The effect of binder concentration and dry mixing time on granules, tablet characteristics and content uniformity of low dose drug in high shear wet granulation

Accepted Manuscript The effect of binder concentration and dry mixing time on granules, tablet characteristics and content uniformity of low dose drug in high shear wet granulation Gamal M. Mahours, Diaa Edin Z. Shaaban, Gamal A. Shazly, Sayed H. Auda PII:

S1773-2247(16)30576-7

DOI:

10.1016/j.jddst.2017.03.014

Reference:

JDDST 321

To appear in:

Journal of Drug Delivery Science and Technology

Received Date: 22 November 2016 Revised Date:

2 March 2017

Accepted Date: 18 March 2017

Please cite this article as: G.M. Mahours, D.E.Z. Shaaban, G.A. Shazly, S.H. Auda, The effect of binder concentration and dry mixing time on granules, tablet characteristics and content uniformity of low dose drug in high shear wet granulation, Journal of Drug Delivery Science and Technology (2017), doi: 10.1016/j.jddst.2017.03.014. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

The effect of binder concentration and dry mixing time on granules, tablet characteristics and content uniformity of low dose drug in high shear wet granulation

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Gamal M. Mahours1, Diaa Edin Z. Shaaban 1, Gamal A. Shazly 1,2, & Sayed H. Auda 1,3*

1 Department of Pharmaceutics, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia.

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2 Department of Industrial pharmacy, Faculty of Pharmacy, Assiut University 71526, Assiut, Egypt.

3 Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Al-Azhar

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University, Assiut, Egypt.

*Corresponding author, Email address: [email protected] , [email protected]

Abstract

High shear mixers are classically used for wet granulation because they provide granules with a high density and a high strength in a short granulation time. In this study, the high shear wet

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granulation process was employed using chlorpheniramine maleate as a model drug, since it represents an example of low dose drug employing 32 randomized full factorial design and optimized in terms of different properties using Response Surface Methodology (RSM)

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approach. The effect of binder concentration (X1) and dry mixing time (X2) at three levels on the granules and tablet properties was studied. Chlorpheniramine maleate tablets were prepared from the produced granules to evaluate the impact of dry mixing time and the binder

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concentration on the produced granules and tablet properties as well as tablet content uniformity. The results show that both evaluated variables had an impact on the produced granules and tablets as well as tablet content uniformity. The high shear mixer is found to be an efficient mixer for dry blending prior to the wet massing of a low dose drug formulation when impeller speed and chopper speed were carefully selected. The results of RSM optimization indicated that formulation containing 4.46% Binder concentration at a dry mixing time of 5 minutes, gave the most desirable properties for the produced granules and tablets. The observed results of the optimized formula were : Carr's Index:12.72 %, Hausner Rati :1.14, angle of repose: 35.18 degrees, hardness: 5.5 Kp, friability: 0.42 %, disintegration

ACCEPTED MANUSCRIPT time: 60 seconds, average of content uniformity: 112.18 % with RSD: 4.6 %, and d (0.9): 982 µ which are in consistence with the predicted results, having low values of standard error.

Keywords: Process parameter, tablet characteristics, high shear, wet granulation

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1. Introduction Pharmaceutical tablets represent the most popular drug delivery systems. They have numerous advantages like relatively easy to manufacture, the convenience of dosing, and their stability moreover, as the ease of usage and the appearance bring on high patient

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compliance [1]. In the pharmaceutical industry, the preferred tablet production method is direct compression technique. However, it is often necessary to improve the material’s compaction and flow properties in order to obtain uniform die filling and to produce tablets

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of adequate quality. These properties are commonly enhanced by converting fine powders into large agglomerate by the process of wet or dry granulation [2]. Wet granulation is traditionally used and performed by spraying a liquid binder onto the particles while they are agitated in a tumbling drum, a fluidized bed, a high shear mixer or similar devices [3]. High shear wet granulation is one of the most commonly used techniques. The main objectives of

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this process are to improve the flow and compressibility characteristics of the particles as well as final product homogeneity [4]. Among the different methods, high shear mixers are classically used for wet granulation because they provide granules with a high density and a high strength in a short granulation time [5, 6]. Classically, the high shear wet granulation

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process is divided into three distinct stages: (i) wetting and nucleation where granulating liquid contacts the powder to form nuclei; (ii) granules grow from nuclei primarily due to

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intergranular collisions and consolidation; and (iii) attrition and breakage of granules due to impact, wear, or compaction [7, 8]. These mechanisms control the obtained granule properties and are influenced by a combination of formulation design and process design [9]. However, one of the most important necessities for granulation is to ensure homogeneity of the formulation ingredients, especially in case of low dosage products. The wet massing of the ingredients should result in granules which are homogeneous in content. They are expected to contain the active pharmaceutical ingredient (API) and the excipients in the same proportion as the original bulk mixture. Thus, despite the ability of some formulations to be directly compacted (despite good compaction characteristics and flow properties), they are granulated to improve confidence in content uniformity.

ACCEPTED MANUSCRIPT The most important process variables studied using a high shear mixer for granulation were the main impeller speed and the wet massing time (10, 11). In this study, the effect of dry mixing time of the powder blend prior to granulation with fixation of the main impeller speed and the wet massing time was studied in an attempt to clarify the importance of this process factor and its impact on the content uniformity of low dose drug.

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Another important factor of wet granulation is the selection of the binder and its concentration. The effect of various binders on wet granulation had been studied (12). Polyvinyl pyrrolidone (PVP) was found to produce granules and subsequent tablets exhibiting good physical properties (13). In the present study, the effect of different PVP

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concentrations on the properties of the produced granules and tablets was studied.

Response Surface Methodology (RSM) is widely used as a tool for designing experiments in the development and optimization of drug delivery systems including preformulation studies.

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RSM is an information based techniques for mapping multidimensional patterns of responses to varying levels of control factors that are identified to govern physical processes. RSM is dependent on the use of regression analysis on data from experiments carried out at multiple levels and can be used to find approximate minima or maxima in response patterns provided

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such optima are within the design space of regressed points. (14-17).

Chlorpheniramine maleate is, a first-generation alkylamine antihistamine, used for treating allergic conditions, e.g. rhinitis and urticaria (18). Chlorpheniramine maleate was used as a model drug for our investigations since it represents an example of low dose drug employing

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32 randomized full factorial design and optimized in terms of different properties using Response Surface Methodology (RSM) approach.

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Materials and methods

Material

Chlorpheniramine maleate (CLM) (Winlab, England), Avicel PH 101 (FMC biopolymer, Ireland), Povidone(PVP K90) (GAF chemicals corp, USA), and magnesium stearate.(Winlab, England).

Experimental design Randomized factorial experimental design 32 (Expert Design 10, Stat-ease Inc., Minneapolis, MN, USA,) was used to characterize the relationship between the selected parameters and the measured properties of the obtained granules and tablets by performing

ACCEPTED MANUSCRIPT experimental design, polynomial fitting and ANOVA results. Appropriate models were selected by comparing p values and R2 values. Graphs were plotted for statistical significant models at desired confidence levels. The general design used is given in table 1. Response surface methodology (RSM) was used to investigate the effect of independent variables (Binder concentration; X1 and Mixing time X2) on a range of dependent variables. The

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independent variables were evaluated at three levels. The higher and lower levels of each factor were coded as +1 and −1, respectively, and the mean value as 0 (Table 2). Carr’s index (Y1), Hausner ratio (Y2), angle of repose (Y3), tablet hardness (Y4), % tablet friability (Y5), tablet disintegration time (Y6), average tablet content (Y7), RSD for tablet content

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uniformity (Y8) and average particle size (Y9) were used as dependent (response) variables (Table 2).

Response surfaces were constructed using the obtained equations and used as an aid in the

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visualization of process parameter effects on resulting granule and tablet characteristics. The desirability, which is a function that ranges from zero (least desirable) to one (most desirable) was calculated.

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Methods

The evaluations were based on the wet granulation of CLM powder using povidone as a binder and water as a granulating agent. Nine formulations were done using 32 full factorial design as shown in table 1 and 2. Granulation of CLM and excipients was done through the

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following procedure. The high shear mixer with the Gentelwing mechanism (Mycromix, Oyster Huttlen, Germany) was charged with 250 g of the powder blend for each formula. The

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powder was mixed at the main impeller speed of 200 rpm and a chopper speed of 2500 rpm for dry mixing time specified by the design prior to the addition of the granulating solution (Distilled water). The powder was then granulated with the granulating solution for a fixed time of four minutes. The wet mass was then transferred and screened through a 2 mm screen. The resulted granules were dried in the oven overnight at 50 °C. The moisture content of the granules was determined using moisture balance (Mettler PM 480 Deltarange, model LP 16 ) and was kept around 1% for all formulations. The dried granules were then removed, sized and stored for subsequent evaluation and tablet compression. For each formula, part of the produced granules was compressed into tablets using an Ereweka tablet press, model EKO with flat 8 mm punches. The compression force was kept constant and the tablet weight was adjusted to about 200 mg. Each run was performed in triplicate.

ACCEPTED MANUSCRIPT Granule evaluation 1. Sizing: The produced granules were sized by passing through 1mm screen opening sieve and received on 0.31 mm screen opening sieve.

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2. Granule flowability: The flow properties of the granules were determined by measuring the angle of repose.

θ = tan -1 h / r 3. Granulation bulk / tapped density:

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Bulk and tap densities were determined using a 100 ml graduated cylinder. The bulk density was measured by carefully pouring the material into a pre-weighed 100 ml graduated cylinder and was calculated by dividing the weight of the material (g) by the

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volume (ml) occupied in the cylinder. The filled cylinder was then placed on the Vankle tap density tester and tapped to a constant volume. Both bulk and tapped densities were measured. In addition, the flow properties were evaluated using Carr’s index and Hausner ratio measurements.



=



=





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−





× 100

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4. Particle size distribution:

Particle size distribution (n=3) of the granules had been determined by light scattering (Mastersizer 2000, with a Scriocco dry disperser, Malvem Instruments Ltd, UK)

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operated at an inlet air pressure of 1 bar and a feed-rate of 30%. Obscuration was maintained between 0.6% and 6%.

Tablet evaluation 1. Tablet hardness: Tablet hardness was measured using hardness tester (Erweka TBH-28, Germany). The crushing strength of 10 tablets with known weight and thickness of each was recorded in (Kp). The average hardness, SD, and RSD were reported.

ACCEPTED MANUSCRIPT 2. Tablet friability: Twenty tablets were selected from each batch, dedusted and weighed. Each group of tablets was rotated at 25 rpm for 4 minutes in the friabilator (Eletrolab EF-2 USP, India). The tablets were then dedusted and reweighed to determine the loss in weight. Friability

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had been calculated as percent loss in weight.

3. Disintegration time:

The time required for the breakdown of a tablet into smaller particles and/or granules

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which will completely pass through a 10 mesh is defined as the tablet disintegration time. Tablet disintegration time was measured according to USP with a disintegration

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tester (Electrolab ED-2L, India).

4. Content uniformity:

Content uniformity test for each batch was conducted using the USP procedure as follows:

Each of ten tablets weighing 200 mg and equivalent to 4 mg Chlorpheniramine maleate

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(CLM) was accurately weighed, finely powdered, and transferred into a volumetric flask. About 40 ml of 0.1 N HCl was added, sonicated for 10 min, then shaken by mechanical means for 30 min and completed to 50 ml with the same solvent and then sonication and filtration were performed using Millipore filters. The drug content was

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determined spectrophotometrically at 260 nm. The test was repeated on individual ten

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tablets and the average content ± SD was presented.

Results and Discussion

A 32 randomized full factorial design which consists of 9 runs (Table 1& 2) was designed and the effect of independent variables (Binder concentration; X1 and; Mixing time X2) was studied on the response variables (Y1 to Y9) using Design Expert® software trial version 10 (Stat- ease Inc., Minneapolis, MN, USA). Table 3 depicts the values of different response variables obtained for different formulations. The obtained values were fitted into different models, namely linear, 2FI (2 factor interaction), Quadratic and cubic using DesignExpert® software and a suitable model was selected based on p-value and R2 values. The different model fitting values are shown in Table 4. The value of correlation coefficient (R2) is an indication of the appropriateness of the selected model. Adjusted R2 represents the amount of

ACCEPTED MANUSCRIPT variation that can be explained by the model. Predicted R2 represents the amount of variation in the predicted values explained by the model. In the present study, the predicted R2 and the adjusted R2 of the selected models are within 0.20 (as shown in Table 4) of each other indicating appropriateness of the selected models. A quadratic model was selected to response Y9, where the linear model was selected to responses Y1 - Y8. The statistical

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validity of the selected models was further established by ANOVA. The results are summarized in Table 5. From the table, it can be observed that for all the responses, the model terms are significant (p< 0.05) which implies that the selected models are statistically significant and can be used for prediction of responses. These regression results are also

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represented diagrammatically in response surface plots in Figures 1-3. These response surfaces aid in the visualization of results and can be overlaid to give information on factor level selection for the production of granules with competing characteristics.

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The following equations represent the relationship between the dependent variable (Y) and independent variable (X1 and X2) obtained for all the responses.

Y1 = 13.037 - 0.37X1 + 0.56X2 Y2 = 1.188-0.005X1 – 0.002X2

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Y3 = 39.36 – 1.9X1 - 0.29X2 Y4 = 1.84 + 0.42X1 + 0.027X2

Y5 = 1.54 – 0.147X1 - 0.006X2 Y6 = -47.77 + 16.66X1 + 2X2

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Y7 = 96.06 + 1.32X1 + 0.407X2 Y8 = 3.01 + 0.2X1 – 0.013X2

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Y9= 885 + 139X1 + 8.33X2- 9.75X1X2

It can be seen that an increase in the binder concentration has a positive impact on the tested responses. Thus, increasing the binder concentration was followed by an increase in the mean particle size, and imoprovment in the flow properties of granules. The effect of X1 on tablet properties was also shown in Table 3 and Figurs 1-3. An increase in binder concentration resulted in an increase in harndess and decrease % friability and disintegration time. Increasing mixing time decreased granulation flowability. The effect of X2 on tablet properties is also shown in Table 3 and Figures 1-3. An increase in mixing time led to a slight increase in hardness and disintegration time. However, the impact of X1 is more pronounced

ACCEPTED MANUSCRIPT as shown from the above equations. Where the values of X1 are higher than that of X2 in all equations. After establishment and analysis of appropriate models for individual responses, the simultaneous optimization of multiple responses was carried out using Design-Expert® software to find a combination of factor levels that simultaneously satisfies the requirements

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placed on each of the responses and factors. This process identifies those areas in design region where the system is likely to give desirable responses (19, 20). This was done graphically by overlaying critical response contours on a contour plot and identifying the area of feasible response values in the factor space (yellow/ lighter area in Figure 4), as well as

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numerically by using desirability approach. This helps in the interpretation of the data. For calculating desirability function, the desired goal (maximum, minimum, target or within range value) was chosen for each independent and response variable. The goals are then

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combined into a simultaneous objective function, desirability D, which is a geometric mean

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of all transformed responses, calculated according to the following formula (21):

Where, (di) is the desirable range for each response, (ri) is the importance attached to various responses and (n) is the number of responses. Desirability is an objective function that ranges

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from zero (least desirable) to one (most desirable). In the present research, after goal setting of all the responses, the maximum value of desirability was found to be 0.822. The

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optimization step gave 6 checkpoint formulations in the desirable range and predicted 4.6% binder concentration and 5 minutes as the most desirable conditions.

Impact of composition and procedure on content uniformity To reach the uniform content of a low dose drug in the final dosage form while

keeping the production costs low, a suitable composition and procedure have to be chosen. The easiest method for tablet production is direct compression, where all constituents are weighted and mixed in a single container. However, with this method, final content uniformity is influenced by the ratio of constituents and the active ingredient, electrostatic charge, particle density, shape, and size. Significant procedural parameters include the

ACCEPTED MANUSCRIPT mixing order of individual components, mixing time and speed, container shape and container load. The highest degree of content uniformity cannot be reached if the shape, size, and density of mixed particles are not as similar as possible; The ratio of active substance and excipients should be equal and there should not be any electrostatic charge (22). On the other hand, The wet granulation method can overcome such difficulties. (23).

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The effect of particle size on content uniformity has been discussed in the literature (24, 25). Particle size is by far the most essential factor in achieving the necessary flow properties and proper mixing of powders. The results of particle size analysis showed a uniform normal particle size distribution with average particle size (d 0.9) ranged from 0.885-0.982 mm as

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shown in Table 3.The bulk density of powder depends on the particle size distribution, particle shape, and tendency of particles to adhere to one another. The effect of bulk density is less important in this mixing study since bulk densities of the drug and microcrystalline

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cellulose are quite close . USP required content uniformity of 10 samples to fall within 85115 % of the average and RSD not more than 6%. All formulations produced tablets that met the USP specification for content uniformity throughout the whole compression run as in all cases the RSD was less than 6 % and the drug content in tablets was within the acceptable limits of 85 to 115 % of the theoretical label claim as shown in Table 3. %, Hausner

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The observed results of the optimized formula where: Carr's Index12.72

Ratio1.14, angle of repose 35.18 degrees, hardness 5.5 Kp, friability 0.42 %, disintegration time 60 seconds, average of content uniformity 112.18 % with RSD 4.6 %, and d (0.9) 982 µ which are in consistence with the predicted results, having low values of standard error as

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shown in table 6. The mean standard error ranged from 0.22 to 9.75. The exception was the disintegration time, which showed marked decrease from 105 seconds of the predicted result

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to 60 seconds for the observed result indicating good tablet properties of the optimized formula.

CONCLUSIONS

A model low API drug Clorpheniramine, was used to evaluate the impact of the binder concentration and dry mixing time on the produced granules and tablet properties as well as tablet content uniformity. The results show that both evaluated variables had an impact on the produced granules and tablets as well as tablet content uniformity. High shear mixer is found to be an efficient mixer for dry blending prior to the wet massing of a low dose drug formulation when impeller speed and chopper speed were carefully selected. The results of RSM optimization indicated that formulation containing 4.46% Binder concentration at

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ACCEPTED MANUSCRIPT dry mixing time of 5 minutes, gave the most desirable properties for the produced granules and tablets.

Acknowledgment The authors would like to extend their sincere appreciation to the Deanship of Scientific

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Research at King Saud University for its funding this Research group No. RGP -299.

ACCEPTED MANUSCRIPT References: 1- S.G. Late, Y. Yu, A.K. Banga, Effects of disintegration-promoting agent, lubricants and moisture treatment on optimized fast disintegrating tablets, Int. J. Pharm. 365 (2009) 4–11. 2- M. Santl, I. Ilic, F. Vrecer, S. Baumgartner, A compressibility and compatibility of real tableting mixture: the impact of wet and dry granulation versus a direct tableting mixture, Int.

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J. Pharm. 414 (2011) 131–139. 3- T. Martinello, T.M. Kaneko, M.V. Robles Velasco, M.E. Santos Taqueda, V.O. Consiglieri, Optimization of poorly compactable drug tablets manufactured by direct compression using the mixture experimental design, Int. J. Pharm. 322 (2006) 87–95.

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4- P. Di Martino, L. Malaj, R. Censi, S. Martelli, Physico-chemical and technological properties of sodium naproxen granules prepared in a high-shear mixer granulator, J. Pharm. Sci. 97 (2008) 5263–5273.

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5- A. Faure, P.Yourk, R.C. Row, process control and scale up of pharmaceutical wet granulation process: a review, Eur. J. Pharm. Biopharm. 52 (2001) 269-277 6- H. Stahl, comparing different granulation techniques, Pharm. Technol. Eur. 11 (2004) 2333

7- A. Bouwman, M. Henstra, D. Westerman, J. Chung, Z. Zhang, A. Ingram, The effect of

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the amount of binder liquid on the granulation mechanisms and structure of microcrystalline cellulose granules prepared by high shear granulation, Int. J. Pharm. 290 (2005) 129–136 8- F. Hoornaert, P. Wauters, G. Meesters, S. Pratsinis, B. Scarlett, Agglomeration behaviour of powders in a Lodige mixer granulator, Powder Technol. 96 (1998) 116–128.

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9- K. Saleh, L. Vialatte, P. Guigon, Wet granulation in a high shear mixer, Chem. Eng. Sci. 60 (2005) 3763–3775.

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10- P. Holm, O. Jungerson, T. Schaefer, H. G. Kristensen, Granulation in high speed mixers. Part 2: Effects of process variables during kneading, Pharm. Ind. 46(1) (1984) 97-101. 11- T. Schaefer, H. H. Bak, A. Jaegerskou, A. Kristensen, J. R. Svensson, P. Holm, H. G. Kristensen, Granulation in different types of high speed mixer. Part 2: Comparisons between mixters, Pharm. Ind. 49(3) (1987) 297-304. 12- N. A. El-Gendy, M. W. Samaha, H. A. El-Maradny, Evaluation of binder activities on the physical properties and compression characteristics of granules prepared by two different modes, Drug Delv. Ind. Pharm. 14(7) (1988) 977-1005. 13- C.F. Dick, R.A. Clsssen, G.E. Amidon, Determination of the sensitivity of a tablet formulation to variations in excipients levels and processing condtions using optimization techniques, Int, J. Pharm. 38(1987)23-31.

ACCEPTED MANUSCRIPT 14- B. Singh, M. Dahiya, V. Saharan, N. Ahuja, Optimizing drug delivery systems using systematic ―design of experiments—part II: retrospect and prospects. Critical Rev. Ther. Drug Carrier Syst. 22 (2005) 215–294. 15- S. Chopra, G. V. Patil, S. K. Motwani, Release modulating hydrophilic matrix systems of losartan potassium: optimization of formulation using statistical experimental design. Eur. J.

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Pharm. Biopharm. 66 (2007) 73–82. 16- A. Hooda, A. Nanda, M. Jain, V. Kumar, P. Rathee, Optimization and evaluation of gastroretentive ranitidine HCl microspheres by using design expert software. Int. J. Bio. Macromol. 51 (2012) 691–700.

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17- N. Mennini, S. Furlanetto, F. Maestrelli, S. Pinzauti, P. Mura, Response surface methodology in the optimization of chitosan-Ca pectinate bead formulations. Eur. J. Pharm. Sci. 35(2008) 318–325.

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18. A. Issa, O. Mansourb, T. Hammada, Comparative Study of Different Formulations of Chlorpheniramine Maleate Orally Disintegrating Tablets, Int. J. Pharm. Sci. Rev. Res. 28(2) (2014) 234-239

19 – F. M. Espinoza-Escalante, C. Pelayo-Ortiz, H. Gutierrez-Pulido, V. Gonzalez-lvarez,

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V. Alcaraz-Gonzalez, A. Bories, Multiple response optimization analysis for pretreatments of Tequila‘s stillages for VFAs and hydrogen production. Bioresour. Technol. 99 (2007) 5822–5829.

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20- L. Vera-Candioti, J. C. Robles, Mantovani, H. C. Goicoechea, Multiple response optimization applied to the development of a capillary electrophoretic method for

21-

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pharmaceutical analysis. Talanta. 69 (2006) 140–147. L. Kumar, M. S. Reddy, S. R. Managuli, P. K. Girish, Full factorial design for

optimization, development and validation of HPLC method to determine valsartan in nanoparticles, Saudi Pharm. J. 23(5) (2015) 549–555. 22- J. Hilden, M. Schrad, J. Kuehne-Willmore and J. Sloan, A first-principles model for prediction of product dose uniformity based on drug substance particle size distribution, J. Pharm. Sci. 101(2012) 2364–2371.

23. Y. Pu, M. Mazumder and C. Cooney, Effects of electrostatic charging on pharmaceutical powder blending homogeneity, J. Pharm. Sci. 98 (2009) 2412–2421.

ACCEPTED MANUSCRIPT 24. S. H. Yalkowsky, and S. Bolton, Particle size and content uniformity. Pharm. Res. 7 (1990) 962-966.

25. Y. Zhang, and K. C. Johnson, Effect of drug particles size on content

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uniformity of low-dose solid dosage forms. Int. J. Pharm. 154 (1997) 179-183.

ACCEPTED MANUSCRIPT Table 1: Variables in 32 full factorial design Independent variable, Factor Low (-1)

middle (0)

high(1)

2

5

8

X2: dry mixing time (min)

5

10

15

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X1: binder concentration (%)

Dependent variable, Response Y 1: Carr's Index % Y 2: Hausner Ratio

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Y 3: Angle of repose (degree) Y 4: Hardness (Kp)

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Y 5: Friability (%) Y 6: Disintegration (Sec) Y 7: Content uniformity(%) Y 8: RSD (%)

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Y 9: d (0.9) (um)

Table 2: Composition of different CLP powder blinds for granulation

Formula

PVP

F1

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CLP (drug) Avicel PH 101 5

225

20

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(run)

Ingredients (g)

F2

5

225

20

F3

5

225

20

F4

5

232.5

12.5

F5

5

240

5

F6

5

240

5

F7

5

240

5

F8

5

232.5

12.5

F9

5

232.5

12.5

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Table 3: A 32 full factorial design and the values obtained for different response variables. Run

Independent variables

Response/Dependent Variables

B

Y2

Binder

Mixing

%

time

1

8

10

12±0.3

1.13±0.01

35.2±0.5

2.52±0.2

0.42±0.08

60±5

2

8

5

11±0.29

1.12±0.008

36.2±0.5

5.93±0.2

0.1±0.05

3

8

15

10±0.27

1.1±0.01

34±0.4

5.75±0.3

4

5

5

13.2±0.3

1.15±0.01

36.7±0.52

5

2

10

14±0.4

1.16±0.011

6

2

5

14±0.4

1.16±0.011

7

2

15

11±0.26

1.13±0.011

8

5

10

12.6±0.3

1.14±0.009

9

5

15

11±0.3

1.13±0.011

Y9

RSD

d (0.9)

(%)

(um)

112.2±5.1

4.6

982±10

120±10

107.8±5

4.7

952±9

0.2±0.06

180±13

111.5±5.3

4.8

943±8

2.64±0.3

0.5±0.07

30±3

104.7±4.6

4.4

886±8

37.4±0.6

2.34±0.21

0.6±0.1

20±2

103.1±3.9

3.8

698±7

36.9±0.55

2.41±0.19

0.6±0.1

20±2

100.5±3

2.9

656±7

34.5±0.5

2.37±0.19

0.6±0.11

20±2

104.2±4.8

4.7

686±7

36.1±0.45

2.33±0.2

0.6±0.11

20±2

107.2±4.5

4.2

872±8

32.5±0.4

2.54±0.2

0.5±0.1

30±3

109.5±3.8

3.5

885±9

repose

Ratio

Hardness Friability (Kp)

(%)

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(Degree)

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%

Y8

Angle of

Hausner

Y5

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Index

Y4

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Carr's

Y3

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Y1

Y6

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A

Disintegration time(Sec)

Y7 Average Content (%)

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Table 4: Model Summary Statistics for different response variables Model

R2

Sequential

R2

R2

Linear

0.0511

0.9726

0.9270

0.7927

Suggested

Hausner Ratio

Linear

0.0038

0.8434

0.7912

0.6504

Suggested

Angle of repose

Linear

0.0187

0.7343

0.6458

0.5496

Suggested

Hardness

Linear

0.0016

0.8838

0.8451

0.7561

Suggested

Friability

Linear

0.0018

0.8793

0.8391

0.7037

Suggested

Disintegration

Linear

0.0642

0.5994

0.4659

0.5090

Suggested

8.71

0.9556

0.9409

0.8884

Suggested

TE D

Content uniformity Linear

M AN U

Carr’s index

SC

p-value

Adjusted Predicted Comment

RI PT

Response

Linear

0.0394

0.7256

0.6158

0.6888

Suggested

D(90)

Quadratic

0.0002

0.9430

0.9240

0.8608

Suggested

AC C

EP

RSD

ACCEPTED MANUSCRIPT

Table 5: Anova results for the selected models for different responses Source

DF##

Sum of squares

Mean square

Linear model

16.27

5

3.25

Y2

Linear model

0.003

2

0.001

Y3

Linear model

14.686

2

7.343

Y4

Linear model

9.94

2

Y5

Linear model

1.18

2

Linear model

1560

2

Y7

Linear model

119.147

2

Y8

Linear model

2.211

2

Y9

Quadratic model

12370

5

Comment

>F 0.0150

Significant

16.15

0.0038

Significant

8.294

0.0187

Significant

4.97

22.82

0.0016

Significant

0.59

21.85

0.0018

Significant

7800

4.490

0.0443

Sgnificant

59.574

64.689

0.0009

Significant

1.106

6.611

0.0394

Significant

24748.98

144.71

0.0009

Significant

M AN U

TE D EP

AC C

p-value prob

21.33

SC

Y1

F-value

RI PT

Response

ACCEPTED MANUSCRIPT

Table 6: The predicted and observed results of the characterization of the optimized formula

Predicted

Observed

Std Dev

Mean

SE for Mean

CI for Mean 95% CI low

95% CI high

RI PT

Response

12.56

12.72

2.12

1.58

7.54

17.58

Hausner Ratio

1.11

1.14

0.03

0.018

1.07

1.16

34.92

35.18

0.94

33.70

36.13

Hardness (Kp)

5.53

5.5

0.47

0.25

4.93

6.14

Friability (%)

0.31

0.42

0.16

0.09

0.09

0.52

105.56

60

41.68

21.97

51.81

159.30

110.71

112.18

0.96

0.51

109.47

111.94

Disintegration time (sec) Content uniformity

4.71

4.6

0.41

0.22

4.14

5.26

D (0.9) ( µ)

973

982

13.08

9.75

941.98

1004.02

AC C

RSD (%)

EP

(%)

0.49

M AN U

(degrees)

TE D

Angle of repose

SC

Carr's Index %

ACCEPTED MANUSCRIPT Fig 1. Response surface plots for responses, namely (a) Carr’s index, (b) Hausner Ratio,

(b)

AC C

EP

TE D

M AN U

(a)

SC

RI PT

(c) Angle of repose

(c)

ACCEPTED MANUSCRIPT Fig 2. Response surface plots for responses, namely (a) Hardness, (b) friability, (c)

TE D

M AN U

(a)

SC

RI PT

Disintegration

AC C

EP

(b)

(c)

ACCEPTED MANUSCRIPT Fig 3. Response surface plots for responses, namely (a Content uniformity, (b) RSD, (c)

(b)

AC C

EP

TE D

M AN U

(a)

SC

RI PT

d(0.9)

(c)

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

Fig 4: The desirability overlay plot