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The Scientific World Journal
Volume 2012 (2012), Article ID 185085, 10 pages
http://dx.doi.org/10.1100/2012/185085
Research Article

Design Space Approach in Optimization of Fluid Bed Granulation and Tablets Compression Process

Department of Pharmaceutical Technology, Faculty of Pharmacy, University of Belgrade, 11221 Belgrade, Serbia

Received 31 January 2012; Accepted 19 March 2012

Academic Editors: S. Baboota and A. Nokhodchi

Copyright © 2012 Jelena Djuriš et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The aim of this study was to optimize fluid bed granulation and tablets compression processes using design space approach. Type of diluent, binder concentration, temperature during mixing, granulation and drying, spray rate, and atomization pressure were recognized as critical formulation and process parameters. They were varied in the first set of experiments in order to estimate their influences on critical quality attributes, that is, granules characteristics (size distribution, flowability, bulk density, tapped density, Carr's index, Hausner's ratio, and moisture content) using Plackett-Burman experimental design. Type of diluent and atomization pressure were selected as the most important parameters. In the second set of experiments, design space for process parameters (atomization pressure and compression force) and its influence on tablets characteristics was developed. Percent of paracetamol released and tablets hardness were determined as critical quality attributes. Artificial neural networks (ANNs) were applied in order to determine design space. ANNs models showed that atomization pressure influences mostly on the dissolution profile, whereas compression force affects mainly the tablets hardness. Based on the obtained ANNs models, it is possible to predict tablet hardness and paracetamol release profile for any combination of analyzed factors.