pH and conductivity control in an integrated biomanufacturing plant

Lu, Amos E.; Paulson, Joel A.; Braatz, Richard D.

doi:10.1109/acc.2016.7525168

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…Bayesian estimation is used to address these uncertainties by updating solution

normalp K_{a}

and concentrations in response to sensor data. For implementation details on the pH modeling and controller design, the reader is referred to previously published work (Lu et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

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Model‐based control for column‐based continuous viral inactivation of biopharmaceuticals

Hong

et al. 2021

Biotech & Bioengineering

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Batch low-pH hold is a common processing step to inactivate enveloped viruses for biologics derived from mammalian sources. Increased interest in the transition of biopharmaceutical manufacturing from batch to continuous operation resulted in numerous attempts to adapt batch low-pH hold to continuous processing. However, control challenges with operating this system have not been directly addressed. This article 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. Viral inactivation testing demonstrates tight control of logarithmic reduction values over extended operation. This study provides tools for the design and operation of continuous viral inactivation systems in service of increasing productivity, improving product quality, and enhancing patient safety.

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“…Bayesian estimation is used to address these uncertainties by updating solution

normalp K_{a}

and concentrations in response to sensor data. For implementation details on the pH modeling and controller design, the reader is referred to previously published work (Lu et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

“…A model-based, reaction-invariant controller with Bayesian estimation was used for pH control (Lu et al, 2016). Bayesian estimation was performed on the tablet (using custom code running in MATLAB 2019a), and the parameter estimates forwarded to the PLC to improve the performance and robustness of the reaction-invariant controller.…”

mentioning

confidence: 99%

Model‐based control for column‐based continuous viral inactivation of biopharmaceuticals

Hong

et al. 2021

Biotech & Bioengineering

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“…31,32 Different variations of model predictive control have also shown excellent performance for various unit operations. 29,[33][34][35][36] Additionally, several methodologies for reducing computation burdens are emerging such as model linearization, and reinforcement learning. [37][38][39] To fully implement the QbC, an additional requirement is to develop a systematic framework that enables the integration of operation control based on hierarchical process automation principles.…”

Section: Quality By Controlmentioning

confidence: 99%

“…For example, neural network‐based control has demonstrated its effectiveness under system perturbations and parametric uncertainties 31,32 . Different variations of model predictive control have also shown excellent performance for various unit operations 29,33–36 . Additionally, several methodologies for reducing computation burdens are emerging such as model linearization, and reinforcement learning 37–39 …”

Section: Quality Control and Process Monitoringmentioning

confidence: 99%

Current challenges and recent advances on the path towards continuous biomanufacturing

Drobnjakovic

Hart²,

Kulvatunyou

et al. 2023

Biotechnology Progress

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Continuous biopharmaceutical manufacturing is currently a field of intense research due to its potential to make the entire production process more optimal for the modern, ever‐evolving biopharmaceutical market. Compared to traditional batch manufacturing, continuous bioprocessing is more efficient, adjustable, and sustainable and has reduced capital costs. However, despite its clear advantages, continuous bioprocessing is yet to be widely adopted in commercial manufacturing. This article provides an overview of the technological roadblocks for extensive adoptions and points out the recent advances that could help overcome them. In total, three key areas for improvement are identified: Quality by Design (QbD) implementation, integration of upstream and downstream technologies, and data and knowledge management. First, the challenges to QbD implementation are explored. Specifically, process control, process analytical technology (PAT), critical process parameter (CPP) identification, and mathematical models for bioprocess control and design are recognized as crucial for successful QbD realizations. Next, the difficulties of end‐to‐end process integration are examined, with a particular emphasis on downstream processing. Finally, the problem of data and knowledge management and its potential solutions are outlined where ontologies and data standards are pointed out as key drivers of progress.

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“…In this way, multiple drugs will be produced in the same time period by an easily programmed switch with chemical induction. For example, Tim Lu's group 43 has already developed and demonstratedthe exposure of the production cells to methanol as an inducer to produce hGH or interferon.…”

Section: Fig 13: Overview Of Inscyt Biomanufacturing Platformmentioning

confidence: 99%

Mass spectrometric methods for analysis of therapeutic products and advanced proteomic analysis

Wu¹

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, etc. for their support and help in my study and life. I'm so grateful for time spent with my friends during these years. Nobody has been more important to me in the pursuit of this degree than the members of my family. I would like to thank my parents for their unconditional love and support on my life and study. And most of all for my loving, encouraging, supportive, and patient husband Boyan Lin whose faithful support during the final stages of this Ph.D. is so appreciated. Thank you.

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pH and conductivity control in an integrated biomanufacturing plant

Cited by 4 publications

References 10 publications

Model‐based control for column‐based continuous viral inactivation of biopharmaceuticals

Model‐based control for column‐based continuous viral inactivation of biopharmaceuticals

Current challenges and recent advances on the path towards continuous biomanufacturing

Mass spectrometric methods for analysis of therapeutic products and advanced proteomic analysis

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