STATISTICAL OPTIMIZATION OF ORALLY DISINTEGRATING TABLETS OF MEMANTINE HYDROCHLORIDE
HTML Full TextSTATISTICAL OPTIMIZATION OF ORALLY DISINTEGRATING TABLETS OF MEMANTINE HYDROCHLORIDE
I. S. Ghogari ^{* 1} and P. S. Jain ^{2}
Pacific Academy of Higher Education and Research University, Udaipur  313003, Rajasthan, India.
ABSTRACT: Objective: The present work deals with the statistical optimization of memantine hydrochloride orally disintegrating tablets by the design of experimentation. The study was aimed at the development of a stable and robust formulation for the wellbeing of society for the treatment of Alzheimer’s disease, which is the most common form of dementia. Methods: Binder (X_{1}), disintegrant (X_{2}), and diluent (X_{3}) concentrations were selected as independent variables, and their levels were optimized by employing a Central composite design, whereas dissolution (Y_{1}) and disintegration time (Y_{2}) were selected as the dependent variables. Results: All formulations were evaluated for physical parameters of lubricated blend and compressed tablets. Mathematical equations and response surface plots were used to relate the dependent and independent variables. The regression equation generated for dissolution was Y_{1} = 93.51  7.00B + 5.20C  4.28B²  2.28C² and for disintegration time (DT) was Y_{2} = 4.80 + 0.0148A + 0.3690B 0.3181C. Conclusion: The statistical significance of each variable with respect to the model was established, and optimized formulation factors were selected by feasibility and grid search. The factorial batches were evaluated by contour plots and response surface methodology.
Keywords: 
Memantine hydrochloride, Statistical optimization, Orallydisintegrating, Tablets, Alzheimer’s disease
INTRODUCTION: Dementia is one of the brain disorders which lead to Alzheimer’s disease (AD) as a result of neurofibrillary degeneration affecting neurons of the central nervous system. The process includes a decrease in cholinergic transmission, higher sensitivity to oxidative stress, alterations in the cytoskeleton, and neuronal death ^{1, 2}. The elderly patients are affected by the formation of two main protein aggregates; senile plaques and neurofibrillary tangles.
Recently, the demand for the development of orally disintegrating tablets (ODTs) has enormously increased as it has a significant impact on patient compliance. The bitterness of various drugs can be masked by formulating ODTs with good taste and flavor and thereby increasing the acceptability.
The conventional dosage forms are associated with a high prevalence of noncompliance and unsuccessful therapy with respect to swallowing, especially in the case of geriatric, pediatric, or in mentally retarded persons. Drugs present in orodispersible tablets are also not affected by firstpass metabolism ^{3}. Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, which is associated with excessive loss of memory ^{46}. It has been shown that AD afflicts about 810% of the population over 65 years of age, and its prevalence doubles every 5 years thereafter ^{7}. In this disease, the change in forgetfulness is more dramatic than normal, which commonly gets increased with age. More importantly, this difficulty is persistent, progressive, and severe, and there is usually a noticeable, rapid decline in cognitive skills ^{8}.
There are two types of Alzheimer’s diseases, one is a familial type that is passed from one generation to another through a dominant gene, is very rare and is seen in only 510% of cases. About 90% of cases are the sporadic type, which can be developed even if nobody in the family has had the disease ^{9, 10}. It is the fourth leading cause of death and the most common cause of dementia in the United States. The total number of people with dementia worldwide is anticipated to nearly double every 20 y, 65.7 million in 2030, and 115.4 million in 2050 ^{1113}. The total number of new cases each year worldwide is nearly 7.7 million, implying one new case every 4 seconds. The fastest growth is taking place in China, India, and their south Asian and western Pacific neighbors. As per projection, by 2050, people aged 60 and over will account for 22% of the world’s population, with 80% living in Asia, Latin America, or Africa ^{14}.
Memantine hydrochloride belongs to a lowmoderate affinity, uncompetitive nmethyldaspartate (NMDA) receptor antagonist with sturdy voltage dependency and quick blocking/unblocking kinetics ^{15, 16}. These pharmacological features appear to allow memantine to block the sustained activation of the receptor by glutamate that may occur under pathological conditions, and to rapidly leave the NMDA receptor channel during normal physiological activation.
Response surface methodology (RSM) is a widely exploited tool for the development and optimization of drug delivery systems. In this investigation, we explored the utility of RSM for the optimization of ODTs. Based on the principle of design of experiments, the methodology encompasses the use of various types of experimental designs, generation of polynomial equations, and mapping of the response over the experimental domain to optimize the tablets. The technique selected based on its benefits as minimum experimentation and time, more effective and costeffective as compared to the conventional methods of formulating dosage forms. The current approach is intended to apply central composite design for optimization of an orally disintegrating drug delivery system of memantine hydrochloride to avoid complexities in the optimization of various dependent and independent variables. as these systems are beneficial for many patients, particularly from paediatric and geriatric populations who have difficulty in swallowing conventional tablets and capsules and also in patients travelling with little or no access to water, leading to noncompliance and ineffective therapy ^{1720}.
MATERIALS AND METHODS:
Chemicals and Reagents: Cadila Healthcare Ltd, India has provided the memantine hydrochloride as gift sample. Microcrystalline cellulose, mannitol, croscarmellose sodium, silica colloidal anhydrous and magnesium stearate procured from Signet chemicals ltd, Eudragit EPO as a gift sample from Evonik GmbH, India. All these suppliers were based in Mumbai, India.
Methods:
Preparation of Orally Disintegrating Tablets of Memantine Hydrochloride: Initial developmental trials by wet granulation to formulate the orally disintegrating tablets of memantine hydrochloride was taken to select excipients and their primary levels. The taste and flavor enhancers were used to mask the bitterness of the drug.
Memantine hydrochloride, microcrystalline cellulose, mannitol were sifted through # 40 sieve and transferred to a bowl of top spray granulator. Eudragit EPO ready mix clear was dispersed in purified water and sprayed on the blend by top spray granulation method and subsequently dried. The dried granules were milled using a suitable screen and blended with sifted extra granular materials viz. croscarmellose sodium, silica colloidal anhydrous, tartaric acid, neotame, acesulfame potassium, and tuttifrutti flavor. The blend was lubricated with magnesium stearate in the blender. The lubricated blend was transferred to the hopper of the compression machine, and tablets were compressed at the hardness of 24 kg/cm^{2} using 8 mm circular, flatfaced beveled edge, plain punches using Cadmach single rotary compression machine.
Experimental Design: The Central composite design was utilized to study the effect of independent variables on dependent variables, as shown in Table 1. The prepared tablets were evaluated for physicochemical characterization. The experimental design with statistical screening was used to optimize the formulation factors and evaluate main effects, interaction effects, and quadratic effects on the disintegration time and dissolution with the help of RSM and possible curvature in the response function. A threefactor, the threelevel design was used to explore linear and quadratic response surfaces and to construct secondorder polynomial models with Design Expert (Version 11.1.2.0, StatEase Inc. Minneapolis, MN, USA). A design matrix comprising of 17 experimental runs was constructed. The nonlinear computergenerated quadratic model is given as Y = b_{0} + b_{1}X_{1} + b_{2}X_{2} + b_{3}X_{3} + b_{12}X_{1}X_{2} + b_{13}X_{1}X_{3} + b_{23}X_{2}X_{3} + b_{11}X_{12} + b_{22}X_{22} + b_{33}X_{32} where Y1 and Y_{2} is the measured response associated with each factor level combination: b_{0} is constant; b_{1}, b_{2}, b_{3} are linear coefficients, b_{11}, b_{22}, b_{33} are quadratic coefficients computed from experimental runs and X_{1}, X_{2} and X_{3} are coded levels of independent variables. The independent variables selected were the concentrations of binder (X_{1}), disintegrant (X_{2}), and diluent (X_{3}). The dependent variables were dissolution (Y_{1}) and disintegration time (Y_{2}) with constraints applied to the formulation of tablets. The concentration range of independent variables was selected based on the observations from preliminary experimentation. The selected concentration range of independent variables under study with their low, medium, and high levels are shown in Table 1.
TABLE 1: VARIABLES AND OBSERVED RESPONSES IN CENTRAL COMPOSITE DESIGN FOR TABLET
Trials  Independent variables  Dependent variable  
EPO Concentration
(%) (X_{1}) 
CCS Concentration
(%) (X_{2}) 
Diluent Concentration
(%) (X_{3}) 
Dissolution
(%) (Y_{1}) 
Disintegration time
(seconds) (Y_{2}) 

F1  2.00  4.00  40.00  99  93 
F2  4.00  4.00  20.00  94  118 
F3  4.00  4.00  40.00  91  104 
F4  6.00  6.00  60.00  84  140 
F5  2.00  6.00  20.00  97  64 
F6  4.00  4.00  60.00  96  118 
F7  2.00  2.00  60.00  87  118 
F8  6.00  2.00  20.00  78  228 
F9  6.00  4.00  40.00  80  205 
F10  2.00  6.00  60.00  100  61 
F11  4.00  6.00  40.00  98  84 
F12  6.00  6.00  20.00  86  125 
F13  2.00  2.00  20.00  89  112 
F14  6.00  2.00  60.00  74  235 
F15  4.00  2.00  40.00  85  195 
F16  4.00  4.00  40.00  93  113 
F17  4.00  4.00  40.00  91  104 
Evaluation of Blend: The lubricated blend was evaluated for flow characteristics: bulk density (BD), tapped density (TD), compressibility index (CI), Hausner’s ratio (HR), and angle of repose. The flow characteristics were determined by using approximately 25 g weighed amount of blend in 100 ml measuring cylinder.
Evaluation of Tablets: The formulations were characterized for physical aspects like hardness, weight variation, thickness, friability, disintegration time, disintegration time in the oral cavity, invitro dispersion time, wetting time and water absorption ratio as well as for chemical tests like assay, content uniformity and in vitro dissolution study.
Tablet Hardness: The crushing strength of the tablet was measured in terms of hardness, which determines the ease of handling and the rigors of transportation. 10 tablets per formulation were used for the study.
Weight Variation Test: The weight variation test was done by individually weighing 20 tablets, calculating the average weight, and comparing the individual tablet weight to the average. The table given below shows the weight variation tolerance for uncoated tablets.
Thickness: The digital vernier caliper was used to measure the thickness of the tablets.
Friability: To assess the effect of friction along with shocks, which may often cause the tablet to chip, cap or break, friability test was performed using Roche friabilator. This device subjects the tablets to the combined effect of abrasion and shock by utilizing a plastic chamber that revolves at 25 rpm dropping the tablets at a distance of 6 inches with each revolution. Preweighed tablet samples were placed in the friabilator, which was then operated for 100 revolutions. Tablets were dedusted prior to reweighing.
Disintegration Time: One tablet was placed in each of six tubes of disintegration test apparatus. The disintegration test was carried out at 37 ± 2 °C with respect to United States Pharmacopoeia (USP) 22^{nd} edition. The time required for the complete passage of tablet fragments through the sieve (#10) was measured as a disintegration time of the tablet.
Disintegration Time in Oral Cavity: The disintegration time in the oral cavity of 6 human volunteers was calculated by placing the tablet on the tongue until no lumps remain. It is expressed in seconds ^{17}.
Invitro Dispersion Time: The measurement of invitro dispersion time was done by dropping tablets in 100 ml of water and stirring until completely dispersed. A smooth dispersion is produced which passes through a screen with a nominal mesh aperture of 710 μm.
Wetting Time and Water Absorption Test: The inner structure of tablets and the hydrophilicity of the excipients are important factors to determine the wetting time. According to the equation proposed by Washburn E. W., the water penetration rate into the powder bed is proportional to the pore radius and is affected by the hydrophilicity of the powder. It is apparent that pore size becomes smaller and wetting time increases with an increase in compression force or a decrease in porosity. A linear relationship exists between wetting time and disintegration time. Thus, wetting time is an important step for the disintegration process. A piece of tissue paper folded twice was placed in a small petri dish (internal diameter = 6.5 cm) containing 6 ml of water. A tablet was placed on the paper, and the time for complete wetting of the tablet was measured in seconds. The method was slightly modified by maintaining water at 37 °C ^{17}.
The same procedure was repeated for determining the water absorption ratio. The wetted tablet was then weighed. Water absorption ratio, R, was determined according to the following equation:
R = {(W_{a} – W_{b}) /W_{a}} × 100
Where, W_{a} = Weight of tablet before the study, W_{b} = Weight of tablet after study.
Assay: 30 tablets were randomly selected from each batch. Out of 30 tablets, 10 tablets were crushed into a fine powder. Powder equivalent to label claim was weighed accurately, dissolved in the media, and analyzed for assay.
Uniformity of Dosage Units (By Content Uniformity): One tablet was taken in a 100 ml volumetric flask, which was shaken with 15 ml of methanol and sonicated for 10 min. 15 ml of 0.2 N sodium hydroxide solution was added and sonicated for 15 min with vigorous shaking. 10 ml of internal standard solution was added and shaken for 15 min and then allowed to separate 2 layers for about 15 min. The top toluene layer was separated by using pasture pipette and dried over anhydrous sodium sulphate. The initial 23 ml was discarded, and the remaining solution was used for analysis.
Invitro Dissolution Studies: The invitro dissolution study was carried out using 900 ml of 0.01 NHCl at 37 ± 0.5 °C temperature at 50 rpm using USP Type 2 (paddle) dissolution test apparatus. Samples were withdrawn at 1, 2, 4, 6, 8, and 10 min time intervals.
RESULTS AND DISCUSSION:
Formulation Development:
Evaluation of Tablets: The tablet parameters observed are depicted in Tables 2, 3, and 4. The tablets were compressed at the acceptable weight variation range of target weight ± 5% as per USP. The hardness ranging from 2.5 to 4.0 kg/cm^{2} for all formulations, was observed. The hardness is not an absolute indicator of the strength, and hence, all formulations complied with the weight variation and hardness test indicated the minimal impact of formulation compositions. The friability value of all the formulations was found less than 0.517%. The results of friability indicated the mechanical strength of the tablets to withstand the rigors of transportation and handling. The thickness of all the formulations was observed between 2.60 – 2.88 mm, indicating fairly acceptable tableting. Disintegration time is a very vital parameter of fast disintegrating tablets. The inner structure of a tablet, pore size distribution, water penetration into tablet, and swelling of disintegrant are key aspects to establish the mechanism of disintegration.
The disintegration time of formulation was gratifying because it disintegrated in the range of 1 to 4 min. The trial, F10, demonstrated the best disintegrating time, i.e., Table 3, which depicts the impact of high concentration of mannitol and croscarmellose sodium.
Invitro Dissolution Study: The dissolution study on formulation F1 – F17 showed the dissolution ranging from 74 ± 2.27% to 100 ± 1.09% in 10 min Table 24. High dissolution resulted due to faster breakdown and rapid dispersion of the tablet. It may be due to rapid diffusion or the porous nature of the tablet. Based on the data, it can be concluded that the addition of super disintegrant improved the dissolution profile of the watersoluble drug besides expediting the disintegration time.
TABLE 2: PHYSICOCHEMICAL CHARACTERISTICS OF FORMULATION F1F5
Parameters  F1  F2  F3  F4  F5 
Bulk density (g/ml)  0.467  0.498  0.477  0.389  0.396 
Tapped density (g/ml)  0.556  0.612  0.538  0.497  0.492 
Compressibility Index (%)  16.007  18.627  11.338  21.730  19.512 
Hausner’s ratio  1.191  1.229  1.128  1.278  1.242 
Angle of repose  27.9  31.2  26.8  29.5  31.2 
Weight variation (mg)  150 ± 4  150 ± 3  150 ± 1  150 ± 2  150 ± 2 
Thickness (mm)  2.75 ± 0.06  2.75 ± 0.08  2.70 ± 0.08  2.75 ± 0.04  2.80 ± 0.08 
Friability (%)  0.419  0.208  0.234  0.312  0.419 
Hardness (kg/cm^{2})  2.8 ± 0.4  3.4 ± 0.2  3.6 ± 0.4  2.7 ± 0.3  3.5 ± 0.4 
Disintegration time (s)  93 ± 3.05  118 ± 2.32  104 ± 1.04  140 ± 3.21  64 ± 2.13 
Dispersion time (s)  113 ± 2.17  138 ± 1.01  126 ± 2.95  157 ± 1.89  99 ± 1.29 
Content uniformity (%)  97.2 ± 1.2  91.4 ± 2.9  98.8 ± 2.1  95.0 ± 1.37  92.6 ± 3.20 
Water absorption ratio  58 ± 0.8  69.6 ± 1.2  63.5 ± 1.1  79.2 ± 1.2  53.1 ± 1.9 
Assay (% w/w)  97.3 ± 1.12  95.4 ± 2.87  98.9 ± 0.93  97.1 ± 1.27  93.2 ± 2.23 
Wetting time (s)  127.1 ± 1.12  152.6 ± 1.55  139.6 ± 1.18  180.0 ± 1.09  110.0 ± 3.1 
Dissolution (%)  99 ± 1.03  94 ± 2.23  91 ± 1.51  84 ± 2.05  97 ± 0.98 
Values are represented as Mean ± standard deviation, n=3 for assay, n=6 for dissolution and n =10 for all physical parameters
TABLE 3: PHYSICOCHEMICAL CHARACTERISTICS OF FORMULATION F6F11
Parameters  F6  F7  F8  F9  F10  F11 
Bulk density (g/ml)  0.315  0.322  0.378  0.477  0.398  0.481 
Tapped density (g/ml)  0.466  0.451  0.488  0.543  0.478  0.539 
Compressibility Index (%)  32.403  28.603  22.541  12.155  16.736  10.761 
Hausner’s ratio  1.479  1.401  1.291  1.138  1.201  1.121 
Angle of repose  26.3  28.4  34.3  28.5  26.6  26.1 
Weight variation (mg)  150 ± 2  150 ± 1  150 ± 5  150 ± 3  150 ± 4  150 ± 1 
Thickness (mm)  2.65 ± 0.04  2.68 ± 0.04  2.80 ± 0.05  2.70 ± 0.06  2.68 ± 0.07  2.70 ± 0.06 
Friability (%)  0.277  0.293  0.437  0.119  0.258  0.311 
Hardness (kg/cm^{2})  2.7 ± 0.2  2.8 ± 0.3  3.0 ± 0.5  3.3 ± 0.4  3.0 ± 0.3  3.4 ± 0.3 
Disintegration time (s)  118 ± 1.48  118 ± 3.65  228 ± 2.11  205 ± 1.10  61 ± 2.08  84 ± 1.27 
Dispersion time (s)  138 ± 2.15  152 ± 0.95  252 ± 2.17  238 ± 2.11  98 ± 1.10  108 ± 1.46 
Content uniformity (%)  98.3 ± 1.70  91.1 ± 1.90  96.2 ± 3.20  99.5 ± 2.10  98.7 ± 1.50  98.3 ± 1.70 
Water absorption ratio  64.3 ± 1.4  70.1 ± 1.0  132.3 ± 1.8  132.0 ± 1.4  51.1 ± 0.9  54.3 ± 1.1 
Assay (% w/w)  97.6 ± 1.08  92.0 ± 2.59  95.5 ± 1.99  99.8 ± 0.23  98.9 ± 0.67  97.6 ± 1.12 
Wetting time (s)  148.1 ± 1.15  162.5 ± 2.10  263.1 ± 1.12  258.1 ± 1.75  109.1 ± 0.85  119.3 ± 1.37 
Dissolution (%)  96 ± 1.12  87 ± 1.23  78 ± 1.17  80 ± 2.21  100 ± 1.09  98 ± 3.01 
Values are represented as Mean ± standard deviation, n=3 for assay, n=6 for dissolution and n =10 for all physical parameters
TABLE 4: PHYSICOCHEMICAL CHARACTERISTICS OF FORMULATION F12F17
Parameters  F12  F13  F14  F15  F16  F17 
Bulk density (g/ml)  0.337  0.331  0.357  0.469  0.480  0.477 
Tapped density (g/ml)  0.499  0.517  0.504  0.537  0.541  0.538 
Compressibility Index (%)  32.465  35.977  29.167  12.663  11.275  11.338 
Hausner’s ratio  1.481  1.562  1.412  1.145  1.127  1.128 
Angle of repose  29.1  32.8  30.7  26.9  26.3  26.8 
Weight variation (mg)  150 ± 2  150 ± 5  150 ± 2  150 ± 3  150 ± 3  150 ± 1 
Thickness (mm)  2.65 ± 0.05  2.78 ± 0.05  2.68 ± 0.06  2.70 ± 0.06  2.70 ± 0.04  2.70 ± 0.08 
Friability (%)  0.517  0.473  0.336  0.112  0.276  0.234 
Hardness (kg/cm^{2})  3.2 ± 0.3  3.5 ± 0.5  2.8 ± 0.3  3.4 ± 0.3  3.3 ± 0.2  3.6 ± 0.4 
Disintegration time (s)  125 ± 0.31  112 ± 1.12  235 ± 2.36  195 ± 1.47  113 ± 1.23  104 ± 1.04 
Dispersion time (s)  145 ± 2.05  139 ± 3.17  267 ± 3.19  218 ± 2.15  142 ± 1.19  126 ± 2.95 
Content uniformity (%)  94.5 ± 4.20  93.6 ± 1.90  98.3 ± 1.70  98.3 ± 1.70  98.5 ± 0.9  98.8 ± 2.1 
Water absorption ratio  72.1 ± 1.9  70.0 ± 1.5  136.2 ± 1.2  109.1 ± 1.8  72.4 ± 1.7  63.5 ± 1.1 
Assay (% w/w)  96.1 ± 2.10  94.3 ± 3.12  98.8 ± 0.78  99.0 ± 0.44  99.1  98.9 
Wetting time (s)  161.3 ± 1.07  148.9 ± 1.01  289.1 ± 1.15  228.6 ± 1.30  157.3 ± 0.99  139.6 ± 1.18 
Dissolution (%)  86 ± 1.09  89 ± 1.61  74 ± 2.27  85 ± 1.17  93 ± 1.18  91 ± 1.51 
Values are represented as Mean ± standard deviation, n=3 for assay, n=6 for dissolution and n=10 for all physical parameters
Experimental Design: The independent variables and the responses for all 17 experimental runs are given in Table 1. The 17 experimental formulations of tablets were prepared using different concentrations of binder, disintegration, and diluent. The responses, dissolution (Y_{1}), and disintegration time (Y_{2}) were found to be 100 ± 1.09 and 61 ± 2.08 s respectively for F10 trial in which mannitol and croscarmellose sodium were used at high concentration levels as diluent and disintegrant respectively. The response, Y_{1,} and Y_{2} vary from 74% to 100% and 61 sec to 235 sec, respectively. For estimation of the quantitative effects of the different combinations of factors and their levels on disintegration time and dissolution profile, the response models were calculated with DesignExpert software by applying coded values of factor levels. The model described could be represented as: Coded level: Dissolution (Y_{1}) = 93.51  7.00B + 5.20C  4.28 B²  2.28 C²and for disintegration time (Y_{2}) = 4.80 + 0.0148A + 0.3690B  0.3181C.
Fitting of Data to the Model: A threefactor, a threelevel central composite statistical experimental design was used, which involved the preparation of 17 trials. Formulation F1 showed a significantly better disintegration time and dissolution among the experimental runs. All the responses observed for 17 formulations prepared were simultaneously fit first order, secondorder, and quadratic models using Design Expert 11.1.2.0. It was observed that the best fit model was the quadratic model with sum of squares is type III  partial for response Y_{1} (dissolution) and linear for response Y_{2} (disintegration time). A positive value represents an effect that favors the optimization, while a negative value indicates an inverse relationship between the factor and response. It is evident that out of three independent variables, the binder (X_{1}) have a negative relationship for response Y_{1} (dissolution) and positive for response Y_{1} (dissolution), whereas, disintegrant (X_{2}) and diluent (X_{3}) have a positive relationship for response Y_{1} (dissolution) and negative for response Y_{2} (disintegration time). The quantitative effects of the different combinations of factors and factor levels on the disintegration time were calculated using response surface models. The significant pvalue (p<0.05), R^{2}, adjusted R^{2,} and coefficient of variation values of this model indicated that the assumed regression model was significant and valid for each considered response. The values of the coefficients in the model are related to the effect of these variables on the response. The 3D response surface Fig. 1 was drawn to estimate the effects of the independent variables on response and to select the optimal formulation.
Data Analysis: The dissolution and disintegration time (dependent variable) obtained at various levels of the three independent variables (X_{1}, X_{2,} and X_{3}) were subjected to multiple regression to yield a polynomial equation. The value of the correlation coefficient (r^{2}) of the equation was found to be 0.9462 and 0.9458 for response Y_{1} and Y_{2,} respectively, indicating a good fit. VIF value found to be 1, indicating the model is significant.
According to Table 5, the result calculated using equation 1 was statistically significant with p<0.005, indicating that the developed model exhibited good agreement between the response Y_{1} and the significant variables. The value for lack of fit for the equation (more than 0.05) indicated that the proposed statistical model fits well. The F values from ANOVA results suggested that the derived models have a significant influence on the responses. R^{2} and adjusted R^{2} value for both the responses, as depicted in Table 6 demonstrate the accuracy of the test and the fitness of the results with the prepared model. The disintegration time measured for the different formulations showed wide variation (i.e., values ranged from 61 to 235 sec). The results indicate that the disintegration time is strongly affected by the variables selected for the study. The main effects of X_{1}, X_{2,} and X_{3} represent the average result of changing one variable at a time from its low level to its high level. The interaction terms (X_{1}X_{2}, X_{1}X_{3}, X_{2}X_{3}, X_{12}, X_{22,} and X_{32}) shows how the disintegration time and dissolution changes when two variables are simultaneously changed.
The negative coefficient for all three independent variables has an unfavorable effect on the disintegration time, while positive coefficients for the interactions between two variables indicate a favorable effect on disintegration time.
TABLE 5A: SUMMARY OF ANOVA FOR RESPONSE Y1 (DISSOLUTION) FOR FITTING TO QUADRATIC MODEL
Source  Sum of Squares  df  Mean Square  Fvalue  pvalue  
Model  898.81  4  224.70  52.80  < 0.0001  significant 
BEPO  490.00  1  490.00  115.13  < 0.0001  
CCCS  270.40  1  270.40  63.53  < 0.0001  
B²  55.56  1  55.56  13.05  0.0036  
C²  15.79  1  15.79  3.71  0.0781  
Residual  51.07  12  4.26  
Lack of Fit  48.41  10  4.84  3.63  0.2352  not significant 
Pure Error  2.67  2  1.33  
Core Total  949.88  16 
TABLE 5B: SUMMARY OF ANOVA FOR RESPONSE Y2 (DT) FOR FITTING TO QUADRATIC MODEL
Source  Sum of Squares  df  Mean Square  Fvalue  pvalue  
Model  2.38  3  0.7919  75.56  < 0.0001  significant 
ADiluent  0.0022  1  0.0022  0.2083  0.6556  
BEPO  1.36  1  1.36  129.95  < 0.0001  
CCCS  1.01  1  1.01  96.53  < 0.0001  
Residual  0.1362  13  0.0105  
Lack of Fit  0.1303  11  0.0118  4.00  0.2167  not significant 
Pure Error  0.0059  2  0.0030  
Core Total  2.51  16 
FIG. 1: (A) COUNTERPLOT SHOWING EFFECT INDEPENDENT VARIABLES ON RESPONSE Y1 (B) COUNTER PLOT SHOWING EFFECT INDEPENDENT VARIABLES ON RESPONSE Y2
FIG. 2: (A) RESPONSE SURFACE PLOT SHOWING EFFECT INDEPENDENT VARIABLES ON RESPONSE Y1 (B) RESPONSE SURFACE PLOT SHOWING EFFECT INDEPENDENT VARIABLES ON RESPONSE Y2
Counterplots and Response Surface Analysis: Twodimensional counterplots and 3D response plots are shown in Fig. 1A, 1B, and Fig. 2A, 2B, which are very useful to study the interaction effects of the factors on the responses. These types of plots are useful in the study of the effects of two factors on the response at one time.
In all the presented figures, the third factor was kept at a constant level. All the relationships among the three variables are nonlinear, although they exhibit a nearly linear relationship of factors.
Optimization: The optimum formulation was selected based on the criteria of attaining a maximum value of dissolution and minimum value of disintegration time by applying constraints on Y_{1} andY_{2}. Upon trading of various response variables and comprehensive evaluation of feasibility search and exhaustive grid search, the formulation F_{1} was found to fulfill the maximum requisite of an optimum formulation because of the desired dissolution, disintegration time values along with other parameters Table 6.
TABLE 6: SIMULTANEOUS OPTIMAL SOLUTION BY RSM
Response  Predicted Mean  Predicted Median*  Std
Dev 
n  SE
Pred 
95% PI
low 
95% PI high 
Disso  93.5094  93.5094  2.063  1  2.2313  88.6479  98.371 
DT  121.541  120.907  12.4098  1  N/A  96.2983  151.805 
Validation of Response Surface Methodology: 17 formulations were obtained from RSM, the composition, and predicted response of which are listed in Table 6.
To confirm the validity of the optimal calculated parameters and predicted responses, the optimum formulations were prepared according to the above value of the factors. From the results presented in Table 5, the predicted error is below 15%, indicating that the observed responses were very close to the predicted values, shown in Table 6.
The linear correlation plots were drawn between the predicted and experimental values. Thus, the low magnitudes of error, as well as the R^{2} values in the present investigation, prove the high prognostic ability of the RSM.
CONCLUSION: The data obtained from the central composite study revealed that independent variables at used concentration levels have significant impacts on selected responses. After the determination of significant parameters by using experimental design methodology, levels of variables were optimized to formulate the stable and robust formulation of memantine hydrochloride orally disintegrating tablets.
The chosen responses were dissolution and disintegration time. The levels of these factors were predicted to obtain an optimal response concerning set constraints. It is essential that experimental design methodology is a very economical way for extracting the maximum complex information, a significant experimental timesaving factor, and it saves the material used for analyses and personal cost as well. It is concluded that by adopting a systematic formulation approach, an optimum can be reached in the shortest time with minimum efforts.
AUTHOR CONTRIBUTION: All authors have equally participated and contributed to the conceptualization of ideas and have read and approved this version of the article. However, the first author has played a role in the inception and execution of concept and manuscript preparation, and the second author reviewed and provided technical support.
ACKNOWLEDGEMENT: Authors would like to acknowledge Evonik GmbH, India, for generously providing the gift sample of Eudragit EPO.
CONFLICTS OF INTEREST: The authors of this scientific publication report no conflict of interest in this work.
REFERENCES:
 Talita HF, Isabella MG, Flavia RS and Fabiola MR: Alzheimer's disease: Targeting the Cholinergic system. Curr Neuropharmacol 2016; 14(1): 10115.
 Onesimus M, Mohamad THB, Nurul HMN, Samaila MC, Saravanan J and Mohamad AMM: Chemicals used for the induction of Alzheimer’s diseaselike cognitive dysfunctions in rodents. Biomedical Research and Therapy 2019; 6(11): 346084.
 Santosh R and Naga SY: Fast dissolving systems – an alternative approach for enhanced therapeutic action. Indo Am J Pharm Res 2018; 8(12): 146472.
 Shreyasi C and Amtpal M: Alzheimer's disease and Type 2 diabetes: a critical assessment of the shared pathological traits. Front Neurosci 2018; 12: 383.
 Katie I, Claire S, Tarun D, Brian L and Lorina N: Healthy Aging and Dementia: Two Roads Diverging in Midlife?. Front Neurosci 2018; 10: 275.
 Roy C and Ghosh T: The antioxidative role of Benincasahispida on colchicine induced experimental rat model of Alzheimer’s disease. Biogenic Amines 2007; 21(12): 4457.
 Ashraf EM, Paivi T, Mashael A, Shanila N, Munazza J, Raghad A, Hira R and Nada A: Epidemiology of Alzheimer’s Disease and Dementia in Arab Countries: A Systematic Review. Behavioural Neurology 2019; 10: 1155.
 Ravi G and Gupta NV: Development and evaluation of transdermal film containing solid lipid nanoparticles of rivastigmine tartrate. Int J Appl Pharm 2017; 9: 8590.
 Alzheimer Society of Canada: Shared Experiences: Suggestions for those with Alzheimer disease. Available at: http://www.alzheimer.ca/docs/brochuresharedexperienceseng.pdf. Accessed on March 26, 2020.
 Alzheimer Society of Canada: Ethical guidelines. Genetic testing. October 2005. Available at: http://www.alzheimer. ca/english/care/ethicsgenetictest.htm. Accessed on March 26, 2020.
 World Population Ageing 2009: New York, United Nations, 2009 Available at: http://www.un.org/esa/ population/publications/WPA2009/WPA2009. Accessed on March 26, 2020.
 Preethi B and Vinyaga MP: A review on Alzheimer’s disease: pathology, molecular conditions, management, and causes. Asian J Pharm Clin Res 2017; 10: 2730.
 Abhinav A, Neha S and Navneet K: Prediction of activity spectra of substances assisted prediction of biological activity spectra of potential antiAlzheimer’s phytoconstituents. Asian J Pharm Clin Res 2017; 10(16): 13.
 World Alzheimer’s Report 2010: The global economic impact of dementia. London, Alzheimer’s disease International 2010.
 Mobius HJ, Stoffler A and Graham SM: Memantine hydrochloride: pharmacological and clinical profile. Drugs Today (Barc) 2004; 40: 68595.
 Jagathi V, Anupama B, Praveen PS and Rao GD: Spectrophotometric determination of memantine in bulk and in pharmaceutical formulations. IJCPR 2010; 2: 1718.
 Jeevitha M and Pandey VP: Formulation and development of orodispersible tablet of memantine hydrochloride. Int J Drug Dev Res 2016; 8: 3840.
 Amin AF, Shah TJ, Bhadani MN and Patel MM: Emerging trends in the development of orally disintegrating tablet technology. Indo Am J Pharm Res 2012; 3: 221.
 AlKhattawi A and Mohammed AR: Challenges and emerging solutions in the development of compressed orally disintegrating tablets. Expert Opin Drug Discovery 2014; 9(10): 110920.
 Parijat P and Mandeep D: Oral Disintegrating Tablets: A Review. Int J Pharma Res Rev 2016; 5(1): 5062.
How to cite this article:
Ghogari IS and Jain PS: Statistical optimization of orally disintegrating tablets of memantine hydrochloride. Int J Pharm Sci & Res 2020; 11(5): 248997. doi: 10.13040/IJPSR.09758232.11(5).248997.
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Article Information
62
24892497
670
54
English
IJPSR
I. S. Ghogari * and P. S. Jain
Pacific Academy of Higher Education and Research University, Udaipur, Rajasthan, India.
imran.ghogari@gmail.com
03 February 2020
25 April 2020
28 April 2020
10.13040/IJPSR.09758232.11(5).248997
01 May 2020