Hybrid Controls Combining First-Principle Calculations with Empirical Modeling for Fully Automated Fluid Bed Processing
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The US Food and Drug Administration has encouraged the use of the guidelines put forth by the International Conference on Harmonization (ICH-Q8) that allow for operational flexibility within a validated design space. These guidelines make possible fully automated control systems that incorporate information about a process back into the system to adjust process variables to consistently hit product quality targets. Traditionally, fluid bed control systems have used either first-principle calculations to control the internal process environment or purely empirical methods that incorporate online process measurements with process models and real-time data management. This study demonstrates the development and implementation of a novel hybrid control system that combines the two traditional approaches.
Material and Methods
Granules containing gabapentin, and hydroxypropyl cellulose were prepared in a high-shear granulator and dried in a fluid bed processing system (Diosna Minilab). The fluid bed dryer was outfitted with near-infrared (NIR), pressure, temperature, and flow sensors which were connected to a distributed control system (DCS) that was used to exercise control of the system. The control system itself consisted of a Delta V DCS (Emerson Process Management, Equipment and Controls, Inc., Lawrence, PA, USA) that was used to interface the fluid bed dryer with SynTQ (Optimal, Bristol, UK). The dried granules were characterized by median particle size and quantity of gabapentin lactam formed (a chemical degradant).
Control of a fluid bed dryer utilizing both a first-principle control strategy and empirical model-based controls was demonstrated. First-principle control was based on an environmental equivalency factor model to maintain a constant thermodynamic environment. Empirical models included a pressure drop across the bed and NIR measurement of water content. These systems were combined effectively to consistently dry granules prepared by high-shear wet granulation. Utilization of this system greatly reduced the number of experiments necessary to characterize the performance of the system and facilitated control of the process with respect to the two properties of interest, median particle size and chemical stability during drying.
KeywordsFluid bed processing Process analytical technology Multivariate modeling Online monitoring Real-time data management
The research team at the Duquesne Center for Pharmaceutical Technology (DCPT) would like to thank the National Institute for Pharmaceutical Technology and Education (NIPTE) and the US Food and Drug Administration (FDA) for providing funds for this research. This study was funded by the FDA-sponsored grant “Development of Quality by Design (QbD) Guidance Elements on Design Space Specifications Across Scales with Stability Considerations.”(#HHSF223200819929C). The DCPT would also like to recognize Dr. Lee Kirsch’s laboratory at the University of Iowa for providing the DCPT with the lactam concentration measurements used in this study.
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