Model-based intensification of CHO cell cultures: One-step strategy from fed-batch to perfusion

Richelle, Anne; Corbett, Brandon; Agarwal, Piyush; Vernersson, Anton; Trygg, Johan; McCready, Chris

doi:10.3389/fbioe.2022.948905

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…This work is focused on the pre‐stages of bioprocesses, where the objective is solely on obtaining the highest cell density possible, while balancing the media consumption, whereas neither titer nor product quality are considered. Previously, modeling of process dynamics was used in the context of pre‐stages to support process intensification (Richelle et al, 2022; Stepper et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Modeling of bioprocess pre‐stages for optimization of perfusion profiles and increased process understanding

Pogodaev,

Hernández Rodríguez,

et al. 2023

Biotech & Bioengineering

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Improving bioprocess efficiency is important to reduce the current costs of biologics on the market, bring them faster to the market, and to improve the environmental footprint. The process intensification efforts were historically focused on the main stage, while intensification of pre‐stages has started to gain attention only in the past decade. Performing bioprocess pre‐stages in the perfusion mode is one of the most efficient options to achieve higher viable cell densities over traditional batch methods. While the perfusion‐mode operation allows to reach higher viable cell densities, it also consumes large amount of medium, making it cost‐intensive. The change of perfusion rate during a process (perfusion profile) determines how much medium is consumed, thereby running a process in optimal conditions is key to reduce medium consumption. However, the selection of the perfusion profile is often made empirically, without full understanding of bioprocess dynamics. This fact is hindering potential process improvements and means for cost reduction. In this study, we propose a process modeling approach to identify the optimal perfusion profile during bioprocess pre‐stages. The developed process model was used internally during process development. We could reduce perfused medium volume by 25%–45% (project‐dependent), while keeping the difference in the final cell within 5%–10% compared to the original settings. Additionally, the model helps to reduce the experimental workload by 30%–70% and to predict an optimal perfusion profile when process conditions need to be changed (e.g., higher seeding density, change of operating mode from batch to perfusion, etc.). This study demonstrates the potential of process modeling as a powerful tool for optimizing bioprocess pre‐stages and thereby guiding process development, improving overall bioprocess efficiency, and reducing operational costs, while strongly reducing the need for wet‐lab experiments.

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Section: Introductionmentioning

confidence: 99%

Modeling of bioprocess pre‐stages for optimization of perfusion profiles and increased process understanding

Pogodaev,

Hernández Rodríguez,

et al. 2023

Biotech & Bioengineering

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Noise aware parameter estimation in bioprocesses: Using neural network surrogate models with nonuniform data sampling

Weir,

Mathias,

Corbett

et al. 2024

AIChE Journal

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This article demonstrates a parameter estimation technique for bioprocesses that utilizes measurement noise in experimental data to determine credible intervals on parameter estimates, with this information of potential use in prediction, robust control, and optimization. To determine these estimates, the work implements Bayesian inference using nested sampling, presenting an approach to develop neural network‐ (NN) based surrogate models. To address challenges associated with nonuniform sampling of experimental measurements, an NN structure is proposed. The resultant surrogate model is utilized within a Nested Sampling Algorithm that samples possible parameter values from the parameter space and uses the NN to calculate model output for use in the likelihood function based on the joint probability distribution of the noise of output variables. This method is illustrated against simulated data, then with experimental data from a Sartorius fed‐batch bioprocess. Results demonstrate the feasibility of the proposed technique to enable rapid parameter estimation for bioprocesses.

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Hybrid modeling for in silico optimization of a dynamic perfusion cell culture process

Agarwal,

McCready,

et al. 2024

Biotechnology Progress

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The bio‐pharmaceutical industry heavily relies on mammalian cells for the production of bio‐therapeutic proteins. The complexity of implementing and high cost‐of‐goods of these processes are currently limiting more widespread patient access. This is driving efforts to enhance cell culture productivity and cost reduction. Upstream process intensification (PI), using perfusion approaches in the seed train and/or the main bioreactor, has shown substantial promise to enhance productivity. However, developing optimal process conditions for perfusion‐based processes remain challenging due to resource and time constraints. Model‐based optimization offers a solution by systematically screening process parameters like temperature, pH, and culture media to find the optimum conditions in silico. To our knowledge, this is the first experimentally validated model to explain the perfusion dynamics under different operating conditions and scales for process optimization. The hybrid model accurately describes Chinese hamster ovary (CHO) cell culture growth dynamics and a neural network model explains the production of mAb, allowing for optimization of media exchange rates. Results from six perfusion runs in Ambr® 250 demonstrated high accuracy, confirming the model's utility. Further, the implementation of dynamic media exchange rate schedule determined through model‐based optimization resulted in 50% increase in volumetric productivity. Additionally, two 5 L‐scale experiments validated the model's reliable extrapolation capabilities to large bioreactors. This approach could reduce the number of wet lab experiments needed for culture process optimization, offering a promising avenue for improving productivity, cost‐of‐goods in bio‐pharmaceutical manufacturing, in turn improving patient access to pivotal medicine.

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Model-based intensification of CHO cell cultures: One-step strategy from fed-batch to perfusion

Cited by 4 publications

References 36 publications

Modeling of bioprocess pre‐stages for optimization of perfusion profiles and increased process understanding

Modeling of bioprocess pre‐stages for optimization of perfusion profiles and increased process understanding

Noise aware parameter estimation in bioprocesses: Using neural network surrogate models with nonuniform data sampling

Hybrid modeling for in silico optimization of a dynamic perfusion cell culture process

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