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Mapping key process parameters to the performance of a continuous dry powder blender in a continuous direct compression system
Powder Technology 362 (2020) 659–670
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Powder Technology journal homepage: www.elsevier.com/locate/powtec
Mapping key process parameters to the performance of a continuous dry powder blender in a continuous direct compression system John Palmer a, Gavin K. Reynolds b,⁎, Furqan Tahir c, Indrajeetsinh K. Yadav d, Elizabeth Meehan b, James Holman a, Gurjit Bajwa d a
Process Development, GEA Group Ltd, Eastleigh, UK Pharmaceutical Technology & Development, AstraZeneca, Macclesfield, UK Perceptive Engineering Ltd, Vanguard House, Sci Tech Daresbury, Cheshire, UK d Product Development and Supply, GlaxoSmithKline Research and Development, Ware, UK b c
a r t i c l e
i n f o
Article history: Received 1 August 2019 Received in revised form 13 December 2019 Accepted 14 December 2019 Available online 16 December 2019 Keywords: Continuous direct compression Continuous blending Material attributes Continuous pharmaceutical manufacturing Residence time distribution Strain
a b s t r a c t This work aims to expand the typical raw material attributes that can successfully be processed on a continuous direct compression line with a particular focus on the continuous dry powder blender. Three grades of Acetaminophen were investigated as model active pharmaceutical ingredients and chosen to span a broad range of material attributes wider than what would normally be considered for a direct compression process. An experimental design was set-up for each grade of Acetaminophen to investigate the effect of throughput, impeller speed and impeller configuration on the critical process responses and attributes. The strain experienced by the in-process material was found critical to improve content uniformity. The impeller speed and design can be optimised at a given throughput to obtain a strain which would deliver the required content homogeneity. The content uniformity of the final tablets can be modelled as a function of the strain using an exponential decay model. © 2019 Elsevier B.V. All rights reserved.
1. Introduction The pharmaceutical manufacturing industry is undergoing a paradigm shift as it moves towards a more agile supply chain. A transition to continuous manufacturing (CM) provides an opportunity to address the changing needs of the supply chain. It allows for a much more integrated approach in the manufacture of oral solid dosage (OSD) forms leading to a reduction of material handling and process steps and overall a more streamlined manufacturing platform [1]. CM also provides further potential for enhanced product quality assurance as continuous processes present a greater opportunity to deploy process analytical technologies (PAT) for inline process and product monitoring that lends itself to real time release testing (RTRT). Abbreviations: APAP, Acetaminophen; API, Active Pharmaceutical Ingredient; CDC, Continuous Direct Compression; CM, Continuous Manufacturing; CU, Content uniformity; DC, Direct Compression; DoE, Design of Experiments; HPLC, High Performance Liquid Chromatography; LiW, Loss in Weight feeding; mAPAP, Micronised Acetaminophen; MRT, Mean Residence Time; MSE, Mean Square Error; NIR, Near Infrared (Spectroscopy); OPC, Open platform communication; OSD, Oral solid dosage; PAT, Process Analytical Technology; pAPAP, Powdered Acetaminophen; QbD, Quality by design; RSD, Relative Standard Deviation; RMSE, Root Mean Square Error; RTD, Residence Time Distribution; RTRT, Real Time Release Testing; SG APAP, Special Granular Acetaminophen; WG, Wet Granulation. ⁎ Corresponding author. E-mail address: [email protected] (G.K. Reynolds).
https://doi.org/10.1016/j.powtec.2019.12.028 0032-5910/© 2019 Elsevier B.V. All rights reserved.
The feasibility for implementation of continuous unit operations is widely reported with a large body of research conducted on many of the independent units. Examples such as process optimisation during product development for continuous wet granulation (WG) processes using twin screw granulation [2,3] and continuous dispensing with Loss-in-weight (LiW) feeding have been reported [4–6]. Significant work has been focussed on continuous blending and different types of heterogeneous blenders such as horizontal and vertical blenders [7] have been investigated. Horizontal blenders are now well established in the industry for continuous blending applications where active pharmaceutical ingredient (API) and excipients are blended for either direct compression (DC) or WG formulations. Horizontal blenders generally consist of an elongated barrel which can be either completely horizontal or inclined and an impeller made of a central shaft with mixing blades attached which can be operated at variable rotational speed. Different mixing blade designs exist and often the angle of mixing blades can also be adjusted to optimise the blending process. A number of research groups [8–10] have demonstrated how the blender process parameters can be manipulated to optimise a pharmaceutical continuous blending process, while Vanarase et al. [11] described how material attributes can impact the blender performance. Traditionally uniformity of the blend produced after mixing the API and excipients in the process has been assessed through a stratified
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Table 1 Composition of final compression blend. Component
Function
% in formulation
APAP Lactose anhydrous Microcrystalline cellulose Croscarmellose sodium Magnesium stearate
API Diluent Diluent Disintegrant Lubricant
10 27 59 3 1
blend sampling approach with off-line analysis for API content. For CM in-line PAT measurements can facilitate an effective approach to assess operating space, that is the interaction of input variables such as material attributes and process parameters, to deliver drug product of an acceptable quality as suggested in regulatory guidance [12]. Being able to either measure or predict the homogeneity of the blend after blending in real time offers an essential quality assurance tool for continuous manufacturing. Near Infrared (NIR) Spectroscopy is well established in other industries such as food [13] and oil and gas [14] and is now the most common spectroscopic PAT tool used for the real time monitoring of blend homogeneity as demonstrated by Varanese et al. [11,15] and Jarvinen et al. [16]. Sierra-Vega et al. [17] evaluate the application of different NIR methodologies within a continuous direct compression (CDC) process with NIR measurements after blending, in the feed frame of the tablet press and on the final tablet core. Other types of measurements are available which can be used to measure the blend homogeneity as described in the review conducted by Asachi et al. [18]. Often with PAT sensors such as NIR, complex multivariate models are required which need to be robust enough to accommodate sources of variability in raw materials and the process as well as being stable over long periods of time [19]. Recently soft sensor approaches have been demonstrated to predict the blend homogeneity after blending from other indirect measurements coupled with dynamic models of the blending process [20–22]. In these cases, the residence time distribution (RTD) of the blending process is characterized typically using a form of tracer experiment and then modelled using a standard technique such as continuous stirred tanks in series or a plug flow reactor with diffusion type model. The exit concentration can then be predicted by using the measured input concentration from the LiW feeders and a representative process RTD model. These models have the added benefit that they can provide evidence of material traceability through the process on which decisions about product collection or rejection can be made thus supporting the overall drug product control strategy [12,23–26]. The DC method of oral solid dose manufacture offers advantages such as reduced number of unit operations, smaller equipment footprint, and simpler process with less material in flight at a given time over wet or dry granulation methods. The latter require the transformation of in process material attributes to improve the processability of the raw materials. If this can be avoided DC is more favourable, however the application of DC is heavily reliant on the target dose of the drug product and raw material attributes and variability. Products containing a high API loading that demonstrates poor flow properties have traditionally given preference to granulation based routes of manufacture in order to reduce the risk of process failure. With the advent of CDC the potential process operating space in which such material attribute limitations can be overcome is the focus of ongoing evaluation. It is recognised that a further limitation of traditional batch DC relates to material segregation that is related to the attributes of the API properties and the drug loading in the formulation. Since continuous DC closely integrates individual unit process operations, eliminates hold times and improves the transport of the blend it can offer greater potential to accommodate a wider range of material attributes and drug product designs by effectively removing the risk of segregation. The successful application of CDC for a variety of drug products has been reported. Van Snick et al. [27] demonstrated the ability of a commercial CDC platform to produce
satisfactory product quality at low drug loadings, while developing the optimised final drug product process. Several groups have successfully investigated the use of a CDC platform in the production of extended release tablets [28–30]. In addition, the successful application of continuous blending and DC to formulations which are prone to segregation has been reported [31–33]. The work described in this paper is a part of a programme of work oncontinuous tablet manufacturing has been completed as part of a UK based collaborative research project (ReMediEs: RE-configuring MEDIcines End-to-end Supply), which was focused on streamlining the supply chain in the pharmaceutical industry. This article describes an assessment of the performance of the ConsiGma™ CDC-50 (GEA Group, Wommelgem, Belgium) across a wide operating space. The paper aims to show the effect of material attributes, throughput rate, key process parameters and blender set-up using a model API formulation. The paper will also thoroughly investigate the impact of changes to the setup of the API blending stage on the final tablet content uniformity (CU). 2. Materials and methods 2.1. Materials and composition Micronised Acetaminophen (mAPAP, Mallinckrodt, USA) was used as a model API and it was formulated using pharmaceutical excipients commonly used for direct compression. Microcrystalline cellulose (Avicel PH102, FMC, Ireland), lactose anhydrous (Supertab 21AN, DFE Pharma, Germany), croscarmellose sodium (Ac-di-sol SD-711, FMC, Ireland) and Magnesium Stearate (Ligamed MF-2-V, Peter Greven, Germany) were combined during processing to form the final blend for compression. The components with functionality and quantitative composition of the final compression blend are shown in Table 1. Two additional grades of Acetaminophen (APAP) of divergent material attributes were also included in the study to assess the impact of material attributes on the process performance. Other grades of APAP used in comparative studies were powder grade APAP (pAPAP, Mallinckrodt, USA) and Special Granular grade APAP (SG APAP, Mallinckrodt, USA). 2.2. Material characterisation The particle size distribution of the raw materials was measured by laser diffraction using a Mastersizer 3000 with Aero S dry disperser
Feeder 1
Feeder 2
Feeder 3
Feeder 4
(Coperion K-Tron KT20)
(Coperion K-Tron KT20)
(GEA Compact Feeder)
(GEA Compact Feeder)
Blender 1
Feeder 5
(GEA CDB)
(Coperion K-Tron KT20)
Blender 2 (GEA CDB)
In-line NIR (J&M)
Tablet Press (GEA R100)
At-line NIR (Bruker MPA II)
Fig. 1. Schematic of proof of concept CDC line.
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Table 2 Calibration and validation runs to be completed for both inline and at-line calibration.
Calibration 1 (CP) Calibration 2 Calibration 3 Calibration 4 Calibration 5 Calibration 6 Calibration 7 Validation 1 Validation 2 Validation 3 Validation 4 Validation 5
APAP
Lactose anhydrous
Microcrystalline cellulose
Croscarmellose sodium
Magnesium Stearate
10.00% 8.00% 8.67% 9.33% 10.67% 11.33% 12.00% 11.68% 8.95% 9.63% 11.77% 10.04%
29.00% 24.61% 36.60% 22.68% 35.49% 22.58% 26.59% 28.58% 31.29% 25.12% 30.96% 27.99%
57.00% 62.92% 50.96% 64.03% 49.43% 62.20% 57.17% 55.37% 55.83% 61.20% 53.28% 57.35%
3.00% 3.42% 2.72% 2.87% 3.34% 2.83% 3.18% 3.25% 2.90% 2.94% 3.20% 3.55%
1.00% 1.05% 1.06% 1.09% 1.08% 1.05% 1.06% 1.12% 1.03% 1.11% 0.80% 1.07%
(Malvern Instruments, UK). The dispersion pressure was 0.5 bar and each sample was measured in triplicate. D(v,0.1), D(v,0.5) and D (v,0.9) values were derived from the measured particle size distribution and used for modelling purposes. The cohesive/adhesive properties of the materials were characterized using a RST-XS Ring Shear Tester (Dietmar Schulze, Germany). A shear cell (30 mL volume) was used for testing powder flowability at a pre-shear normal stress of 4 kPa, flow function coefficient (FFC) and wall friction angle (WFA) were measured in duplicate. Bulk and tapped density were measured in duplicate using a 100 mL measuring cylinder. A weighed sample of material was added, and the volume measured to record bulk density. It was then subjected to tapping using a JEL STAV2003 jolting volumeter (J. Engelsmann, Germany) until no discernible change in volume occurred in three consecutive readings at which point the tapped volume was measured to record tapped density. Particle and surface morphology were evaluated using a TM1000 scanning electron microscope, SEM, (Hitachi, Japan) at an accelerating voltage of 15 kV. Images were captured at appropriate magnifications, up to 2000×, for a detailed visualisation of the particle and surface attributes.
2.3. CDC process overview A prototype CDC-50 system, depicted in Fig. 1, was constructed by integrating three units into a single processing platform; LiW feeding of individual formulation components, continuous blending to combine the individual components into a homogenous blend and compression of the blend via a rotary tablet press into the final dosage form. On the prototype CDC system, four LiW feeders were used to continuously feed individual raw materials into the inlet of the first continuous blender (GEA Wommelgem, Belgium). Depending upon the set-up of the mixing blades on the blender shaft within the main blending stage
(blender 1), the amount of shear imparted on the powder can be optimised as required to ensure sufficient mixing. The exit of the main blender was directly coupled to the inlet of a secondary blender (blender 2) along with a LiW feeder for the lubricant. A secondary blender was included to blend any components of the formulation which are shear sensitive such as in this case the lubricant. Having a main and secondary blending stage allows for maximum flexibility as it allows for these two blending stages to be decoupled. The secondary blender was configured to minimise shear and in process material mean residence time (MRT). In the case of lubrication blending this minimises the risk of over lubrication of the blend, which can lead to issues with the final product, such as poor tensile strength and poor wettability. Finally, material leaving the secondary blender is used to continuously feed a rotary tablet press (R100, GEA Halle, Belgium) for tablet compression. PAT was used to monitor the process either in-line or at-line. Blend uniformity was measured prior to compression using in-line NIR (Tidas P analyzer, J&M Analytik, Essingen, Germany) located in the feed tube of the tablet press, whilst tablet assay was determined at-line using transmission NIR (MPA II, Bruker, Germany). 2.3.1. Loss-in-weight feeding LiW feeders use the change in net weight of the material in the hopper to calculate a mass flow rate. The motor speed is then regulated using a feedback control loop to maintain the calculated mass flow rate at the set-point. Screw type, motor gearbox ratios and agitator types were selected to optimise the feeding process. Feeders 1 to 5 were used to dose microcrystalline cellulose, lactose anhydrous, croscarmellose sodium, APAP and magnesium stearate, respectively. The LiW feeders were set up to deliver a consistent mass flow to the system, even during refills of the hoppers which were controlled by switching the feeders from gravimetric control to volumetric control prior to refilling. On the commercial system this is done automatically by the integrated control system for all GEA compact feeders. The GEA compact feeders update and store a feed factor profile which provides
Fig. 2. Calibration model compared with validation data.
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Fig. 3. PharmaMV dashboard for monitoring an RTD spike.
an estimate of the screw speed during volumetric mode, minimising any mass flow errors. The KT-20 feeders were fitted with large hoppers (20 L for microcrystalline cellulose and 10 L for lactose anhydrous), which minimised the number of refills and therefore time in volumetric mode. 2.3.2. Continuous blenders The GEA linear blender is a single shaft paddle blender inclined at an angle of 15o to the horizontal. Each blender has a mixing length of 70 cm and an internal diameter of 12 cm. The drive of the motor enables the blender shaft speed to be set to any value between 0 and 450 rpm. For this study the minimum speed selected was 150 rpm, the maximum was 450 rpm and the midpoint 300 rpm, corresponding to a range of Froude (Fr) numbers between 1.5 and 13.4. The Froude number is calculated from:
Fr ¼
r blender ω2 g
Equation 1: Definition of Froude number, where rblender is the blender radius, ω is the impeller rotation rate and g is the acceleration due to gravity. Table 3 The three factor Box-Behnken experimental plan with three centre-points used for the micronised APAP grade. The rows shaded in grey indicate the 22 full factorial design which were run for powder and special granular APAP grades. Run
System throughput (kg/h)
B1 Impeller Speed (rpm)
B1 Mixing blade arrangement
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
2.5 50 2.5 50 2.5 50 2.5 50 26.25 26.25 26.25 26.25 26.25 26.25 26.25
150 150 450 450 300 300 300 300 150 450 150 450 300 300 300
8 8 8 8 0 0 16 16 0 0 16 16 8 8 8
The blender consists of a configurable shaft made up of 56 paddles, and each of the paddles can be angled in two directions, transport (45° to shaft) or mixing (0° to shaft). Increasing the number of blades at 0° to the shaft will decrease the ability of the blender to pump the inprocess material along the blender and therefore will increase the volume of powder within the blender and extend the mean residence time. The level of blending imparted on the blend is a critical control factor in ensuring sufficient blending, both at a micro and macro level. 2.3.3. Tablet press Tablets were compressed using a GEA R100 rotary tablet press. For the throughputs of 26.25 kg/h and 50 kg/h the press was fitted with 30 stations of 10 mm round, concave punches and the press set up to make tablets 350 mg in weight, corresponding to speeds of 42 and 79 rpm, respectively. For the low rate of 2.5 kg/h the press was partially tooled with only 5 stations of the same tooling and the same tablet weights were targeted, with a corresponding press speed of 24 rpm. The press settings (fill depth, pre-compression height, main compression height,) were adjusted to ensure the final tensile strength of the tablets between the 1.7–2.0 MPa target, to ensure tablets were robust enough to determine tablet content uniformity. For the 10 mm tablets, this corresponded to a hardness range of approximately 10–12 kp [34]. 2.4. Analytical tools and calibration model development 2.4.1. Blend uniformity via reflectance NIR An in-line NIR reflectance probe and spectrometer (Light House Probe™, GEA Wommelgem, Belgium) were positioned after the outlet of the second blender in the feed tube above the tablet press. The realtime NIR spectra were used within a calibration (chemometric) model to predict the composition of the blend before tabletting. The design of the calibration model is described as follows. 2.4.1.1. Inline NIR calibration for API content in the bulk blend. The measurements for the calibration samples were completed at the centre point throughput and the blender set-up which was expected to yield the best blend homogeneity. For each of the runs the system was started from a cleaned state. The feeder mass flow rates were all set at the correct value for the desired final blend composition and the system was then run till it was at steady state. While the system was at steady state, measurements of the blend were taken every second from the
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A Shaft Angle (°) Shaft Angle (°) 0 45 45 45 45 45 45 45 45 45 0 60 45 45 45 45 45 45 45 45 45 60 120 45 45 45 45 45 45 45 45 45 120 180 45 45 45 45 45 45 45 45 45 45 180 240 45 45 45 45 45 45 45 45 45 240 300 45 45 45 45 45 45 45 45 45 300 Outlet
Inlet
B Shaft Angle (°) Shaft Angle (°) 0 45 45 45 45 0 45 0 45 45 0 60 45 45 45 45 0 45 45 45 45 60 120 45 45 45 45 0 45 45 45 45 120 180 45 45 45 45 45 0 45 45 45 45 180 240 45 45 45 45 45 0 45 45 45 240 300 45 45 45 0 45 0 45 45 45 300 Inlet
Outlet
C Shaft Angle (°) Shaft Angle (°) 0 45 45 45 45 0 45 0 45 45 0 60 45 45 0 45 0 45 0 45 45 60 120 45 45 0 45 0 45 0 45 45 120 180 45 45 45 0 45 0 45 0 45 45 180 240 45 45 45 0 45 0 45 0 45 240 300 45 45 45 0 45 0 45 45 45 300 Outlet
Inlet
Fig. 4. Shows the three-standard linear blender blade configurations used in this work (a) Full transport (0H), (b) 8 Blade helical (8H) and (c) 16 blade helical (16H).
inline NIR system above the press and tablet samples were taken every 0.5 system mean residence times for a total of 2 mean residence times for content measurement. The calibration and validation experimental formulation matrices can be seen in Table 2. For the calibration experiments, the API was varied +/− 20% in equal increments around the target drug loading. As there were no quantitative measurements giving the absolute API concentration the average API concentration as calculated from the LiW feeder mass flow rates were used. The average API concentration was then regressed, using a PLS algorithm, with the pre-processed spectra during steady state operation. As part of the pre-processing, the NIR spectra was first normalised using SNV (standard normal variate). Then, a Savitzky-Golay 1st order derivative filter (2nd order polynomial with nine points) was applied to smooth out the spectra. A 3 latent variable (LV) PLS calibration model was developed in PharmaMV™ software (Perceptive Engineering Ltd., Daresbury, UK) using the steady state spectral data from each of the seven calibration runs. The developed model accounted for 97% of variability within the response signal. The model demonstrates good prediction accuracy which is reflected in similar RMSE values of 0.237% and 0.262% for both training and validation datasets, respectively. Fig. 2 illustrates the model prediction against unseen data, demonstrating good prediction accuracy. 2.4.2. Content uniformity via transmission NIR Tablet assay was determined at-line using transmission NIR (MPA II, Bruker, Germany) by sampling tablets as detailed in Section 2.6. Calibration was completed through the measurement of a reference tablets whose API assay had been measured using HPLC. The calibration was
then undertaken by regressing the NIR spectra to the final Acetaminophen content in each tablet using PLS algorithm. This model development is described next. 2.4.2.1. NIR calibration for tablet API assay. A limited region of the NIR spectrum (8000–12,590 cm−1) for each tablet was pre-processed using a standard normal variate and Savitzky-Golay first derivative procedure. The PLS model was developed to correlate the measured spectra to the APAP assay measured by HPLC in each tablet. The model consisted of two LVs which accounted for around 98% of the Y-variance. A model with more than two LVs was found to be over-fitting. The final model was found to have RMSE values of 0.171% and 0.167% for training and validation datasets, respectively. 2.4.3. System mean residence time and residence time distribution When determining the performance of a continuous blending system it is necessary to characterise the RTD of the blender under a given set of process conditions. The RTD is a probability distribution, defining the probability of how long a particle will stay in the blender. The RTD will define the macro mixing capability of the blender and therefore determine the amount of variability from the feeders that the blender can buffer before blend uniformity are observed. Table 4 Sampling Strategy for each run. Sample #
1
2
3
4
5
6
7
8
9
MRT No. Tablets Tested
0 10
0.5 3
1 3
1.5 3
2 10
2.5 3
3 3
3.5 3
4 10
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Table 5 Material characterisation of materials used in study. Material
D(v,0.1) (μm)
D(v,0.5) (μm)
D(v,0.9) (μm)
FFC
WFA (°)
Bulk density (g cm−3)
Tapped density (g cm−3)
Micronised APAP Powdered APAP Special granular APAP Lactose anhydrous Microcrystalline cellulose Croscarmellose sodium Magnesium stearate
3 8 152 29 38 18 2
10 53 299 181 124 41 10
33 225 498 353 302 98 33
1.4 1.9 19.4 7.4 5.4 4.2 3.4
13.2 12.8 15.0 20.4 19.6 9.8 8.0
0.19 0.31 0.73 0.68 0.32 0.53 0.20
0.30 0.53 0.83 0.89 0.42 0.73 0.33
The RTD is linked to the MRT that material spends in the process. The MRT is calculated from the measured mass of powder in the system (in this work this mass will be called the residence mass) divided by the mass flow rate through the system (Eq. (2)). t¼
Z∞ t¼
t:^eðt Þdt
0
m _ m
Equation 2: Equation defining the mean residence time of the system or unit _ operation wheret is the mean residence time, m is the residence mass and m is the mass flow rate. The residence mass can also be expressed as a volumetric fill level, which can be used to quantify the free volume for dynamic powder flow and allow comparison with other blending systems. m
F¼
It is also possible to determine the mean residence time by taking the first moment of the normalised residence time distribution:
=ρb;m 2 πL rmixer −r 2shaft
Equation 3: Equation defining the volumetric fill level, F, where m is the residence mass, ρb,m is the powder bulk density, L is the mixer length, and rmixer and rshaft are the mixer and shaft radii, respectively.
Equation 4: Equation defining the mean residence time, t, of the system or unit operation, where ^eðtÞ is the normalised residence time distribution. The degree of back mixing or axial dispersion within a system can be quantified using the Peclet number, which expresses the relative rates of convection and dispersion. The Peclet number can be estimated from the normalised residence time distribution using the following equation [35]: 2 2 − 2 1−e−Pe ¼ Pe Pe
R∞ 0
2 t−t :^eðt Þdt t
2
Equation 5: Equation relating the Peclet number, Pe, and the residence time distribution. The RTD was determined experimentally using tracer experiments. After the system had been deemed to reach steady state for a given set of conditions 2% by mass of the total system residence mass of
Fig. 5. SEM micrographs of the three grades of APAP used in the study: (a) micronised APAP, (b) powder APAP, (c) special granular APAP.
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Table 6 Measured residence mass for each run. Run
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 a b
Throughput (kg/h)
2.5 50 2.5 50 2.5 50 2.5 50 26.25 26.25 26.25 26.25 26.25 26.25 26.25
Impeller Speed (RPM)
150 150 450 450 300 300 300 300 150 450 150 450 300 300 300
No. Mixing Blades
Residence Mass (kg)
8 8 8 8 0 0 16 16 0 0 16 16 8 8 8
Fill Level (%)
Pe
mAPAP
pAPAP
SG APAP
mAPAP
pAPAP
SG APAP
mAPAP
1.073 1.652 0.219 0.284 0.081 0.229 0.980 1.268 0.307a 0.087 1.561 0.156 0.556 0.571 ****b
– – – – – – – – 1.223 0.143 1.860 0.685 0.644 0.615 0.633
– – – – – – – – 1.112 0.180 1.730 0.490 0.630 0.640 0.630
41.7 64.3 8.5 11.0 3.2 8.9 38.1 49.3 11.9 3.4 60.7 6.1 21.6 22.2 –
– – – – – – – – 46.2 5.4 70.3 25.9 24.3 23.2 23.9
– – – – – – – – 38.2 6.2 59.5 16.8 21.7 22.0 21.7
5.2 19.6 36.0 31.3 50.3 52.8 3.8 12.6 68.8 24.9 17.0 59.6 8.0 9.9 –
Run at 200RPM impeller speed. Residence mass not collected.
APAP was added to the inlet of the blender in a single aliquot. The system was then run for 5 mean residence times with measurements and the sampling strategy as described in Section 2.6. 2.4.4. System strain and micro-mixing The tablet assay RSD can be directly related to the amount of blending imparted by the system using the strain as previously reported by Van Snick et al. [27]. The strain for each unit can be calculated based on the mean residence time and the impeller frequency as shown by Eq. (6). ϵ ¼ t:ω Equation 6: Equation for the strain for a unit operation where ε is the strain, t is the mean residence time and ω is the impeller rotation rate. The total strain imparted on the powder is a sum of the strain imparted by each of the unit operations. The scope of this study was to investigate the impact of the main blending stage on tablet properties and therefore the downstream contributions were kept constant, such as the settings for the secondary blender and the mass hold-up between the secondary blender and the tablet press feed-frame. Therefore, only the strain contribution from the main blending stage was considered in subsequent analysis. Gao et al. [36] has previously shown a method of predicting blend uniformity using an exponential decay model. In batch blending, the blend RSD is described as a function of time whereas for a continuous blender the RSD is a function of the position within the blender. In this case the final tablet RSD was described as a function of the strain of the entire system using Eq. (7). Three different free parameters need to be parameterised by minimising the error between the
predictions and the measured values. RSDmin is the minimum RSD that is achievable, and any further blending will result in no further RSD reduction. RSD0 is the initial RSD of the unblended mixture and kb is the RSD decay rate constant. RSDðεÞ ¼
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi RSD2min þ RSD20 −RSD2min eð−kb εÞ
Equation 7: RSD as a function of the system strain where RSDmin is the minimum achievable RSD, RSD0 is the initial RSD, kb is the RSD decay rate constant and ε is the strain. 2.4.5. On-line system monitoring using PharmaMV™ During the trials, all data logging, calibration model execution and process monitoring was performed, in real-time, using PharmaMV™ software (Perceptive Engineering Ltd., Daresbury, UK). This included logging and time aligning process data from feeders and blenders as well as PAT data from the in-line NIR spectrometer through the OPC (open platform communications) interface. The software allows for real-time monitoring, control and process optimisation as well as data analysis, modelling and visualisation. Process dashboards were also implemented to allow the operators to easily monitor various mass flows, calibration model predictions and RTD spikes. An example of the data collection dashboard for one of the experimental runs is shown in Fig. 3. In this dashboard, the data related to the four feeder mass flowrates is shown in the two top-half trends while the net weight in the feeders is shown in the form of dials and a table underneath. The bottom right trend shows the blender load cell measurement from the start of the RTD spike test. Finally, the bottom left trend shows the blend APAP calibration prediction in blue, overlaid with the red crosses corresponding
Table 7 DoE model coded parameters, with p-value in brackets. Material
mAPAP
Response
Residence Mass
Fill Level
Pe
Residence Mass
pAPAP Fill Level
Residence Mass
SG APAP Fill Level
Constant Throughput Impeller Speed No. Mixing Blades Impeller Speed × No. Mixing Blades Impeller Speed × Impeller Speed No. Mixing Blades × No. Mixing Blades R2 Q2
0.65 0.14 (0.763) −0.49 (b0.0001) 0.40 (0.0002) −0.27 (0.0288) – – 0.9185 0.7425
25.28 5.25 (0.763) −19.22 (b0.0001) 15.49 (0.0002) −10.49 (0.0288) – – 0.9185 0.7425
8.08 – 3.20 (0.459) −14.98 (0.0052) 25.50 (0.0031) 15.37 (0.0464) 22.45 (0.0066) 0.8415 0.0511
0.83 – −0.56 (0.0079) 0.29 (0.0615) – – – 0.8856 0.5225
31.33 – −21,31 (0.0079) 11,14 (0.0615) – – – 0.8856 0.5225
0.77 0 −0.54 (0.0036) 0.23 (0.0594) – – – 0.9169 0.5985
26.57 −18.66 (0.0036) 7.97 (0.0594) – – – 0.9169 0.5985
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Fig. 6. Contour plots showing the change in fill level with the three factors: thoughput, impeller speed and impeller configuration for the micronised APAP design (a) 2.5 kg/h, (b) 26.25 kg/h and (c) 50 kg/h.
to the tablet APAP prediction. Notice that, as expected, the real-time tablet APAP concentration prediction is essentially a lagged version of the blend APAP concentration prediction which further serves to verify the accuracy of the two designed calibration models (see Sections 2.4.1.1 and 2.4.2.1, respectively).
2.5. Experimental outline 2.5.1. Design of experiments (DoE) The DoE was constructed and analysed using Design Expert 8 software (Stat-Ease, Inc., Minneapolis, 2010). For the micronised acetaminophen a three factor Box-Behnken design was implemented with three centre point replicates with the list of experiments can be seen in Table 3. The experimental study assessed three main control variables namely line throughput (kg/h), Blender 1 impeller rotational speed (rpm) and Blender 1 impeller configuration (number of axial blades) and the responses measured were Blender 1 residence mass (kg) and uniformity of tablet API assay (% relative standard deviation). As part of this study three different blade configurations were selected to impart differing levels of mixing intensities during main blending. To allow for a quantitative analysis the pattern of mixing blades to transport blades were kept consistent, with only the number of mixing blades being increased. The high mixing configuration consisted of 16 blades positioned in the axial direction in a helical pattern (16H), centred along the length of the blender. The mid mixing configuration
employed 8 blades, in the same helical pattern (8H) and central to the shaft whilst the low mixing configuration used zero blades in the axial configuration (0H). Fig. 4 shows the positioning of the blender blades for the three different configurations. Blender 1 was located on three load cells (K-SFT-II-M, Coperion, Switzerland) and the combined readings gave an in-line measurement of the residence mass within the blending stage. The setup of Blender 2 was fixed, with all blades positioned in the transport position and the blender speed fixed at 250 rpm. The secondary blender was not located on load cells, the residence mass was measured by vacuuming out the contents of the blender after an instantaneous stop of the system, and measure the mass collected in the vacuum supply. For the other two grades of APAP a reduced DoE was conducted consisting of only a 22 factorial design with centre point replicates varying blender impeller rotational speed and blender impeller configuration with a fixed throughput of 26.25 kg/h. For analysis of each DoE, linear and interaction terms were considered and selected based on p-values below 0.1. Quadratic terms were also considered for the micronised APAP DoE. 2.6. Sampling strategy After system start-up, steady state was determined by monitoring on-line measurement of the residence mass of blender 1 and using the signal from the in-line NIR probe. The first part of the experiment was to run the system at steady state and to monitor the key process responses. As the experimental plan was run at different throughputs and the blender residence mass would be changing between experimental points, it was important to take into account the transient nature of the system when defining the sample plan. For this work tablet samples at the outlet of the tablet press were taken over 4 total system mean residence times at 0.5 mean residence time intervals. This gave a total of 9 sample sets per experimental point. For tablet assay analysis at the 0, 2 and 4 mean residence time points, ten individual tablets were analysed. A summary of the sampling strategy is shown in Table 4. 3. Results and discussion 3.1. Material characterisation
Fig. 7. Contour plot showing the change in Peclet number with impeller speed and number of mixing blades.
The material attributes measured for each component in the formulation are summarised in Table 5. The values demonstrate the broad range of material attributes for this typical CDC formulation that must be accommodated in each stage of the process. Excipient grades deemed suitable for DC processing were selected for the model formulation and therefore in particular the diluents, making up the majority of the
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667
Peclet Number
100
10
1 0
10
20
30
40
50
60
70
Fill Level (%) Fig. 8. Relationship between Peclet number (Pe) and fill level.
formulation, exhibit relatively good flow properties as intended. Magnesium stearate and croscarmellose sodium are minor components in the formulation and exhibit poorer flow properties. Fig. 5 shows SEM micrographs of the three grades of APAP, illustrating the difference in particle size and shape. For the API the three grades of APAP span a wide range of material properties between two extremes; micronsized, cohesive material to significantly larger particle size, very good flowing material. This diversity of material attributes provides a strong basis to assess the ability of CDC to process a typical drug product formulation with a particular emphasis on segregation tendency. For example, micronised APAP and Special Granular APAP would be expected to be prone to segregation from the DC excipients due to the significant differences in FFC. 3.2. Residence mass, fill level and Peclet number mapping As described previously, the residence mass of Blender 1 was monitored during the runs and the mean can be observed in Table 6. The three blending process parameters used as factors in this DoE analysis were all found to affect the final residence mass that was measured in the blender with varying levels of significance, as shown in Table 7. Increasing the impeller speed caused the final mass and fill level in the blender to decrease, and increasing the number of mixing blades increased the residence mass and fill level significantly. For micronised APAP the influence of throughput was marginally significant, with an increase in throughput leading to a slight increase in residence mass and fill level. These relationships are consistent with those found by Van Snick et al. (2017) using a GEA linear blender. Vanarase and Muzzio (2011) also found a decrease in residence mass with
increasing impeller speed and an increase in residence mass with increasing throughput, although they used a Gericke GCM250 continuous mixer, which is horizontally positioned and includes a weir. The three contour plots shown in Fig. 6 demonstrate how the chosen factors affect the blender fill level. It has been observed that if the impeller is set-up as the full transport configuration, the fill level becomes largely independent of the impeller speed. Although satisfactory tablet content uniformity for all conditions were obtained, the narrow operating space for this blade configuration would make it unsuitable for the main blending stage. Further, the full transport set-up would be appropriate for processes such as lubrication blending where low intensity blending is typically required. This highlights the importance of setting the correct impeller configuration to obtain a wide enough operating space to achieve the desired mean residence time to achieve a satisfactory blend and uniformity. It is also possible to see from these plots that it is possible to achieve an equivalent residence mass with different combinations of impeller configuration and impeller speeds. In general, unless the blend being processed is shear sensitive, the parameters should be set in a way as to achieve the highest impeller speed for a given mean residence time which will maximise the strain. Peclet number was found to be a non-linear function of impeller speed and number of mixing blades, with some significant quadratic terms. Fig. 7 shows how the model predicts Peclet number to change with these factors. The model shows that the lowest Peclet numbers, which correspond to the greatest extent of axial dispersion, are generated at intermediate impeller speeds with a higher number of mixing blades. The model Q2 was quite poor, so this should not be
Fig. 9. Contour plots showing the change in fill level with the two factors: impeller speed and impeller configuration for each grade of APAP tested at 26.5 kg/h (a) micronised APAP, (b) powder APAP and (c) special granular APAP.
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Table 8 Summary of the measured tablet assay RSD for the mAPAP experimental runs. Run 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Throughput (kg/h)
Impeller Speed (rpm)
No. Mixing Blades
Tablet Assay RSD (%)
2.5 50 2.5 50 2.5 50 2.5 50 26.25 26.25 26.25 26.25 26.25 26.25
150 150 450 450 300 300 300 300 200 450 150 450 300 300
8 8 8 8 0 0 16 16 0 0 16 16 8 8
0.64 1.44 0.77 1.32 0.66 1.68 0.59 0.9 1.37 1.02 0.76 1.35 0.81 0.77
over-interpreted. Another way of considering this relationship is to view it in terms of fill level. Fig. 8 shows a non-linear relationship between Peclet number and fill level. For low fill levels, Pe is high, indicating poor backmixing in the system. There appears to be a minimum Peclet number around a fill level of 30–40%. Higher fill levels appear to exhibit slightly higher Peclet numbers again. Van Snick et al. [27] also made a similar observation, attributing the reduction in axial dispersion at high impeller speeds to the high conveying rate moving the bed in a more plug flow like state and the reduction in axial dispersion at very low impeller speeds due to an inability of the impeller to impart significant homogenisation force to intermingle individual particles. Conversely, Vanarase and Muzzio [37] found that an increase in impeller speed improved axial dispersion in the horizontal Gericke GCM250 mixer. This suggests an optimum fill level is required to maximise the extent of axial dispersion in the inclined GEA linear blender. Fig. 9 shows that at a throughput of 26.25 kg/h both the impeller speed and the impeller configuration had a similar effect on the fill level for all three grades of APAP. It can also be observed that the material properties of the different grades of APAP in the blend have no significant effect on the fill level in the blender. Although throughput was not tested for all three grades of APAP in this study, the influence of throughput on the fill level of the micronised APAP formulation was found to be consistent with other formulations [29] and therefore, a similar fill level response to throughput changes for the powder and special granular grades would not be expected to be different to the micronised APAP grade. 3.3. Tablet assay variability vs key process parameters It is important to assess the blend homogeneity achieved by changing the key process parameters and to determine which parameters are
Table 9 DoE model coded parameters for tablet assay RSD, with p-value in brackets. Model Term
Coded Factor (p-value)
Constant Throughput Impeller Speed No. Mixing Blades Throughput × Impeller Speed Throughput × No. Mixing Blades Impeller Speed × No. Mixing Blades Impeller Speed × Impeller Speed No. Mixing Blades × No. Mixing Blades R2 Q2
0.79 0.34 (b0.0001) −7.9 × 10−4 (0.9712) −0.17 (0.0004) −0.06 (0.0761) −0.18 (0.0014) 0.30 (0.0002) 0.25 (0.0007) 0.15 (0.0045) 0.99 0.91
the most significant in order to optimise the blending process. To quantify the impact on tablet properties, the assay variability of the tablets was assessed for each run by calculating the RSD for assay of the tablets across a run using Eq. (8). σ RSD ¼
σ 100 μ
Equation 8: σRSD is the assay RSD (%), σ is the sample standard deviation and μ is the sample mean. The NIR tablet assay method was only developed for micronised APAP. Table 8 shows the consistently low tablet RSD (b 1.68%) obtained across the experimental design using micronised APAP. This shows the ability of the CDC process to minimise the risk of segregation anticipated from the material attributes. Statistical analysis of the tablet results showed throughput and blade configuration to be significant along with interactions with impeller speed and several quadratic terms. The fitted model is shown in Fig. 10 and detailed in Table 9. This shows for a given blade configuration there is an optimum impeller speed range required in order to minimise tablet assay RSD. Although, there were no significant univariate relationships between the tablet assay variability and the parameters assessed, the key observation was that the lowest tablet RSD values were observable at the 2.5 kg/h throughput. To explain this further, the strain in the system is examined. To quantify the relationship between strain and tablet assay RSD, the parameters in Eq. (7) were estimated by minimising the mean square error. The estimated parameters are shown in Table 10. As the strain increases, the RSD decreases before plateauing at the RSDmin parameter as shown in Fig. 11. To provide some context, the theoretical dose RSD can be estimated to be 0.1%, based on API particle size and density [38], indicating that the ranges measured for the size of tablets show a significant variation in content uniformity. This shows that the parameterised model predictions are in good agreement with the
Fig. 10. Contour plots showing the change in tablet assay RSD with the three factors: thoughput, impeller speed and impeller configuration for the micronised APAP design (a) 2.5 kg/h, (b) 26.25 kg/h and (c) 50 kg/h.
J. Palmer et al. / Powder Technology 362 (2020) 659–670 Table 10 Free parameters for Eq. (7) obtained after meansquare-error minimisation between training data and predictions. RSDmin RSD0 kb
0.637% 1.688% 0.00427
measured tablet assay uniformity data, with a root mean square error (RMSE) of 0.183%. The strain-RSD model provides a good description of the tablet assay RSD and serves to demonstrate the underlying mechanism behind the statistical contour plots in Fig. 10. This shows that by changing the blender parameters the tablet API assay RSD could be minimised. In general running at the lowest throughput will give a low RSD independent of the impeller speed or configuration used. This is due to the high mean residence time and therefore high strain when running at a low mass flow rate. The only exception to this is
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when the blender is run at its highest impeller speed with all the blades set to transport. By running with a higher number of blades configured as mixing blades the tablet API assay RSD decreases as would be expected due to the increase in residence mass as shown in Fig. 6 and therefore strain. It is observable that as the number of mixing blades is increased, the tablet API assay RSD is lower over the available operating space, while the RSD is also typically higher at the extremes of the impeller speed ranges run. This indicates that for a given blade configuration, there will be an optimum impeller speed to minimise tablet RSD, due to the trade-off between increasing shear and reduction in mean residence time with increasing impeller speed. At a superficial level, there are some similarities between the relationships observed for tablet assay RSD (Fig. 10) and Pe (Fig. 7), whereby there appears to be an optimum impeller speed for a given blade configuration, although throughput was not found to be a significant factor for Pe. Fig. 12 shows that there is a degree of correlation between Pe and strain for a given throughput. Higher values of strain
1.7
Tablet Assay RSD (%)
1.5
1.3
1.1
0.9
0.7
0.5 0
1000
2000
3000
4000
5000
6000
7000
8000
Strain Fig. 11. Tablet content RSD reducing as a function of the total system mean residence time. Continuous line shows the tablet content RSD prediction using Eq. (7) with the parameters shown in Table 10.
Peclet Number
100
2.5 kg/h
10
26.25 kg/h 50 kg/h
1 10
100
1000
10000
Strain Fig. 12. Relationship between Peclet number and strain, annotated by throughput.
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correspond to lower values of Pe and therefore greater axial dispersion in the Blender 1. Over the broad range of throughputs studied, strain was found to be a better predictor of tablet assay RSD compared with Pe. However, it can be observed that, in general, optimising Blender 1 for axial dispersion is also likely to coincide with maximising strain and therefore minimising tablet assay. This analysis shows the potential to develop a strain based model for micro-mixing. The results quantitatively support the hypotheses that increasing strain leads to a reduction in tablet content RSD and that above a critical level of strain the tablet content RSD is minimised. The development of a robust strain based model, for example including additional process steps across different material properties, is beyond the scope of this study and would be an area of interest for further research.
4. Conclusions The DoE approach was successfully used to process a wide range of APAP grades and other typical tablet formulation excipients with divergent material properties. This study demonstrated that it was possible to process formulations prone to segregation on a close coupled CDC line that would normally not be selected for manufacturing using DC. This shows the advantage of close coupled integrated systems to minimise the risk of segregation associated with the DC process. It was found that the residence mass in the linear blender could be optimised through adjusting the impeller speed and configuration at a given throughput. It was also observed that the content uniformity of the tablets was a function of the number of impeller rotations per mean residence time as described by the strain and that the RSD of APAP assay in the final tablet could be minimised through maximising this. It was also possible to fit an exponential decay model to describe the final tablet RSD as a function of the strain. It would then be possible to optimise the available blender parameters such as impeller speed and impeller configuration to minimise the variance in final tablet content uniformity.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was supported by the Advanced Manufacturing Supply Chain Initiative (AMSCI) programme ReMediES Project, United Kingdom. (https://www.financebirmingham.com/amsci/). The authors would also like to acknowledge the support from Paul Ferguson from AstraZeneca for the extensive HPLC analytical support during this project. References [1] S. Byrn, M. Futran, H. Thomas, E. Jayjock, N. Maron, R.F. Meyer, et al., Achieving continuous manufacturing for final dosage formation: challenges and how to meet them, J. Pharm. Sci. 104 (2014) 792–802. [2] D. Djuric, Continuous Granulation With a Twin-Screw Extruder, University of Düsseldorf, Düsseldorf, Germany, 2008. [3] E.I. Keleb, A. Vermeire, C. Vervaet, J.P. Remon, Twin screw granulation as a simple and efficient tool for continuous wet granulation, Int. J. Pharm. 273 (2004) 183–194. [4] Peddapatla RVG, C.A. Blackshields, M.F. Cronin, A.M. Crean, Behaviour of magnesium stearate in continuous feeding, AIChE Annual Meeting; 13–18 November; San Francisco, 2016. [5] C.A. Blackshields, A.M. Crean, Continuous powder feeding for pharmaceutical solid dosage form manufacture: a short review, Pharm. Dev. Technol. 23 (2017) 554–560. [6] I.K. Yadav, J. Holman, E. Meehan, F. Tahir, J. Khoo, J. Taylor, et al., Influence of material properties and equipment configuration on loss-in-weight feeder performance for drug product continuous manufacture, Powder Technol. 348 (2019) 126–137.
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Contents lists available at ScienceDirect
Powder Technology journal homepage: www.elsevier.com/locate/powtec
Mapping key process parameters to the performance of a continuous dry powder blender in a continuous direct compression system John Palmer a, Gavin K. Reynolds b,⁎, Furqan Tahir c, Indrajeetsinh K. Yadav d, Elizabeth Meehan b, James Holman a, Gurjit Bajwa d a
Process Development, GEA Group Ltd, Eastleigh, UK Pharmaceutical Technology & Development, AstraZeneca, Macclesfield, UK Perceptive Engineering Ltd, Vanguard House, Sci Tech Daresbury, Cheshire, UK d Product Development and Supply, GlaxoSmithKline Research and Development, Ware, UK b c
a r t i c l e
i n f o
Article history: Received 1 August 2019 Received in revised form 13 December 2019 Accepted 14 December 2019 Available online 16 December 2019 Keywords: Continuous direct compression Continuous blending Material attributes Continuous pharmaceutical manufacturing Residence time distribution Strain
a b s t r a c t This work aims to expand the typical raw material attributes that can successfully be processed on a continuous direct compression line with a particular focus on the continuous dry powder blender. Three grades of Acetaminophen were investigated as model active pharmaceutical ingredients and chosen to span a broad range of material attributes wider than what would normally be considered for a direct compression process. An experimental design was set-up for each grade of Acetaminophen to investigate the effect of throughput, impeller speed and impeller configuration on the critical process responses and attributes. The strain experienced by the in-process material was found critical to improve content uniformity. The impeller speed and design can be optimised at a given throughput to obtain a strain which would deliver the required content homogeneity. The content uniformity of the final tablets can be modelled as a function of the strain using an exponential decay model. © 2019 Elsevier B.V. All rights reserved.
1. Introduction The pharmaceutical manufacturing industry is undergoing a paradigm shift as it moves towards a more agile supply chain. A transition to continuous manufacturing (CM) provides an opportunity to address the changing needs of the supply chain. It allows for a much more integrated approach in the manufacture of oral solid dosage (OSD) forms leading to a reduction of material handling and process steps and overall a more streamlined manufacturing platform [1]. CM also provides further potential for enhanced product quality assurance as continuous processes present a greater opportunity to deploy process analytical technologies (PAT) for inline process and product monitoring that lends itself to real time release testing (RTRT). Abbreviations: APAP, Acetaminophen; API, Active Pharmaceutical Ingredient; CDC, Continuous Direct Compression; CM, Continuous Manufacturing; CU, Content uniformity; DC, Direct Compression; DoE, Design of Experiments; HPLC, High Performance Liquid Chromatography; LiW, Loss in Weight feeding; mAPAP, Micronised Acetaminophen; MRT, Mean Residence Time; MSE, Mean Square Error; NIR, Near Infrared (Spectroscopy); OPC, Open platform communication; OSD, Oral solid dosage; PAT, Process Analytical Technology; pAPAP, Powdered Acetaminophen; QbD, Quality by design; RSD, Relative Standard Deviation; RMSE, Root Mean Square Error; RTD, Residence Time Distribution; RTRT, Real Time Release Testing; SG APAP, Special Granular Acetaminophen; WG, Wet Granulation. ⁎ Corresponding author. E-mail address: [email protected] (G.K. Reynolds).
https://doi.org/10.1016/j.powtec.2019.12.028 0032-5910/© 2019 Elsevier B.V. All rights reserved.
The feasibility for implementation of continuous unit operations is widely reported with a large body of research conducted on many of the independent units. Examples such as process optimisation during product development for continuous wet granulation (WG) processes using twin screw granulation [2,3] and continuous dispensing with Loss-in-weight (LiW) feeding have been reported [4–6]. Significant work has been focussed on continuous blending and different types of heterogeneous blenders such as horizontal and vertical blenders [7] have been investigated. Horizontal blenders are now well established in the industry for continuous blending applications where active pharmaceutical ingredient (API) and excipients are blended for either direct compression (DC) or WG formulations. Horizontal blenders generally consist of an elongated barrel which can be either completely horizontal or inclined and an impeller made of a central shaft with mixing blades attached which can be operated at variable rotational speed. Different mixing blade designs exist and often the angle of mixing blades can also be adjusted to optimise the blending process. A number of research groups [8–10] have demonstrated how the blender process parameters can be manipulated to optimise a pharmaceutical continuous blending process, while Vanarase et al. [11] described how material attributes can impact the blender performance. Traditionally uniformity of the blend produced after mixing the API and excipients in the process has been assessed through a stratified
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Table 1 Composition of final compression blend. Component
Function
% in formulation
APAP Lactose anhydrous Microcrystalline cellulose Croscarmellose sodium Magnesium stearate
API Diluent Diluent Disintegrant Lubricant
10 27 59 3 1
blend sampling approach with off-line analysis for API content. For CM in-line PAT measurements can facilitate an effective approach to assess operating space, that is the interaction of input variables such as material attributes and process parameters, to deliver drug product of an acceptable quality as suggested in regulatory guidance [12]. Being able to either measure or predict the homogeneity of the blend after blending in real time offers an essential quality assurance tool for continuous manufacturing. Near Infrared (NIR) Spectroscopy is well established in other industries such as food [13] and oil and gas [14] and is now the most common spectroscopic PAT tool used for the real time monitoring of blend homogeneity as demonstrated by Varanese et al. [11,15] and Jarvinen et al. [16]. Sierra-Vega et al. [17] evaluate the application of different NIR methodologies within a continuous direct compression (CDC) process with NIR measurements after blending, in the feed frame of the tablet press and on the final tablet core. Other types of measurements are available which can be used to measure the blend homogeneity as described in the review conducted by Asachi et al. [18]. Often with PAT sensors such as NIR, complex multivariate models are required which need to be robust enough to accommodate sources of variability in raw materials and the process as well as being stable over long periods of time [19]. Recently soft sensor approaches have been demonstrated to predict the blend homogeneity after blending from other indirect measurements coupled with dynamic models of the blending process [20–22]. In these cases, the residence time distribution (RTD) of the blending process is characterized typically using a form of tracer experiment and then modelled using a standard technique such as continuous stirred tanks in series or a plug flow reactor with diffusion type model. The exit concentration can then be predicted by using the measured input concentration from the LiW feeders and a representative process RTD model. These models have the added benefit that they can provide evidence of material traceability through the process on which decisions about product collection or rejection can be made thus supporting the overall drug product control strategy [12,23–26]. The DC method of oral solid dose manufacture offers advantages such as reduced number of unit operations, smaller equipment footprint, and simpler process with less material in flight at a given time over wet or dry granulation methods. The latter require the transformation of in process material attributes to improve the processability of the raw materials. If this can be avoided DC is more favourable, however the application of DC is heavily reliant on the target dose of the drug product and raw material attributes and variability. Products containing a high API loading that demonstrates poor flow properties have traditionally given preference to granulation based routes of manufacture in order to reduce the risk of process failure. With the advent of CDC the potential process operating space in which such material attribute limitations can be overcome is the focus of ongoing evaluation. It is recognised that a further limitation of traditional batch DC relates to material segregation that is related to the attributes of the API properties and the drug loading in the formulation. Since continuous DC closely integrates individual unit process operations, eliminates hold times and improves the transport of the blend it can offer greater potential to accommodate a wider range of material attributes and drug product designs by effectively removing the risk of segregation. The successful application of CDC for a variety of drug products has been reported. Van Snick et al. [27] demonstrated the ability of a commercial CDC platform to produce
satisfactory product quality at low drug loadings, while developing the optimised final drug product process. Several groups have successfully investigated the use of a CDC platform in the production of extended release tablets [28–30]. In addition, the successful application of continuous blending and DC to formulations which are prone to segregation has been reported [31–33]. The work described in this paper is a part of a programme of work oncontinuous tablet manufacturing has been completed as part of a UK based collaborative research project (ReMediEs: RE-configuring MEDIcines End-to-end Supply), which was focused on streamlining the supply chain in the pharmaceutical industry. This article describes an assessment of the performance of the ConsiGma™ CDC-50 (GEA Group, Wommelgem, Belgium) across a wide operating space. The paper aims to show the effect of material attributes, throughput rate, key process parameters and blender set-up using a model API formulation. The paper will also thoroughly investigate the impact of changes to the setup of the API blending stage on the final tablet content uniformity (CU). 2. Materials and methods 2.1. Materials and composition Micronised Acetaminophen (mAPAP, Mallinckrodt, USA) was used as a model API and it was formulated using pharmaceutical excipients commonly used for direct compression. Microcrystalline cellulose (Avicel PH102, FMC, Ireland), lactose anhydrous (Supertab 21AN, DFE Pharma, Germany), croscarmellose sodium (Ac-di-sol SD-711, FMC, Ireland) and Magnesium Stearate (Ligamed MF-2-V, Peter Greven, Germany) were combined during processing to form the final blend for compression. The components with functionality and quantitative composition of the final compression blend are shown in Table 1. Two additional grades of Acetaminophen (APAP) of divergent material attributes were also included in the study to assess the impact of material attributes on the process performance. Other grades of APAP used in comparative studies were powder grade APAP (pAPAP, Mallinckrodt, USA) and Special Granular grade APAP (SG APAP, Mallinckrodt, USA). 2.2. Material characterisation The particle size distribution of the raw materials was measured by laser diffraction using a Mastersizer 3000 with Aero S dry disperser
Feeder 1
Feeder 2
Feeder 3
Feeder 4
(Coperion K-Tron KT20)
(Coperion K-Tron KT20)
(GEA Compact Feeder)
(GEA Compact Feeder)
Blender 1
Feeder 5
(GEA CDB)
(Coperion K-Tron KT20)
Blender 2 (GEA CDB)
In-line NIR (J&M)
Tablet Press (GEA R100)
At-line NIR (Bruker MPA II)
Fig. 1. Schematic of proof of concept CDC line.
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Table 2 Calibration and validation runs to be completed for both inline and at-line calibration.
Calibration 1 (CP) Calibration 2 Calibration 3 Calibration 4 Calibration 5 Calibration 6 Calibration 7 Validation 1 Validation 2 Validation 3 Validation 4 Validation 5
APAP
Lactose anhydrous
Microcrystalline cellulose
Croscarmellose sodium
Magnesium Stearate
10.00% 8.00% 8.67% 9.33% 10.67% 11.33% 12.00% 11.68% 8.95% 9.63% 11.77% 10.04%
29.00% 24.61% 36.60% 22.68% 35.49% 22.58% 26.59% 28.58% 31.29% 25.12% 30.96% 27.99%
57.00% 62.92% 50.96% 64.03% 49.43% 62.20% 57.17% 55.37% 55.83% 61.20% 53.28% 57.35%
3.00% 3.42% 2.72% 2.87% 3.34% 2.83% 3.18% 3.25% 2.90% 2.94% 3.20% 3.55%
1.00% 1.05% 1.06% 1.09% 1.08% 1.05% 1.06% 1.12% 1.03% 1.11% 0.80% 1.07%
(Malvern Instruments, UK). The dispersion pressure was 0.5 bar and each sample was measured in triplicate. D(v,0.1), D(v,0.5) and D (v,0.9) values were derived from the measured particle size distribution and used for modelling purposes. The cohesive/adhesive properties of the materials were characterized using a RST-XS Ring Shear Tester (Dietmar Schulze, Germany). A shear cell (30 mL volume) was used for testing powder flowability at a pre-shear normal stress of 4 kPa, flow function coefficient (FFC) and wall friction angle (WFA) were measured in duplicate. Bulk and tapped density were measured in duplicate using a 100 mL measuring cylinder. A weighed sample of material was added, and the volume measured to record bulk density. It was then subjected to tapping using a JEL STAV2003 jolting volumeter (J. Engelsmann, Germany) until no discernible change in volume occurred in three consecutive readings at which point the tapped volume was measured to record tapped density. Particle and surface morphology were evaluated using a TM1000 scanning electron microscope, SEM, (Hitachi, Japan) at an accelerating voltage of 15 kV. Images were captured at appropriate magnifications, up to 2000×, for a detailed visualisation of the particle and surface attributes.
2.3. CDC process overview A prototype CDC-50 system, depicted in Fig. 1, was constructed by integrating three units into a single processing platform; LiW feeding of individual formulation components, continuous blending to combine the individual components into a homogenous blend and compression of the blend via a rotary tablet press into the final dosage form. On the prototype CDC system, four LiW feeders were used to continuously feed individual raw materials into the inlet of the first continuous blender (GEA Wommelgem, Belgium). Depending upon the set-up of the mixing blades on the blender shaft within the main blending stage
(blender 1), the amount of shear imparted on the powder can be optimised as required to ensure sufficient mixing. The exit of the main blender was directly coupled to the inlet of a secondary blender (blender 2) along with a LiW feeder for the lubricant. A secondary blender was included to blend any components of the formulation which are shear sensitive such as in this case the lubricant. Having a main and secondary blending stage allows for maximum flexibility as it allows for these two blending stages to be decoupled. The secondary blender was configured to minimise shear and in process material mean residence time (MRT). In the case of lubrication blending this minimises the risk of over lubrication of the blend, which can lead to issues with the final product, such as poor tensile strength and poor wettability. Finally, material leaving the secondary blender is used to continuously feed a rotary tablet press (R100, GEA Halle, Belgium) for tablet compression. PAT was used to monitor the process either in-line or at-line. Blend uniformity was measured prior to compression using in-line NIR (Tidas P analyzer, J&M Analytik, Essingen, Germany) located in the feed tube of the tablet press, whilst tablet assay was determined at-line using transmission NIR (MPA II, Bruker, Germany). 2.3.1. Loss-in-weight feeding LiW feeders use the change in net weight of the material in the hopper to calculate a mass flow rate. The motor speed is then regulated using a feedback control loop to maintain the calculated mass flow rate at the set-point. Screw type, motor gearbox ratios and agitator types were selected to optimise the feeding process. Feeders 1 to 5 were used to dose microcrystalline cellulose, lactose anhydrous, croscarmellose sodium, APAP and magnesium stearate, respectively. The LiW feeders were set up to deliver a consistent mass flow to the system, even during refills of the hoppers which were controlled by switching the feeders from gravimetric control to volumetric control prior to refilling. On the commercial system this is done automatically by the integrated control system for all GEA compact feeders. The GEA compact feeders update and store a feed factor profile which provides
Fig. 2. Calibration model compared with validation data.
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Fig. 3. PharmaMV dashboard for monitoring an RTD spike.
an estimate of the screw speed during volumetric mode, minimising any mass flow errors. The KT-20 feeders were fitted with large hoppers (20 L for microcrystalline cellulose and 10 L for lactose anhydrous), which minimised the number of refills and therefore time in volumetric mode. 2.3.2. Continuous blenders The GEA linear blender is a single shaft paddle blender inclined at an angle of 15o to the horizontal. Each blender has a mixing length of 70 cm and an internal diameter of 12 cm. The drive of the motor enables the blender shaft speed to be set to any value between 0 and 450 rpm. For this study the minimum speed selected was 150 rpm, the maximum was 450 rpm and the midpoint 300 rpm, corresponding to a range of Froude (Fr) numbers between 1.5 and 13.4. The Froude number is calculated from:
Fr ¼
r blender ω2 g
Equation 1: Definition of Froude number, where rblender is the blender radius, ω is the impeller rotation rate and g is the acceleration due to gravity. Table 3 The three factor Box-Behnken experimental plan with three centre-points used for the micronised APAP grade. The rows shaded in grey indicate the 22 full factorial design which were run for powder and special granular APAP grades. Run
System throughput (kg/h)
B1 Impeller Speed (rpm)
B1 Mixing blade arrangement
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
2.5 50 2.5 50 2.5 50 2.5 50 26.25 26.25 26.25 26.25 26.25 26.25 26.25
150 150 450 450 300 300 300 300 150 450 150 450 300 300 300
8 8 8 8 0 0 16 16 0 0 16 16 8 8 8
The blender consists of a configurable shaft made up of 56 paddles, and each of the paddles can be angled in two directions, transport (45° to shaft) or mixing (0° to shaft). Increasing the number of blades at 0° to the shaft will decrease the ability of the blender to pump the inprocess material along the blender and therefore will increase the volume of powder within the blender and extend the mean residence time. The level of blending imparted on the blend is a critical control factor in ensuring sufficient blending, both at a micro and macro level. 2.3.3. Tablet press Tablets were compressed using a GEA R100 rotary tablet press. For the throughputs of 26.25 kg/h and 50 kg/h the press was fitted with 30 stations of 10 mm round, concave punches and the press set up to make tablets 350 mg in weight, corresponding to speeds of 42 and 79 rpm, respectively. For the low rate of 2.5 kg/h the press was partially tooled with only 5 stations of the same tooling and the same tablet weights were targeted, with a corresponding press speed of 24 rpm. The press settings (fill depth, pre-compression height, main compression height,) were adjusted to ensure the final tensile strength of the tablets between the 1.7–2.0 MPa target, to ensure tablets were robust enough to determine tablet content uniformity. For the 10 mm tablets, this corresponded to a hardness range of approximately 10–12 kp [34]. 2.4. Analytical tools and calibration model development 2.4.1. Blend uniformity via reflectance NIR An in-line NIR reflectance probe and spectrometer (Light House Probe™, GEA Wommelgem, Belgium) were positioned after the outlet of the second blender in the feed tube above the tablet press. The realtime NIR spectra were used within a calibration (chemometric) model to predict the composition of the blend before tabletting. The design of the calibration model is described as follows. 2.4.1.1. Inline NIR calibration for API content in the bulk blend. The measurements for the calibration samples were completed at the centre point throughput and the blender set-up which was expected to yield the best blend homogeneity. For each of the runs the system was started from a cleaned state. The feeder mass flow rates were all set at the correct value for the desired final blend composition and the system was then run till it was at steady state. While the system was at steady state, measurements of the blend were taken every second from the
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A Shaft Angle (°) Shaft Angle (°) 0 45 45 45 45 45 45 45 45 45 0 60 45 45 45 45 45 45 45 45 45 60 120 45 45 45 45 45 45 45 45 45 120 180 45 45 45 45 45 45 45 45 45 45 180 240 45 45 45 45 45 45 45 45 45 240 300 45 45 45 45 45 45 45 45 45 300 Outlet
Inlet
B Shaft Angle (°) Shaft Angle (°) 0 45 45 45 45 0 45 0 45 45 0 60 45 45 45 45 0 45 45 45 45 60 120 45 45 45 45 0 45 45 45 45 120 180 45 45 45 45 45 0 45 45 45 45 180 240 45 45 45 45 45 0 45 45 45 240 300 45 45 45 0 45 0 45 45 45 300 Inlet
Outlet
C Shaft Angle (°) Shaft Angle (°) 0 45 45 45 45 0 45 0 45 45 0 60 45 45 0 45 0 45 0 45 45 60 120 45 45 0 45 0 45 0 45 45 120 180 45 45 45 0 45 0 45 0 45 45 180 240 45 45 45 0 45 0 45 0 45 240 300 45 45 45 0 45 0 45 45 45 300 Outlet
Inlet
Fig. 4. Shows the three-standard linear blender blade configurations used in this work (a) Full transport (0H), (b) 8 Blade helical (8H) and (c) 16 blade helical (16H).
inline NIR system above the press and tablet samples were taken every 0.5 system mean residence times for a total of 2 mean residence times for content measurement. The calibration and validation experimental formulation matrices can be seen in Table 2. For the calibration experiments, the API was varied +/− 20% in equal increments around the target drug loading. As there were no quantitative measurements giving the absolute API concentration the average API concentration as calculated from the LiW feeder mass flow rates were used. The average API concentration was then regressed, using a PLS algorithm, with the pre-processed spectra during steady state operation. As part of the pre-processing, the NIR spectra was first normalised using SNV (standard normal variate). Then, a Savitzky-Golay 1st order derivative filter (2nd order polynomial with nine points) was applied to smooth out the spectra. A 3 latent variable (LV) PLS calibration model was developed in PharmaMV™ software (Perceptive Engineering Ltd., Daresbury, UK) using the steady state spectral data from each of the seven calibration runs. The developed model accounted for 97% of variability within the response signal. The model demonstrates good prediction accuracy which is reflected in similar RMSE values of 0.237% and 0.262% for both training and validation datasets, respectively. Fig. 2 illustrates the model prediction against unseen data, demonstrating good prediction accuracy. 2.4.2. Content uniformity via transmission NIR Tablet assay was determined at-line using transmission NIR (MPA II, Bruker, Germany) by sampling tablets as detailed in Section 2.6. Calibration was completed through the measurement of a reference tablets whose API assay had been measured using HPLC. The calibration was
then undertaken by regressing the NIR spectra to the final Acetaminophen content in each tablet using PLS algorithm. This model development is described next. 2.4.2.1. NIR calibration for tablet API assay. A limited region of the NIR spectrum (8000–12,590 cm−1) for each tablet was pre-processed using a standard normal variate and Savitzky-Golay first derivative procedure. The PLS model was developed to correlate the measured spectra to the APAP assay measured by HPLC in each tablet. The model consisted of two LVs which accounted for around 98% of the Y-variance. A model with more than two LVs was found to be over-fitting. The final model was found to have RMSE values of 0.171% and 0.167% for training and validation datasets, respectively. 2.4.3. System mean residence time and residence time distribution When determining the performance of a continuous blending system it is necessary to characterise the RTD of the blender under a given set of process conditions. The RTD is a probability distribution, defining the probability of how long a particle will stay in the blender. The RTD will define the macro mixing capability of the blender and therefore determine the amount of variability from the feeders that the blender can buffer before blend uniformity are observed. Table 4 Sampling Strategy for each run. Sample #
1
2
3
4
5
6
7
8
9
MRT No. Tablets Tested
0 10
0.5 3
1 3
1.5 3
2 10
2.5 3
3 3
3.5 3
4 10
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Table 5 Material characterisation of materials used in study. Material
D(v,0.1) (μm)
D(v,0.5) (μm)
D(v,0.9) (μm)
FFC
WFA (°)
Bulk density (g cm−3)
Tapped density (g cm−3)
Micronised APAP Powdered APAP Special granular APAP Lactose anhydrous Microcrystalline cellulose Croscarmellose sodium Magnesium stearate
3 8 152 29 38 18 2
10 53 299 181 124 41 10
33 225 498 353 302 98 33
1.4 1.9 19.4 7.4 5.4 4.2 3.4
13.2 12.8 15.0 20.4 19.6 9.8 8.0
0.19 0.31 0.73 0.68 0.32 0.53 0.20
0.30 0.53 0.83 0.89 0.42 0.73 0.33
The RTD is linked to the MRT that material spends in the process. The MRT is calculated from the measured mass of powder in the system (in this work this mass will be called the residence mass) divided by the mass flow rate through the system (Eq. (2)). t¼
Z∞ t¼
t:^eðt Þdt
0
m _ m
Equation 2: Equation defining the mean residence time of the system or unit _ operation wheret is the mean residence time, m is the residence mass and m is the mass flow rate. The residence mass can also be expressed as a volumetric fill level, which can be used to quantify the free volume for dynamic powder flow and allow comparison with other blending systems. m
F¼
It is also possible to determine the mean residence time by taking the first moment of the normalised residence time distribution:
=ρb;m 2 πL rmixer −r 2shaft
Equation 3: Equation defining the volumetric fill level, F, where m is the residence mass, ρb,m is the powder bulk density, L is the mixer length, and rmixer and rshaft are the mixer and shaft radii, respectively.
Equation 4: Equation defining the mean residence time, t, of the system or unit operation, where ^eðtÞ is the normalised residence time distribution. The degree of back mixing or axial dispersion within a system can be quantified using the Peclet number, which expresses the relative rates of convection and dispersion. The Peclet number can be estimated from the normalised residence time distribution using the following equation [35]: 2 2 − 2 1−e−Pe ¼ Pe Pe
R∞ 0
2 t−t :^eðt Þdt t
2
Equation 5: Equation relating the Peclet number, Pe, and the residence time distribution. The RTD was determined experimentally using tracer experiments. After the system had been deemed to reach steady state for a given set of conditions 2% by mass of the total system residence mass of
Fig. 5. SEM micrographs of the three grades of APAP used in the study: (a) micronised APAP, (b) powder APAP, (c) special granular APAP.
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Table 6 Measured residence mass for each run. Run
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 a b
Throughput (kg/h)
2.5 50 2.5 50 2.5 50 2.5 50 26.25 26.25 26.25 26.25 26.25 26.25 26.25
Impeller Speed (RPM)
150 150 450 450 300 300 300 300 150 450 150 450 300 300 300
No. Mixing Blades
Residence Mass (kg)
8 8 8 8 0 0 16 16 0 0 16 16 8 8 8
Fill Level (%)
Pe
mAPAP
pAPAP
SG APAP
mAPAP
pAPAP
SG APAP
mAPAP
1.073 1.652 0.219 0.284 0.081 0.229 0.980 1.268 0.307a 0.087 1.561 0.156 0.556 0.571 ****b
– – – – – – – – 1.223 0.143 1.860 0.685 0.644 0.615 0.633
– – – – – – – – 1.112 0.180 1.730 0.490 0.630 0.640 0.630
41.7 64.3 8.5 11.0 3.2 8.9 38.1 49.3 11.9 3.4 60.7 6.1 21.6 22.2 –
– – – – – – – – 46.2 5.4 70.3 25.9 24.3 23.2 23.9
– – – – – – – – 38.2 6.2 59.5 16.8 21.7 22.0 21.7
5.2 19.6 36.0 31.3 50.3 52.8 3.8 12.6 68.8 24.9 17.0 59.6 8.0 9.9 –
Run at 200RPM impeller speed. Residence mass not collected.
APAP was added to the inlet of the blender in a single aliquot. The system was then run for 5 mean residence times with measurements and the sampling strategy as described in Section 2.6. 2.4.4. System strain and micro-mixing The tablet assay RSD can be directly related to the amount of blending imparted by the system using the strain as previously reported by Van Snick et al. [27]. The strain for each unit can be calculated based on the mean residence time and the impeller frequency as shown by Eq. (6). ϵ ¼ t:ω Equation 6: Equation for the strain for a unit operation where ε is the strain, t is the mean residence time and ω is the impeller rotation rate. The total strain imparted on the powder is a sum of the strain imparted by each of the unit operations. The scope of this study was to investigate the impact of the main blending stage on tablet properties and therefore the downstream contributions were kept constant, such as the settings for the secondary blender and the mass hold-up between the secondary blender and the tablet press feed-frame. Therefore, only the strain contribution from the main blending stage was considered in subsequent analysis. Gao et al. [36] has previously shown a method of predicting blend uniformity using an exponential decay model. In batch blending, the blend RSD is described as a function of time whereas for a continuous blender the RSD is a function of the position within the blender. In this case the final tablet RSD was described as a function of the strain of the entire system using Eq. (7). Three different free parameters need to be parameterised by minimising the error between the
predictions and the measured values. RSDmin is the minimum RSD that is achievable, and any further blending will result in no further RSD reduction. RSD0 is the initial RSD of the unblended mixture and kb is the RSD decay rate constant. RSDðεÞ ¼
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi RSD2min þ RSD20 −RSD2min eð−kb εÞ
Equation 7: RSD as a function of the system strain where RSDmin is the minimum achievable RSD, RSD0 is the initial RSD, kb is the RSD decay rate constant and ε is the strain. 2.4.5. On-line system monitoring using PharmaMV™ During the trials, all data logging, calibration model execution and process monitoring was performed, in real-time, using PharmaMV™ software (Perceptive Engineering Ltd., Daresbury, UK). This included logging and time aligning process data from feeders and blenders as well as PAT data from the in-line NIR spectrometer through the OPC (open platform communications) interface. The software allows for real-time monitoring, control and process optimisation as well as data analysis, modelling and visualisation. Process dashboards were also implemented to allow the operators to easily monitor various mass flows, calibration model predictions and RTD spikes. An example of the data collection dashboard for one of the experimental runs is shown in Fig. 3. In this dashboard, the data related to the four feeder mass flowrates is shown in the two top-half trends while the net weight in the feeders is shown in the form of dials and a table underneath. The bottom right trend shows the blender load cell measurement from the start of the RTD spike test. Finally, the bottom left trend shows the blend APAP calibration prediction in blue, overlaid with the red crosses corresponding
Table 7 DoE model coded parameters, with p-value in brackets. Material
mAPAP
Response
Residence Mass
Fill Level
Pe
Residence Mass
pAPAP Fill Level
Residence Mass
SG APAP Fill Level
Constant Throughput Impeller Speed No. Mixing Blades Impeller Speed × No. Mixing Blades Impeller Speed × Impeller Speed No. Mixing Blades × No. Mixing Blades R2 Q2
0.65 0.14 (0.763) −0.49 (b0.0001) 0.40 (0.0002) −0.27 (0.0288) – – 0.9185 0.7425
25.28 5.25 (0.763) −19.22 (b0.0001) 15.49 (0.0002) −10.49 (0.0288) – – 0.9185 0.7425
8.08 – 3.20 (0.459) −14.98 (0.0052) 25.50 (0.0031) 15.37 (0.0464) 22.45 (0.0066) 0.8415 0.0511
0.83 – −0.56 (0.0079) 0.29 (0.0615) – – – 0.8856 0.5225
31.33 – −21,31 (0.0079) 11,14 (0.0615) – – – 0.8856 0.5225
0.77 0 −0.54 (0.0036) 0.23 (0.0594) – – – 0.9169 0.5985
26.57 −18.66 (0.0036) 7.97 (0.0594) – – – 0.9169 0.5985
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Fig. 6. Contour plots showing the change in fill level with the three factors: thoughput, impeller speed and impeller configuration for the micronised APAP design (a) 2.5 kg/h, (b) 26.25 kg/h and (c) 50 kg/h.
to the tablet APAP prediction. Notice that, as expected, the real-time tablet APAP concentration prediction is essentially a lagged version of the blend APAP concentration prediction which further serves to verify the accuracy of the two designed calibration models (see Sections 2.4.1.1 and 2.4.2.1, respectively).
2.5. Experimental outline 2.5.1. Design of experiments (DoE) The DoE was constructed and analysed using Design Expert 8 software (Stat-Ease, Inc., Minneapolis, 2010). For the micronised acetaminophen a three factor Box-Behnken design was implemented with three centre point replicates with the list of experiments can be seen in Table 3. The experimental study assessed three main control variables namely line throughput (kg/h), Blender 1 impeller rotational speed (rpm) and Blender 1 impeller configuration (number of axial blades) and the responses measured were Blender 1 residence mass (kg) and uniformity of tablet API assay (% relative standard deviation). As part of this study three different blade configurations were selected to impart differing levels of mixing intensities during main blending. To allow for a quantitative analysis the pattern of mixing blades to transport blades were kept consistent, with only the number of mixing blades being increased. The high mixing configuration consisted of 16 blades positioned in the axial direction in a helical pattern (16H), centred along the length of the blender. The mid mixing configuration
employed 8 blades, in the same helical pattern (8H) and central to the shaft whilst the low mixing configuration used zero blades in the axial configuration (0H). Fig. 4 shows the positioning of the blender blades for the three different configurations. Blender 1 was located on three load cells (K-SFT-II-M, Coperion, Switzerland) and the combined readings gave an in-line measurement of the residence mass within the blending stage. The setup of Blender 2 was fixed, with all blades positioned in the transport position and the blender speed fixed at 250 rpm. The secondary blender was not located on load cells, the residence mass was measured by vacuuming out the contents of the blender after an instantaneous stop of the system, and measure the mass collected in the vacuum supply. For the other two grades of APAP a reduced DoE was conducted consisting of only a 22 factorial design with centre point replicates varying blender impeller rotational speed and blender impeller configuration with a fixed throughput of 26.25 kg/h. For analysis of each DoE, linear and interaction terms were considered and selected based on p-values below 0.1. Quadratic terms were also considered for the micronised APAP DoE. 2.6. Sampling strategy After system start-up, steady state was determined by monitoring on-line measurement of the residence mass of blender 1 and using the signal from the in-line NIR probe. The first part of the experiment was to run the system at steady state and to monitor the key process responses. As the experimental plan was run at different throughputs and the blender residence mass would be changing between experimental points, it was important to take into account the transient nature of the system when defining the sample plan. For this work tablet samples at the outlet of the tablet press were taken over 4 total system mean residence times at 0.5 mean residence time intervals. This gave a total of 9 sample sets per experimental point. For tablet assay analysis at the 0, 2 and 4 mean residence time points, ten individual tablets were analysed. A summary of the sampling strategy is shown in Table 4. 3. Results and discussion 3.1. Material characterisation
Fig. 7. Contour plot showing the change in Peclet number with impeller speed and number of mixing blades.
The material attributes measured for each component in the formulation are summarised in Table 5. The values demonstrate the broad range of material attributes for this typical CDC formulation that must be accommodated in each stage of the process. Excipient grades deemed suitable for DC processing were selected for the model formulation and therefore in particular the diluents, making up the majority of the
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Peclet Number
100
10
1 0
10
20
30
40
50
60
70
Fill Level (%) Fig. 8. Relationship between Peclet number (Pe) and fill level.
formulation, exhibit relatively good flow properties as intended. Magnesium stearate and croscarmellose sodium are minor components in the formulation and exhibit poorer flow properties. Fig. 5 shows SEM micrographs of the three grades of APAP, illustrating the difference in particle size and shape. For the API the three grades of APAP span a wide range of material properties between two extremes; micronsized, cohesive material to significantly larger particle size, very good flowing material. This diversity of material attributes provides a strong basis to assess the ability of CDC to process a typical drug product formulation with a particular emphasis on segregation tendency. For example, micronised APAP and Special Granular APAP would be expected to be prone to segregation from the DC excipients due to the significant differences in FFC. 3.2. Residence mass, fill level and Peclet number mapping As described previously, the residence mass of Blender 1 was monitored during the runs and the mean can be observed in Table 6. The three blending process parameters used as factors in this DoE analysis were all found to affect the final residence mass that was measured in the blender with varying levels of significance, as shown in Table 7. Increasing the impeller speed caused the final mass and fill level in the blender to decrease, and increasing the number of mixing blades increased the residence mass and fill level significantly. For micronised APAP the influence of throughput was marginally significant, with an increase in throughput leading to a slight increase in residence mass and fill level. These relationships are consistent with those found by Van Snick et al. (2017) using a GEA linear blender. Vanarase and Muzzio (2011) also found a decrease in residence mass with
increasing impeller speed and an increase in residence mass with increasing throughput, although they used a Gericke GCM250 continuous mixer, which is horizontally positioned and includes a weir. The three contour plots shown in Fig. 6 demonstrate how the chosen factors affect the blender fill level. It has been observed that if the impeller is set-up as the full transport configuration, the fill level becomes largely independent of the impeller speed. Although satisfactory tablet content uniformity for all conditions were obtained, the narrow operating space for this blade configuration would make it unsuitable for the main blending stage. Further, the full transport set-up would be appropriate for processes such as lubrication blending where low intensity blending is typically required. This highlights the importance of setting the correct impeller configuration to obtain a wide enough operating space to achieve the desired mean residence time to achieve a satisfactory blend and uniformity. It is also possible to see from these plots that it is possible to achieve an equivalent residence mass with different combinations of impeller configuration and impeller speeds. In general, unless the blend being processed is shear sensitive, the parameters should be set in a way as to achieve the highest impeller speed for a given mean residence time which will maximise the strain. Peclet number was found to be a non-linear function of impeller speed and number of mixing blades, with some significant quadratic terms. Fig. 7 shows how the model predicts Peclet number to change with these factors. The model shows that the lowest Peclet numbers, which correspond to the greatest extent of axial dispersion, are generated at intermediate impeller speeds with a higher number of mixing blades. The model Q2 was quite poor, so this should not be
Fig. 9. Contour plots showing the change in fill level with the two factors: impeller speed and impeller configuration for each grade of APAP tested at 26.5 kg/h (a) micronised APAP, (b) powder APAP and (c) special granular APAP.
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Table 8 Summary of the measured tablet assay RSD for the mAPAP experimental runs. Run 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Throughput (kg/h)
Impeller Speed (rpm)
No. Mixing Blades
Tablet Assay RSD (%)
2.5 50 2.5 50 2.5 50 2.5 50 26.25 26.25 26.25 26.25 26.25 26.25
150 150 450 450 300 300 300 300 200 450 150 450 300 300
8 8 8 8 0 0 16 16 0 0 16 16 8 8
0.64 1.44 0.77 1.32 0.66 1.68 0.59 0.9 1.37 1.02 0.76 1.35 0.81 0.77
over-interpreted. Another way of considering this relationship is to view it in terms of fill level. Fig. 8 shows a non-linear relationship between Peclet number and fill level. For low fill levels, Pe is high, indicating poor backmixing in the system. There appears to be a minimum Peclet number around a fill level of 30–40%. Higher fill levels appear to exhibit slightly higher Peclet numbers again. Van Snick et al. [27] also made a similar observation, attributing the reduction in axial dispersion at high impeller speeds to the high conveying rate moving the bed in a more plug flow like state and the reduction in axial dispersion at very low impeller speeds due to an inability of the impeller to impart significant homogenisation force to intermingle individual particles. Conversely, Vanarase and Muzzio [37] found that an increase in impeller speed improved axial dispersion in the horizontal Gericke GCM250 mixer. This suggests an optimum fill level is required to maximise the extent of axial dispersion in the inclined GEA linear blender. Fig. 9 shows that at a throughput of 26.25 kg/h both the impeller speed and the impeller configuration had a similar effect on the fill level for all three grades of APAP. It can also be observed that the material properties of the different grades of APAP in the blend have no significant effect on the fill level in the blender. Although throughput was not tested for all three grades of APAP in this study, the influence of throughput on the fill level of the micronised APAP formulation was found to be consistent with other formulations [29] and therefore, a similar fill level response to throughput changes for the powder and special granular grades would not be expected to be different to the micronised APAP grade. 3.3. Tablet assay variability vs key process parameters It is important to assess the blend homogeneity achieved by changing the key process parameters and to determine which parameters are
Table 9 DoE model coded parameters for tablet assay RSD, with p-value in brackets. Model Term
Coded Factor (p-value)
Constant Throughput Impeller Speed No. Mixing Blades Throughput × Impeller Speed Throughput × No. Mixing Blades Impeller Speed × No. Mixing Blades Impeller Speed × Impeller Speed No. Mixing Blades × No. Mixing Blades R2 Q2
0.79 0.34 (b0.0001) −7.9 × 10−4 (0.9712) −0.17 (0.0004) −0.06 (0.0761) −0.18 (0.0014) 0.30 (0.0002) 0.25 (0.0007) 0.15 (0.0045) 0.99 0.91
the most significant in order to optimise the blending process. To quantify the impact on tablet properties, the assay variability of the tablets was assessed for each run by calculating the RSD for assay of the tablets across a run using Eq. (8). σ RSD ¼
σ 100 μ
Equation 8: σRSD is the assay RSD (%), σ is the sample standard deviation and μ is the sample mean. The NIR tablet assay method was only developed for micronised APAP. Table 8 shows the consistently low tablet RSD (b 1.68%) obtained across the experimental design using micronised APAP. This shows the ability of the CDC process to minimise the risk of segregation anticipated from the material attributes. Statistical analysis of the tablet results showed throughput and blade configuration to be significant along with interactions with impeller speed and several quadratic terms. The fitted model is shown in Fig. 10 and detailed in Table 9. This shows for a given blade configuration there is an optimum impeller speed range required in order to minimise tablet assay RSD. Although, there were no significant univariate relationships between the tablet assay variability and the parameters assessed, the key observation was that the lowest tablet RSD values were observable at the 2.5 kg/h throughput. To explain this further, the strain in the system is examined. To quantify the relationship between strain and tablet assay RSD, the parameters in Eq. (7) were estimated by minimising the mean square error. The estimated parameters are shown in Table 10. As the strain increases, the RSD decreases before plateauing at the RSDmin parameter as shown in Fig. 11. To provide some context, the theoretical dose RSD can be estimated to be 0.1%, based on API particle size and density [38], indicating that the ranges measured for the size of tablets show a significant variation in content uniformity. This shows that the parameterised model predictions are in good agreement with the
Fig. 10. Contour plots showing the change in tablet assay RSD with the three factors: thoughput, impeller speed and impeller configuration for the micronised APAP design (a) 2.5 kg/h, (b) 26.25 kg/h and (c) 50 kg/h.
J. Palmer et al. / Powder Technology 362 (2020) 659–670 Table 10 Free parameters for Eq. (7) obtained after meansquare-error minimisation between training data and predictions. RSDmin RSD0 kb
0.637% 1.688% 0.00427
measured tablet assay uniformity data, with a root mean square error (RMSE) of 0.183%. The strain-RSD model provides a good description of the tablet assay RSD and serves to demonstrate the underlying mechanism behind the statistical contour plots in Fig. 10. This shows that by changing the blender parameters the tablet API assay RSD could be minimised. In general running at the lowest throughput will give a low RSD independent of the impeller speed or configuration used. This is due to the high mean residence time and therefore high strain when running at a low mass flow rate. The only exception to this is
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when the blender is run at its highest impeller speed with all the blades set to transport. By running with a higher number of blades configured as mixing blades the tablet API assay RSD decreases as would be expected due to the increase in residence mass as shown in Fig. 6 and therefore strain. It is observable that as the number of mixing blades is increased, the tablet API assay RSD is lower over the available operating space, while the RSD is also typically higher at the extremes of the impeller speed ranges run. This indicates that for a given blade configuration, there will be an optimum impeller speed to minimise tablet RSD, due to the trade-off between increasing shear and reduction in mean residence time with increasing impeller speed. At a superficial level, there are some similarities between the relationships observed for tablet assay RSD (Fig. 10) and Pe (Fig. 7), whereby there appears to be an optimum impeller speed for a given blade configuration, although throughput was not found to be a significant factor for Pe. Fig. 12 shows that there is a degree of correlation between Pe and strain for a given throughput. Higher values of strain
1.7
Tablet Assay RSD (%)
1.5
1.3
1.1
0.9
0.7
0.5 0
1000
2000
3000
4000
5000
6000
7000
8000
Strain Fig. 11. Tablet content RSD reducing as a function of the total system mean residence time. Continuous line shows the tablet content RSD prediction using Eq. (7) with the parameters shown in Table 10.
Peclet Number
100
2.5 kg/h
10
26.25 kg/h 50 kg/h
1 10
100
1000
10000
Strain Fig. 12. Relationship between Peclet number and strain, annotated by throughput.
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correspond to lower values of Pe and therefore greater axial dispersion in the Blender 1. Over the broad range of throughputs studied, strain was found to be a better predictor of tablet assay RSD compared with Pe. However, it can be observed that, in general, optimising Blender 1 for axial dispersion is also likely to coincide with maximising strain and therefore minimising tablet assay. This analysis shows the potential to develop a strain based model for micro-mixing. The results quantitatively support the hypotheses that increasing strain leads to a reduction in tablet content RSD and that above a critical level of strain the tablet content RSD is minimised. The development of a robust strain based model, for example including additional process steps across different material properties, is beyond the scope of this study and would be an area of interest for further research.
4. Conclusions The DoE approach was successfully used to process a wide range of APAP grades and other typical tablet formulation excipients with divergent material properties. This study demonstrated that it was possible to process formulations prone to segregation on a close coupled CDC line that would normally not be selected for manufacturing using DC. This shows the advantage of close coupled integrated systems to minimise the risk of segregation associated with the DC process. It was found that the residence mass in the linear blender could be optimised through adjusting the impeller speed and configuration at a given throughput. It was also observed that the content uniformity of the tablets was a function of the number of impeller rotations per mean residence time as described by the strain and that the RSD of APAP assay in the final tablet could be minimised through maximising this. It was also possible to fit an exponential decay model to describe the final tablet RSD as a function of the strain. It would then be possible to optimise the available blender parameters such as impeller speed and impeller configuration to minimise the variance in final tablet content uniformity.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was supported by the Advanced Manufacturing Supply Chain Initiative (AMSCI) programme ReMediES Project, United Kingdom. (https://www.financebirmingham.com/amsci/). The authors would also like to acknowledge the support from Paul Ferguson from AstraZeneca for the extensive HPLC analytical support during this project. References [1] S. Byrn, M. Futran, H. Thomas, E. Jayjock, N. Maron, R.F. Meyer, et al., Achieving continuous manufacturing for final dosage formation: challenges and how to meet them, J. Pharm. Sci. 104 (2014) 792–802. [2] D. Djuric, Continuous Granulation With a Twin-Screw Extruder, University of Düsseldorf, Düsseldorf, Germany, 2008. [3] E.I. Keleb, A. Vermeire, C. Vervaet, J.P. Remon, Twin screw granulation as a simple and efficient tool for continuous wet granulation, Int. J. Pharm. 273 (2004) 183–194. [4] Peddapatla RVG, C.A. Blackshields, M.F. Cronin, A.M. Crean, Behaviour of magnesium stearate in continuous feeding, AIChE Annual Meeting; 13–18 November; San Francisco, 2016. [5] C.A. Blackshields, A.M. Crean, Continuous powder feeding for pharmaceutical solid dosage form manufacture: a short review, Pharm. Dev. Technol. 23 (2017) 554–560. [6] I.K. Yadav, J. Holman, E. Meehan, F. Tahir, J. Khoo, J. Taylor, et al., Influence of material properties and equipment configuration on loss-in-weight feeder performance for drug product continuous manufacture, Powder Technol. 348 (2019) 126–137.
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