Evaluation of in-line spatial filter velocimetry as PAT monitoring tool for particle growth during fluid bed granulation

被引:63
作者
Burggraeve, A.
Van Den Kerkhof, T. [2 ]
Hellings, M. [2 ]
Remon, J. P. [3 ]
Vervaet, C. [3 ]
De Beer, T. [1 ]
机构
[1] Univ Ghent, Dept Pharmaceut Anal, Lab Pharmaceut Proc Analyt Technol, B-9000 Ghent, Belgium
[2] Johnson & Johnson Pharmaceut Res & Dev, Beerse, Belgium
[3] Univ Ghent, Dept Pharmaceut, B-9000 Ghent, Belgium
关键词
Spatial filter velocimetry (SFV); Process analytical technology (PAT); Fluid bed granulation; Particle size (distribution); Process understanding; In-line measurements; IMAGE-PROCESSING SYSTEM; HIGH-SHEAR GRANULATION; NEAR-INFRARED SPECTROSCOPY; END-POINT DETERMINATION; SIZE DISTRIBUTION; VALIDATION; MOISTURE; VELOCITY; PROBE; SHAPE;
D O I
10.1016/j.ejpb.2010.06.001
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
In this study, the feasibility of spatial filter velocimetry (SFV) as process analytical technology tool for the in-line monitoring of the particle size distribution during top spray fluidized bed granulation was examined. The influence of several process (inlet air temperature during spraying and drying) and formulation variables (HPMC and Tween 20 concentration) upon the particle size distribution during processing, and the end product particle size distribution, tapped density and Hausner ratio was examined using a design of experiments (DOE) (2-level full factorial design, 19 experiments). The trend in end granule particle size distributions of all DOE batches measured with in-line SFV was similar to the off-line laser diffraction (LD) data. Analysis of the DOE results showed that mainly the HPMC concentration and slightly the inlet air temperature during drying had a positive effect on the average end granule size. The in-line SFV particle size data, obtained every 10 s during processing, further allowed to explain and better understand the (in)significance of the studied DOE variables, which was not possible based on the LD data as this technique only supplied end granule size information. The variation in tapped density and Hausner ratio among the end granules of the different DOE batches could be explained by their difference in average end granule size. Univariate, multivariate PLS and multiway N-PLS models were built to relate these end granule properties to the in-line-measured particle size distribution. The multivariate PLS tapped density model and the multiway N-PLS Hausner ratio model showed the highest R-2 values in combination with the lowest RMSEE values (R-2 of 82% with an RMSEE of 0.0279 for tapped density and an R-2 of 52% with an RMSEE of 0.0268 for Hausner ratio, respectively). (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码:138 / 146
页数:9
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