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This study demonstrates the beneficial use of a spatial-filter velocimetry particle-size analyzer during granulation.
The pharmaceutical industry has used fluid-bed granulation extensively for several decades to improve powder properties (e.g., flowability and compressibility) for downstream processing. During this two-phase process that includes spraying and drying, the addition of a binder liquid causes primary particles to aggregate and form granules (1). The granule size distribution (GSD) is of major importance to the final quality of the granulated product because it influences density, flowability, and dustiness. Hence, the understanding and control of granule growth during manufacturing are of major importance to the delivery of a high-quality end product.
Sieve analysis, image analysis, and laser diffraction are common off-line particle-size determination techniques. These methods are usually time-consuming and labor-intensive because they require sample preparation. In recent years, interest in real-time process analysis has increased, partly because of FDA's process analytical technology (PAT) initiative. Several studies have examined at-line, on-line, and in-line particle-size analysers. The application of image analysis, near-infrared spectroscopy, acoustic-emission spectroscopy, focused-beam reflectance spectroscopy, and spatial-filter velocimetry for real-time granulation monitoring has been investigated (2–24).
The authors applied spatial-filter velocimetry (SFV) in-line during top-spray fluid-bed granulation to obtain GSD information continuously. During SFV measurements, particles pass through a laser beam, and the corresponding shadow thrown onto the detector helps determine the chord-length distribution of the measured particles. The measurement zone at the probe tip is equipped with sapphire windows that are kept clean by an internal compressed-air supply system, thus preventing window fouling. A two-level full-factorial design was used to examine the influence of process and formulation variables on end product GSD, measured in-line with SFV and compared with off-line laser diffraction (LD) results. The granule-size data obtained continuously in-line were analyzed in detail to improve understanding of the influence of the examined process and formulation variables on the granule growth mechanism. Furthermore, the in-line quantified GSD was related to the off-line-measured tapped density using univariate, multivariate, and multiway models, thus allowing early estimation of this end-product property during granulation.
Materials and methods
Materials. The dry powder mass consisted of dextrose monohydrate (700 g, Roquette Frères) and unmodified maize starch (Cargill Benelux). This was granulated with an aqueous binder solution of hydroxypropyl methylcellulose (HPMC, type 2910, 15 mPa, Dow Chemical) and Tween 20 (Croda Chemicals Europe). The amounts of HPMC and Tween 20 were varied according to the design of experiments (DOE, see Table I). The HPMC binder was always sprayed as a 4% solution, and the total amount of solids was kept constant at 1 kg by varying the amount of maize starch accordingly.
Table I: Lower and upper levels of the examined process and formulation variables.
Fluid-bed granulation set-up. Granulations were performed in a laboratory-scale fluid-bed granulator (GPCG 1, Glatt). An SFV probe (Parsum IPP 70; Gesellschaft für Partikel-, Strömungs- und Umweltmesstechnik) was installed in the fluid-bed granulator at a height of 200 mm above the distributor plate and at approximately 50 mm from the side wall of the granulator. Granules passed through a 4-mm aperture, and an internal and external air connection prevented fouling of the measurement zone and ensured the dispersion of the powder mass. SFV data were collected every second during the entire granulation processes, but an average granule-size distribution was saved every 10 s. The granulation process finished when an outlet air temperature of 37 °C and a product temperature of 45 °C were reached.
DOE. A two-level full-factorial design was applied to study the effects of HPMC concentration, Tween 20 concentration, inlet-air temperature during spraying, and inlet-air temperature during drying (see Table I) on the end-product's GSD. Three design center point repetitions were performed (i.e., 19 experiments in total).
Off-line granule characterization. For each DOE granulation experiment, the end-product particle-size distribution was determined with LD (Mastersizer S long bench, Malvern Instruments). Average D10, D50, and D90 values were determined based on three measurements of each batch.
End-product tapped-density measurements (1250 taps, J. Englesmann) were performed in triplicate, and the average tapped density was used.
Results and discussion
Comparison of in-line SFV and off-line LD particle-size measurements. The GSD of the end products measured in-line using SFV was compared with the off-line determined LD granule sizes for all DOE batches. Although SFV and LD are based on different measurement principles (LD assumes spherical particles, whereas SFV does not), similar GSDs were expected. Figure 1 displays the average D50 values measured with SFV (i.e., the average of the last granulation minute, or six data points) and LD for the end granules of all 19 DOE batches. Similar D50 differences between the 19 experiments were obtained by the two particle-sizing techniques. However, D50 sizes measured with LD were always lower than those obtained by SFV. The same observations were made for the D10 and D90 values.
Figure 1: Average D50 results obtained with spatial-filter velocimetry (SFV) and laser diffraction (LD) for all design-of-experiments batches. (ALL FIGURES ARE COURTESY OF THE AUTHORS)
The authors believe that this difference in GSD is caused by the LD measurement technique. The quantified particles experience rapid accelerations as the air stream passes through a venturi. The high shear applied during this process and the subsequent collisions with the wall of the apparatus may cause granules to break or crumble. Pressurized air also disperses the granules during the SFV measurements, but the particles pass directly through the measurement zone. No collisions occur because high shear is not applied. The authors' hypothesis was confirmed by the GSD measurement of low-friability spherical granules (i.e., Cellets 100, 200, and 350, Pharmatrans Sanaq Pharmaceuticals) using SFV and LD under software and experimental settings identical to those for the DOE granules. The LD measurements did not systematically underestimate the GSD, in contrast to observations obtained for the breakable granules. An additional explanation for the discrepancy between SFV and LD values might be found in the assumption of a spherical shape during LD measurements.
Figure 2a: Differences between average D50 values measured with spatial-filter velocimetry (SFV) and laser diffraction (LD), arranged according to increasing SFV particle size.
Size segregation during fluidization should also be addressed because it influences in-line SFV measurements (23). Inappropriate fluidization can cause a high amount of larger granules to be present in the lower part of the chamber and a high amount of smaller granules to appear in the upper part of the chamber. The SFV probe is placed in the upper part of the chamber, which the largest particles cannot reach under low inlet airflow rates, potentially resulting in an underestimation of granule size. Figures 2a and 2b display the difference between SFV and LD D50 results for the 19 DOE batches, arranged according to increasing SFV end-granule size and LD end-granule size in the x axes, respectively. Because these differences did not increase as a function of increasing end granule size, the authors concluded that no size segregation had occurred.
Figure 2b: Differences between average D50 values measured with spatial-filter velocimetry (SFV) and laser diffraction (LD), arranged according to increasing LD particle size.
These primary results suggest that although a systematic difference exists between LD and SFV data, the SFV technique can successfully measure the actual particle-size distribution during granulation.
Improved process understanding through continuously gathered GSD information. The average D50 values, obtained from the in-line SFV measurements during the last minute of granulation, were used as DOE responses. The experimental results revealed three significant DOE variables. HPMC concentration, inlet air temperature during drying, and the interaction between these factors had a positive effect on the D50 values. Because GSD information was obtained every 10 s through in-line SFV measurements, the authors took a closer look at the individual granule-size profiles of several DOE batches to better understand the significance of the studied DOE factors.
Influence of the HPMC concentration. The distinctive particle-size trajectory during fluid-bed granulation consisting of three phases can be distinguished clearly in Figure 3. Between the first captured granule-size data and time point 1, the particle size remains constant, which corresponds to the mixing phase. Between time points 1 and 2, the granule size increases because of the agglomeration of powder particles. The drying period is depicted after time point 2. The graph reveals that the positive effect of the HPMC concentration on granule size was caused by the following two factors:
Figure 3: D50 profile of batches 13 and 14 (1%/3% hydroxypropyl methylcellulose, 0,2% Tween 20, 50 Â°C spraying T, 70 Â°C drying T) at (1) the beginning of the spraying phase, (2) the end of the spraying phase, and (3) the end of the drying period.
Influence of the inlet air temperature during the drying period. DOE analysis showed that the drying inlet air temperature was of less importance (statistical significance p < 0.05) to the GSD than the HPMC concentration (statistical significance p < 0.001). An explanation can be found in Figure 4. Figure 4a displays the in-line measured D50 profiles of Batches 6 (50 °C drying temperature) and 14 (70 °C drying temperature). The other settings were the same for both batches. These batches showed a similar granule-size trajectory throughout the spraying phase. During the subsequent drying period, Batch 6 yielded smaller end-sized granules because of higher levels of attrition during a longer drying phase.
Figure 4: D50 profile of (a) batches 6 and 14 (3% hydroxypropyl methylcellulose [HPMC], 0.2% Tween 20, 50 Â°C spraying T, 50 Â°C, and 70 Â°C drying T) and (b) batches 1 and 9 (1% HPMC, 0.2% Tween 20, 40 Â°C spraying T, 50 Â°C, and 70 Â°C drying T).
Figure 4b displays the granule-size data of two other batches with different drying conditions. Batch 1 was dried at 50 °C, and Batch 9 at 70 °C. Both batches had identical initial process conditions, but a different granule growth profile occurred during the spraying period. Although the difference in drying temperature between the two batches caused a clear difference in particle-size evolution during the drying period (i.e., a larger decrease in particle size at the lower temperature of Batch 1), the end granule sizes used for the DOE analysis were similar. Hence, for these batches, a similar response value was used in the DOE analysis, despite the fact that the batches showed different granulation trajectories. This result might explain why the drying temperature was of limited significance according to the DOE. Only through the information obtained continuously in-line from the SFV probe was it possible to develop this in-depth understanding.
Influence of the inlet air temperature during the spraying period. Figure 5 shows the D50 data obtained in-line during the spraying period of two batches manufactured at different spraying temperatures. During the agglomeration phase of Batch 5 (spraying temperature of 50 °C), a higher fluctuation in granule size was observed, compared with Batch 1 (spraying temperature of 40 °C). This result was caused by the continuous entrapment of small particles in and subsequent discharge from the filter bags. At the higher temperature, small particles were present for a longer period of time than they were in Batch 1 because the faster evaporation of binder liquid led to slower agglomeration. However, at the end of the spraying periods, a similar particle size was observed at both temperatures. Although the detailed profile of granule growth through in-line granule-size monitoring showed a difference in agglomeration kinetics, the different spraying temperature did not create a difference in granule size at the end of the spraying period.
Figure 5: D50 profile during the spraying period of batches 1 and 5 (1% hydroxypropyl methylcellulose, 0.2% Tween 20, 40 Â°C/50 Â°C spraying T, 50 Â°C drying T).
Estimation of granule tapped density from in-line SFV measurements. The tapped density of the end product is, like GSD, also important to further processing. Univariate, multivariate, and multiway approaches were considered to relate the in-line determined GSD to the tapped density of the 19 DOE batches (tapped density as dependent variable: 19 × 1 Y-vector). A univariate linear model was built using the D50 SFV values of the end granules of the 19 DOE batches as independent variables (19 × 1 X-vector). A multivariate partial least squares (PLS) model was built using the D01, D10, D25, D50, D63, D75, D90, and D99 SFV values of the end granules of the 19 DOE experiments as independent variables (19 × 8 X-matrix). A multiway N-way PLS (N-PLS) model was built using the D01, D10, D25, D50, D63, D75, D90, and D99 SFV values of the 19 DOE experiments in a function of complete batch process time as independent variables (three-way X-matrix).
Table II compares the goodness of fit (R2) and root mean square error of estimation (RMSEE) of the different models. The multivariate PLS tapped-density model had the highest R2 value and the lowest RMSEE value. The low RMSEE of the model suggests the ability of the in-line SFV data to predict the off-line measured tapped density. Nevertheless, the model performance should be tested independently because the RMSEE value relates to the error within the calibration set and may overestimate the actual model performance.
Table II: R2 and RMSEE of univariate, multivariate, and multiway tapped-density models.
The results of this study show the feasibility of SFV for the real-time monitoring of GSD during fluid-bed granulation. The technique was sensitive to PS changes during the performed granulations and did not underestimate the granule size because of size segregation. Probe fouling did not occur. The continuously obtained GS information enabled a better understanding of the significance of the studied DOE factors on granulation. This understanding was not possible based on the off-line LD data of the end product. Finally, a multivariate PLS model was built to estimate end-product tapped density using continuously obtained GSD during granulation, which may improve batch-release time.
Next to real-time process monitoring of critical quality attributes, which helps to improve process understanding, another goal of PAT is to use the real-time critical information to steer or adjust the process toward its desired state based on feedback and feed-forward loops. Granule particle-size distribution and density are important granule quality attributes. In a future study, the authors aim to use in-line collected granule information gathered during the spraying phase to adjust process variables from the consecutive drying phase, hence guiding the drying process to the desired granule properties.
The Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen) provided financial support for this study.
A. Burggraeve* is a doctoral student, J.P. Remon and C. Vervaet are professors of pharmaceutical technology, and T. De Beer is a professor of process analytical technology, all at Ghent University, Harelbekestraat 72, B-9000 Ghent, Belgium, tel. +32 9 264 83 55, fax +32 9 222 82 36, email@example.com. T. Van Den Kerkhof and M. Hellings are senior scientists, both at Johnson & Johnson.
*To whom all correspondence should be addressed.
Submitted: Feb. 22, 2011. Accepted: May 13, 2011.
1. S.M. Iveson, et al., Powder Technol. 117 (1–2), 3–39 (2001).
2. T. Naervanen, et al., AAPS PharmSciTech 9 (1), 282–287 (2008).
3. S. Watano, Powder Technol. 117 (1–2), 163–172 (2001).
4. S. Watano and K. Miyanami, Powder Technol. 83 (1), 55–60 (1995).
5. S. Watano et al., Chem. Pharm. Bull. 48 (8), 1154–1159 (2000).
6. S. Watano et al., Powder Technol. 115 (2), 124–130 (2001).
7. S. Watano, Y. Sato, and K. Miyanami, Chem. Pharm. Bull. 44 (8), 1556–1560 (1996).
8. S. Watano, Y. Sato, and K. Miyanami, Adv. Powder. Technol. 8 (4), 269–277 (1997).
9. M. Alcala et al., J. Pharm. Sci. 99 (1), 336–345 (2010).
10. W.P. Findlay, G.R. Peck, and K.R. Morris, J. Pharm. Sci. 94 (3), 604–612 (2005).
11. P. Frake et al., Int. J. Pharm. 151 (1), 75–80 (1997).
12. S.G. Goebel and K.J. Steffens, Pharm. Industrie 60 (10), 889–895 (1998).
13. A.A. Kaddour and B. Cuq, Powder Technol. 190 (1–2), 10–18 (2009).
14. P. Luukkonen et al., J. Pharm. Sci. 97 (2), 950–959 (2008).
15. A. Tok et al., AAPS PharmSciTech 9 (4), 1083–1091 (2008).
16. J.F. Gamble, A.B. Dennis, and M. Tobyn, Pharm. Dev. Technol. 14 (3), 299–304 (2009).
17. M. Halstensen, P. de Bakker, and K.H. Esbensen, Chemom. Intell. Lab. Syst. 84 (1–2), 88–97 (2006).
18. S. Matero et al., Chemom. Intell. Lab. Syst. 97 (1), 75–81 (2009).
19. M. Whitaker et al., Int. J. Pharm. 205 (1–2), 79–91 (2000).
20. X.H. Hu, J.C. Cunningham, and D. Winstead, Int. J. Pharm. 347 (1–2), 54–61 (2008).
21. J. Huang et al., J. Pharm. Sci. 99 (7), 3205–3212 (2010).
22. T. Lipsanen et al., AAPS PharmSciTech. 9 (4), 1070–1077 (2008).
23. T. Narvanen et al., J. Pharm. Sci. 98 (3), 1110–1117 (2009).
24. T. Narvanen et al., Int. J. Pharm. 357 (1–2), 132-138 (2008).
Citation: When referring to this article, please cite it as "A. Burggraeve, T. Van Den Kerkhof, M. Hellings, J.P. Remon, C. Vervaet, T. De Beer, "Understanding Fluidized-Bed Granulation," Pharmaceutical Technology 35 (8) 63-67(2011)."