(645c) Industrial Application of Mechanistic Model for Fluid-Bed Granulation for Technology Transfer and Design Space Exploration | AIChE

(645c) Industrial Application of Mechanistic Model for Fluid-Bed Granulation for Technology Transfer and Design Space Exploration

Authors 

Billups, M. W., Janssen
Hong, B., Janssen
Schäfer, E., Janssen
Marchut, A. J., Abbott Laboratories
Lyngberg, O., Bristol-Myers Squibb Company
Fluid bed granulation is a frequently-used formulation step whose benefits typically include improved flowability and compressibility of powders. The process consists of two main stages: a spraying phase, during which fluidized particles are coated with an aqueous binder and agglomerate, and a drying phase used to evaporate away the water, resulting in a dry powder.

While the exact process outcome depends on a large variety of factors, making this process difficult to model in its entirety, it is found that the moisture-level profile experienced by the powder during the granulation is a useful and scale-independent measure of the process performance within a sizeable region of the parameter space [1]. Hence, here, a mechanistic model for this moisture-level profile is presented.

The tool, a parameterized heat- and mass-balance with a customized graphical user interface, has been used extensively during a technology transfer of multiple products from an old to a new site in Shaanxi province, China. Specifically, the model was used to plan pilot-scale experiments at different conditions, used to estimate model parameters and to study the effects of different parameters on outputs such as tablet hardness and dissolution. Subsequently, this information was used together with the model to design a set of target conditions and scale-up the process to full-scale.

In this work, we give an overview of the results obtained at the different scales, together with the challenges faced during development and implementation. Furthermore, we provide insight into how such a model can be leveraged in order to assess the effects of input fluctuations and disturbances to derive a more meaningful set of acceptable parameter ranges, founded on detailed process understanding.

[1] Lyngberg, O., et al. (2016). Applications of Modeling in Oral Solid Dosage Form Development and Manufacturing. Process Simulation and Data Modeling in Solid Oral Drug Development and Manufacture. M. G. Ierapetritou and R. Ramachandran. New York, NY, Springer New York: 1-42.

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