stretching cm -1 C=N stretching cm -1 K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 193

25 X- Ray Diffraction Figure ( ) represented the X- ray diffraction pattern of ondansetron HCl, Eudragit EPO, drug-polymer physical mixture and drug polymer complex. Figure 6.9 X-ray diffractogram of ondansetron HCl Figure 6.10 X-ray diffractogram of Eudragit EPO K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 194

26 Figure 6.11 X-ray diffractogram of physical mixture Figure 6.12 X-ray diffractogram DPC K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 195

27 The x-ray diffractogram of ondansetron HCl confirms its crystalline nature, as evidenced from the number of sharp and intense peak, (Figure 6.9). The diffractogram of polymer (Eudragit EPO) showed diffused peak, indicating the amorphous nature (Figure 6.10) while the diffraction pattern of drug polymer physical mixture showed simply the sum of characteristic peaks of pure drug and the diffused peaks of polymer, indicating presence of drug in crystalline state. However the diffraction pattern of DPC represents complete disappearance of crystalline peaks of drug (Figure 6.12) especially those situated between 20 0 and 60 0 (2θ). These finding suggest the formation of new solid phase with a lower degree of crystallinity due to complexation which coincides with the conclusion of Fernandes and Veiga [206]. 6.8 Evaluation of prepared using different ratio of mcc and lactose Characterization of tablet The preliminary trial batches were prepared using the formula given in Table 6.1 by direct compression technique in order to study the effect of superdisintegrants and diluents on the disintegration time and hardness. Results of the different batches showed a wide variation in the disintegration time (22-51 seconds) and hardness ( kg/cm 2 ). On the basis of these results, dependent and independent variable were selected and to systematically study, different factorial batches (OH1 to OH12) were prepared and evaluated. Formulations OH1 to OH12 were characterized for different parameters as shown in Table (6.20) Friability of all the formulation was below 1% indicates that the tablets had good mechanical resistance. Good uniformity of drug content was observed in all the formulations. The weight variation results revealed that average % deviation of 20 tablets of each formulation was less than ±7.5%, which provide good uniformity in all formulations. K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 196

28 Table 6.20 Characterization of fast dissolving tablets Parameters Thickness Weight Friability Drug Content Wetting time Formulations (mm) (mg) (%) (%) (Seconds) OH1 3.8± ± ± ± ±1.29 OH2 3.9± ± ± ± ±1.42 OH3 3.9± ± ± ± ±1.20 OH4 3.9± ± ± ± ±1.38 OH5 OH6 OH7 3.9± ± ± ± ± ± ± ± ± ± ± ± ± ± ±1.14 OH8 3.9±0.50 OH9 3.9±0.30 OH10 3.8±0.21 OH11 3.8± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±1.19 OH12 3.9± ± ± ± ±1.28 Data are expressed as mean S.D. (n = 3) Statistical design A statistical model incorporating interactive and polynomial terms was used to evaluate the responses. Y= b 0 + b 1 X 1 + b 2 X 2 + b 12 X 1 X 2 + b 2 1 X b 2 2 X b 2 1 b 2 X 2 1 X 2 Y is the measured response associated with each factor-level combination, b 0 is the arithmetic mean response of the total 12 runs ; X 1 and X 2 are the factors studied, b i is the regression coefficient for factor X i computed from the observed response Y. The main effects (X 1 and X 2 ) represent the average result of changing one factor at a time from its low to high value. The interaction terms (X 1 X 2 ) show how the response changes when two 2 factors are simultaneously changed. The polynomial terms (X 1 and X 2 2 ) are included to K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 197

29 investigate nonlinearity. Two conclusions could be drawn from the equation: (1) a coefficient with a negative sign increases the response when the factor level is decreased from a higher level to a lower level, and (2) the factor with a higher absolute value of the coefficient and a lower significance value P has a major effect on the response variables. The dependent variables, disintegration time and hardness showed a wide variation (Table 6.21). The data clearly indicates that the response variables are strongly dependent on the selected independent variables. The high values of the correlation coefficient for disintegration time and the hardness indicate a close fit. Table 6.21 Results of each experimental run in full factorial design Batch Disintegration time (Y1) Response Hardness (Y2) OH1 30± ±0.29 OH2 30± ±0.17 OH3 34± ±0.44 OH4 30± ±0.98 OH5 35± ±1.0 OH6 30± ±0.29 OH7 30± ±0.37 OH8 22± ±0.59 OH9 32± ±0.71 OH10 26± ±0.33 OH11 29± ±0.20 OH12 26± ±0.51 Data are expressed as mean S.D. (n = 3) The fitted equations (full and reduced) relating the responses to the transformed factor are shown in Table Analysis of variance (ANOVA) was carried out to identify the K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 198

30 insignificant factors, which were then removed from the full model to generate the reduced model. Results of ANOVA is represented in Table ( ). Table 6.22 ANOVA for response surface reduced cubic model for disintegration time Response model Sum of square Df Mean square F value P value R 2 Adeq. Precision DT < Response model Table 6.23 ANOVA for response surface reduced quadratic model Sum of square Df Mean square F value P value R 2 Adeq. Precision Hardness < The Model F-value of and for didintegration time and hardness implies the model is significant. There is only a 0.01% chance that a "Model F-Value" this large could occur due to noise. Value of prob>f less than indicate that model term are significant. Values greater than indicate the model term are not significant. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable, ratio of and indicates an adequate signal. This model can be used to navigate the design space. Table 6.24 Summary of result of regression analysis Model* (DT) b 0 b 1 b 2 b 12 b 1 2 b 2 2 b 1 2 b 2 FM RM Model* (Hardness) b 0 b 1 b 2 b 12 b 1 2 b 2 2 b 1 2 b 2 RM *FM indicate full model; RM indicate reduced model For disintegration time, the coefficients of X 1 and X 2 that is, b 1 and b 2 respetively, bear a negative sign, thus on increasing the concentration of MCC in MCC-Lactose K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 199

31 combination and conentration of Ac-Di-Sol, a decrease in disintegration time is observed. For hardness, the coefficients of X 1 and X 2 that is, b 1 and b 2 respetively, bear a positive sign thus on increasing the concentration of MCC in MCC-Lactose combination and conentration of Ac-Di-Sol, a increase in hardness is observed. Validation of statistical model To validate the statistical model checkpoint batches, CP1 and CP2 were prepared according to the formula. Comparison of predicted values and experimental values for check point batches are shown in Table From the response surface plot (Figure ) and the calculations from the statistical equation obtained by regression, the results revealed the close match of the experimental results. Thus, we can conclude that the statistical model is mathematically valid. Overlay plot for hardness and DT is shown in Figure Table 6.25 Comparison of predicted values and experimental values for check point batches Formulation code CP1 X 1 = +0.5 X 2 = 1 CP2 X 1 = +1 X 2 = +0.5 Predicted Values (DT) Experimental Values (DT) Residual Predicted Values (Hardness) Experimental Values (Hardness) Residual ± ± ± ± The best batch was selected after considering the requirements of an FDT. To full fill these requirements, disintegration time and hardness was targeted to 25 s and 4.5 kg/cm 2 respectively. The batches dissolution rates were also considered and batches with higher dissolution rates were given priority. Different constraints were applied; responses were predicted at 95% CI and they are found in range, which showed the robustness of the statistical model (Table 6.26). Further, solution with desirability 1 was selected, as shown in (Table 6.27). K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 200

32 D T Table 6.26 Predicted response at 95% confidence (n=1) Response Prediction Std Dev SE (n=1) 95% PI low 95% PI high DT Hardness Table 6.27 Predicted desirability Number MCC Ratio Ac-Di-Sol DT Hardness Desirability Design-Expert Software Factor Coding: Actual DT Design points above predicted value Design points below predicted value X1 = A: MCC RATIO X2 = B: Ac-Di-Sol B: Ac-Di-Sol A: MCC RATIO Figure 6.13 Response surface plot for disintegration time K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 201

33 B : A c - D i - S o l Figure 6.14 Response surface plot for hardness Design-Expert Software Factor Coding: Actual Overlay Plot DT Hardness Design Points X1 = A: MCC RATIO X2 = B: Ac-Di-Sol Overlay Plot DT: Hardness: X1 X2 DT: Hardness: A: MCC RATIO Figure 6.15 Overlay plot for disintegration time and hardness K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 202

34 6.8.2 Characterization of optimized batch (OFDT1) To determine the suitability of the powder blend for tablet compression, optimized FDT (OFDT 1) was characterized for various flow properties as shown in Table (6.28). Sr no. Formulation Code Table 6.28 Physical properties of optimized tablet blend Bulk density (mg/ml) Tapped Density (mg/ml) Hausner s Ratio Carr s Index (%) Angle of Repose ( θ) 1 OFDT ± ± ± ± ±0.28 Data are expressed as mean S.D. (n = 3) The tablet blend showed good flow ability (angle of repose < 30 0 ).Further optimized FDT (OFDT 1) was characterized for different parameters as shown in Table (6.29). Table 6.29 Characterization of optimized tablet (OFDT 1) Parameters Thickness Diameter Weight Friability Drug Content Wetting time Formulations (mm) (mm) (mg) (%) (%) (Seconds) OFDT 1 3.9± ± ± ± ± ±0.88 Data are expressed as mean S.D. (n = 3) Friability of all the formulation was below 1% indicates that the tablets had good mechanical resistance. Good uniformity of drug content was observed in all the formulations. The weight variation results revealed that average % deviation of 20 tablets of each formulation was less than ±7.5 %, which provide good uniformity in all formulations. Comparison of predicted responses and observed values for the disintegration time and hardness (Table 6.30) were in close agreement, and the models were found to be valid. Thus, full factorial design with two factors can be successfully used to optimize the formulations. Table 6.30 Comparison of predicted responses and observed values Predicted Values (Disintegration time) Experimental Values (Disintegration time) Predicted Values (Hardness) Experimental Values (Hardness) 25± ± ± ±0.42 Data are expressed as mean S.D. (n = 3) K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 203

35 %CDR Figure 6.16 showed the in vitro drug release profile of all factorial batches and optimized batch and it was found to be more than 95% in 4 minutes than compare to 90 % in 10 minutes for marketed product (ONDEM MD8) Time (min.) OFDT1 OH1 OH2 OH3 OH4 OH5 OH6 OH7 OH8 OH9 OH10 OH11 OH12 Figure 6.16 In vitro drug release profile of OFDT1 and formulation OH1- OH Evaluation of fast dissolving tablets prepared using vacuum drying technique Characterization of tablets The preliminary trial batches were prepared using the formula given in Table 6.12 using vacuum drying technique in order to study the effect of subliming material and diluents on the disintegration time and hardness. Results of the different batches showed a wide variation in the disintegration time (5-91 seconds) and hardness (1-4.9 kg/cm 2 ). On the basis of these results, dependent and independent variable were selected and to systematically study, different factorial batches (OH1 to OH12) were prepared and evaluated. Formulations OV1 to OV12 were characterized for different parameters as shown in Table (6.31). K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 204

36 Table 6.31 Characterization of fast dissolving tablets Parameters Thickness Weight Friability Drug Content Wetting time Formulations (mm) (mg) (%) (%) (Seconds) OV1 3.9± ± ± ± ±1.48 OV2 3.9± ± ± ± ±1.59 OV3 3.8± ± ± ± ±1.28 OV4 3.9± ± ± ± ±1.39 OV5 OV6 OV7 OV8 3.9±0.58 OV9 3.8±0.39 OV10 3.9±0.71 OV11 3.9±0.28 OV12 3.8± ± ± ± ± ± ± ± ± ±1.49 4± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±0.63 Data are expressed as mean S.D. (n = 3) 99.50±1.25 5± ± ± ± ± ±1.48 7± ± ±1.29 Friability of all the formulation was below 1% indicates that the tablets had good mechanical resistance except formulations OV6, OV8 and OV9 (% friability was more than 1). Uniformity of drug content was observed in all the formulations. The weight variation results revealed that average % deviation of 20 tablets of each formulation was less than ±7.5%, which provide good uniformity in all formulations. Statistical design A statistical model incorporating interactive and polynomial terms was used to evaluate the responses. Y= b 0 + b 1 X 1 + b 2 X 2 + b 12 X 1 X 2 + b 1 2 X b 2 2 X b 1 b 2 2 X 1 X 2 2 Y is the measured response associated with each factor-level combination, b 0 is the arithmetic mean response of the total 12 runs ; X 1 and X 2 are the factors studied, b i is the regression coefficient for factor X i computed from the observed response Y. The main K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 205

37 effects (X 1 and X 2 ) represent the average result of changing one factor at a time from its low to high value. The interaction terms (X 1 X 2 ) show how the response changes when two 2 factors are simultaneously changed. The polynomial terms (X 1 and X 2 2 ) are included to investigate nonlinearity. Two conclusions could be drawn from the equation: (1) a coefficient with a negative sign increases the response when the factor level is decreased from a higher level to a lower level, and (2) the factor with a higher absolute value of the coefficient and a lower significance value P has a major effect on the response variables. A statistical model incorporating interactive and polynomial terms was used to evaluate the responses. The dependent variables, disintegration time and hardness showed a wide variation (Table 6.32) 11 s to 35 s and 1.2 to 4.9 kg/cm 2 respectively. The data clearly indicates that the response variables are strongly dependent on the selected independent variables. The high values of the correlation coefficient for disintegration time and the hardness indicate a close fit. The fitted equations (full and reduced) relating the responses to the transformed factor are shown in Table Analysis of variance (ANOVA) was carried out to identify the insignificant factors, which were then removed from the full model to generate the reduced model. Results of ANOVA are shown in Table ( ). Table 6.32 Results of each experimental run in full factorial design Batch Disintegration time (Y1) Response Hardness (Y2) OV1 35± ±1.51 OV2 23±0.57 3±1.32 OV3 23±0.73 3±1.57 OV4 33± ±1.28 OV5 28± ±1.34 OV6 8± ±1.52 OV7 23± ±1.24 OV8 11± ±1.29 K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 206

38 Response model OV9 12± ±1.47 OV10 22±0.85 3±1.19 OV11 15± ±1.28 OV12 32± ±1.30 Data are expressed as mean S.D. (n = 3) Table 6.33 ANOVA for response surface reduced cubic model for disintegration time Sum of square Df Mean square F value P value R 2 Adeq. Precision DT < The Model F-value of implies the model is significant. There is only a 0.01% chance that a "Model F-Value" this large could occur due to noise. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. The ratio of indicates an adequate signal. This model can be used to navigate the design space. Response model Table 6.34 ANOVA for response surface reduced quadratic model for hardness Sum of square df Mean square F value P value R 2 Adeq. precision Hardness < The Model F-value of implies the model is significant. There is only a 0.01% chance that a "Model F-Value" this large could occur due to noise.values of "Prob > F" less than indicate model terms are significant. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. The ratio of indicates an adequate signal. This model can be used to navigate the design space. K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 207

39 Table 6.35 Summary of result of regression analysis Model* (DT) b 0 b 1 b 2 b 12 b 1 2 b 2 2 b 1 b 2 2 FM RM Model* (Hardness) b 0 b 1 b 2 b 12 b 1 2 b 2 2 b 1 b 2 2 RM For disintegration time, the coefficients of X 1 and X 2 that is, b 1 and b 2 respetively, bear a negative sign, thus on increasing the concentration of mannitol and conentration of camphor, a decrease in disintegration time is observed. For hardness, the coefficients of X 1 and X 2 that is b 1 and b 2 respetively, bears a positive sign and negative sign respectively, thus on increasing the concentration of mannitol an increase in hardness andon increasing the conentration of camphor, decrease in hardness is observed. Validation of statistical model To validate the statistical model checkpoint batches, CP1 and CP2 were prepared according to the formula. Comparison of predicted values and experimental values for check point batches are shown in Table From the response surface plot (Figure ) and the calculations from the statistical equation obtained by regression, the results revealed the close match of the experimental results. Thus, we can conclude that the statistical model is mathematically valid. Overlay plot for disintegration time and hardness are shown in Figure K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 208

40 Table 6.36 Comparison of predicted values and experimental values for check point batches Formulation code CP1 X 1 = +1 X 2 = CP2 X 1 = X 2 = +1 Predicted Values (DT) Experimental Values (DT) Residual Predicted Values (Hardness) Experimental Values (Hardness) Residual ± ± ± ± Data are expressed as mean S.D. (n = 3) The best batch was selected after considering the requirements of an FDT. To full fill these requirements; concentration of mannitol was set 38% and concentration of camphor 13%. The batches dissolution rates were also considered and batches with higher dissolution rates were given priority. Different constraints were applied; responses were predicted at 95% CI and they are found in range, which showed the robustness of the statistical model (Table 6.37). Further, solution with desirability 1 was selected, as shown in Table (6.38). Table 6.37 Predicted response at 95% confidence (n=1) Response Prediction Std Dev SE (n=1) 95% PI low 95% PI high Hardness DT Table 6.38 Predicted desirability Number Mannitol Camphor Hardness DT Desirability Selected K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 209

41 H a r d n e s s D T Design-Expert Software Factor Coding: Actual DT Design points above predicted value Design points below predicted value 35 8 X1 = A: Mannitol X2 = B: Camphor B: Camphor A: Mannitol Figure 6.17 Response surface plot for disintegration time Design-Expert Software Factor Coding: Actual Hardness Design points above predicted value Design points below predicted value X1 = A: Mannitol X2 = B: Camphor B: Camphor A: Mannitol Figure 6.18 Response surface plot for hardness K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 210

42 B : C a m p h o r Design-Expert Software Factor Coding: Actual Overlay Plot DT Hardness Design Points 1.00 Overlay Plot X1 = A: Mannitol X2 = B: Camphor DT: Hardness: X X2Hardness: DT: DT: A: Mannitol Figure 6.19 Overlay plot for DT Characterization of optimized batch (OFDT2) To determine the suitability of the powder blend for tablet compression, optimized FDT (OFDT 2) was characterized for various flow properties as shown in Table (6.39). Sr no. Formulation Code Table 6.39 Physical properties of optimized tablet blend Bulk density (mg/ml) Tapped Density (mg/ml) Hausner s Ratio Carr s Index (%) Angle of Repose ( θ) 1 OFDT ± ± ± ± ±0.54 Data are expressed as mean S.D. (n = 3) The tablet blend showed good flow ability (angle of repose < 30 0 ). Further optimized FDT (OFDT 1) was characterized for different parameters as shown in Table (6.40). Table 6.40 Characterization of optimized tablet (OFDT 2) Parameters Thickness Diameter Weight Friability Drug Content Wetting time Formulations (mm) (mm) (mg) (%) (%) (Seconds) OFDT 2 3.9± ± ± ± ± ±0.68 Data are expressed as mean S.D. (n = 3) K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 211

43 Friability of all the formulation was below 1% indicates that the tablets had good mechanical resistance. Good uniformity of drug content was observed in all the formulations. The weight variation results revealed that average % deviation of 20 tablets of each formulation was less than ±7.5%, which provide good uniformity in all formulations. Figure 6.20 showed a micrograph of the cross section of a high porosity fast dissolving tablet. It was found that many porous cavities in the tablet were formed due to the sublimation of camphor. Figure 6.20 SEM micrograph of the cross sectional view of optimized tablet after sublimation Comparison of predicted responses and observed values for the disintegration time and hardness (Table 6.41) were in close agreement, and the models were found to be valid. Thus, full factorial design with two factors can be successfully used to optimize the formulations. Predicted Values (Disintegration time) Table 6.41 Comparison of predicted vs observed response Experimental Values (Disintegration time) Predicted Values (Hardness) Experimental Values (Hardness) ± ± ± ±1.025 Data are expressed as mean S.D. (n = 3) K. B. I. P. E. R. Kadi Sarva Vishwavidyalaya Page 212