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Impact of material properties and process parameters on residence time distributions in commercial scale continuous
Scott M Krull1, Naresh Pavurala1, Thomas F O'Connor1
1U.S. Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Quality, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA.
Abstract:
Continuous manufacturing of powder-based dosage forms is an emerging technology in the pharmaceutical industry and residence time distributions (RTDs) offer insights into the flow processes involved. Use of RTDs as a component of the control strategy is common in continuous manufacturing, but the methods and types of data used vary. This work investigates multiple aspects of the continuous powder blending process that affect the RTD. Comparing pulse disturbances and step changes at equivalent conditions revealed the two approaches only yielded comparable results when the same analytical method was used. Out of two RTD fitting models, only the model most representative of experimental conditions produced accurate estimates of material flow and dispersion despite both models fitting the RTDs well. The similarity between pulse disturbance tests with different spike amounts suggested that a range of spike amounts may reliably produce similar RTDs, provided the spike is large enough to be detected and small enough not to alter the flow properties of the blend. There was no observable difference between RTDs when varying the ratio of bulk materials, and grouping these RTDs showed they were similar enough to be analysed collectively. Blender speed, blender shaft configuration, and total throughput all affected the time required for a disturbance to pass through the blender and the steady-state mass hold-up inside the blender. The level of process understanding demonstrated when employing RTD models should be commensurate with how the RTD is used as part of the control strategy. Additionally, RTD model assumptions should be justified with appropriate considerations for the limitations of the model.
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