Optimization of cultivation conditions for monoclonal IgM antibody production by M1A2 hybridoma using artificial neural network

Bashokouh, Fatemeh; Abbasiliasi, Sahar; Tan, Joo Shun

doi:10.1007/s10616-019-00330-5

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…FBS contains a variety of factors essential for cell growth, such as trace elements (vitamins and minerals), hormones, free radical scavengers and mitogenic growth factors 45 , which are absent in the basal media 46 . Generally, 10–20% FBS in the media is suitable for cell culture, as reported in enterocytes 47 , stem cells 48 , and hybridomas 49 . This reduced amount of FBS should be a preferable trend, considering the risk of contamination, the cost, and the batch-to-batch variability in cell production 50 , 51 .…”

Section: Resultsmentioning

confidence: 99%

Employing active learning in the optimization of culture medium for mammalian cells

2023

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Medium optimization is a crucial step during cell culture for biopharmaceutics and regenerative medicine; however, this step remains challenging, as both media and cells are highly complex systems. Here, we addressed this issue by employing active learning. Specifically, we introduced machine learning to cell culture experiments to optimize culture medium. The cell line HeLa-S3 and the gradient-boosting decision tree algorithm were used to find optimized media as pilot studies. To acquire the training data, cell culture was performed in a large variety of medium combinations. The cellular NAD(P)H abundance, represented as A450, was used to indicate the goodness of culture media. In active learning, regular and time-saving modes were developed using culture data at 168 h and 96 h, respectively. Both modes successfully fine-tuned 29 components to generate a medium for improved cell culture. Intriguingly, the two modes provided different predictions for the concentrations of vitamins and amino acids, and a significant decrease was commonly predicted for fetal bovine serum (FBS) compared to the commercial medium. In addition, active learning-assisted medium optimization significantly increased the cellular concentration of NAD(P)H, an active chemical with a constant abundance in living cells. Our study demonstrated the efficiency and practicality of active learning for medium optimization and provided valuable information for employing machine learning technology in cell biology experiments.

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Section: Resultsmentioning

confidence: 99%

Employing active learning in the optimization of culture medium for mammalian cells

2023

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“…FBS contained a variety of factors essential for cell growth, including trace elements (vitamins and minerals), hormones, free radical scavengers, mitogenic growth factors, and so on 37 , which were absent in the basal media 38 . Generally, 10∼20% of FBS in the media was considered suitable for cell culture, as reported in enterocytes 39 , stem cells 40 , and hybridoma 41 . Such a reduced amount of FBS was supposed to be a preferred trend, considering the risk of contamination, the cost, and the batch-to-batch variability in cell production 42, 43 .…”

Section: Resultsmentioning

confidence: 99%

Employing active learning in the optimization of culture medium for mammalian cells

Hashizume

Ozawa

Ying

2022

Preprint

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Medium optimization is a crucial step of cell culture for biopharmaceutics and regeneration medicine. It remains challenging, as both media and cells are highly complex systems. To address the issue, we tried active learning to fine-tune the culture medium by combining the high-throughput assay and machine learning. As a pilot study, the cell line HeLa-S3 and the gradient-boosting decision tree algorithm were used. The regular and time-saving approaches were developed, and both successfully fine-tuned 29 components to achieve improved cell culture than the original medium. The fine-tuned media showed a significant decrease in fetal bovine serum and the differentiation in vitamins and amino acids. Unexpectedly, the medium optimization raised the cellular NAD(P)H abundance but not the cell concentration owing to the conventional method used for cell culture assay. Our study demonstrated the efficiency of active learning for medium optimization and provided valuable hints for employing machine learning in cell culture.

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“…Studies on cell therapy process optimization using RSM are scarce, and seem to indicate that the majority only consider one parameter at a time (Kasemiire et al, 2021; Reddy et al, 2020). Those who did apply RSM report up to twofold improvement over standard culture conditions (Bashokouh et al, 2019; Jeon et al, 2010; Knöspel et al, 2010; Lee et al, 2022). In contrast to BO, however, RSM is purely exploitative , primarily focusing on the advancement of the model optimum without regard for model accuracy, which can harm the optimization efficiency in the long run.…”

Section: Discussionmentioning

confidence: 99%

Bayesian cell therapy process optimization

Claes,

Heck,

Coddens

et al. 2024

Biotech & Bioengineering

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Optimizing complex bioprocesses poses a significant challenge in several fields, particularly in cell therapy manufacturing. The development of customized, closed, and automated processes is crucial for their industrial translation and for addressing large patient populations at a sustainable price. Limited understanding of the underlying biological mechanisms, coupled with highly resource‐intensive experimentation, are two contributing factors that make the development of these next‐generation processes challenging. Bayesian optimization (BO) is an iterative experimental design methodology that addresses these challenges, but has not been extensively tested in situations that require parallel experimentation with significant experimental variability. In this study, we present an evaluation of noisy, parallel BO for increasing noise levels and parallel batch sizes on two in silico bioprocesses, and compare it to the industry state‐of‐the‐art. As an in vitro showcase, we apply the method to the optimization of a monocyte purification unit operation. The in silico results show that BO significantly outperforms the state‐of‐the‐art, requiring approximately 50% fewer experiments on average. This study highlights the potential of noisy, parallel BO as valuable tool for cell therapy process development and optimization.

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Optimization of cultivation conditions for monoclonal IgM antibody production by M1A2 hybridoma using artificial neural network

Cited by 10 publications

References 15 publications

Employing active learning in the optimization of culture medium for mammalian cells

Employing active learning in the optimization of culture medium for mammalian cells

Employing active learning in the optimization of culture medium for mammalian cells

Bayesian cell therapy process optimization

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