Nicotinamide pelletization by fluidized hot melt granulation: L18 Hunter design to screen high risk variables

International Journal of Pharmaceutics 466 (2014) 83–95

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International Journal of Pharmaceutics journal homepage: www.elsevier.com/locate/ijpharm

Nicotinamide pelletization by fluidized hot melt granulation: L18 Hunter design to screen high risk variables Ahmed S Zidan a,b, *, Mohamed Ebeed b,c, Hanaa Elghamry b , Alaia Badawy d a

Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Zagazig University, Zagazig, Egypt c Deef Pharmaceuticals Inc., Al Badaye, Al Qassim, Saudi Arabia d Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Cairo University, Cairo, Egypt b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 31 December 2013 Received in revised form 5 February 2014 Accepted 2 March 2014 Available online 10 March 2014

L18 Hunter design was used to investigate the practicability of applying QbD approaches to fluidized hot melt granulation (FHMG) in preparing oral controlled release systems. Eight high-risk variables obtained from risk analysis were classified into chemical factors (type and percentage of meltable binder, matrix viscosity and percentage and filler type) and process variables (size fraction of meltable binder, inlet air volume and fluidization time). The variables were screened for their impacts on pellets characteristics. The obtained results showed that the meltable binder percentage was the significant variable affecting most responses. Flow properties, size distribution, bulk, and tapped densities were significantly (P < 0.05) affected by the filler type, inlet air volume, and fluidization time. On the other hand, the matrix variables were non-significant to the dissolution parameters. Out of eight critical variables, it was found that the meltable binder percentage and size fraction, inlet air volume had the most significant effects and will be optimized in the second part of the study. In conclusion, QbD paradigm not only offered a robust FHMG technique to formulate controlled release formulations of hydrophilic drugs but also provided a time and cost saving advantage to pharmaceutical industry. ã 2014 Elsevier B.V. All rights reserved.

Keywords: Quality by design Fluidized hot melt granulation Risk analysis Nicotinamide Hunter design

1. Introduction Fluidized hot melt granulation (FHMG) is a technique experiencing considerable attention as it can be used in a wide array of pharmaceutical applications (Kukec et al., 2012). In the classical granulation techniques, binder, filler, solubilizer and many other raw material may be utilized for achieving the formulation goal; however in FHMG, the multifunctional excipients are highly considerable. It employs binders with a relatively low melting range (50–80
C) to act as molten binding liquid. The advantages offered by meltable binders make FHMG more preferred than other granulation methods, considering not only the process is simple, rapid, suitable for moisture-sensitive drugs and cost effective but also no risks are associated with residual solvents in the final product (Kraciuk and Sznitowska, 2011; Masic et al., 2012). Moreover, better control of the product temperature is achieved with FHMG, which simplifies the whole process allowing the cooling phase to be done easily and rapidly

* Corresponding author at: Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, King Abdulaziz University, P.O. Box: 80260, Jeddah 21589, Saudi Arabia. Tel.: +966 564 266 682; fax: +966 126 951 696. E-mail addresses: [email protected], [email protected] (A.S. Zidan). http://dx.doi.org/10.1016/j.ijpharm.2014.03.008 0378-5173/ ã 2014 Elsevier B.V. All rights reserved.

in the same equipment. Regarding the low shearing forces in fluid bed processor, FHMG is capable of producing spherical agglomerates with high binder concentration than high-shear mixer and rotary processor (Vilhelmsen et al., 2004). The meltable binder can be added while processing by two methods. The first is spraying the molten binder on the powder through a nozzle. The second is incorporating the meltable binder within the powder initially. Hence, the mechanism for powder agglomeration would be either coating the particle surface with the meltable binder or immersing the solid particles within the molten binder droplets (Bukovec et al., 2009). Schæfer et al. it has reported that the occurrence of either mechanism or both was mainly controlled by the proportion of the size of molten binder droplets to that of solid particles (Schæfer and Mathiesen, 1996). It was found that agglomeration by immersion was achieved when larger molten binder droplets than the solid particles existed. However, agglomeration by distribution occurred when the molten binder droplets were smaller than the solid particles. Recent studies showed that uniform spherical pellets can be produced in fluid bed granulator when the main mechanism of agglomeration is immersion and layering (PauliBruns et al., 2010). Selecting the appropriate meltable binder allows for preparation of controlled or immediate release dosage forms (Pauli-Bruns et al., 2010; Sandhu et al., 2007) as well as enhancing the bioavailability of poorly water soluble drugs (Passerini et al., 2010).

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Hydrophilic meltable binders such as PEGs, gelucire and poloxamer can be used to achieve the desired product profile (Seo et al., 2002). Regarding the immediate release products, it can be used as binders and solubilizers; whilst in modified release products, it is used as binders and pore formers (channeling agent). On the other hand, hydrophobic meltable binders such as stearic acid, glyceryl behenate, and hydrogenated castor oil are employed in modified release products as binders and dissolution modifiers (Kukec et al., 2012). Previous studies have provoked FHMG for the preparation of granules with excellent flow characteristics and suitability for processing into tablets, capsules, granules and/or pellets. The synchronized combination of sustained release pellet formulation with FHMG as a one-step and one-machine process can have an economical positive interest to pharmaceutical industry. To achieve the proper results of this combination, quality by design (QbD) paradigm was utilized in the current research as one of the recent tools in pharmaceutical development (Adam et al., 2011; Aksu et al., 2013). According to ICH guidelines Q8 (R2), two important elements must be covered during the pharmaceutical development (ICH-Q8, 2006). The first is quality target product profile (QTTP) while the second is critical quality attributes (CQA) (Charoo et al., 2012). QTTP is the basic point in the design of product. It describes the criteria that ensures the quality, safety, and efficacy of the product and usually includes intended use, dose, strength, dosage form, and route of administration. CQAs identify what is drug product attributes which are critical to the patient. After determination of CQAs, a suitable strategy is applied to control these attributes. Risk management is one of the most common and accepted strategies in this regard. Application of risk management shall be done at sequential steps as initial risk assessment followed by appropriate justification and risk reduction. These shall be done for the drug substance, formulation, and process attributes. In this prospect, nicotinamide was employed in the current study as a model drug to prepare sustained release pellets by FHMG. Nicotinamide is freely soluble in water and is heat-stable with melting range 128–131
C. It can be assayed UV-spectrophotometrically at 261 nm. Nicotinamide has a biological half-life of 4 h (Bussink et al., 2002). Therapeutically, nicotinamide is used to protect the specialized cells in the pancreas, favoring a normal response to glucose metabolism and glucose disposal (Bussink et al., 2002). This study covers the continual study following quality risk assessment and screening design, which includes design of experiment campaign, in conjunction with multivariate data analysis, to achieve enhanced process understanding. In particular, using L18 hunter design as a case study of QbD, the aim of the present study was to screen and evaluate effects of the FHMG design factors on manufacturability and final nicotinamide sustained release pellets CQAs. 2. Materials and methods Nicotinamide was supplied from BDH Laboratories (Poole, UK). Microcrystalline cellulose (Avicel PH 101) was purchased from FMC BioPolymer (Philadelphia, PA, USA). Methocel K4M, methocel K15M, and methocel K100M were received from Dow Chemical Corporation, Midland, MI, USA). Dibasic calcium phosphate anhydrous

(anhydrous Emcompress) and starch 1500 were purchased from JRS pharma LP (Patterson, NY, USA). PEG 3350 was purchased from Sasolwax Gmbh (Hamburg, Germany). Poloxamer 407 was purchased from BASF Corporation (Tarrytown, NY, USA). PEG 3350 and Poloxamer 407 were pre-milled through 1000 micron sieve using CHITRA multi-mill (Chitra Machineries Private Limited, Gujarat, India) and two fractions from each material was collected. First fraction is 355–500 mm to represent the small size while the second fraction of 500–710 mm to represent the coarse fraction. Characteristics of the utilized excipients were summarized in Table 1. 2.1. Quality target product profile QTPP is one of the important elements of QbD to include CQAs as well as product and process design and control strategies. QTPP is defined based on the properties of the drug substance, characterization of the reference listed drug (RLD) product, and consideration of the RLD label and intended patient population (Xu et al., 2012). On the other hand, CQA is a physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality (Charoo et al., 2012). The identification of a CQA from the QTPP is based on the severity of harm to a patient should the product fall outside the acceptable range for that attribute. All quality attributes are target elements of the drug product and should be achieved through a good quality management system, appropriate formulation/process design and development. From the perspective of pharmaceutical development, we investigated the QTTP and subset of CQAs of nicotinamide sustained release pellets prepared by FHMG that were mostly impacted by the formulation or process variables (Table 2). Our investigation culminated in an appropriate control strategy. 2.2. L18 Hunter screening design The design of experiment is a very powerful methodology to mine critical factors for a process and/or formulation which may be investigated in their optimization. For the last decade, regulatory agencies reinforce to use design of experiment strategies for screening and optimization designs as it provide plenty of data while using minimal resources. Thus it is a cost effective strategy of pharmaceutical development compared to traditional methods. In the present investigation, Hunter L18 screening design was constructed using JMP software (version 10, SAS inc., NC, USA). In the preliminary experiments, initial risk assessment was performed for a set of different possible process and formulation factors using risk priority number (RPN). The independent variables selected were filler type (X1), polymer viscosity (X2), polymer loading (X3), meltable binder percentage (X4), meltable binder type (X5), meltable binder size fraction (X6), inlet air volume (X7), and fluidization time (X8) (Table 3). Out of these eight independent variables, X1, X2, X5 and X6 were the categorical variables and rests were continuous nature. Three levels were investigated for X1, X2, X3, X4 and X8 and two levels

Table 1 Characteristics of the employed excipients. Avicel PH101 Starch 1500 Anhydrous emcompress Methocel K4M Methocel K15M Methocel K100M PEG 3350 Bulk density (g/ml) Tapped density (g/ml) Mean particle size (mm) Specific surface area (m2/g) Viscosity (cP) Molecular weight (Da) Melting point (
C)

0.32 0.45 50 1.06–1.12 – – Chars at 265
C

0.586 0.879 52 0.261 – – –

0.78 0.82 136 20–30 – – Decomposed at 425
C

0.341 0.557 – – 4000 – Chars at 230
C

0.341 0.557 – – 15,000 – Chars at 230
C

0.341 0.557 – – 100,000 – Chars at 228
C

– – – – 76–110 3000–3700 51
C

Poloxamer 407 – – – – 1000 9840–14,600 55
C

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Table 2 QTPP and CQAs for nicotinamide 250 mg sustained release pellets. QTTP element

Target

Therapeutic indication

Prophylaxis and treatment of vitamin B3 deficiency

Dosage form Patient population Route of administration Dosage strength Stability Container closure system

Justification

Nicotinamide is part of co-enzymes, NAD and NADP, regulating glycogenolysis, lipid metabolism and tissue respiration for electron transfer reactions Sustained release Pellets Suitable for tableting or encapsulated For adults, except pregnant, lactating, and geriatrics Effect on these groups still non-reported Oral According to RLD 250–500 mg According to RLD At least 24 months shelf-life below 30
C Equivalence or better than reference product shelf-life Suitable container closure system to achieve the target shelf-life PET bottles were selected based on similarity to the RLD packaging. No and to ensure tablet integrity during shipping further special protection is needed due to the stability of Nicotinamide

CQAs

Target

Justification

Appearance

Spherical, symmetrical, and smooth free-flowing pellets

To ensure a high degree of dose and mass uniformity during tableting or encapsulation Critically related to efficacy and quality Compressibility Carr's compressibility index: NMT 20% Good compressibility ensures smooth tableting process. Critically related to quality Flowability Flow rate: NLT 10 g/min Good flowability ensures smooth tableting process and high degree of Hausner's ratio: NLT 1.10 dose and mass uniformity Critically related to efficacy and quality Porosity/surface area – Porosity and surface area of pellets can affect drug release. Critically related to efficacy and quality Assay 90–110% USP monograph for nicotinamide tablets Critically related to efficacy and quality Particle size NLT 80% of pellets shall be between 355–800 mm Pellets between 355–800 mm were the most suitable for tableting or encapsulation Critically related to efficacy and quality Dissolution Dissolution profile shall be zero order or almost zero order during 12 h Releasing of 250 mg over 12 h in zero order manners will be more acceptable by the patient Critically related to safety, efficacy and quality

were studied for the rest (Table 3). The responses measured were bulk, tapped, and true densities, Carr’s index, Hausner’s ratio, flow rate, angle of repose, yield percentage, particle size, drug assay and release characteristics. The responses were aimed to meet the USP specification of dissolution and potency (USP35/ NF30, 2012). A synchronized combination of risk assessment and Hunter design was used to understand the process and product characteristics. The combination was done in different stages.

In first stage, RPN was utilized to filter the factors into two main categories: factors with negligible RPN values (less than 40) and factors with considerable RPN values (Fig. 1). In second stage, factors with considerable RPN values were screened by incorporation into L18 Hunter design as mentioned. In third stage, multiple regression and ANOVA analysis were used to determine the most critical factors. Finally, a study is in progress to incorporate the critical factors in response surface design for final optimization.

Table 3 Factors combinations used for Hunter L18 screening design. Trial No.

Filler type (X1)

Polymer viscosity (X2)

Polymer loading (%w/ w) (X3)

Meltable binder (%w/w) (X4)

Meltable polymer type (X5)

Size fraction meltable polymer (mm) (X6)

Inlet air volume (m3/h) (X7)

Fluidization time (min) (X8)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Avicel Avicel Avicel Avicel Avicel Avicel Avicel Avicel Avicel Emcompress Emcompress Emcompress Emcompress Emcompress Emcompress Starch Starch Starch Starch Starch Starch

K100M K100M K15M K15M K4M K4M K4M K4M K4M K100M K100M K15M K15M K4M K4M K100M K100M K15M K15M K4M K4M

10 20 10 20 10 15 15 15 20 10 20 10 20 10 20 10 20 10 20 10 20

25 15 25 15 15 20 20 20 25 15 25 15 25 25 15 25 15 15 25 15 25

PEG 3350 Poloxamer Poloxamer PEG 3350 PEG 3350 PEG 3350 PEG 3350 PEG 3350 Poloxamer PEG 3350 Poloxamer PEG 3350 Poloxamer Poloxamer PEG 3350 PEG 3350 Poloxamer Poloxamer PEG 3350 Poloxamer PEG 3350

355–500 500–710 355–50 500–710 500–710 355–500 355–500 500–710 355–500 355–500 500–710 355–500 500–710 500–710 355–500 500–710 355–500 355–500 500–710 500–710 355–500

50 30 30 50 50 30 50 30 30 30 50 30 50 50 30 30 50 50 30 30 50

5 20 20 5 20 12.5 12.5 12.5 5 5 20 5 20 5 20 20 5 20 5 5 20

407 407

407 407 407 407

407 407 407

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Fig. 1. Risk assessment using risk priority number (RPN) determination.

2.3. Fluidized hot melt granulation

from the equation (USP35/NF30, 2012):

A laboratory fluid bed dryer (Hüttlin Solid lab-1, Bosch GmbH, Stuttgart, Germany) was used for the hot melt granulation process. The fluid bed consisted of stainless steel container (0.25–1.0 kg capacity) with a unique structure of diskjet diffusion bottom providing optimum fresh air distribution and toroidal product movement and a three filter bags at the top. The hot melt granulation process was done in three steps. The first step was heating the powder using inlet air temperature at 65  2
C to ensure complete melting of the meltable binder. When product temperature reached 65
C, the second step, fluidization started. In this step, the powder was fluidized at the specified inlet air volume (30, 40, or 50 m3/h) for the specified time (5, 12.5, or 20 min) and maintained inlet air temperature of 65  2
C. The third step, cooling, started after ending of fluidization step. In the cooling step, an inlet air volume was maintained as specified and inlet air temperature was adjusted to 25  2
C. Finally, the process stopped when product temperature reached 25  2
C.

Tan u ¼

2.4. Determination of bulk and tapped densities Tapped density of the prepared pellets was determined using the automated tapped-density tester (PT-TD1, Pharma test Inc., Hainburg, Germany). The device includes a cam-shaft assembly, which causes a stage to tap through a vertical height of 3 mm at a rate of 250 taps per minute. The pellets powder (100 g) was added to the 100 mL graduated cylinder and the initial volume was recorded to calculate the bulk density. The number of taps was then set to 1500 taps and the tapped volume was recorded. The procedure was repeated at increments of 400 taps until the difference in tapped volume was less than 1 mL and the final tapped volume was measured. 2.5. Calculation of Carr’s index and Hausner’s ratio Carr’s compressibility index and Hausner’s ratio were calculated according to the following equations: Corr0 s compressiblity index ¼

Tapped density  Bulk density  100 Tapped density

Cone height base radius

(3)

On the other hand, powder flow rate was calculated by measuring the time required for a predetermined powder mass to settle down from the fixed hopper into the base. 2.7. Calculation of yield percentage Yield percentage was calculated using the following equation: Yield percentage ¼

W2  100 W1

(4)

where W2 is the actual weight of pellets at the end of the process and W1 is the theoretical weight of the powder before process. 2.8. Particle size distribution Sieve fraction assay analysis was performed to determine the composition of the pellets in each sieve fraction. 100 g sample of the granular powder was sieved through 6 sieves using a mechanical dry sieve shaker (AS 200 basic, Retsch GmbH, Haan, Germany), at an amplitude of 0.5 mm for 10 min, to measure the granule size distribution and to collect at least 15 g of pellets in the a range sieve fractions of 125–800 mm. Granular sizes expressed as d10, d50, and d90 were further calculated from linear interpolation of the cumulative percentage frequency curve. 2.9. Determination of nicotinamide assay Nicotinamide assay was done on pellets fraction between 500 and 800 mm. Accurate weights of pellets equivalent to 250 mg nicotinamide were finely powdered using pastel and mortar then transferred to 1000 mL volumetric flask. Double distilled Milli-Q water was added to the mark and samples were sonicated for 10 min. 10 mL of the solution was filtered and quantitated by UV spectrophotometer (UV-1650PC, Shimadzu Corp, Kyoto, Japan) at 261 nm against standard calibration curve with a range from 0.025 to 0.25 mg/mL. All determinations were performed in triplicate.

(1) Housner0 s ratio ¼

Tapped density Bulk density

2.10. In-vitro nicotinamide dissolution (2)

2.6. Determination of flow rate and angle of repose The angle of repose was done using fixed hopper (200 mL) and fixed base with a retaining lip to allow formation of symmetric cone by the powder. The hopper was vibration free and was posed at distance of 4 cm from the top of powder cone. The height and base of powder cone were measured and angle of repose calculated

Dissolution studies were performed using dissolution tester equipped with online auto-sampler (PTWS, PTFCII, Pharma test Inc., Hainburg, Germany). Dissolution test parameters were apparatus II at 50 rpm using 900 mL of purified water as a dissolution media. Dissolution media samples were withdrawn at the predetermined time intervals of 1, 2, 4, 8, and 12 h. Withdrawn samples were filtered and UV spectrophotometrically assayed (UV1650PC, Shimadzu Corp, Kyoto, Japan) at 261 nm. All determinations were performed in triplicate.

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DSC analyses of individual ingredients and the produced pellets were performed using Diamond differential scanning calorimeter (PerkinElmer Corp., Waltham, MA, USA). Accurately weighed samples (2–3 mg) were placed in hermetically pierced aluminum pans at a heating rate of (10
C/min) over a temperature range of 25–300
C in a nitrogen atmosphere.

factors were classified as low risk variables, namely channeling agent type and concentration, inlet air temperature, filter bags shaking time and pressure. Inlet air temperature was a low risk factor due to nature of the granulation process as the melting point of PEG and poloxamer were about 60
C. Hence, the process was performed at a temperature above 65
C and performing at proposed levels of 65, 75 and 85
C were not significant. It is also worth noting that performing at 85
C was out of fluid bed working range and running at 75
C resulted in a fluidization time more than 20 min and a heater fault error in the machine was produced. Filter bags shaking time and pressure displayed low risks due to location of filters at the upper part of fluidization chamber. Therefore, a batch size of 150 g that occupied almost 15% of total capacity of fluid bed container was unlikely to be affected by the filter bags shaking time and pressure. On the other hand, the other variables, namely filler type, polymer viscosity, polymer loading, meltable binder percentage, meltable binder type, meltable binder size fraction, inlet air volume, and fluidization time, were deemed high risk factors and were investigated in details during this screening study using L18 Hunter design.

2.13. Surface morphology

3.2. Bulk density

The shape and surface of the aggregates were inspected using a scanning electron microscope (SEM) (SSX-500, Shimadzu, Japan) at an accelerating voltage of 15 kV after sputter coating with gold/ palladium under vacuum. Thin sectors of the pellets samples were also prepared and examined for the presence or absence of drug particles.

Bulk density has remarkable influence on pellets handling and storage characteristics. It is affected by pellets’ composition, size, shape, moisture content, specific density and processing parameters. Bulk density of pellets rises during shipping, handling, and storage due to vibration and/or tapping. Hence, density variation of the produced pellets is significant for capacity sizing and supply logistics. Throughout the studied design space of the variables, bulk density of the produced pellets ranged from 0.48 g/mL (batch 3) to 0.72 g/mL (batch 18). The regression analysis results revealed that both filler type and inlet air volume were the most significant factors affecting bulk density (Table 4). Among the three investigated fillers, starch 1500 was found to be highly significant and showed a direct effect on the bulk density. On the other hand, avicel and emcompress exhibited inverse relationships. This may be attributed to the starch 1500 median particle size (52 mm) and bulk density (0.586 g/mL) that allowed pelletization via nucleation. Direct relationship was also observed for the influence of inlet air volume on bulk density. Inlet air volume was deemed critical factor during the granulation process. Increasing inlet air volume led to an increase in movement of particles during agglomerates growth to result in the formation of slightly dense and highly bounded pellets with high bulk density (Fig. 2). Bulk density was also increased by increasing the polymer viscosity, percentage and size fraction of the meltable binder (Table 4). Increasing the percentage of meltable binder within the powder matrix led to the formation of meltable nuclei for agglomerates growth to augment the pellets bulk density. On the other hand, despite the similar properties of PEG 3350 and poloxamer 407 as meltable binders, both showed variation in the bulk density. PEG 3350 and poloxamer 407 have almost similar melting points of 54–55
C and comparable densities of 1.07 g/mL. However, the higher viscosity of poloxamer 407 (1000 cP) than that of PEG 3350 (83–120 cP) resulted in higher bulk densities of its pellets (Table 4). Despite being nonsignificant to bulk density, inverse relationship was observed with the fluidization time (Table 4). The massive attrition of the primary pounded pellets on fluid bed walls by increasing the fluidization time caused softening of the formed pellets with a decrease in its bulk density. One of the important functions of the multiple regression models is the predictability of each response based on the equation that explains response–variables relationships. The predictability of bulk density by the model was acceptable (p = 0.013) with a regression coefficient (R2) of 0.8183 for plotting the predicted bulk density versus the actual values. After neglecting the insignificant

2.11. Fourier transform infrared spectroscopy (FTIR) FTIR spectra of the prepared pellets as well as the individual components were recorded using a Nicolet(tm) iS(tm) 10 FT-IR spectrometer (Thermo Fisher Scientific Inc., Madison, WI, USA) coupled with attenuated total reflectance (ATR) sampling accessory. Ten mg of each sample was placed on the diamond surface plate of the ATR. The piston handle was then positioned on the sample to generate enough pressure for close contact compression. The scanning range of 100 scans per sample was 650–4000 cm1 and the resolution was 4 cm1. 2.12. Differential scanning calorimetry (DSC)

2.14. Statistical analysis Statistical significance was determined using analysis of variance. In all measurements, at least three replicates were recorded and in all cases, P < 0.05 denoted significance. For all the obtained results, standard deviations less than 4% of the stated values were obtained. 3. Results and discussion 3.1. Determination of high and low risk factors according to RPN Risk assessment of the formulation factors was performed using RPN determination, which could identify the high risk factors that have the highest impact of triggering product failure, i.e., not fulfilling the QTPP. The relative risk that each formulation or process factor disseminates was ranked according to its RPN (Vora et al., 2013). Those variables that showed high impact on the drug product performance were considered for the screening study whereas those factors that had low risk were neglected. RPNs were calculated according to the following equation: RPN ¼ ð1; 2; or 3ÞO  ð1; 2; 3; 4 or 5ÞS  ð1; 2; or 3ÞD

(5)

where O is the occurrence or the chance of an event to occur; it was ranked as 3, frequent; 2, occasional; 1, improbable to occur. The next term S is the severity to express how severe of a variable to cause failure; it was ranked as 5, catastrophic; 4, critical; 3, serious; 2, minor and 1, negligible. The final term D is the detectability to express the easiness to detect failure due to a variable; it was ranked as 1, easily detectable; 2 moderately detectable and 3 as hard to detect or absolute uncertain. Therefore, the more detectable failure occurred due to a factor, the less risk it presented to product quality. Fig. 1 shows the variables that were considered in development oral controlled release systems of nicotinamide while performing risk assessment. In the present study, the RPN  40 was considered as high risk, and <40 was considered as low risk. Fig. 1 shows that five

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Table 4 Regression analysis results of Hunter L18 screening design. Factors

X1 Avicel X1 Starch X1 Emcompress X2 K4M X2 K15M X2 K100M X3 X4 X5 PEG 3350 X5 Poloxamer 407 X6 355–500 (mm) X6 500–710 (mm) X7 X8 R2

Estimate P-value Estimate P-value Estimate P-value Estimate P-value Estimate P-value Estimate P-value Estimate P-value Estimate P-value Estimate P-value Estimate P-value Estimate P-value Estimate P-value Estimate P-value Estimate P-value Estimate P-value

Tapped density (g/ml)

Hausner's ratio

Carr's index (%)

Flow rate (g/ min)

Angle of repose (
) Pellets size d10

0.6 0.0001 0.01 0.1652 0.05 0.0013 0.03 0.0127 0.01 0.5345 0.01 0.7934 0.01 0.7471 4.65 0.9954 0.01 0.0983 0.01 0.1275 0.01 0.1275 0.01 0.0836 0.01 0.0836 0.02 0.0355 0.01 0.1126 0.8183

0.7 0.00001 0.01 0.4956 0.04 0.0162 0.03 0.0502 0.01 0.5061 0.02 0.0905 0.02 0.2470 0.02 0.0882 0.02 0.0361 0.01 0.6432 0.01 0.6432 0.03 0.0050 0.03 0.0050 0.01 0.2397 0.03 0.0053 0.8288

1.1 0.0001 0.01 0.1588 0.01 0.4936 0.01 0.4936 0.02 0.0102 0.01 0.0958 0.01 0.2942 0.01 0.0492 0.01 0.0494 0.01 0.0034 0.01 0.0034 0.01 0.9456 0.01 0.9456 0.01 0.5757 0.01 0.5921 0.8305

11.9 0.0001 0.57 0.1554 0.37 0.3801 0.21 0.6170 1.24 0.0080 0.72 0.1020 0.52 0.2279 0.83 0.0191 0.81 0.0212 0.99 0.0057 0.99 0.0057 0.04 0.8950 0.04 0.8950 0.17 0.5434 0.30 0.3316 0.8363

12.9 0.0001 0.59 0.0435 0.39 0.1903 0.21 0.4698 0.32 0.2447 0.16 0.5764 0.48 0.1141 0.48 0.0397 1.65 0.0001 0.81 0.0020 0.81 0.0020 0.14 0.4796 0.14 0.4796 0.62 0.0084 0.54 0.2400 0.9138

26.5 0.0001 0.18 0.4963 0.18 0.5450 0.01 0.9748 0.15 0.5831 0.49 0.1107 0.34 0.2502 0.42 0.0672 0.83 0.0026 0.56 0.0183 0.56 0.0183 0.09 0.6502 0.09 0.6502 0.39 0.0696 0.42 0.0672 0.7898

Pellets size d50

131.4 546.5 0.00017 0.0001 37.52 33.7 0.0097 0.0007 18.2 27.02 0.1780 0.0048 19.26 6.69 0.1574 0.3925 23.31 8.22 0.0756 0.2669 34.91 7.86 0.0198 0.3187 11.59 0.38 0.3792 0.9625 17.42 12.77 0.0902 0.0433 21.82 40.47 0.0407 0.0001 19.22 11.49 0.0568 0.0556 19.22 11.49 0.0568 0.0556 32.23 5.84 0.0038 0.2800 32.23 5.84 0.0038 0.2800 27.83 11.95 0.009 0.0417 21.82 13.59 0.0407 0.0335 0.8470 0.9176

Pellets size d90

Drug assay (%)

Yield (%) Q 1 h

Q 12 h

684.2 0.0001 24.08 0.0006 43.04 0.0001 18.95 0.0049 6.08 0.2448 8.04 0.1583 1.95 0.7182 2.74 0.4962 22.12 0.0002 13.29 0.0052 13.29 0.0052 12.37 0.0064 12.37 0.0064 12.12 0.0072 5.62 0.1784 0.9384

96.1 0.0001 1.41 0.0385 3.95 0.0001 2.54 0.0025 0.61 0.3205 0.96 0.1582 0.34 0.5947 0.42 0.3918 0.95 0.0685 2.04 0.0011 2.04 0.0011 0.02 0.9623 0.02 0.9623 0.02 0.9695 0.44 0.3640 0.8809

94.6 0.0001 1.82 0.0001 0.15 0.5238 1.67 0.0001 0.56 0.0238 0.67 0.0135 1.23 0.0003 0.41 0.0344 0.78 0.0009 0.27 0.1228 0.27 0.1228 1.31 0.0001 1.31 0.0001 0.16 0.3150 0.83 0.0005 0.9596

95.9 0.0001 1.32 0.1287 2.91 0.0067 1.58 0.0922 0.48 0.5582 0.62 0.4791 0.15 0.8681 0.59 0.3680 1.48 0.0397 2.83 0.0008 2.83 0.0008 0.69 0.2602 0.69 0.2602 0.37 0.5383 0.73 0.2713 0.8250

65.9 0.0001 0.61 0.4743 2.95 0.0072 2.34 0.0236 1.35 0.1289 2.07 0.0399 0.72 0.4336 5.01 0.0001 5.75 0.0001 5.93 0.0001 5.93 0.0001 0.22 0.7236 0.22 0.7236 0.93 0.1517 1.97 0.0121 0.9636

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Intercept

Bulk density (g/ ml)

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variables, the reduced linear model equation that explains effect of significant factors on bulk density can be expressed as: Bulk densityðg=mLÞ ¼ 0:56

0

Avicel þðfiller typeÞ@ Startch Emcompress

1 ) 0:01 ) 0:04 A ) 0:03

þ0:001  Inlet air volume ðm3 =hÞ 3.3. Tapped density Tapped density calculated after 1500 tapings was highest for batch 7 (0.857 g/mL) and lowest for batch 3 (0.545 g/mL) with an average tapped density of all formulations of 0.73 g/mL. Similar trends were obtained for effects of the studied variables on both bulk density and tapped bulk density with some variations. Fluidization time and size fraction of the meltable binder were the most significant factors affecting tapped density of the pellets followed by percentage of meltable binder and using starch 1500 as a filler (Table 4). The other variables were nonsignificant for their contribution to tapped density. Negative effects were observed for fluidization time and using small size fraction of the meltable binder; whereas positive effects were observed for percentage and large size fraction of meltable binder as well as using starch 1500 (Fig. 2). The contribution of fluidization time to increase the attrition and friction among the primary pellets and the fluidization chamber resulted in formation of fragile pellets with low tapped density. The variation in tapped density by

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changing the size fraction of the meltable binder can be explained based on the variation observed in the absolute compressibility of the pellets occurred during tapping, and abridged effect of particle rearrangement due to smaller size fraction (Chevanan et al., 2010). On the other hand, the positive effect of binder percentage can be explained by the formation of harder pellets with more resistance for compaction by tapping.The positive effect of starch on tapped density can be explained by its fairly uniform physical characteristics. On the other hand, avicel and emcompress contained both fibrous and other irregular shaped particles that affected the particle arrangement and packing during filling. This may have led to negative estimate values for avicel and emcompress within the prediction expression of tapped density. The predictability of tapped density by the model was acceptable (p = 0.0100) with a regression coefficient (R2) of 0.8288 for plotting the predicted tapped density versus the experimental tapped density. After neglecting the insignificant variables, the reduced linear model equation that explains effect of only the significant variables on pellets’ tapped density can be expressed as: Tapped density ðg=mLÞ ¼ 0:65 þ 0:036  ðStarchÞ   ½Percentage of meltable binder  25 þ0:02  5 þðSize fraction of the meltable polymerÞ     Fluidization time  12:5 355  500 mm ) 0:03  0:03  500  710 mm ) 0:03 7:5

Fig. 2. Response surface and contour plots of the investigated factors on the responses.

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3.4. Carr’s index and Hausner’s ratio Carr’s index and Hausner’s ratio are measures of the flow properties of pellets. The higher Hausner’s ratio indicated that the pellets are more cohesive and less able to flow freely (Sune-Negre et al., 2011). In particular, a Hausner’s ratio more than 1.25 indicates a free flowing pellets and values less than 1.25 indicates poor flow characteristics. The smaller the Carr’s index the better the flow properties. For example, 5–15 indicates excellent flow, 12–16 is good flow, 18–21 is fair flow; and above 23 indicates poor flow (Lee and Hsu, 2007). Within the investigated design space of the studies variables, the produced pellets from all batches were freely flowing with Hausner's ratio less than 1.25 and Carr's index less than 18%. Since Carr’s index and Hausner’s ratio are derived from bulk and tapped density value, the effects of factors on both attributes were more or less close. The most significant factors affecting both responses were the meltable binder type and percentage (Table 4). The direct relationship of Poloxamer 407 as a meltable binder with the bulk density resulted in a reduction of the numerical value of Carr’s index and Hausner’s ratio. Hence, an improvement of pellets flowability was obtained. These results are observed in response surface and contour plots in Fig. 2. Similar trend was observed by increasing the meltable binder percentage on both responses (Fig. 2). This finding revealed that the sphericity of prepared agglomerates increased at higher amount of the meltable binder which might be due to the roughness imparted by the other ingredients on growing agglomerates (Garala et al., 2012). The influences of the other nonsignificant factors on both responses were comparable to those on bulk and tapped bulk densities. Using the larger size fraction of the meltable binder, higher inlet air volume, or lower fluidization time led to the formation of pellets with greater bulk density and excellent flowability (Table 4). Effects of polymer viscosity and percentages were minimal with confidence p-values approaching the nonsignificant limit (p = 0.49); hence they were neglected to develop the predictability function. The constructed model showed good predictability of p = 0.0096 and 0.0083 for Hausner’s ratio and Carr’s index with quantile–qualntile correlation coefficients of 0.8305 and 0.8363, respectively. The linear reduced model equations to predict both responses are: Hausner0 s ratios

  ½Percentage of meltable binderð%w=wÞ  25 5   PEG 3350 ) 0:014 þ ðMeltable polymer typeÞ Polaxmer 407 ) 0:014

and advanced formation of bridges and arches in the pellets’ powder and an impaired flow would result (Radjai and Richefeu, 2009). Table 4 shows the regression results of angle of repose and flow rate of the produced batches. Pellets produced from all trials were smooth-surfaced, spherical, and free flowing. No problems or limitation of flowability was observed. Angle of repose for all trials ranged from 25o to 30o (excellent flow) with flow rates ranged from 7.2 to 16.7 g/min. The type and percentage of meltable binder type and percentage as well as the polymer percentage were the most significant factors affecting both flow rate and angle of repose (Table 4). Direct relationships were observed between both responses and these variables. However, flowability increased by using PEG 3350 as a meltable binder rather than using poloxamer 407 (Fig. 2). The effect of the meltable binder and polymer percentage on the produced size distribution of the pellets could explain these results. Reduction in pellets particle size with subsequent increment in the surface area per unit mass led to an increase in the cohesive strength of the pellets’ powder bed thereby reducing flowability (Fitzpatrick et al., 2004). On the other hand, compared to PEG 3350, the surface irregularity of poloxamer 407 led to an increase in friction between the produced pellets. The effect of filler type on the flowability parameters was positive for starch 1500 and emcompress; whereas it was negative for avicel PH 101. This can be related to initial good flowability of starch 1500 and emcompress and poor flowability of avicel PH 101 (Ohwoavworhua and Adelakun, 2005). The developed prediction model showed good predictability of p = 0.0004 and 0.0241 for angle of repose and flow rate with quantile–qualntile correlation coefficients of 0.7898 and 0.9138, respectively. The linear reduced model equations to predict both responses are given below: Angle of respose

0

Avicel ¼ 28:04 þ MatchðFiller typeÞ@ Starch Emcompress 0

K4M þ MatchðPolymer viscosityÞ@ K15M K100M   0:82 

1 ) 0:18 ) 0:175 A ) 0:009

1 0:14 0:4 A 0:34

ðPercentage of meltable binderð% w=wÞ  25Þ 5



¼ 1:14 þ 0:01 

Carr0 s index

  ðPercentage of meltable binder ð%w=wÞ  25Þ ¼ 12:58 þ 0:08  5   PEG 3350 ) 0:99 þ ðMeltable polymer typeÞ Polaxmer 407 )¼ 0:97

3.5. Flow rate and angle of repose Although the angle of repose is one of the mostly used techniques to measure the powder flowability, it has drawbacks for lacking reproducibility due to the variability of the angle formed within the powder cone. In some instances, pellets builds up to form a steep angle; and sometimes collapses to form a shallow angle (Zhu et al., 2013). In the current study, the combination of the angle of repose along with pellets flow rate would be more indicative of the pellets’ flow pattern than solely by the angle of repose. It is worth noting that higher values of the angle of repose along with low flow rate would suggest considerable cohesiveness

Flow rate

0

Avicel ¼ 10:5 þ MatchðFiller typeÞ@ Starch Emcompress

1 ) 0:5 ) 0:3 A ) 0:2

0

1 ) 0:32 K4M þMatchðPolymer viscosityÞ@ K15M ) 0:16 A K100M ) 0:4   ðPercentage of meltable binderð% w=wÞ  25Þ þ1:6  5   ) 0:814 PEG3350 þ MatchðMeltable polymer typeÞ Polaxmer 407 ) 0:81 3.6. Pellets size distribution For oral pellets, the impact of size distribution on drug product performance and subsequent manufacturability is of significant importance due to the fact that it affects drug release, stability and flow properties as well as the coating process, if needed. Concerning mixing and blending of pellets with other processing excipients, size differences among these components shows also a large impact on blend homogeneity. Therefore, understanding the impact of

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processing as well as chemical factors involved to prepare the proposed pellets is critical and should be controlled. Once it is determined that a pellet size distribution is critical for a certain quality attribute, the next step would be establishing an appropriate size specification. d10, d50, and d90 are often used as the limits to specify the acceptance of pellet size distribution. These responses describe the cumulative size distribution which defines the percentages of pellets are below a certain size. In particular, d10, d50, and d90 refer to the median diameter values corresponding to cumulative size distributions at 10%, 50% and 90% of the sample weights, respectively. Hence, d10, d50, and d90 are controlled by establishing their upper and lower acceptance criteria to be considered during the optimization process. Table 4 shows that size fraction of meltable binder and inlet air volume were the most significant factors affecting d10. On the other hand, d10 increased and decreased by using meltable binder size fraction of 355–500 mm and 500–710 mm, respectively. Theoretically, pellet’s diameter should equal to diameter of the original binder particle after subtracting the contraction that occurs when solid particles are immersed into the droplet and adding thickness of the layer layered on the core (Pauli-Bruns et al., 2010). If the pellet’s diameter increased significantly more than the core binder particle, this would suggest that the thickness of the layer layered onto the binder droplet is not constant (Abberger and Henck, 2000). The obtained results showed that smaller binder particles seem to have high binding capacity for a thicker layer than the larger ones to increase the d10. On the other hand, d10 decreased by increasing inlet air volume due to the increased friction time and force between the formed pellets and fluid bed chamber during agglomerate growth and finally formation of pellets with small particle size (Fig. 2). d10 increased by using avicel and decrease by both starch 1500 and emcompress as the employed fillers. This can be explained by the differences in initial particle size of the filler; avicel has the smallest diameter of 50 mm. Therefore, the nongranulated amount of filler could appear as an added percentage to the total fines. Direct and inverse relationships were also obtained for the effects of polymer viscosity and percentages on d10, respectively. On the other hand, Poloxamer 407 was more effective than PEG 3350 to increase d10 (Table 4). An acceptable predictability of D10 (p = 0.0061 and R2 = 0.8470) was obtained using the following model equation. The linear model equation is: d10 ¼ 241:4 

 ðPercentage of meltable binder ð% w=wÞ  25Þ 5   355  500 um ) 32:23 þðSize fraction of the meltable polymerÞ 500  710 um ) 32:2 þ21:8 

2:7  Inlet air volume ðm3 =hÞ d50 (also called median pellet size) and d90 were significantly affected by the employed percentage of meltable binder and filler type (Table 4). The increased binder percentage allowed for enhanced filling of inter-particular capillaries of the layered solid pellets to increase both d50 and d90 (Schaafsma et al., 1997). Using starch 1500 and emcompress were decreasing both d50 and d90; whereas avicel was increasing them (Table 4 and Fig. 2). As starch 1500 and emcompress exhibited higher pycnometric densities than avicel, increased amount of either former filler meant that a larger volume of solid particles was available for layering on the core binder droplets to result in larger pellets. The fraction of employed polymer was also a significant key factor for d50. The more polymer loadings were used, the smaller d50 was resulted. More polymer was associated with less solid particles per binder particle, hence a thinner layer and smaller size was suggested (Pauli-Bruns et al., 2010). On the other hand, type and size fraction of the meltable

91

binder were the prominent factors affecting d90. Compared to poloxamer 407 and small size fraction, using PEG 3350 along and large size fraction of the binder were associated with increased values of d90. The higher molten viscosity of poloxamer 407 would explain this result. Poloxamer 407 had a flow index of 0.98  0.01 and a consistency of 7.20  0.48 Pa sn; whereas PEG 3350 had a flow index of 0.23  0.02 and a consistency of 1.29  0.17 Pa sn (Zhai et al., 2010). As expected, due to intra- and intermolecular interactions within both binders, the consistency of poloxamer 407 at the working temperature was significantly higher than that of PEG 3350 (Table 1). The obtained results for the larger binder particle size (500–710 mm) may be attributed to the concurrent processes operating during granulation. Initially, binder consistency promoted pellet’s growth through coalescence as well as decreasing the deformability of agglomerates (Schæfer and Mathiesen, 1996). When using a larger binder size, the pellet’s growth was more dependent on the contact and/or collision of filler particles with the surface of binder rather than upon the binder distribution (Zhai et al., 2010). Predictability of d50 and d90 by the model was acceptable (d50: p = 0.0061 and R2 = 0.9176; whereas d90: p = 0.0001 and R2 = 0.9384) according to the following equations: 0 1 Avicel ) 33:7 @ d50 ¼ 593:8 þ ðFiller typeÞ Starch ) 27:0 A Emcompress ) 6:69   ðPolymer loading ð% w=wÞ  15Þ 12:7  5   ðPercentage of meltable binder ð% w=wÞ  25Þ þ 40:47  5 þ  1:19  Inlet air volume ðm3 =hÞ þ 13:5   ðFluidization timeðminutesÞ  12:5Þ 7:5 0

d90

1 Avicel ) 24:08 ¼ 732:2 þ ðFiller typeÞ@ Starch ) 43:0 A Emcompress ) 18:95   ðPercentage of meltable binder  25Þ þ22:1 5   ) 13:29 PEG 3350 þðMeltable Polymer typeÞ Polaxmer 407 ) 13:2 þðSize fraction of the meltable polymerÞ   355  500 um ) 12: 500  710 um ) 12:3

3.7. Drug assay Uniformity of pellets in shape, flowability, and particle size distribution were observed also in assay of nicotinamide in pellets fraction of 500–800 mm. The employed types of meltable binder and filler were the most significant factors affecting drug assay (Table 4). The result of drug assay analysis was described regarding the ability of the meltable binder and filler to incorporate nicotinamide into the formed pellets during hot melt granulation process. The use of PEG 3350 and poloxamer 407 as meltable binders increased and decreased drug assay, respectively (Table 4). Considering the lower melting range of PEG 3350 (48–54
C) than that of poloxamer 407 (52–58
C) provided a shorter time for the melting stage. This fact allowed faster onset of agglomeration and greater opportunities for nicotinamide incorporation into pellets prepared with PEG 3350. On the other hand, effect of filler type on drug assay could be explained depending on variation in particle size, density, and specific surface area of the employed filler. It is

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noteworthy that filler with greater specific surface area can enhance particles’ agglomeration. According to this suggestion, the effects of emcompress and avicel (specific surface areas of 20 and 1.12 m2/g, respectively) were positive while the effect of starch 1500 (specific surface area of 0.26 m2/g) was negative (Fig. 2). Although nonsignificant, polymer viscosity and percentage showed positive and negative impacts on drug assay, respectively. In addition, increasing the surface area by using small size fraction of the binder allowed for drug loading within the formed pellets. Positive effect was also observed for the fluidization time on nicotinamide loading. In contrary, negative effect was observed for inlet air volume on drug loading. Excessive inlet air volume might cause powder segregation especially when different densities and particle size of the ingredients existed. Predictability of drug assay by the model was acceptable (p = 0.002 and R2 = 0.8809) according to the following linear model equation: 0 1 Avicel ) 1:41 @ Drug loading ¼ 96:1 þ ðFiller typeÞ Starch ) :9 A Emcompress ) 2:54   ) 2:04 PEG 3350 þðMeltable polymer typeÞ Polaxmer 407 ) 2:0 3.8. Batch yield percentage Although the theoretical batch size in each trial was about 150 g, but calculation of batch yield and understanding impact of different variables are useful key elements especially in the scaleup stage as the process control and cost reduction are the most important challenges. Batch yield percentage ranged throughout all trials from 91 to 100%. Size fraction of meltable binder and filler type were the most significant factors affecting batch yield percentage (Table 4 and Fig. 2). Increasing the size fraction of the meltable binder was associated with an increase the yield percentages. Large size fraction of 500–710 mm resulted in the formation of strong bonded agglomerates with minimum fines. Effect of filler type was positive for starch 1500 and emcompress; whereas it was negative for avicel. These observations could be explained depending on bulk density and particle size of the filler. Avicel bulk density of 0.32 g/mL with particle size of 50 mm allowed greater chances for segregation and formation of fines than starch 1500 and emcompress (bulk densities of 0.59 and 0.88 g/mL and particle sizes of 52 and 136 mm, respectively). The following multiple regression model generated an acceptable predictability of yield percentage with p = 0.0001 and R2 = 0.9596. Yeild percentage 0

1 ) 1:8 ) 0:14 A ) 1:67   355  500 um ) 1:3 þðSize fraction of the meltable polymerÞ 500  710 um ) 1:31

Avicel ¼ 95:3 þ ðFiller typeÞ@ Starch Emcompress

3.9. In vitro dissolution Methocel with different viscosities were used as hydrophilic polymer to formulate pellets with sustained release properties over 12 h. Dissolution data in the current screening stage were able to achieve this goal. Moreover, analysis of dissolution results was done to understand effect of variables and mining the significant factors. These significant factors will be utilized in further optimization study to elucidate the optimized formula with the required sustained release properties. The in vitro dissolution rate of all prepared pellets using both meltable binders (PEG 3350 or poloxamer 407) was slower as compared to

Fig. 3. Dissolution profiles of nicotinamide loaded pellets in purified water for 21 batches of L18 Hunter design (n = 3).

the pure drug. The differences in the dissolution behaviours of all batches were generated within the first hour and up to 12 h (Fig. 3). After initial burst dissolution, nicotinamide was then slowly released throughout the remaining time, dependently of the different investigated chemical and processing variables. Hence, nicotinamide dissolution percentages after 1 and 12 h were selected to study the initial burst and sustained release properties, respectively. Meltable binder type and percentage were the most significant factors affecting both the initial burst and sustained dissolution properties with nonsignificant contribution of the binder size fraction. Pellets prepared with PEG 3350 showed fast drug release (Fig. 3 and Table 4). Higher PEG content resulted in higher percentages of nicotinamide release after 1 and 12 h. Formulations with poloxamer 407 showed considerably faster burst and slower sustained phases of drug release compared to the formulations with PEG 3350 (Fig. 2). The higher HLB value of poloxamer 407 (29–31) than PEG 3350 (20) can explain the faster burst release phase due to its solubilizing activity. On the other hand, the higher viscosity of the dissolved poloxamer 407 as the binder dissolved could account for the slower sustained release phase of the drug. Although nonsignificant, faster burst and sustained release phases were observed for the smaller size fraction of the binder compared to 500–710 mm size fraction (Table 4). An explanation for these influences of binder size fractions on dissolution characteristics might be different mechanisms of agglomerate formation. Schæfer et al. reported that distribution and coalescence mechanisms led to homogenous distribution of the binder within the pellet matrix than the immersion and layering mechanisms (Schæfer and Mathiesen, 1996). The more homogenous binder distribution was, more firmly the drug particles were entrapped in the formed pellets, which might explain slower release from pellets prepared with larger binder particles. Significant and nonsignificant negative effects were observed for methocel loading on the burst and sustained release phases, respectively (Fig. 2). Increasing in polymer concentration within the formed pellets created more barrier action for the dissolution media penetration to dissolve the drug. The polymer viscosity showed a nonsignificant negative effect on both responses. The high solubility of hydrophilic meltable binders to act as pore formers (channeling agents) would abolish the retarding effect of the polymer and deemed nonsignificant. The developed prediction model showed good predictability of p < 0.0001 and 0.0481 for the initial burst and sustained phases with quantile–qualntile correlation coefficients of 0.9636 and 0.8250, respectively. The linear reduced model

A.S. Zidan et al. / International Journal of Pharmaceutics 466 (2014) 83–95

93

equations to predict both responses are given below: Percentage drug release after 1 h ¼ 69:6  5:01  

  ðPolymer loading  15Þ 5

 ðPercentage of meltable binder  25Þ 5    ) 5:9 PEG 3350 þ Meltable polymer typeÞ Polaxmer 407 ) 5:93

þ 5:7 

Percentage drug release after 12 h   ðPolymer loading  15Þ ¼ 97:4  0:59  5   ðPercentage of meltable binder  25Þ þ 1:4  5 PEG 3350 þ ðMeltable polymer typeÞð Polaxmer 407 Þ

) 2:83 ) 2:8

3.10. Solid state analysis Fig. 4. DSC thermograms of nicotinamide, individual excipients, the binary physical mixtures of the drug and each excipient at 1:1 weight ratio and granules prepared with either PEG 3350 or poloxamer as meltable binders.

Solid state analysis of the pellets was done not only to figure out whether the hot melt granulation process changed the physical properties of both the raw meltable binder and the drug but also to

Fig. 5. FTIR spectra of nicotinamide, individual excipients and granules prepared with either PEG 3350 or poloxamer as meltable binders.

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explore any interaction that might exist between the drug and the excipients. Fig. 4 shows DSC thermograms of nicotinamide, individual excipients, the binary physical mixtures of the drug and each excipient at 1:1 weight ratio and granules prepared with either PEG 3350 or poloxamer as meltable binders. The melting heats involved during the thermal analysis of DSC were directly proportional to the powder weight employed. Consequently, the DSC thermograms were normalized to a weight of 1 mg. DSC thermogram of raw nicotinamide showed single sharp melting endothermic peak at 128–131
C corresponding to its melting peak (Hino et al., 2001). DSC thermogram of PEG 3350 exhibited melting peak at 51
C, while poloxamer showed slightly wider melting range with melting endotherm at 55
C. Thermal decompositions were observed at 265
C, 240
C and 425
C for avicel PH101, methocel K4M and emcompress, respectively. Thermograms of pellets prepared with PEG 3350 showed characteristic peaks of the binder and the drug. Melting peak of nicotinamide could be easily recognized in the thermograms but with lower intensities than the raw drug. During the heating process, it might be speculated that nicotinamide partially dissolved in the molten mass of the meltable binder. FTIR analysis was done to explore any interaction that might exist between the drug and the components of pellets (Fig. 5). The FTIR spectrum of nicotinamide showed characteristic peaks at 3061 cm1 due to C H stretching vibrations, in-plane C H bending vibration at 1124, 1092, and 1029 cm1. Two CQC stretching vibrations were also observed at 1540 and 1480 cm1, respectively. Two CC stretching vibrations at 1420 and 1350 cm1, two CCC bending vibrations at 625 and 603 cm1, CQN stretching vibration at 1575 cm1, C N stretch at 1340 cm1, tow N H stretching vibration at 3367 and 3158 cm1, tow N H in-plane bending vibrations at 1580 and 1593 cm1, CQO stretch at 1698 cm1, in-plane CQO bending vibration at 703 cm1 and out of plane CQO bending vibration at 511 cm1 were also shown for nicotinamide (Bayarı et al., 2003). PEG 3350 exhibited the absorption bands of O H stretching vibration at 3200–3600 cm1, CH stretch peaks at 2850–2960 cm1 and bending peaks at 1300–1450 cm1, C O stretching vibration at 1000–1260 cm1 and C O C stretching vibration at 1050–1150 cm1 that are characteristic for its chemical structure (Tunç and Duman, 2008). The infrared spectrum of poloxamer 407 was characterized by functional groups absorbance of C H aliphatic stretching vibration at 2891 cm1, O H in-plane bending vibration at 1343 cm1 and C O stretching vibration at 1111 cm1 (Vyas et al., 2009). According to the structure of nicotinamide, CQO, CQN, and NH2 groups are the most capable for interaction. The OH groups in PEG and Poloxamer are the active sites for possible interaction. The infrared spectrum of nicotinamide pellets prepared with PEG as the meltable binder showed disappearance of tow C H in-plane bending peaks, minor shifting of the C C and CQO stretches. On the other hand, disappearance of O H stretching vibration of PEG was also observed in the same spectrum. This result suggested that nicotinamide CQO interacted with the hydroxyl groups of PEG 3350 through hydrogen bonding. Similarly, the infrared spectrum of nicotinamide pellets prepared with poloxamer showed significant shifting and intensity decreasing CNH2 stretching vibration with the disappearance of the O H stretch of poloxamer to suggest hydrogen bonding interaction. 3.11. Shape and surface morphology Microscopic images (Fig. 6) of the pellets sections indicated the absence of nicotinamide crystals in the pellets prepared with either PEG or poloxamer as meltable binders. Moreover, the formed pellets were regular and spherical shape to indicate that immersion and layering was dominant agglomeration mechanism at the investigated meltable binder content (Masic et al., 2012).

Fig. 6. SEM images (100X) showing nicotinamide pellets prepared by FHMG.

4. Conclusion Experimental data obtained in this study suggest that the FHMG technique could be a successful method to prepare sustained release pellets of nicotinamide. Using L18 Hunter screening design, the optimal operating parameters for the pellets’ formulation were set according to the characteristics of each meltable binder melt and equipment/procedure specifications. Uniform and spherical shaped pellets with smooth surface were obtained through an immersion and layering mechanism of granule growth. The type, size fraction and percentage of the meltable binder incorporated were the most important factors to affect the resultant pellets size, physical characteristics and sustained release parameters. The pellets size data showed narrow size distribution and good flow properties at a sufficient binder concentration. Dissolution rate was determined by agglomeration mechanism involved and the extent of interaction between the drug and the meltable binder. Acknowledgments The authors would like to thank Deef Pharmaceutical Inc., Al Badaye, Al Qassim, KSA for the assistance and unlimited help with the experimentation and data analysis. References Abberger, T., Henck, J.O., 2000. Granule formation mechanisms in fluid-bed melt granulation and their effects on tablet properties. Die Pharmazie 55, 521–526. Adam, S., Suzzi, D., Radeke, C., Khinast, J.G., 2011. An integrated Quality by Design (QbD) approach towards design space definition of a blending unit operation by Discrete Element Method (DEM) simulation. European Journal of Pharmaceutical Sciences: Official Journal of the European Federation for Pharmaceutical Sciences 42, 106–115. Aksu, B., Paradkar, A., de Matas, M., Ozer, O., Guneri, T., York, P., 2013. A quality by design approach using artificial intelligence techniques to control the critical quality attributes of ramipril tablets manufactured by wet granulation. Pharmaceutical Development and Technology 18, 236–245. Bayarı, S., Ataç, A., Yurdakul, Ş., 2003. Coordination behaviour of nicotinamide: an infrared spectroscopic study. Journal of Molecular Structure 655, 163–170. Bukovec, P., Kroselj, V., Turk, S., Vrecer, F., 2009. Optimization of melt pelletization in a high shear mixer. International Journal of Pharmaceutics 381, 192–198. Bussink, J., Stratford, M.R., van der Kogel, A.J., Folkes, L.K., Kaanders, J.H., 2002. Pharmacology and toxicity of nicotinamide combined with domperidone during fractionated radiotherapy. Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology 63, 285–291. Charoo, N.A., Shamsher, A.A., Zidan, A.S., Rahman, Z., 2012. Quality by design approach for formulation development: a case study of dispersible tablets. International Journal of Pharmaceutics 423, 167–178. Chevanan, N., Womac, A.R., Bitra, V.S., Igathinathane, C., Yang, Y.T., Miu, P.I., Sokhansanj, S., 2010. Bulk density and compaction behavior of knife mill chopped switchgrass, wheat straw, and corn stover. Bioresource Technology 101, 207–214. Fitzpatrick, J., Barringer, S.A., Iqbal, T., 2004. Flow property measurements of food powders and sensitivity of Jenike’s hopper design methodology to the measured values. Journal of Food Engineering 61, 339–405.

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