Application of Model-based Online Monitoring and Robust Optimizing Control to Fed-Batch Bioprocesses

Hille, Rubin; Brandt, Heiko; Colditz, Vera; Claßen, Jens; Hebing, Lukas; Langer, Matthäus; Kreye, Steffen; Neymann, Tobias Claus; Krämer, Stefan; Tränkle, Jens; Brod, Helmut; Jockwer, Alexander

doi:10.1016/j.ifacol.2020.12.1204

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…For example, biocapacitance measurements used to estimate biomass on-line and infrequent sampling of ammonia and lactate can be coupled along with process dynamic equations to continuously estimate glucose and glutamine concentrations [141]. A similar study was realized with Raman measurements that were combined with dynamic metabolic models through adaptive, constrained, extended Kalman filters for the purpose of metabolite concentration tracking, which could then be applied in setpoint tracking of glucose [142].…”

Section: Hybrid Modelsmentioning

confidence: 99%

Modern Sensor Tools and Techniques for Monitoring, Controlling, and Improving Cell Culture Processes

et al. 2022

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The growing biopharmaceutical industry has reached a level of maturity that allows for the monitoring of numerous key variables for both process characterization and outcome predictions. Sensors were historically used in order to maintain an optimal environment within the reactor to optimize process performance. However, technological innovation has pushed towards on-line in situ continuous monitoring of quality attributes that could previously only be estimated off-line. These new sensing technologies when coupled with software models have shown promise for unique fingerprinting, smart process control, outcome improvement, and prediction. All this can be done without requiring invasive sampling or intervention on the system. In this paper, the state-of-the-art sensing technologies and their applications in the context of cell culture monitoring are reviewed with emphasis on the coming push towards industry 4.0 and smart manufacturing within the biopharmaceutical sector. Additionally, perspectives as to how this can be leveraged to improve both understanding and outcomes of cell culture processes are discussed.

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Section: Hybrid Modelsmentioning

confidence: 99%

Modern Sensor Tools and Techniques for Monitoring, Controlling, and Improving Cell Culture Processes

et al. 2022

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“…Model predictive control (MPC) has been widely attempted for bioprocess optimization [57,58]. Its primary requirement is a predictive process model, through which the dynamic and static interactions among the input, output, and disturbance variables can be apprehended and the control estimate can be synchronized with the optimum set points calculations [59,60]. Successful implementation of MPC to track the variable trajectory [49,61] and to maximize process variables has been reported in biomanufacturing [62,63].…”

Section: Model Predictive Controlmentioning

confidence: 99%

Bioprocess Control: Current Progress and Future Perspectives

Rathore

Mishra

Nikita

et al. 2021

Life

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Typical bioprocess comprises of different unit operations wherein a near optimal environment is required for cells to grow, divide, and synthesize the desired product. However, bioprocess control caters to unique challenges that arise due to non-linearity, variability, and complexity of biotech processes. This article presents a review of modern control strategies employed in bioprocessing. Conventional control strategies (open loop, closed loop) along with modern control schemes such as fuzzy logic, model predictive control, adaptive control and neural network-based control are illustrated, and their effectiveness is highlighted. Furthermore, it is elucidated that bioprocess control is more than just automation, and includes aspects such as system architecture, software applications, hardware, and interfaces, all of which are optimized and compiled as per demand. This needs to be accomplished while keeping process requirement, production cost, market value of product, regulatory constraints, and data acquisition requirements in our purview. This article aims to offer an overview of the current best practices in bioprocess control, monitoring, and automation.

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“…For example, kinetic models can be used as a filter to remove measurement errors and systematic noise, and add missing data points, so that high-quality data of the bioprocess are generated (Zhang et al 2019 ). Additionally, kinetic models have been applied to aid online monitoring and control of bioprocesses (Hille et al 2020 ; Krämer and King 2016 ). This application is encouraged by the regulatory agency through Process Analytical Technology (PAT) initiatives, promoting quantitative tools for real-time quality assurance (Narayanan et al 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Construction of a novel kinetic model for the production process of a CVA6 VLP vaccine in CHO cells

Xing,

Nguyen,

Kanai-Bai

et al. 2023

Cytotechnology

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Bioprocess development benefits from kinetic models in many aspects, including scale-up, optimization, and process understanding. However, current models are unable to simulate the production process of a coxsackievirus A6 (CVA6) virus-like particle (VLP) vaccine using Chinese hamster ovary cell culture. In this study, a novel kinetic model was constructed, correlating (1) cell growth, death, and lysis kinetics, (2) metabolism of major metabolites, and (3) CVA6 VLP production. To construct the model, two batches of a laboratory-scale 2 L bioreactor cell culture were prepared and various pH shift strategies were applied to examine the effect of pH shift. The proposed model described the experimental data under various conditions with high accuracy and quantified the effect of pH shift. Next, cell culture performance with various pH shift timings was predicted by the calibrated model. A trade-off relationship was found between product yield and quality. Consequently, multiple objective optimization was performed by integrating desirability methodology with model simulation. Finally, the optimal operating conditions that balanced product yield and quality were predicted. In general, the proposed model improved the process understanding and enabled in silico process development of a CVA6 VLP vaccine.

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Application of Model-based Online Monitoring and Robust Optimizing Control to Fed-Batch Bioprocesses

Cited by 4 publications

References 6 publications

Modern Sensor Tools and Techniques for Monitoring, Controlling, and Improving Cell Culture Processes

Modern Sensor Tools and Techniques for Monitoring, Controlling, and Improving Cell Culture Processes

Bioprocess Control: Current Progress and Future Perspectives

Construction of a novel kinetic model for the production process of a CVA6 VLP vaccine in CHO cells

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