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

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.

Formulation and pharmaceutical development of quetiapine fumarate sustained release matrix tablets using a QbD approach

“…DoE provides the largest amount of information from the least number of experimental runs by systematic variation of the factors and simultaneous evaluation of the effects of multiple variables. Quality assurance has changed from the need to show that the final product meets predefined specifications to a new situation where it needs to be demonstrated that the product is controlled within a well-defined design space (10,11). The design space is defined as an established multidimensional range of input variables (e.g., formulation factors) and process parameters demonstrated to provide assurance of quality (12).…”

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“…More recent publications on optimization of fluidized bed granulation processes use design space approaches to generate feasible optimal solutions that can be implemented in multiple unit processes. Djuris et al [181] uses the design space approach to optimize the outcomes of both granulation and compression processes by identifying the critical formulation and process parameters. Dry granulation processes have also been modeled and optimized using surrogate-based methods [182][183][184].…”

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