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Model-based Control for Continuous Viral Inactivation of Biopharmaceuticals

Presented at: Bioprocessing 2021
C.E. Credits: P.A.C.E. CE Florida CE
Speaker
  • Researcher at the Massachusetts Institute of Technology (MIT)
    Biography
      Moo Sun Hong is a researcher at the Massachusetts Institute of Technology (MIT) in the group of Prof. Richard D. Braatz. Moo Sun received a B.S. in Chemical and Biological Engineering from Seoul National University and was the top graduate in its College of Engineering. He received an M.S. in Chemical Engineering Practice from MIT and the Jefferson W. Tester Prize for enthusiasm and leadership in the Practice School. He has constructed mathematical models for multiple bioreactor configurations and does research in the model-based design and control of continuous protein crystallization and viral inactivation.

    Abstract

    Biologics derived from mammalian sources are expected to undergo two orthogonal virus removal processes. One common processing step is a batch low-pH hold to inactivate enveloped viruses. Attempts to adapt this technique to continuous processing have involved cyclic batch operation, continuous flow tubular reactors, or continuous flow column-based reactors. None of the continuous flow reactors, however, address the control challenges with operating this system. This presentation describes a low-cost, column-based continuous viral inactivation system constructed with off-the-shelf components. Model-based, reaction-invariant pH controller is implemented to account for the nonlinearities with Bayesian estimation addressing variations in the operation. The residence time distribution is modeled as a plug flow reactor with axial dispersion in series with a continuously stirred tank reactor, and is periodically estimated during operation through inverse tracer experiments. The estimated residence time distribution quantifies the minimum residence time, which is used to adjust feed flow rates. Controller validation experiments demonstrate that pH and minimum residence time setpoint tracking and disturbance rejection are achieved with fast and accurate response and no instability.

    Learning Objectives:

    1. Discuss current trends in biopharmaceutical manufacturing operations and control

    2. Describe a mathematical model that incorporates pH and residence time to more accurately control continuous bioprocessing


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