(69a) Optimising High Shear Wet Granulation with the Combined Use of in-Line and at-Line Testing
World Congress on Particle Technology
2018
8th World Congress on Particle Technology
Particle & Bulk Powder Characterization
Flow Properties of Particulate Solids III
Tuesday, April 24, 2018 - 3:30pm to 3:45pm
A Drag Force Flow (DFF) sensor (Lenterra Inc, USA) is a thin, hollow cylindrical pin containing two optical strain gages which detect deflections caused by the force of flow. These sensors can provide highly sensitive in-line measurements of flow forces within granulators. Previous research has also demonstrated that these measurements correlate well with granule densification and resulting tablet properties.
In this study, three formulations were granulated with 40% wt/wt water in a GEA PharmaConnectTM High Shear Wet Granulator. The relationship between the in-line response of a Drag Force Flow (DFF) sensor and at-line flow property measurements of wet granules measured using an FT4 Powder Rheometer® (Freeman Technology Ltd, UK) was investigated.
By evaluating the real-time DFF sensor output, expressed as a Force Pulse Magnitude (FPM), it was possible to clearly identify the different phases of the granulation process, including water addition and end-point. The Basic Flowability Energy (BFE) values generated from FT4 dynamic flow testing displayed a similar trend to the FPM data, displaying an increase and subsequent decrease as a function of time after the addition of water. A relationship between BFE and binder level was also observed with higher levels of binder resulting in higher BFE values, indicating stronger, denser and larger granules.
Wet mass consistency measured in-line by the DFF sensor is a good indicator of the rheological properties of the granulate and correlates well with at-line BFE measurements that are known to influence Critical Quality Attributes (CQAs) of a tablet. The data provided by both the DFF sensor and FT4 can be correlated to granulation end-point and help define a design space of parameters that correlate to optimal process configuration.