Dynamic Optimisation of Fed-Batch Bioreactors for mAbs: Sensitivity Analysis of Feed Nutrient Manipulation Profiles

Wil Jones; Dimitrios Gerogiorgis

doi:10.3390/pr11113065

Dynamic Optimisation of Fed-Batch Bioreactors for mAbs: Sensitivity Analysis of Feed Nutrient Manipulation Profiles

Wil Jones, Dimitrios Gerogiorgis

School of Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

Successful cultivation of mammalian cells must consider careful formulation of culture media consisting of a variety of substrates and amino acids. A widely cited method for quantifying metabolic networks of mammalian cultures is dynamic flux balance modelling. Application of in-silico techniques allows researchers to circumvent time-consuming and costly in-vivo experimentation. Dynamic simulation and optimisation of reliable models allows for the visualization of opportunities to improve throughputs of target protein products, such as monoclonal antibodies (mAbs). This study presents a sensitivity analysis comparing dynamic optimisation results for industrial-scale fed-batch bioreactors, considering a variety of initial conditions. Optimized feeding trajectories are computed via Nonlinear Programming (NLP) model, employing the established IPOPT solver. Glucose, then glutamine, then asparagine, can lead to improved mAb yields and viable cell counts.

Original language	English
Article number	3065
Number of pages	16
Journal	Processes
Volume	11
Issue number	11
Early online date	25 Oct 2023
DOIs	https://doi.org/10.3390/pr11113065
Publication status	Published - Nov 2023

Keywords / Materials (for Non-textual outputs)

dynamic optimisation
fed-batch reactor
monoclonal antibody/mAb
sensitivity analysis

Access to Document

10.3390/pr11113065Licence: Creative Commons: Attribution (CC-BY)

processes-11-03065Final published version, 2.09 MBLicence: Creative Commons: Attribution (CC-BY)

RAPID: ReAl-time Process ModellIng and Diagnostics: Powering Digital Factories
Polydorides, N. (Principal Investigator) & Gerogiorgis, D. (Co-investigator)
Engineering and Physical Sciences Research Council
1/08/21 → 20/11/25
Project: Research
A Digital Twin via First-Principles Modelling and Data Analytics for Process Optimisation in Pharmaceutical Manufacturing
Gerogiorgis, D. (Principal Investigator)
UK-based charities
1/10/20 → 31/01/22
Project: Research

Cite this

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title = "Dynamic Optimisation of Fed-Batch Bioreactors for mAbs: Sensitivity Analysis of Feed Nutrient Manipulation Profiles",

abstract = "Successful cultivation of mammalian cells must consider careful formulation of culture media consisting of a variety of substrates and amino acids. A widely cited method for quantifying metabolic networks of mammalian cultures is dynamic flux balance modelling. Application of in-silico techniques allows researchers to circumvent time-consuming and costly in-vivo experimentation. Dynamic simulation and optimisation of reliable models allows for the visualization of opportunities to improve throughputs of target protein products, such as monoclonal antibodies (mAbs). This study presents a sensitivity analysis comparing dynamic optimisation results for industrial-scale fed-batch bioreactors, considering a variety of initial conditions. Optimized feeding trajectories are computed via Nonlinear Programming (NLP) model, employing the established IPOPT solver. Glucose, then glutamine, then asparagine, can lead to improved mAb yields and viable cell counts.",

keywords = "dynamic optimisation, fed-batch reactor, monoclonal antibody/mAb, sensitivity analysis",

author = "Wil Jones and Dimitrios Gerogiorgis",

note = "Funding Information: The authors gratefully acknowledge the Engineering and Physical Sciences Research Council (EPSRC) and UKRI, for a Doctoral Training Partnership (DTP) PhD Scholarship awarded to W.J., as well as financial support under the auspices of an ongoing grant (RAPID—ReAltime Process ModellIng and Diagnostics—Powering Digital Factories, Grant No. EP/V028618/1). D.I.G. also gratefully acknowledges a recent Royal Society Short Industrial Fellowship (2020-22). Publisher Copyright: {\textcopyright} 2023 by the authors.",

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month = nov,

doi = "10.3390/pr11113065",

language = "English",

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N2 - Successful cultivation of mammalian cells must consider careful formulation of culture media consisting of a variety of substrates and amino acids. A widely cited method for quantifying metabolic networks of mammalian cultures is dynamic flux balance modelling. Application of in-silico techniques allows researchers to circumvent time-consuming and costly in-vivo experimentation. Dynamic simulation and optimisation of reliable models allows for the visualization of opportunities to improve throughputs of target protein products, such as monoclonal antibodies (mAbs). This study presents a sensitivity analysis comparing dynamic optimisation results for industrial-scale fed-batch bioreactors, considering a variety of initial conditions. Optimized feeding trajectories are computed via Nonlinear Programming (NLP) model, employing the established IPOPT solver. Glucose, then glutamine, then asparagine, can lead to improved mAb yields and viable cell counts.

AB - Successful cultivation of mammalian cells must consider careful formulation of culture media consisting of a variety of substrates and amino acids. A widely cited method for quantifying metabolic networks of mammalian cultures is dynamic flux balance modelling. Application of in-silico techniques allows researchers to circumvent time-consuming and costly in-vivo experimentation. Dynamic simulation and optimisation of reliable models allows for the visualization of opportunities to improve throughputs of target protein products, such as monoclonal antibodies (mAbs). This study presents a sensitivity analysis comparing dynamic optimisation results for industrial-scale fed-batch bioreactors, considering a variety of initial conditions. Optimized feeding trajectories are computed via Nonlinear Programming (NLP) model, employing the established IPOPT solver. Glucose, then glutamine, then asparagine, can lead to improved mAb yields and viable cell counts.

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KW - monoclonal antibody/mAb

KW - sensitivity analysis

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Dynamic Optimisation of Fed-Batch Bioreactors for mAbs: Sensitivity Analysis of Feed Nutrient Manipulation Profiles

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Keywords / Materials (for Non-textual outputs)

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Projects

RAPID: ReAl-time Process ModellIng and Diagnostics: Powering Digital Factories

A Digital Twin via First-Principles Modelling and Data Analytics for Process Optimisation in Pharmaceutical Manufacturing

Cite this