Real-Time Process Management and Knowledge Management for Continuous Pharmaceutical Manufacturing

  • Type:
    Conference Presentation
  • Skill Level:
    Intermediate
  • PDHs:
    0.50

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The manufacture of pharmaceutical drug products has benefited significantly from a QbD approach, including the enabling of continuous production over batch processing. Developing and improving processes for continuous manufacture requires the combined use of process data with process and formulation knowledge. Process data can be generated across multiple unitops simultaneously, and correcting deviations in one critical process parameter or critical quality attribute may require manipulations of a totally different process unit. Such integrated control must be done in a manner that is both easy to scale to different sizes and easy to transfer to different materials, requiring an integrated approach in gathering and managing process knowledge and using it flexibly for new processes.

In this work, we present the results and case studies from the Engineering Research Center for Structured Organic Particulate Systems (ERC-SOPS), in developing a real-time process management system for continuous pharmaceutical manufacture, that makes use of an ontological informatics framework to manage process knowledge of multiple types, including material properties (such as stress-density relationships), process parameters (such as process setpoints), equipment parameters (such as die and punch sizes in a tablet press), and process control knowledge (mathematical models for PID and model predictive control, process signatures for fault diagnosis, mathematical models to plan future setpoint changes, etc). The models taken together operate on a wide range of time scales, and the knowledge management framework is designed to be flexible for all uses. Further, we show how the system architecture allows for the use of various control systems and modeling programs in a flexible manner. Finally, we present case studies in tablet manufacturing and drop-on-demand systems, and summarize our current results.

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