RTD-based material tracking in a fully-continuous dry granulation tableting line

Abstract

Continuous manufacturing (CM) offers quality and cost-effectiveness benefits over currently dominating batch processing. One challenge that needs to be addressed when implementing CM is traceability of materials through the process, which is needed for the batch/lot definition and control strategy. In this work the residence time distributions (RTD) of single unit operations (blender, roller compactor and tablet press) of a continuous dry granulation tableting line were captured with NIR based methods at selected mass flow rates to create training data. RTD models for continuous operated unit operations and the entire line were developed based on transfer functions. For semi-continuously operated bucket conveyor and pneumatic transport an assumption based the operation frequency was used. For validation of the parametrized process model, a pre-defined API step change and its propagation through the manufacturing line was computed and compared to multi-scale experimental runs conducted with the fully assembled continuous operated manufacturing line. This novel approach showed a very good prediction power at the selected mass flow rates for a complete continuous dry granulation line. Furthermore, it shows and proves the capabilities of process simulation as a tool to support development and control of pharmaceutical manufacturing processes.

Measurement of residence time distributions and material tracking on three continuous manufacturing lines

“…This was one key reason why different techniques were chosen for RTD measurements at different lines in the current study to show the different possibilities in conducting the RTD characterization. RTD measurements have previously been conducted to a variety of continuous process units such as continuous blending (Gao et al, 2011;Portillo et al, 2009Portillo et al, , 2008, dry granulation (Kruisz et al, 2017;Martinetz et al, 2018), wet granulation (Kumar et al, 2016(Kumar et al, , 2014, hot-melt extrusion (Rehrl et al, 2018;Wahl et al, 2018) and tableting (Dülle et al, 2018;Mendez et al, 2010). In these studies, NIR was most often used to measure the RTD response but also video recording for color tracer was used.…”

“…In these studies, NIR was most often used to measure the RTD response but also video recording for color tracer was used. When RTDs for the line or a specific part of the line have been characterized, process models can be established to predict API content at a certain point down the manufacturing line based on a known material composition from the feeding unit (Kruisz et al, 2017;Martinetz et al, 2018;Rehrl et al, 2018). This type of process model can also serve as a so-called soft sensor to replace or supplement NIR measurement in control strategy (Rehrl et al, 2018).…”

Model-based approach to the design of pharmaceutical roller-compaction processes

“…Unlike wet granulation, RC does not require solvents and a drying step to remove them, which is especially advantageous for moisture- and heat-sensitive active pharmaceutical ingredients (APIs). Moreover, since RC involves fewer processing steps, it is suitable for continuous processing lines (Kleinebudde, 2004, Miller, 2005, Guigon et al, 2007, Leane et al, 2015, Rowe et al, 2013, Martinetz et al, 2018). However, an RC process poses several challenges.…”

Explicit Residence Time Distribution of a Generalised Cascade of Continuous Stirred Tank Reactors for a Description of Short Recirculation Time (Bypassing)

“…The θ 0 = 0.5 Equation (12) simplifies to a non-dimensional version of Equation (11). Equation (12) can also be rewritten to a dimensional version with mean residence time τ and breakthrough time t 0 with 0 ≤ t 0 ≤ τ:…”

“…The individual RTDs are then chained together by convolution integrals, in order to calculate the RTD of the overall process. With this information it is possible to predict how long the material remains, on average, in the process (mean residence time, MRT), the response of the system to fluctuations in the material stream (e.g., feeder refills), and to develop process control strategies [7][8][9][10][11].…”