Peer-Reviewed Journal Details
Mandatory Fields
Shirazian S.;Ismail H.;Singh M.;Shaikh R.;Croker D.;Walker G.
2019
July
International journal of pharmaceutics
Multi-dimensional population balance modelling of pharmaceutical formulations for continuous twin-screw wet granulation: Determination of liquid distribution
Published
30 ()
Optional Fields
Continuous pharmaceutical manufacture Numerical simulation Population balance model Twin-screw granulator Wet granulation
566
352
360
2019 Elsevier B.V. Two-dimensional population balance model (PBM) is developed in order to model pharmaceutical granules formation in a twin-screw wet granulator. Granule size and liquid content are considered as internal coordinates, while axial length of granulator is considered as external coordinate. Two types of initial liquid distribution are considered for the model development, i.e. constant and linear distributions. The main focus is on modeling and validation of liquid content distribution of granules. Regime-separated approach was used in order to capture the non-homogeneity of the granulator. The plug flow regime is considered for the conveying zone, while well-mixed regime is assumed for the kneading zone of twin-screw granulator. Aggregation and breakage are considered as the main mechanisms for granule formation and size control. Cell average method is used for solution of the PBM based on lumped parameter approach. In order to determine experimentally the distribution of liquid, liquid binder by dye addition was used in the process. The model findings are calibrated and validated by comparing with measured liquid content in each size fraction. The measured data is collected on a 12 mm twin-screw wet granulator using microcrystalline cellulose (MCC) and water soluble dye plus water as binder. The model indicated to be valid for MCC and needs to be validated with further excipients. The results revealed that increasing screw speed led to more uniform liquid distribution. Finally, the model findings indicated that 2D PBM is capable of predicting liquid distribution, and can be used as predictive tool in pharmaceutical continuous granulation.
0378-5173
10.1016/j.ijpharm.2019.06.001
Grant Details