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Understanding die fill variation during mini-tablet production
Accepted Manuscript Title: Understanding die fill variation during mini-tablet production Authors: Hui Ping Goh, Paul Wan Sia Heng, Celine Valeria Liew PII: DOI: Reference:
S0378-5173(17)31022-0 https://doi.org/10.1016/j.ijpharm.2017.10.042 IJP 17098
To appear in:
International Journal of Pharmaceutics
Received date: Revised date: Accepted date:
19-8-2017 12-10-2017 21-10-2017
Please cite this article as: Goh, Hui Ping, Heng, Paul Wan Sia, Liew, Celine Valeria, Understanding die fill variation during mini-tablet production.International Journal of Pharmaceutics https://doi.org/10.1016/j.ijpharm.2017.10.042 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.
Understanding die fill variation during mini-tablet production
Hui Ping Goh, Paul Wan Sia Heng, Celine Valeria Liew* GEA-NUS Pharmaceutical Processing Research Laboratory, Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore
*Corresponding Author: Celine Valeria Liew GEA-NUS Pharmaceutical Processing Research Laboratory, Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore Tel: +65 6516 3870; Fax: +65 6775 2265 Email: [email protected]
Graphical abstract
Abstract
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Reproducibility of die fill during tablet production is critical to ensure consistent tablet drug content and mechanical attributes. In the production of mini-tablets, tablets smaller than 6 mm, achievement of uniform die fill is much more challenging. Powder flow is often associated with die fill accuracy but this relationship has not been well characterised especially for mini-tablets. In this study, flow properties of different types of granules were characterised. Mini-tablets of 1.8 and 3 mm diameters were prepared from the granules using a rotary press with multiple-tip compression tooling. A methodology was established to evaluate mini-tablet die fill variation within and across compaction cycles using data from compression roller displacement and mini-tablet weight. Both sizes of mini-tablets showed similar extents of inter-cycle weight variation that could be associated with granules’ inter-particulate friction. However, smaller mini-tablets had higher intra-cycle weight variation due to their narrower die orifices. Multivariate and univariate analyses suggested that gravity fill influenced intra-cycle weight variation of 3 mm mini-tablets while suction fill was associated with that of 1.8 mm mini-tablets. Possible differences in die fill mechanisms between the mini-tablet sizes were identified and this provided a better insight into die fill variations during the production of minitablets.
200 words
Keywords: Mini-tablet, weight variation, powder flow, multiple-tip tooling, die fill
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Introduction
Manufacture of pharmaceutical tablets involves filling of tableting feed into dies followed by compaction. Reproducibility of the die filling process is very critical as it determines tablet weight and weight variation, which in turn reflects drug content 2
uniformity and other critical product quality attributes such as hardness, disintegration and drug release (Peeters et al., 2015). In the case of mini-tablets, generally taken as tablets with diameters less than 6 mm, the attainment of consistency in die fill is much more challenging than for conventional-sized tablets due to the narrowness of the die orifices presented (Flemming and Mielck, 1995; Lennartz and Mielck, 1998). Additionally, the lower weights of mini-tablets will grossly magnify normal relative fill variations of the granular materials encountered with tableting, in terms of tablet weight or potency variations. Nonetheless, there is strong interest in the rational development of mini-tablets due to their wide array of applications, such as ameliorating dysphagia in geriatrics and paediatrics, controlling drug release and achieving individualised patient drug therapy (Aleksovski et al., 2015). The final act of die filling on a rotary tablet press comprises various mechanisms working in tandem. These mechanisms include gravity fill where the granular feed is assisted by gravity to fill the die, forced feeding where paddles in the feed frame physically direct feed into die orifices, and suction fill where the rapid descent of the lower punch creates a partial vacuum in the die to draw the feed in (Jackson et al., 2007; Peeters et al., 2015; Sinka et al., 2004). Inevitably, tablet feed flow properties are crucial to die fill performance. A complex interaction of particulate properties and processing conditions affect feed flow properties (Kachrimanis et al., 2005; Sinka et al., 2004). Powder flow may be characterised by a variety of techniques, including compendial (angle of repose, compressibility and shear cell) and non-compendial methods (powder rheometry and avalanche flow). Each of these methods describes different aspects of powder flow behaviour and no single test can be accepted as the standard for powder flowability
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(Shah et al., 2008). While it is generally accepted that powders with good flowability are likely to produce tablets with lower weight variation, it is unclear which powder flow parameters are truly indicative of die fill performance. Fassihi and Kanfer found that powder mixtures that had higher angles of repose produced tablets with greater weight variations (Fassihi and Kanfer, 1986). Correlations between powder flow and die fill performance were also established in another study where a die filling rig was used to evaluate flow properties of glass beads, aluminium granules and tungsten powder (Freeman and Fu, 2008). On the contrary, it has been suggested that powder flow parameters are not able to fully predict die fill performance. Poor flowing powders had been found to achieve more uniform die fill than those with better flow (Mills and Sinka, 2013; Monedero Perales et al., 1996; Sinka et al., 2004). Furthermore, such findings are usually not specific to mini-tablets. During production, variations in die fill or tablet weight may be assessed by periodically collecting samples of tablets and weighing them individually. Alternatively, acquired process parameters can be utilised. For compacting tablets to the same thickness, the peak force in each compaction cycle is proportional to the die fill and hence can be used as a surrogate indicator of die fill variation. Automated tablet weight control systems in most modern rotary tablet presses rely on the measured compaction force to predict tablet weight and a force range is used to reject tablets that are outside the range as they are likely not to be within specifications (Ridgway et al., 1972). Some rotary presses rely on an air piston, also known as the air compensator, installed at the compression roller to control the amount of compaction force applied regardless of tablet weight (Felton, 2013). The compensator is able to move vertically in a cylinder filled with compressed air that allows the roller to “float” rather than being fixed and this modification enables the regulation of the applied peak compaction force independent 4
of tablet weight. A linear variable displacement transducer sensor accurately measures the movement of the compression roller as determined by the tablet weight. Thus, a heavy tablet causes the roller to move more while a lighter tablet, less. The linear relationship between roller displacement and tablet weight provides a more accurate measure of tablet weight variation than compaction force, especially when the applied force is low as for smaller tablets such as mini-tablets. For multiple-tip tools to produce mini-tablets, die fill variation during tableting can arise from within a compaction station as well as across the compaction stations. To the best of the authors’ knowledge, little literature information could be found on this subject. The purpose of this study was twofold, to gain a better understanding of weight variation in mini-tablet production and to find possible relationships between powder flow and mini-tablet weight variation. Pharmaceutical granules with different flow properties were produced and compacted into mini-tablets on a rotary tablet press. Both compendial and non-compendial flow characterisation techniques were used to characterise granule flow. A methodology developed from a combination of mini-tablet weight and in-line compression roller displacement data was used to examine die fill variations during mini-tablet production within and across compaction cycles. 2 2.1
Materials and Methods Materials
Metformin hydrochloride (Granules India, India) was used as the model drug and it was mixed with lactose (Sorbolac 400M, Meggle, Germany) for granulation. Polyvinylpyrrolidone (PVP K30; Kollidon 30, BASF, Germany) was dissolved in purified water to prepare the granulating liquid. Acetone (Aik Moh, Singapore) was the dispersing agent for particle size analysis. 5
2.2
Granule preparation
2.2.1 High shear granulation Granules comprising metformin hydrochloride, lactose and PVP K30 (ratio, 25:24:1) were prepared by wet granulation in a high shear mixer (PMA-1, GEA Niro Pharma Systems, United Kingdom). In order to produce granules with different particle size distributions and flow properties, critical parameters during the wet massing stage were varied: impeller speed (650 or 800 rpm), wet massing time (3 or 5 min) and amount of granulating liquid added (6, 8 or 10 %, w/w). The amount of granulating liquid was expressed as a percentage of the total dry powder weight. Chopper speed was kept constant at 1000 rpm during wet massing. The metformin hydrochloride and lactose mixture was first dry blended for 3 min at an impeller speed of 650 rpm. Wet massing was initiated by the introduction of the granulating liquid via a pressure pot. The granules produced were de-agglomerated (Comil® 197S, Idex-Quadro Engineering, Canada) through a 1143 μm round aperture screen aided by a round impeller operating at 1700 rpm before drying in a fluid bed processor (Strea-1, GEA-Aeromatic, Switzerland) with an inlet air temperature of 60 °C. Drying was stopped when an exhaust temperature of 40 °C was attained. Dried granules were further milled using the conical screen mill fitted with a 457 μm round aperture screen and a round impeller operating at 1700 rpm. A total of twelve granule batches were prepared. 2.2.2 Granule lubrication Each granule batch was blended with 1 %, w/w magnesium stearate in a double cone blender rotated at 15 rpm for 5 min prior to flow characterisation and tableting.
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2.3
Granule characterisation
2.3.1 Particle size and size distribution All batches of granules were sized by laser diffraction (LS230, Beckman Coulter, USA) using the dry powder module. Granule feed rate was adjusted to achieve an obscuration level of about 10 %. Granule sizes corresponding to the 10th, 50th and 90th percentiles (D10, D50 and D90) were determined from the cumulative undersize curve. Span, an indicator of particle size distribution, was calculated using Equation (1). Span = (D90 − D10 )/D50
(1)
2.3.2 Particle shape Using a stereomicroscope (SZH, Olympus, Japan) with an attached video camera (DXC-390, Sony, Japan) linked to a computer, images of 600 granules were collected for each batch and analysed using an image analysis software (Image Pro Plus 6.3, Media Cybernetics, USA). Aspect ratio (AR) of the granules was calculated using Equation (2). A larger aspect ratio indicates more elongated granules. AR = Major axis/Minor axis
(2)
where the major and minor axes are the largest granule diameter and the smallest diameter orthogonal to it, respectively. Granule roundness was determined using Equation (3). A value of 1 indicates a perfect circle. Roundness = P 2 /(4πA)
(3)
where P is the perimeter and A is the area of the granule image. 7
2.3.3 Compendial flow characterisation 2.3.3.1 Angle of repose For each granule batch, approximately 100 g of sample was dispensed through a funnel onto a round 10 cm base plate (Copley Scientific, United Kingdom). The funnel height was adjusted such that the orifice was about 2 - 4 cm above the expected tip of the powder heap to be formed. Height of the cone formed on the 10 cm base plate was recorded to calculate angle of repose (AoR) using Equation (4). The procedure was triplicated for each granule batch and the average AoR determined. Lower AoR values indicate better powder flow. AoR = tan−1(h/r)
(4)
where h is the height of the symmetrical cone and r is the radius of the base plate. 2.3.3.2 Bulk and tapped densities Granule samples were discharged through a 1 mm aperture size sieve into a preweighed cylinder of 100 mL. Excess granules were carefully scraped off the top of the cylinder, and weight obtained after removal of adhering material. Granule bulk density (ρbulk) was calculated using Equation (5). ρbulk = Mass of granules/100 mL
(5)
The filled cylinder was tapped (TD2, Sotax, Switzerland) at 250 taps per min until a constant volume. Granule tapped density (ρtapped) was calculated using Equation (6). ρtapped = Mass of granules/Tapped volume
(6)
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Hausner ratio (HR) was derived from the bulk and tapped densities using Equation (7). The measurements were triplicated for each granule batch and averaged values calculated. Lower HR values are indicative of better flow. HR = ρtapped /ρbulk
(7)
where ρtapped is the tapped density and ρbulk is the bulk density. 2.3.3.3 Shear cell test A FT4 powder rheometer (FT 4, Freeman Technology, United Kingdom) fitted with shear cell accessories was used to carry out rotational shear testing of the granules. The test sequence employed was similar to that of the Jenike shear cell but utilised the same sample to generate all points for the yield loci instead of new samples for each data point. From the yield loci, granule cohesion (COH), unconfined yield strength (UYS), flow function coefficient (ffc) and angle of internal friction (AIF) were determined. The shear cell tests were triplicated and average values reported. Good flowability is associated with low COH and UYS values, and high ffc values. 2.3.4 Non-compendial flow characterisation 2.3.4.1 Powder rheometry A FT4 powder rheometer (FT 4) was also used to measure the basic flow energy (BFE) and specific energy (SE) of the granules. The tests involved turning a twisted blade in a series of downward clockwise and upward anti-clockwise rotations through preconditioned granule samples. BFE was the energy expended in moving the blade downwards with the granules under a state of high stress flow. SE was measured during the blade’s upward traverse where the granules were lifted by the blade and made to
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flow in a gentler manner. The entire procedure was triplicated for each granule batch. In general, high BFE and low SE values are desired for good flowability. 2.3.4.2 Avalanche flow The avalanche flowability (Revolution® Powder Analyzer, Mercury Scientific Inc., USA) of the granules was determined. The granule sample was transferred into a drum and rotated at a constant speed of 0.6 rpm. A digital camera captured the images of avalanches as the drum revolved continuously until 150 avalanches were captured. During this process, the avalanche angle (AvA) - angle of inclination just prior to occurrence of the avalanche, avalanche energy (AvE) - amount of energy released by an avalanche event, and avalanche time (AvT) - average time between consecutive avalanche events, were monitored by the computer software and calculated as parameters to indicate powder flowability. It is generally accepted that low values for AvA, AvE and AvT are related to good flow (Nalluri and Kuentz, 2010). 2.4
Mini-tablet production
2.4.1 Mini-tablet compaction on rotary tablet press All twelve batches of granules were used to produce mini-tablets of 1.8 and 3 mm diameters on a rotary tablet press (R190FT, GEA, Belgium). Multiple-tip (9 tips – 1.8 mm diameter; 8 tips – 3 mm diameter), standard concave punches (Natoli Engineering Company, USA) were employed (Fig. 1A). The tablet press was fitted with a fill cam of 14 mm depth and a two-paddle feed frame. The feeder and metering paddles were set at rotational speeds of 11 and 24 rpm, respectively. Press linear tableting speed was 415 mm/s. Tablet compaction was carried out under controlled ambient temperature and relative humidity conditions (25 °C, 50 %). Equal force compaction technology was
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used; for each mini-tablet diameter, mini-tablets were compacted at the same peak compaction force, independent of tablet weight. Compaction pressure applied per minitablet was about 100 MPa. 2.4.2 Sampling protocol for mini-tablet collection A compaction cycle represents the traverse of a compaction station on the turret. The rotary press was operated for a total of 505 compaction cycles in each experimental run and this included an initial 100 conditioning cycles to ensure smooth feed flow and a completely filled feed frame. After the conditioning phase, mini-tablets were collected over five consecutive compaction cycles (consecutive sampling) at cycles 101 - 105, 301 - 305 and 501 - 505. Mini-tablets were also collected once at every 40 compaction cycles (individual sampling) from cycle 120 to cycle 480. Hence, mini-tablets were sampled over 25 cycles in each experimental run (Fig. 1B) and were stored for at least 24 h under controlled ambient temperature and relative humidity conditions (25 °C, 50 %) before characterisation. 2.5
Mini-tablet characterisation
2.5.1 Inter-cycle mini-tablet weight variation Mini-tablets require much lower compaction forces to attain similar compaction pressures as conventional tablets due to their smaller surface area of force application. However, the relationship between compaction force and tablet weight is non-linear and becomes less sensitive at low compaction forces. As a result, variation in compaction force might not be sensitive enough to detect mini-tablet weight variation during production.
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During mini-tablet production, peak compression roller displacement values were recorded after the conditioning phase (n = 405) to monitor weight variation across the compaction cycles. Standard deviation of all the roller displacement values was used in Equation (8) to calculate the coefficient of variation (COV), yielding inter-COV for indicating mini-tablet weight variation across the 405 compaction cycles.
Inter-COVM =
Standard deviation of roller displacement across cycles Mean roller displacement across cycles
x 100 %
(8)
where M is the mini-tablet diameter in mm. Suitability of inter-COV as a surrogate indicator for mini-tablet weight variation was determined. Mini-tablets from 25 sampled compaction cycles were weighed individually. Mean and standard deviation of mini-tablet weight across the compaction cycles were then determined and used in Equation (9) to calculate inter-COVRef, which was compared against inter-COV.
Inter-COVRef =
Standard deviation of mini−tablet weight across cycles Mean mini−tablet weight across cycles
x 100 %
(9)
2.5.2 Intra-cycle mini-tablet weight variation Mini-tablets from each sampled cycle were weighed individually. The standard deviation of mini-tablet weight within each sampled cycle was determined and used in Equation (10) to yield the intra-COV for each sampled cycle. Intra-COV values from all 25 sampled cycles were then averaged to report the mean intra-COV.
Intra-COVM =
Standard deviation of mini−tablet weight within sampled cycle Mean mini−tablet weight within sampled cycle
x 100 %
(10)
where M is the mini-tablet diameter in mm.
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2.5.3 Mini-tablet tensile strength Breaking force of three randomly selected mini-tablets from each sampled compaction cycle was determined via diametrical compression between two platens using a tensile tester (EZ Test, Shimadzu, Japan) fitted with a 100 N load cell. A micrometer screw gauge was used to measure the tablet thickness. Tensile strength (σt) of each mini-tablet was calculated using Equation (11) and the average σt calculated for each compaction cycle. σt = 2F/(πMT)
(11)
where F is the breaking force, M and T are mini-tablet diameter and thickness in mm, respectively. 2.6
Statistical analysis
Pearson’s correlations (r) were used to identify significant linear correlations between two independent data sets (IBM SPSS Statistics for Windows version 22, IBM Corporation, USA). One-way analysis of variance (ANOVA) and independent samples t-tests were also conducted at the 95 % confidence interval. Using the same software, agglomerative hierarchical clustering analysis (HCA) was used to organise granule batches into clusters based on their properties. Similar granule clusters were merged at successive steps. Ward’s linkage algorithm and squared Euclidean distances between observation sets were used for the analysis (Kaufman and Rousseeuw, 2009). Partial least squares regression (PLSR) was performed using multivariate software (The Unscrambler® X v.10.3, CAMO Software, Norway). Cross-validation and data scaling were performed prior to regression analysis.
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3 3.1
Results Granule particle size, size distribution, shape and bulk density
Particle size and size distribution, shape and bulk density of the granules prepared are presented in Table 1. In general, the granules had different particle size, size distributions and bulk densities. However, particle shape descriptors (AR and roundness) appeared to be comparable. 3.2
Granule flow properties
Granule flow is a bulk response resulting from the intrinsic material properties (particle size, size distribution and shape). Flow parameters from compendial and noncompendial methods are summarised in Tables 2 and 3 respectively. Cluster analysis of the 12 granule batches (B1- B12) based on their measured flow parameters (AoR, HR, COH, UYS, ffc, AIF, BFE, SE, AvE, AvA and AvT) by HCA is summarised in the dendrogram (Fig. 2). The relative positions of the granule batches along the vertical axis indicated their degree of similarity to each other, with batches in closer proximity being more similar in flow properties. Joined clusters are represented as vertical lines and the position of each vertical line on the scale indicates the distance at which clusters were joined. Large distances between sequential vertical lines indicate combination of dissimilar clusters and can be used as the optimum point of differentiation. From the dendrogram, the granule batches can be generally divided into two main clusters (C1 and C2). Cluster C1 comprised granules B1, B3, B4, B6, B7, B10 and B12. All the other granule batches were classified under cluster C2. Analysis of the granules’ flow properties showed no significant differences in BFE and AIF between the two clusters. However, in terms of the remaining flow parameters (AoR, HR, SE, COH, UYS, ffc, AvE, AvA and AvT), flowability of granules in cluster C1 was significantly worse 14
compared to those of cluster C2 (p < 0.05 for all cases). Granules produced in this study had thus showed differences in their flow properties. 3.3
Mini-tablet physical characteristics
Mean weights for both 3 and 1.8 mm mini-tablet samples ranged between 18.0 – 21.0 mg and 4.0 – 4.6 mg while their average tensile strengths were 1.25 ± 0.26 MPa and 1.49 ± 0.31 MPa, respectively. None of the mini-tablets produced had shown mechanical failure such as capping and/or lamination. A random sample of 20 minitablets was also taken for each batch and it was found that all the batches had complied with compendial criteria for uniformity of tablet mass (British Pharmacopoeia Commission, 2016). 3.4
Inter-cycle mini-tablet weight variation and relationship with granule flow properties
For both inter-COV3 and inter-COV1.8, statistically significant linear correlations were obtained with inter-COVRef (Fig. 3A, R2 = 0.694 and 0.855, respectively) and demonstrated that inter-COV could be used as a surrogate marker of mini-tablet weight variation across compaction cycles. Compared to inter-COVRef, inter-COV had reflected the compression roller displacement, a more readily available and convenient surrogate process parameter. Furthermore, inter-COV accounted for all of the compaction cycles after the conditioning phase and gave a more representative picture of mini-tablet weight variation present during production. Inter-COV3 and inter-COV1.8 are shown in Fig. 3B. Higher inter-COV values indicated greater variations in the compression roller displacement, and in turn, mini-tablet
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weights, across compaction cycles. Both inter-COV3 and inter-COV1.8 varied between 10 – 25 % and were correlated with each other (r = 0.643, p = 0.024). Variation in inter-COV across the granule batches (Fig. 3B) suggested that granule properties rather than mini-tablet diameters had affected die fill across compaction cycles. PLSR modelling was done for inter-COV using flow parameters as predictor variables. Correlation loadings plot from the PLSR model is shown in Fig. 4A. It was observed that 80 % of variation in the flow parameters accounted for 71 % of variation in inter-COV. There was relatively good fit of the dataset to the PLSR model (R2 = 0.742 and 0.680 for inter-COV3 and inter-COV1.8, respectively). Variables of importance were identified based on statistical significance of the weighted regression coefficients as shown in Fig. 4B. Granules with higher HR and SE but lower AIF values showed higher inter-COV for both mini-tablet diameters (p < 0.05 for all three flow parameters). BFE values was also positively associated with inter-COV3 (p < 0.01) but not for inter-COV1.8. 3.5
Intra-cycle mini-tablet weight variation and relationship with granule flow properties
Intra-COV3 did not exceed 2 % which indicated low intra-cycle weight variation across all granule batches (Fig. 3C). In contrast, intra-COV1.8 values ranged between 2 - 6 %. No significant correlation was found between intra-COV3 and intra-COV1.8. PLSR modelling was done for each of the two mini-tablet sizes with intra-COV as the response variable and granule flow parameters as predictor variables. Plots of correlation loadings from the PLSR models are shown in Fig. 5A–B. Variables of importance are shown in Fig. 5C.
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For the 3 mm mini-tablets (Fig. 5A), 73 % of variation in the predictor variables accounted for 68 % of variation in intra-COV3 (R2 = 0.488). Weighted regression coefficients for AoR, HR, AvE and AvT were found to be significant (Fig. 5C, p < 0.05 for all four parameters) which suggested that these parameters were best associated with intra-cycle die fill variation in 3 mm mini-tablets among all the other flow parameters. Close proximity of intra-COV3 and these flow parameters in the loadings plot suggested significant positive correlations which were verified by univariate analyses (Fig. 6A–C). HR had the strongest association with intra-COV3 (R2 = 0.796), followed by AoR (R2 = 0.680) and AvT (R2 = 0.612). PLSR model for the 1.8 mm mini-tablets had a better fit of the dataset (R2 = 0.733). However, only 23 % of variation in the predictor variables explained 73 % of variation in intra-COV1.8 (Fig. 5B). Amongst the various flow parameters, AIF had greatest influence on intra-COV1.8 (Fig. 5C). A negative, non-linear relationship between AIF and intra-COV1.8 was also found (Fig. 6D). 4 4.1
Discussion Mini-tablet weight variation within and across compaction cycles
Currently there is no standard method for evaluating mini-tablet weight variation and most studies still rely on pharmacopoeial guidelines that were established for conventional-sized tablets. Multiple mini-tablets were produced in each compaction cycle and it was of interest to determine tablet weight variation within as well as across cycles. The use of inter- and intra-COVs in this study made it possible to analyse minitablet weight variation in these two dimensions separately. In this study, despite both sizes of mini-tablets complying with the compendial standards for uniformity of mass, it was clear that they had different extents of weight variation. 17
Inter-COV (variation in compression roller displacement) regarded the multiple die orifices as a single aggregate and reflected the stability of die fill during tableting while intra-COV represented how well the granules were distributed across the multiple die orifices within each compaction cycle. Even though inter-COV values were comparable for 3 and 1.8 mm mini-tablets, intra-COV values were markedly different. Narrower die orifices of the 1.8 mm mini-tablets were more challenging to fill uniformly which led to higher intra-COV1.8 values as compared to intra-COV3. Consequently, information gathered from inter- and intra-COVs reflected mini-tablet weight variation during the production run more accurately. 4.2
Powder flow parameters and inter-cycle mini-tablet weight variation
For both 3 and 1.8 mm mini-tablets, flow parameters which had significant impact on inter-cycle weight variation were identified via PLSR. Granules with higher HR, BFE and SE values but lower AIF generally produced mini-tablets with higher inter-COV (Fig. 4B). HR reflects inter-particulate friction under dynamic conditions (Grey and Beddow, 1969); the act of tapping the cylinder results in particle rearrangement and repacking which involves overcoming inter-particulate friction coupled with bulk powder movement. Higher HR values are indicative of poorer powder flow and greater extent of inter-particulate friction. Likewise, BFE and SE also indicate inter-particulate friction under dynamic conditions. During the powder rheometry test, the blade must overcome resistance from the inter-particulate friction first before it is able to traverse through the granules. Higher BFE and SE values are indicative of greater inter-particulate friction since more energy is required by the blade to generate the flow pattern. During minitablet production, efficient fluidisation of the tablet feed by the rotating paddles in the feed frame is required to ensure consistency in the amount of granules passed over the 18
die orifice at the filling stage of each compaction cycle. Granules with higher dynamic inter-particulate friction could have been harder to fluidise leading to higher variation in the amount of granules delivered to the die orifice with each pass under the feed frame. Similar to BFE and HR, AIF is also a measure of inter-particulate friction. However, the granule sample was under consolidation stress during the shear cell test with little bulk movement unlike in the powder rheometry and tapping studies. It follows that AIF reflects inter-particulate friction under static conditions: higher AIF values suggest greater static inter-particulate friction. In contrast to HR, BFE and SE, AIF was negatively correlated with inter-COV1.8 and inter-COV3. Static inter-particulate friction could have been important during suction fill of granules from the feed frame into the die orifice. Suction fill occurs when the lower punch descends at the fill cam and creates a partial vacuum to draw powder into the die. Granule particles with larger AIF and greater extent of static inter-particulate friction may be able to latch onto one another more closely during the suction fill event at each compaction cycle. This in turn could have improved the reproducibility of die fill across compaction cycles and possibly explain why AIF was negatively associated with inter-COV. 4.3
Powder flow parameters and intra-cycle mini-tablet weight variation
Flow parameters which best correlate with intra-COV3 and intra-COV1.8 were also identified. While inter-COV3 and inter-COV1.8 were affected by similar flow parameters, this was not the case for intra-COV. Both gravity and suction fills were the main die fill mechanisms during mini-tablet production. However, for the 1.8 mm minitablets, gravity fill may be less dominant since there was less effective evacuation of residual air trapped inside the die, leading to greater back pressure that opposed granules entering the orifice by gravity effect alone. Moreover, granules which were 19
more cohesive may also form stable arches over the small orifices. Suction effect would have been necessary to break the arch and draw particles into the die orifice (Baserinia et al., 2016). The importance of static inter-particulate friction and suction fill for the 1.8 mm mini-tablets could be seen from AIF having significant effect on both inter- and intra-COV1.8. In contrast, HR and SE only affected inter-COV1.8, suggesting a high importance of granule dynamic flow and mixing inside the feed frame when considering inter-cycle weight variation. Conversely, powder flow parameters derived from bulk powder movement under its own weight (AoR, HR and avalanche flow) were best associated with intra-COV3, implying the increased influence of gravity fill for the larger die orifices. With the exception of HR, flow parameters associated with intra-COV3 differed from those associated with inter-COV3. When considering weight variation of all eight 3 mm orifices across the compaction cycles, dynamic and static inter-particulate friction (granule fluidisation in the feed frame and suction fill) were important. Gravity flow into the individual orifice only became apparent when considering weight variation within each compaction cycle. It follows that the die filling process was a complex interplay of several mechanisms working together simultaneously. By studying die fill variation within and across compaction cycles, different die fill mechanisms were identified. 5
Conclusion
In this study, a methodology was established to evaluate mini-tablet weight variation within and across compaction cycles. It was demonstrated that the use of data from compression roller displacement and mini-tablet weight gave better insight on die fill variation during mini-tablet production with multiple-tip tooling. Both 1.8 and 3 mm 20
mini-tablet diameters showed comparable inter-cycle weight variation which was likely to be affected by efficiency of granule fluidisation in the feed frame and reproducibility of suction fill into the die. Higher intra-cycle die fill variation in 1.8 mm mini-tablets as compared to 3 mm mini-tablets was attributed to greater difficulty to fill narrower die orifices uniformly. While intra-cycle die fill variation for 3 mm mini-tablets was related to gravity fill, suction fill continued to play a dominant role in intra-cycle die fill variation of 1.8 mm mini-tablets. The relationship between mini-tablet die fill variation and powder flow was hence highly dependent on the die fill mechanisms involved. Findings from this study may be useful in addressing problems associated with minitablet weight variability and aid in selection of powder flow measurement techniques that best relate to mini-tablet die fill performance. 6
Acknowledgement
Goh Hui Ping is a recipient of the National University of Singapore Graduate Research Scholarship. Financial support for the project was from the GEA-NUS PPRL fund (N148-000-008-001).
References: Aleksovski, A., Dreu, R., Gašperlin, M., Planinšek, O., 2015. Mini-tablets: a contemporary system for oral drug delivery in targeted patient groups. Expert Opinion on Drug Delivery 12, 65-84. Baserinia, R., Sinka, I.C., Rajniak, P., 2016. Vacuum assisted flow initiation in arching powders. Powder Technology 301, 493-502. British Pharmacopoeia Commission, 2016. British Pharmacopoeia 2017. Stationery Office. 21
Fassihi, A.R., Kanfer, I., 1986. Effect of Compressibility and Powder Flow Properties on Tablet Weight Variation. Drug Development and Industrial Pharmacy 12, 19471966. Felton, L.A., 2013. Remington - Essentials of Pharmaceutics. Pharmaceutical Press. Flemming, J., Mielck, J.B., 1995. Requirements for the Production of Microtablets: Suitability of Direct-Compression Excipients Estimated from Powder Characteristics and Flow Rates. Drug Development and Industrial Pharmacy 21, 2239-2251. Freeman, R., Fu, X., 2008. Characterisation of powder bulk, dynamic flow and shear properties in relation to die filling. Powder Metallurgy 51, 196-201. Grey, R.O., Beddow, J.K., 1969. On the Hausner Ratio and its relationship to some properties of metal powders. Powder Technology 2, 323-326. Jackson, S., Sinka, I.C., Cocks, A.C., 2007. The effect of suction during die fill on a rotary tablet press. European Journal of Pharmaceutics and Biopharmaceutics 65, 253256. Kachrimanis, K., Petrides, M., Malamataris, S., 2005. Flow rate of some pharmaceutical diluents through die-orifices relevant to mini-tableting. International Journal of Pharmaceutics 303, 72-80. Kaufman, L., Rousseeuw, P.J., 2009. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley. Lennartz, P., Mielck, J.B., 1998. Minitabletting: improving the compactability of paracetamol powder mixtures. International Journal of Pharmaceutics 173, 75-85.
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Mills, L.A., Sinka, I.C., 2013. Effect of particle size and density on the die fill of powders. European Journal of Pharmaceutics and Biopharmaceutics 84, 642-652. Monedero Perales, M.C., Muñoz-Ruiz, A., Velasco Antequera, M.V., Muñoz, N.M., Ballesteros, M.R.J.-C., 1996. Comparative tableting and microstructural properties of a new starch for direct compression. Drug Development and Industrial Pharmacy 22, 689695. Nalluri, V.R., Kuentz, M., 2010. Flowability characterisation of drug–excipient blends using a novel powder avalanching method. European Journal of Pharmaceutics and Biopharmaceutics 74, 388-396. Peeters, E., De Beer, T., Vervaet, C., Remon, J.P., 2015. Reduction of tablet weight variability by optimizing paddle speed in the forced feeder of a high-speed rotary tablet press. Drug Development and Industrial Pharmacy 41, 530-539. Ridgway, K., Deer, J.J., Finlay, P.L., Lazarou, C., 1972. Automatic weight-control in a rotary tabletting machine. Journal of Pharmacy and Pharmacology 24, 203-210. Shah, R.B., Tawakkul, M.A., Khan, M.A., 2008. Comparative Evaluation of Flow for Pharmaceutical Powders and Granules. AAPS PharmSciTech 9, 250-258. Sinka, I.C., Schneider, L.C.R., Cocks, A.C.F., 2004. Measurement of the flow properties of powders with special reference to die fill. International Journal of Pharmaceutics 280, 27-38.
Figure Captions
23
Fig. 1. Schematics of (A) multiple-tip tooling employed in this study and (B) mini-tablet sampling protocol for individual and consecutive sampling during the 505 compaction cycles. Fig. 2. Mapping of the twelve granule batches into clusters based on their flow parameters using HCA. Fig. 3. (A) Relationship between inter-COVRef and () inter-COV3 and (○) interCOV1.8. Plots of (B) inter-COV and (C) intra-COV for () 3 mm and () 1.8 mm mini-tablets. Fig. 4. (A) Correlation loadings plot in the PLSR model for inter-COV. (B) Weighted regression coefficients of flow parameters in the PLSR model for () inter-COV3 and () inter-COV1.8. Variables of importance are indicated by *. Fig. 5. Correlation loadings plot in the PLSR models for (A) intra-COV3 and (B) intraCOV1.8. (C) Weighted regression coefficients of flow parameters in the PLSR models for () intra-COV3 and () intra-COV1.8. Variables of importance are indicated by *. Fig. 6. Relationship between intra-COVs and selected flow parameters.
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27
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Table 1. Granule particle size, size distribution, bulk density and particle shape Impeller Wet Granulating D10 Batch speed massing time liquid (μm) (rpm) (min) (%, w/w) B1 650 3 6 34.4 (0.4) B2 650 3 8 44.3 (2.2) B3 650 3 10 28 (1.3) B4 650 5 6 42 (1.6) B5 650 5 8 50.4 (2.9) B6 650 5 10 41.7 (1.2) B7 800 3 6 35.9 (1.4) B8 800 3 8 45.6 (2.0) B9 800 3 10 46.7 (2.5) B10 800 5 6 38.1 (1.7) B11 800 5 8 48.2 (1.6) B12 800 5 10 34.2 (6.3) Parenthesised values indicate the respective standard deviations
D50 (μm)
D90 (μm)
Span
ρbulk (g/cm3)
AR
Roundness
79.5 (0.3) 127 (2.1) 167.4 (9.1) 91.9 (3.0) 154.6 (6.1) 171.1 (1.3) 82.0 (2.2) 137.4 (2.7) 174.1 (5.1) 87.9 (0.9) 151.3 (6.7) 152.6 (2.7)
211.9 (3.4) 258 (2.4) 348.5 (7.9) 195.6 (8.4) 317.8 (2.4) 364.6 (0.6) 227.5 (15.5) 282.5 (1.7) 353.1 (2.4) 192.4 (5.1) 313.8 (5.8) 358.8 (5.0)
2.23 1.68 1.91 1.67 1.73 1.89 2.34 1.72 1.76 1.76 1.76 2.13
0.53 0.59 0.58 0.54 0.64 0.61 0.54 0.59 0.61 0.52 0.61 0.59
1.42 (0.28) 1.39 (0.26) 1.41 (0.27) 1.39 (0.25) 1.37 (0.26) 1.42 (0.26) 1.43 (0.28) 1.38 (0.25) 1.43 (0.27) 1.42 (0.29) 1.45 (0.30) 1.33 (0.31)
1.41 (0.27) 1.38 (0.20) 1.43 (0.21) 1.4 (0.21) 1.35 (0.17) 1.4 (0.23) 1.38 (0.22) 1.36 (0.18) 1.44 (0.23) 1.35 (0.21) 1.45 (0.30) 1.44 (0.23)
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Table 2. Granule flow parameters from compendial flow characterisation methods ρtapped (g/cm3) 40.0 (1.5) 1.3 (0.0) 0.71 B1 33.0 (1.2) 1.2 (0.0) 0.73 B2 42.2 (1.9) 1.3 (0.0) 0.78 B3 B4 34.9 (0.7) 1.3 (0.1) 0.7 33.2 (0.6) 1.2 (0.0) 0.76 B5 36.7 (0.7) 1.2 (0.0) 0.75 B6 37.4 (0.9) 1.3 (0.0) 0.71 B7 33.4 (1.3) 1.2 (0.0) 0.71 B8 35.0 (0.4) 1.3 (0.0) 0.76 B9 39.0 (0.1) 1.3 (0.0) 0.65 B10 36.0 (1.6) 1.2 (0.0) 0.73 B11 38.7 (0.5) 1.3 (0.0) 0.74 B12 Parenthesised values indicate the respective standard deviations Batch AOR (°)
HR
COH (kPa)
UYS (kPa)
ffc
AIF (°)
0.46 (0.07) 0.41 (0.10) 0.66 (0.09) 0.41 (0.10) 0.31 (0.05) 0.43 (0.07) 0.45 (0.11) 0.27 (0.07) 0.27 (0.06) 0.75 (0.09) 0.47 (0.05) 0.43 (0.06)
1.68 (0.26) 1.52 (0.36) 2.52 (0.36) 1.53 (0.35) 1.14 (0.14) 1.71 (0.23) 1.65 (0.41) 1.01 (0.26) 1.05 (0.23) 2.81 (0.33) 1.77 (0.21) 1.66 (0.22)
8.62 (1.27) 10.03 (2.26) 6.28 (0.80) 10.08 (2.41) 12.74 (1.96) 9.32 (1.28) 9.13 (2.10) 15.24 (4.04) 15.17 (3.61) 5.30 (0.65) 8.26 (0.84) 9.17 (1.35)
32.52 (0.18) 33.07 (0.17) 34.98 (0.54) 33.84 (0.43) 32.45 (1.63) 36.78 (1.63) 32.91 (0.23) 33.67 (0.39) 35.68 (0.41) 33.65 (0.28) 33.82 (0.29) 34.91 (1.10)
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Table 3. Granule flow parameters from non-compendial flow characterisation methods Batch AvE (kJ/kg)
AvA (°)
AvT (sec)
25.01 (1.10) 56.40 (3.21) 2.60 (0.12) B1 17.74 (1.48) 51.30 (1.28) 2.10 (0.23) B2 26.03 (0.66) 60.40 (0.79) 2.80 (0.05) B3 B4 22.68 (0.23) 56.10 (2.76) 2.40 (0.08) 18.59 (0.72) 51.40 (0.96) 2.10 (0.14) B5 23.09 (1.13) 56.60 (0.88) 2.60 (0.12) B6 25.86 (0.84) 56.70 (0.90) 2.80 (0.07) B7 17.84 (0.81) 50.40 (0.06) 2.10 (0.11) B8 20.06 (1.07) 54.70 (0.75) 2.20 (0.16) B9 25.71 (0.84) 58.80 (0.93) 2.60 (0.06) B10 20.18 (0.42) 52.50 (0.07) 2.10 (0.08) B11 26.74 (0.16) 59.90 (0.07) 2.70 (0.14) B12 Parenthesised values indicate the respective standard deviations
BFE (mJ)
SE (mJ/g)
810.32 (85.18) 812.20 (24.14) 761.62 (39.68) 928.04 (20.89) 865.33 (83.99) 777.68 (13.86) 841.89 (61.75) 958.34 (24.34) 799.39 (16.46) 804.40 (65.29) 895.15 (53.10) 809.84 (51.03)
4.21 (0.21) 3.65 (0.05) 4.26 (0.09) 4.16 (0.04) 3.48 (0.17) 3.50 (0.05) 4.15 (0.09) 3.86 (0.02) 3.45 (0.06) 4.06 (0.10) 3.72 (0.19) 3.80 (0.10)
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S0378-5173(17)31022-0 https://doi.org/10.1016/j.ijpharm.2017.10.042 IJP 17098
To appear in:
International Journal of Pharmaceutics
Received date: Revised date: Accepted date:
19-8-2017 12-10-2017 21-10-2017
Please cite this article as: Goh, Hui Ping, Heng, Paul Wan Sia, Liew, Celine Valeria, Understanding die fill variation during mini-tablet production.International Journal of Pharmaceutics https://doi.org/10.1016/j.ijpharm.2017.10.042 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.
Understanding die fill variation during mini-tablet production
Hui Ping Goh, Paul Wan Sia Heng, Celine Valeria Liew* GEA-NUS Pharmaceutical Processing Research Laboratory, Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore
*Corresponding Author: Celine Valeria Liew GEA-NUS Pharmaceutical Processing Research Laboratory, Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117543, Singapore Tel: +65 6516 3870; Fax: +65 6775 2265 Email: [email protected]
Graphical abstract
Abstract
1
Reproducibility of die fill during tablet production is critical to ensure consistent tablet drug content and mechanical attributes. In the production of mini-tablets, tablets smaller than 6 mm, achievement of uniform die fill is much more challenging. Powder flow is often associated with die fill accuracy but this relationship has not been well characterised especially for mini-tablets. In this study, flow properties of different types of granules were characterised. Mini-tablets of 1.8 and 3 mm diameters were prepared from the granules using a rotary press with multiple-tip compression tooling. A methodology was established to evaluate mini-tablet die fill variation within and across compaction cycles using data from compression roller displacement and mini-tablet weight. Both sizes of mini-tablets showed similar extents of inter-cycle weight variation that could be associated with granules’ inter-particulate friction. However, smaller mini-tablets had higher intra-cycle weight variation due to their narrower die orifices. Multivariate and univariate analyses suggested that gravity fill influenced intra-cycle weight variation of 3 mm mini-tablets while suction fill was associated with that of 1.8 mm mini-tablets. Possible differences in die fill mechanisms between the mini-tablet sizes were identified and this provided a better insight into die fill variations during the production of minitablets.
200 words
Keywords: Mini-tablet, weight variation, powder flow, multiple-tip tooling, die fill
1
Introduction
Manufacture of pharmaceutical tablets involves filling of tableting feed into dies followed by compaction. Reproducibility of the die filling process is very critical as it determines tablet weight and weight variation, which in turn reflects drug content 2
uniformity and other critical product quality attributes such as hardness, disintegration and drug release (Peeters et al., 2015). In the case of mini-tablets, generally taken as tablets with diameters less than 6 mm, the attainment of consistency in die fill is much more challenging than for conventional-sized tablets due to the narrowness of the die orifices presented (Flemming and Mielck, 1995; Lennartz and Mielck, 1998). Additionally, the lower weights of mini-tablets will grossly magnify normal relative fill variations of the granular materials encountered with tableting, in terms of tablet weight or potency variations. Nonetheless, there is strong interest in the rational development of mini-tablets due to their wide array of applications, such as ameliorating dysphagia in geriatrics and paediatrics, controlling drug release and achieving individualised patient drug therapy (Aleksovski et al., 2015). The final act of die filling on a rotary tablet press comprises various mechanisms working in tandem. These mechanisms include gravity fill where the granular feed is assisted by gravity to fill the die, forced feeding where paddles in the feed frame physically direct feed into die orifices, and suction fill where the rapid descent of the lower punch creates a partial vacuum in the die to draw the feed in (Jackson et al., 2007; Peeters et al., 2015; Sinka et al., 2004). Inevitably, tablet feed flow properties are crucial to die fill performance. A complex interaction of particulate properties and processing conditions affect feed flow properties (Kachrimanis et al., 2005; Sinka et al., 2004). Powder flow may be characterised by a variety of techniques, including compendial (angle of repose, compressibility and shear cell) and non-compendial methods (powder rheometry and avalanche flow). Each of these methods describes different aspects of powder flow behaviour and no single test can be accepted as the standard for powder flowability
3
(Shah et al., 2008). While it is generally accepted that powders with good flowability are likely to produce tablets with lower weight variation, it is unclear which powder flow parameters are truly indicative of die fill performance. Fassihi and Kanfer found that powder mixtures that had higher angles of repose produced tablets with greater weight variations (Fassihi and Kanfer, 1986). Correlations between powder flow and die fill performance were also established in another study where a die filling rig was used to evaluate flow properties of glass beads, aluminium granules and tungsten powder (Freeman and Fu, 2008). On the contrary, it has been suggested that powder flow parameters are not able to fully predict die fill performance. Poor flowing powders had been found to achieve more uniform die fill than those with better flow (Mills and Sinka, 2013; Monedero Perales et al., 1996; Sinka et al., 2004). Furthermore, such findings are usually not specific to mini-tablets. During production, variations in die fill or tablet weight may be assessed by periodically collecting samples of tablets and weighing them individually. Alternatively, acquired process parameters can be utilised. For compacting tablets to the same thickness, the peak force in each compaction cycle is proportional to the die fill and hence can be used as a surrogate indicator of die fill variation. Automated tablet weight control systems in most modern rotary tablet presses rely on the measured compaction force to predict tablet weight and a force range is used to reject tablets that are outside the range as they are likely not to be within specifications (Ridgway et al., 1972). Some rotary presses rely on an air piston, also known as the air compensator, installed at the compression roller to control the amount of compaction force applied regardless of tablet weight (Felton, 2013). The compensator is able to move vertically in a cylinder filled with compressed air that allows the roller to “float” rather than being fixed and this modification enables the regulation of the applied peak compaction force independent 4
of tablet weight. A linear variable displacement transducer sensor accurately measures the movement of the compression roller as determined by the tablet weight. Thus, a heavy tablet causes the roller to move more while a lighter tablet, less. The linear relationship between roller displacement and tablet weight provides a more accurate measure of tablet weight variation than compaction force, especially when the applied force is low as for smaller tablets such as mini-tablets. For multiple-tip tools to produce mini-tablets, die fill variation during tableting can arise from within a compaction station as well as across the compaction stations. To the best of the authors’ knowledge, little literature information could be found on this subject. The purpose of this study was twofold, to gain a better understanding of weight variation in mini-tablet production and to find possible relationships between powder flow and mini-tablet weight variation. Pharmaceutical granules with different flow properties were produced and compacted into mini-tablets on a rotary tablet press. Both compendial and non-compendial flow characterisation techniques were used to characterise granule flow. A methodology developed from a combination of mini-tablet weight and in-line compression roller displacement data was used to examine die fill variations during mini-tablet production within and across compaction cycles. 2 2.1
Materials and Methods Materials
Metformin hydrochloride (Granules India, India) was used as the model drug and it was mixed with lactose (Sorbolac 400M, Meggle, Germany) for granulation. Polyvinylpyrrolidone (PVP K30; Kollidon 30, BASF, Germany) was dissolved in purified water to prepare the granulating liquid. Acetone (Aik Moh, Singapore) was the dispersing agent for particle size analysis. 5
2.2
Granule preparation
2.2.1 High shear granulation Granules comprising metformin hydrochloride, lactose and PVP K30 (ratio, 25:24:1) were prepared by wet granulation in a high shear mixer (PMA-1, GEA Niro Pharma Systems, United Kingdom). In order to produce granules with different particle size distributions and flow properties, critical parameters during the wet massing stage were varied: impeller speed (650 or 800 rpm), wet massing time (3 or 5 min) and amount of granulating liquid added (6, 8 or 10 %, w/w). The amount of granulating liquid was expressed as a percentage of the total dry powder weight. Chopper speed was kept constant at 1000 rpm during wet massing. The metformin hydrochloride and lactose mixture was first dry blended for 3 min at an impeller speed of 650 rpm. Wet massing was initiated by the introduction of the granulating liquid via a pressure pot. The granules produced were de-agglomerated (Comil® 197S, Idex-Quadro Engineering, Canada) through a 1143 μm round aperture screen aided by a round impeller operating at 1700 rpm before drying in a fluid bed processor (Strea-1, GEA-Aeromatic, Switzerland) with an inlet air temperature of 60 °C. Drying was stopped when an exhaust temperature of 40 °C was attained. Dried granules were further milled using the conical screen mill fitted with a 457 μm round aperture screen and a round impeller operating at 1700 rpm. A total of twelve granule batches were prepared. 2.2.2 Granule lubrication Each granule batch was blended with 1 %, w/w magnesium stearate in a double cone blender rotated at 15 rpm for 5 min prior to flow characterisation and tableting.
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2.3
Granule characterisation
2.3.1 Particle size and size distribution All batches of granules were sized by laser diffraction (LS230, Beckman Coulter, USA) using the dry powder module. Granule feed rate was adjusted to achieve an obscuration level of about 10 %. Granule sizes corresponding to the 10th, 50th and 90th percentiles (D10, D50 and D90) were determined from the cumulative undersize curve. Span, an indicator of particle size distribution, was calculated using Equation (1). Span = (D90 − D10 )/D50
(1)
2.3.2 Particle shape Using a stereomicroscope (SZH, Olympus, Japan) with an attached video camera (DXC-390, Sony, Japan) linked to a computer, images of 600 granules were collected for each batch and analysed using an image analysis software (Image Pro Plus 6.3, Media Cybernetics, USA). Aspect ratio (AR) of the granules was calculated using Equation (2). A larger aspect ratio indicates more elongated granules. AR = Major axis/Minor axis
(2)
where the major and minor axes are the largest granule diameter and the smallest diameter orthogonal to it, respectively. Granule roundness was determined using Equation (3). A value of 1 indicates a perfect circle. Roundness = P 2 /(4πA)
(3)
where P is the perimeter and A is the area of the granule image. 7
2.3.3 Compendial flow characterisation 2.3.3.1 Angle of repose For each granule batch, approximately 100 g of sample was dispensed through a funnel onto a round 10 cm base plate (Copley Scientific, United Kingdom). The funnel height was adjusted such that the orifice was about 2 - 4 cm above the expected tip of the powder heap to be formed. Height of the cone formed on the 10 cm base plate was recorded to calculate angle of repose (AoR) using Equation (4). The procedure was triplicated for each granule batch and the average AoR determined. Lower AoR values indicate better powder flow. AoR = tan−1(h/r)
(4)
where h is the height of the symmetrical cone and r is the radius of the base plate. 2.3.3.2 Bulk and tapped densities Granule samples were discharged through a 1 mm aperture size sieve into a preweighed cylinder of 100 mL. Excess granules were carefully scraped off the top of the cylinder, and weight obtained after removal of adhering material. Granule bulk density (ρbulk) was calculated using Equation (5). ρbulk = Mass of granules/100 mL
(5)
The filled cylinder was tapped (TD2, Sotax, Switzerland) at 250 taps per min until a constant volume. Granule tapped density (ρtapped) was calculated using Equation (6). ρtapped = Mass of granules/Tapped volume
(6)
8
Hausner ratio (HR) was derived from the bulk and tapped densities using Equation (7). The measurements were triplicated for each granule batch and averaged values calculated. Lower HR values are indicative of better flow. HR = ρtapped /ρbulk
(7)
where ρtapped is the tapped density and ρbulk is the bulk density. 2.3.3.3 Shear cell test A FT4 powder rheometer (FT 4, Freeman Technology, United Kingdom) fitted with shear cell accessories was used to carry out rotational shear testing of the granules. The test sequence employed was similar to that of the Jenike shear cell but utilised the same sample to generate all points for the yield loci instead of new samples for each data point. From the yield loci, granule cohesion (COH), unconfined yield strength (UYS), flow function coefficient (ffc) and angle of internal friction (AIF) were determined. The shear cell tests were triplicated and average values reported. Good flowability is associated with low COH and UYS values, and high ffc values. 2.3.4 Non-compendial flow characterisation 2.3.4.1 Powder rheometry A FT4 powder rheometer (FT 4) was also used to measure the basic flow energy (BFE) and specific energy (SE) of the granules. The tests involved turning a twisted blade in a series of downward clockwise and upward anti-clockwise rotations through preconditioned granule samples. BFE was the energy expended in moving the blade downwards with the granules under a state of high stress flow. SE was measured during the blade’s upward traverse where the granules were lifted by the blade and made to
9
flow in a gentler manner. The entire procedure was triplicated for each granule batch. In general, high BFE and low SE values are desired for good flowability. 2.3.4.2 Avalanche flow The avalanche flowability (Revolution® Powder Analyzer, Mercury Scientific Inc., USA) of the granules was determined. The granule sample was transferred into a drum and rotated at a constant speed of 0.6 rpm. A digital camera captured the images of avalanches as the drum revolved continuously until 150 avalanches were captured. During this process, the avalanche angle (AvA) - angle of inclination just prior to occurrence of the avalanche, avalanche energy (AvE) - amount of energy released by an avalanche event, and avalanche time (AvT) - average time between consecutive avalanche events, were monitored by the computer software and calculated as parameters to indicate powder flowability. It is generally accepted that low values for AvA, AvE and AvT are related to good flow (Nalluri and Kuentz, 2010). 2.4
Mini-tablet production
2.4.1 Mini-tablet compaction on rotary tablet press All twelve batches of granules were used to produce mini-tablets of 1.8 and 3 mm diameters on a rotary tablet press (R190FT, GEA, Belgium). Multiple-tip (9 tips – 1.8 mm diameter; 8 tips – 3 mm diameter), standard concave punches (Natoli Engineering Company, USA) were employed (Fig. 1A). The tablet press was fitted with a fill cam of 14 mm depth and a two-paddle feed frame. The feeder and metering paddles were set at rotational speeds of 11 and 24 rpm, respectively. Press linear tableting speed was 415 mm/s. Tablet compaction was carried out under controlled ambient temperature and relative humidity conditions (25 °C, 50 %). Equal force compaction technology was
10
used; for each mini-tablet diameter, mini-tablets were compacted at the same peak compaction force, independent of tablet weight. Compaction pressure applied per minitablet was about 100 MPa. 2.4.2 Sampling protocol for mini-tablet collection A compaction cycle represents the traverse of a compaction station on the turret. The rotary press was operated for a total of 505 compaction cycles in each experimental run and this included an initial 100 conditioning cycles to ensure smooth feed flow and a completely filled feed frame. After the conditioning phase, mini-tablets were collected over five consecutive compaction cycles (consecutive sampling) at cycles 101 - 105, 301 - 305 and 501 - 505. Mini-tablets were also collected once at every 40 compaction cycles (individual sampling) from cycle 120 to cycle 480. Hence, mini-tablets were sampled over 25 cycles in each experimental run (Fig. 1B) and were stored for at least 24 h under controlled ambient temperature and relative humidity conditions (25 °C, 50 %) before characterisation. 2.5
Mini-tablet characterisation
2.5.1 Inter-cycle mini-tablet weight variation Mini-tablets require much lower compaction forces to attain similar compaction pressures as conventional tablets due to their smaller surface area of force application. However, the relationship between compaction force and tablet weight is non-linear and becomes less sensitive at low compaction forces. As a result, variation in compaction force might not be sensitive enough to detect mini-tablet weight variation during production.
11
During mini-tablet production, peak compression roller displacement values were recorded after the conditioning phase (n = 405) to monitor weight variation across the compaction cycles. Standard deviation of all the roller displacement values was used in Equation (8) to calculate the coefficient of variation (COV), yielding inter-COV for indicating mini-tablet weight variation across the 405 compaction cycles.
Inter-COVM =
Standard deviation of roller displacement across cycles Mean roller displacement across cycles
x 100 %
(8)
where M is the mini-tablet diameter in mm. Suitability of inter-COV as a surrogate indicator for mini-tablet weight variation was determined. Mini-tablets from 25 sampled compaction cycles were weighed individually. Mean and standard deviation of mini-tablet weight across the compaction cycles were then determined and used in Equation (9) to calculate inter-COVRef, which was compared against inter-COV.
Inter-COVRef =
Standard deviation of mini−tablet weight across cycles Mean mini−tablet weight across cycles
x 100 %
(9)
2.5.2 Intra-cycle mini-tablet weight variation Mini-tablets from each sampled cycle were weighed individually. The standard deviation of mini-tablet weight within each sampled cycle was determined and used in Equation (10) to yield the intra-COV for each sampled cycle. Intra-COV values from all 25 sampled cycles were then averaged to report the mean intra-COV.
Intra-COVM =
Standard deviation of mini−tablet weight within sampled cycle Mean mini−tablet weight within sampled cycle
x 100 %
(10)
where M is the mini-tablet diameter in mm.
12
2.5.3 Mini-tablet tensile strength Breaking force of three randomly selected mini-tablets from each sampled compaction cycle was determined via diametrical compression between two platens using a tensile tester (EZ Test, Shimadzu, Japan) fitted with a 100 N load cell. A micrometer screw gauge was used to measure the tablet thickness. Tensile strength (σt) of each mini-tablet was calculated using Equation (11) and the average σt calculated for each compaction cycle. σt = 2F/(πMT)
(11)
where F is the breaking force, M and T are mini-tablet diameter and thickness in mm, respectively. 2.6
Statistical analysis
Pearson’s correlations (r) were used to identify significant linear correlations between two independent data sets (IBM SPSS Statistics for Windows version 22, IBM Corporation, USA). One-way analysis of variance (ANOVA) and independent samples t-tests were also conducted at the 95 % confidence interval. Using the same software, agglomerative hierarchical clustering analysis (HCA) was used to organise granule batches into clusters based on their properties. Similar granule clusters were merged at successive steps. Ward’s linkage algorithm and squared Euclidean distances between observation sets were used for the analysis (Kaufman and Rousseeuw, 2009). Partial least squares regression (PLSR) was performed using multivariate software (The Unscrambler® X v.10.3, CAMO Software, Norway). Cross-validation and data scaling were performed prior to regression analysis.
13
3 3.1
Results Granule particle size, size distribution, shape and bulk density
Particle size and size distribution, shape and bulk density of the granules prepared are presented in Table 1. In general, the granules had different particle size, size distributions and bulk densities. However, particle shape descriptors (AR and roundness) appeared to be comparable. 3.2
Granule flow properties
Granule flow is a bulk response resulting from the intrinsic material properties (particle size, size distribution and shape). Flow parameters from compendial and noncompendial methods are summarised in Tables 2 and 3 respectively. Cluster analysis of the 12 granule batches (B1- B12) based on their measured flow parameters (AoR, HR, COH, UYS, ffc, AIF, BFE, SE, AvE, AvA and AvT) by HCA is summarised in the dendrogram (Fig. 2). The relative positions of the granule batches along the vertical axis indicated their degree of similarity to each other, with batches in closer proximity being more similar in flow properties. Joined clusters are represented as vertical lines and the position of each vertical line on the scale indicates the distance at which clusters were joined. Large distances between sequential vertical lines indicate combination of dissimilar clusters and can be used as the optimum point of differentiation. From the dendrogram, the granule batches can be generally divided into two main clusters (C1 and C2). Cluster C1 comprised granules B1, B3, B4, B6, B7, B10 and B12. All the other granule batches were classified under cluster C2. Analysis of the granules’ flow properties showed no significant differences in BFE and AIF between the two clusters. However, in terms of the remaining flow parameters (AoR, HR, SE, COH, UYS, ffc, AvE, AvA and AvT), flowability of granules in cluster C1 was significantly worse 14
compared to those of cluster C2 (p < 0.05 for all cases). Granules produced in this study had thus showed differences in their flow properties. 3.3
Mini-tablet physical characteristics
Mean weights for both 3 and 1.8 mm mini-tablet samples ranged between 18.0 – 21.0 mg and 4.0 – 4.6 mg while their average tensile strengths were 1.25 ± 0.26 MPa and 1.49 ± 0.31 MPa, respectively. None of the mini-tablets produced had shown mechanical failure such as capping and/or lamination. A random sample of 20 minitablets was also taken for each batch and it was found that all the batches had complied with compendial criteria for uniformity of tablet mass (British Pharmacopoeia Commission, 2016). 3.4
Inter-cycle mini-tablet weight variation and relationship with granule flow properties
For both inter-COV3 and inter-COV1.8, statistically significant linear correlations were obtained with inter-COVRef (Fig. 3A, R2 = 0.694 and 0.855, respectively) and demonstrated that inter-COV could be used as a surrogate marker of mini-tablet weight variation across compaction cycles. Compared to inter-COVRef, inter-COV had reflected the compression roller displacement, a more readily available and convenient surrogate process parameter. Furthermore, inter-COV accounted for all of the compaction cycles after the conditioning phase and gave a more representative picture of mini-tablet weight variation present during production. Inter-COV3 and inter-COV1.8 are shown in Fig. 3B. Higher inter-COV values indicated greater variations in the compression roller displacement, and in turn, mini-tablet
15
weights, across compaction cycles. Both inter-COV3 and inter-COV1.8 varied between 10 – 25 % and were correlated with each other (r = 0.643, p = 0.024). Variation in inter-COV across the granule batches (Fig. 3B) suggested that granule properties rather than mini-tablet diameters had affected die fill across compaction cycles. PLSR modelling was done for inter-COV using flow parameters as predictor variables. Correlation loadings plot from the PLSR model is shown in Fig. 4A. It was observed that 80 % of variation in the flow parameters accounted for 71 % of variation in inter-COV. There was relatively good fit of the dataset to the PLSR model (R2 = 0.742 and 0.680 for inter-COV3 and inter-COV1.8, respectively). Variables of importance were identified based on statistical significance of the weighted regression coefficients as shown in Fig. 4B. Granules with higher HR and SE but lower AIF values showed higher inter-COV for both mini-tablet diameters (p < 0.05 for all three flow parameters). BFE values was also positively associated with inter-COV3 (p < 0.01) but not for inter-COV1.8. 3.5
Intra-cycle mini-tablet weight variation and relationship with granule flow properties
Intra-COV3 did not exceed 2 % which indicated low intra-cycle weight variation across all granule batches (Fig. 3C). In contrast, intra-COV1.8 values ranged between 2 - 6 %. No significant correlation was found between intra-COV3 and intra-COV1.8. PLSR modelling was done for each of the two mini-tablet sizes with intra-COV as the response variable and granule flow parameters as predictor variables. Plots of correlation loadings from the PLSR models are shown in Fig. 5A–B. Variables of importance are shown in Fig. 5C.
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For the 3 mm mini-tablets (Fig. 5A), 73 % of variation in the predictor variables accounted for 68 % of variation in intra-COV3 (R2 = 0.488). Weighted regression coefficients for AoR, HR, AvE and AvT were found to be significant (Fig. 5C, p < 0.05 for all four parameters) which suggested that these parameters were best associated with intra-cycle die fill variation in 3 mm mini-tablets among all the other flow parameters. Close proximity of intra-COV3 and these flow parameters in the loadings plot suggested significant positive correlations which were verified by univariate analyses (Fig. 6A–C). HR had the strongest association with intra-COV3 (R2 = 0.796), followed by AoR (R2 = 0.680) and AvT (R2 = 0.612). PLSR model for the 1.8 mm mini-tablets had a better fit of the dataset (R2 = 0.733). However, only 23 % of variation in the predictor variables explained 73 % of variation in intra-COV1.8 (Fig. 5B). Amongst the various flow parameters, AIF had greatest influence on intra-COV1.8 (Fig. 5C). A negative, non-linear relationship between AIF and intra-COV1.8 was also found (Fig. 6D). 4 4.1
Discussion Mini-tablet weight variation within and across compaction cycles
Currently there is no standard method for evaluating mini-tablet weight variation and most studies still rely on pharmacopoeial guidelines that were established for conventional-sized tablets. Multiple mini-tablets were produced in each compaction cycle and it was of interest to determine tablet weight variation within as well as across cycles. The use of inter- and intra-COVs in this study made it possible to analyse minitablet weight variation in these two dimensions separately. In this study, despite both sizes of mini-tablets complying with the compendial standards for uniformity of mass, it was clear that they had different extents of weight variation. 17
Inter-COV (variation in compression roller displacement) regarded the multiple die orifices as a single aggregate and reflected the stability of die fill during tableting while intra-COV represented how well the granules were distributed across the multiple die orifices within each compaction cycle. Even though inter-COV values were comparable for 3 and 1.8 mm mini-tablets, intra-COV values were markedly different. Narrower die orifices of the 1.8 mm mini-tablets were more challenging to fill uniformly which led to higher intra-COV1.8 values as compared to intra-COV3. Consequently, information gathered from inter- and intra-COVs reflected mini-tablet weight variation during the production run more accurately. 4.2
Powder flow parameters and inter-cycle mini-tablet weight variation
For both 3 and 1.8 mm mini-tablets, flow parameters which had significant impact on inter-cycle weight variation were identified via PLSR. Granules with higher HR, BFE and SE values but lower AIF generally produced mini-tablets with higher inter-COV (Fig. 4B). HR reflects inter-particulate friction under dynamic conditions (Grey and Beddow, 1969); the act of tapping the cylinder results in particle rearrangement and repacking which involves overcoming inter-particulate friction coupled with bulk powder movement. Higher HR values are indicative of poorer powder flow and greater extent of inter-particulate friction. Likewise, BFE and SE also indicate inter-particulate friction under dynamic conditions. During the powder rheometry test, the blade must overcome resistance from the inter-particulate friction first before it is able to traverse through the granules. Higher BFE and SE values are indicative of greater inter-particulate friction since more energy is required by the blade to generate the flow pattern. During minitablet production, efficient fluidisation of the tablet feed by the rotating paddles in the feed frame is required to ensure consistency in the amount of granules passed over the 18
die orifice at the filling stage of each compaction cycle. Granules with higher dynamic inter-particulate friction could have been harder to fluidise leading to higher variation in the amount of granules delivered to the die orifice with each pass under the feed frame. Similar to BFE and HR, AIF is also a measure of inter-particulate friction. However, the granule sample was under consolidation stress during the shear cell test with little bulk movement unlike in the powder rheometry and tapping studies. It follows that AIF reflects inter-particulate friction under static conditions: higher AIF values suggest greater static inter-particulate friction. In contrast to HR, BFE and SE, AIF was negatively correlated with inter-COV1.8 and inter-COV3. Static inter-particulate friction could have been important during suction fill of granules from the feed frame into the die orifice. Suction fill occurs when the lower punch descends at the fill cam and creates a partial vacuum to draw powder into the die. Granule particles with larger AIF and greater extent of static inter-particulate friction may be able to latch onto one another more closely during the suction fill event at each compaction cycle. This in turn could have improved the reproducibility of die fill across compaction cycles and possibly explain why AIF was negatively associated with inter-COV. 4.3
Powder flow parameters and intra-cycle mini-tablet weight variation
Flow parameters which best correlate with intra-COV3 and intra-COV1.8 were also identified. While inter-COV3 and inter-COV1.8 were affected by similar flow parameters, this was not the case for intra-COV. Both gravity and suction fills were the main die fill mechanisms during mini-tablet production. However, for the 1.8 mm minitablets, gravity fill may be less dominant since there was less effective evacuation of residual air trapped inside the die, leading to greater back pressure that opposed granules entering the orifice by gravity effect alone. Moreover, granules which were 19
more cohesive may also form stable arches over the small orifices. Suction effect would have been necessary to break the arch and draw particles into the die orifice (Baserinia et al., 2016). The importance of static inter-particulate friction and suction fill for the 1.8 mm mini-tablets could be seen from AIF having significant effect on both inter- and intra-COV1.8. In contrast, HR and SE only affected inter-COV1.8, suggesting a high importance of granule dynamic flow and mixing inside the feed frame when considering inter-cycle weight variation. Conversely, powder flow parameters derived from bulk powder movement under its own weight (AoR, HR and avalanche flow) were best associated with intra-COV3, implying the increased influence of gravity fill for the larger die orifices. With the exception of HR, flow parameters associated with intra-COV3 differed from those associated with inter-COV3. When considering weight variation of all eight 3 mm orifices across the compaction cycles, dynamic and static inter-particulate friction (granule fluidisation in the feed frame and suction fill) were important. Gravity flow into the individual orifice only became apparent when considering weight variation within each compaction cycle. It follows that the die filling process was a complex interplay of several mechanisms working together simultaneously. By studying die fill variation within and across compaction cycles, different die fill mechanisms were identified. 5
Conclusion
In this study, a methodology was established to evaluate mini-tablet weight variation within and across compaction cycles. It was demonstrated that the use of data from compression roller displacement and mini-tablet weight gave better insight on die fill variation during mini-tablet production with multiple-tip tooling. Both 1.8 and 3 mm 20
mini-tablet diameters showed comparable inter-cycle weight variation which was likely to be affected by efficiency of granule fluidisation in the feed frame and reproducibility of suction fill into the die. Higher intra-cycle die fill variation in 1.8 mm mini-tablets as compared to 3 mm mini-tablets was attributed to greater difficulty to fill narrower die orifices uniformly. While intra-cycle die fill variation for 3 mm mini-tablets was related to gravity fill, suction fill continued to play a dominant role in intra-cycle die fill variation of 1.8 mm mini-tablets. The relationship between mini-tablet die fill variation and powder flow was hence highly dependent on the die fill mechanisms involved. Findings from this study may be useful in addressing problems associated with minitablet weight variability and aid in selection of powder flow measurement techniques that best relate to mini-tablet die fill performance. 6
Acknowledgement
Goh Hui Ping is a recipient of the National University of Singapore Graduate Research Scholarship. Financial support for the project was from the GEA-NUS PPRL fund (N148-000-008-001).
References: Aleksovski, A., Dreu, R., Gašperlin, M., Planinšek, O., 2015. Mini-tablets: a contemporary system for oral drug delivery in targeted patient groups. Expert Opinion on Drug Delivery 12, 65-84. Baserinia, R., Sinka, I.C., Rajniak, P., 2016. Vacuum assisted flow initiation in arching powders. Powder Technology 301, 493-502. British Pharmacopoeia Commission, 2016. British Pharmacopoeia 2017. Stationery Office. 21
Fassihi, A.R., Kanfer, I., 1986. Effect of Compressibility and Powder Flow Properties on Tablet Weight Variation. Drug Development and Industrial Pharmacy 12, 19471966. Felton, L.A., 2013. Remington - Essentials of Pharmaceutics. Pharmaceutical Press. Flemming, J., Mielck, J.B., 1995. Requirements for the Production of Microtablets: Suitability of Direct-Compression Excipients Estimated from Powder Characteristics and Flow Rates. Drug Development and Industrial Pharmacy 21, 2239-2251. Freeman, R., Fu, X., 2008. Characterisation of powder bulk, dynamic flow and shear properties in relation to die filling. Powder Metallurgy 51, 196-201. Grey, R.O., Beddow, J.K., 1969. On the Hausner Ratio and its relationship to some properties of metal powders. Powder Technology 2, 323-326. Jackson, S., Sinka, I.C., Cocks, A.C., 2007. The effect of suction during die fill on a rotary tablet press. European Journal of Pharmaceutics and Biopharmaceutics 65, 253256. Kachrimanis, K., Petrides, M., Malamataris, S., 2005. Flow rate of some pharmaceutical diluents through die-orifices relevant to mini-tableting. International Journal of Pharmaceutics 303, 72-80. Kaufman, L., Rousseeuw, P.J., 2009. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley. Lennartz, P., Mielck, J.B., 1998. Minitabletting: improving the compactability of paracetamol powder mixtures. International Journal of Pharmaceutics 173, 75-85.
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Mills, L.A., Sinka, I.C., 2013. Effect of particle size and density on the die fill of powders. European Journal of Pharmaceutics and Biopharmaceutics 84, 642-652. Monedero Perales, M.C., Muñoz-Ruiz, A., Velasco Antequera, M.V., Muñoz, N.M., Ballesteros, M.R.J.-C., 1996. Comparative tableting and microstructural properties of a new starch for direct compression. Drug Development and Industrial Pharmacy 22, 689695. Nalluri, V.R., Kuentz, M., 2010. Flowability characterisation of drug–excipient blends using a novel powder avalanching method. European Journal of Pharmaceutics and Biopharmaceutics 74, 388-396. Peeters, E., De Beer, T., Vervaet, C., Remon, J.P., 2015. Reduction of tablet weight variability by optimizing paddle speed in the forced feeder of a high-speed rotary tablet press. Drug Development and Industrial Pharmacy 41, 530-539. Ridgway, K., Deer, J.J., Finlay, P.L., Lazarou, C., 1972. Automatic weight-control in a rotary tabletting machine. Journal of Pharmacy and Pharmacology 24, 203-210. Shah, R.B., Tawakkul, M.A., Khan, M.A., 2008. Comparative Evaluation of Flow for Pharmaceutical Powders and Granules. AAPS PharmSciTech 9, 250-258. Sinka, I.C., Schneider, L.C.R., Cocks, A.C.F., 2004. Measurement of the flow properties of powders with special reference to die fill. International Journal of Pharmaceutics 280, 27-38.
Figure Captions
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Fig. 1. Schematics of (A) multiple-tip tooling employed in this study and (B) mini-tablet sampling protocol for individual and consecutive sampling during the 505 compaction cycles. Fig. 2. Mapping of the twelve granule batches into clusters based on their flow parameters using HCA. Fig. 3. (A) Relationship between inter-COVRef and () inter-COV3 and (○) interCOV1.8. Plots of (B) inter-COV and (C) intra-COV for () 3 mm and () 1.8 mm mini-tablets. Fig. 4. (A) Correlation loadings plot in the PLSR model for inter-COV. (B) Weighted regression coefficients of flow parameters in the PLSR model for () inter-COV3 and () inter-COV1.8. Variables of importance are indicated by *. Fig. 5. Correlation loadings plot in the PLSR models for (A) intra-COV3 and (B) intraCOV1.8. (C) Weighted regression coefficients of flow parameters in the PLSR models for () intra-COV3 and () intra-COV1.8. Variables of importance are indicated by *. Fig. 6. Relationship between intra-COVs and selected flow parameters.
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Table 1. Granule particle size, size distribution, bulk density and particle shape Impeller Wet Granulating D10 Batch speed massing time liquid (μm) (rpm) (min) (%, w/w) B1 650 3 6 34.4 (0.4) B2 650 3 8 44.3 (2.2) B3 650 3 10 28 (1.3) B4 650 5 6 42 (1.6) B5 650 5 8 50.4 (2.9) B6 650 5 10 41.7 (1.2) B7 800 3 6 35.9 (1.4) B8 800 3 8 45.6 (2.0) B9 800 3 10 46.7 (2.5) B10 800 5 6 38.1 (1.7) B11 800 5 8 48.2 (1.6) B12 800 5 10 34.2 (6.3) Parenthesised values indicate the respective standard deviations
D50 (μm)
D90 (μm)
Span
ρbulk (g/cm3)
AR
Roundness
79.5 (0.3) 127 (2.1) 167.4 (9.1) 91.9 (3.0) 154.6 (6.1) 171.1 (1.3) 82.0 (2.2) 137.4 (2.7) 174.1 (5.1) 87.9 (0.9) 151.3 (6.7) 152.6 (2.7)
211.9 (3.4) 258 (2.4) 348.5 (7.9) 195.6 (8.4) 317.8 (2.4) 364.6 (0.6) 227.5 (15.5) 282.5 (1.7) 353.1 (2.4) 192.4 (5.1) 313.8 (5.8) 358.8 (5.0)
2.23 1.68 1.91 1.67 1.73 1.89 2.34 1.72 1.76 1.76 1.76 2.13
0.53 0.59 0.58 0.54 0.64 0.61 0.54 0.59 0.61 0.52 0.61 0.59
1.42 (0.28) 1.39 (0.26) 1.41 (0.27) 1.39 (0.25) 1.37 (0.26) 1.42 (0.26) 1.43 (0.28) 1.38 (0.25) 1.43 (0.27) 1.42 (0.29) 1.45 (0.30) 1.33 (0.31)
1.41 (0.27) 1.38 (0.20) 1.43 (0.21) 1.4 (0.21) 1.35 (0.17) 1.4 (0.23) 1.38 (0.22) 1.36 (0.18) 1.44 (0.23) 1.35 (0.21) 1.45 (0.30) 1.44 (0.23)
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Table 2. Granule flow parameters from compendial flow characterisation methods ρtapped (g/cm3) 40.0 (1.5) 1.3 (0.0) 0.71 B1 33.0 (1.2) 1.2 (0.0) 0.73 B2 42.2 (1.9) 1.3 (0.0) 0.78 B3 B4 34.9 (0.7) 1.3 (0.1) 0.7 33.2 (0.6) 1.2 (0.0) 0.76 B5 36.7 (0.7) 1.2 (0.0) 0.75 B6 37.4 (0.9) 1.3 (0.0) 0.71 B7 33.4 (1.3) 1.2 (0.0) 0.71 B8 35.0 (0.4) 1.3 (0.0) 0.76 B9 39.0 (0.1) 1.3 (0.0) 0.65 B10 36.0 (1.6) 1.2 (0.0) 0.73 B11 38.7 (0.5) 1.3 (0.0) 0.74 B12 Parenthesised values indicate the respective standard deviations Batch AOR (°)
HR
COH (kPa)
UYS (kPa)
ffc
AIF (°)
0.46 (0.07) 0.41 (0.10) 0.66 (0.09) 0.41 (0.10) 0.31 (0.05) 0.43 (0.07) 0.45 (0.11) 0.27 (0.07) 0.27 (0.06) 0.75 (0.09) 0.47 (0.05) 0.43 (0.06)
1.68 (0.26) 1.52 (0.36) 2.52 (0.36) 1.53 (0.35) 1.14 (0.14) 1.71 (0.23) 1.65 (0.41) 1.01 (0.26) 1.05 (0.23) 2.81 (0.33) 1.77 (0.21) 1.66 (0.22)
8.62 (1.27) 10.03 (2.26) 6.28 (0.80) 10.08 (2.41) 12.74 (1.96) 9.32 (1.28) 9.13 (2.10) 15.24 (4.04) 15.17 (3.61) 5.30 (0.65) 8.26 (0.84) 9.17 (1.35)
32.52 (0.18) 33.07 (0.17) 34.98 (0.54) 33.84 (0.43) 32.45 (1.63) 36.78 (1.63) 32.91 (0.23) 33.67 (0.39) 35.68 (0.41) 33.65 (0.28) 33.82 (0.29) 34.91 (1.10)
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Table 3. Granule flow parameters from non-compendial flow characterisation methods Batch AvE (kJ/kg)
AvA (°)
AvT (sec)
25.01 (1.10) 56.40 (3.21) 2.60 (0.12) B1 17.74 (1.48) 51.30 (1.28) 2.10 (0.23) B2 26.03 (0.66) 60.40 (0.79) 2.80 (0.05) B3 B4 22.68 (0.23) 56.10 (2.76) 2.40 (0.08) 18.59 (0.72) 51.40 (0.96) 2.10 (0.14) B5 23.09 (1.13) 56.60 (0.88) 2.60 (0.12) B6 25.86 (0.84) 56.70 (0.90) 2.80 (0.07) B7 17.84 (0.81) 50.40 (0.06) 2.10 (0.11) B8 20.06 (1.07) 54.70 (0.75) 2.20 (0.16) B9 25.71 (0.84) 58.80 (0.93) 2.60 (0.06) B10 20.18 (0.42) 52.50 (0.07) 2.10 (0.08) B11 26.74 (0.16) 59.90 (0.07) 2.70 (0.14) B12 Parenthesised values indicate the respective standard deviations
BFE (mJ)
SE (mJ/g)
810.32 (85.18) 812.20 (24.14) 761.62 (39.68) 928.04 (20.89) 865.33 (83.99) 777.68 (13.86) 841.89 (61.75) 958.34 (24.34) 799.39 (16.46) 804.40 (65.29) 895.15 (53.10) 809.84 (51.03)
4.21 (0.21) 3.65 (0.05) 4.26 (0.09) 4.16 (0.04) 3.48 (0.17) 3.50 (0.05) 4.15 (0.09) 3.86 (0.02) 3.45 (0.06) 4.06 (0.10) 3.72 (0.19) 3.80 (0.10)
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