A novel method based on nonparametric regression with a Gaussian kernel algorithm identifies the critical components in CHO media and feed optimization

Zou, Mao; Zhou, Ziwei; Fan, Li; Zhang, Weijian; Zhao, Liang; Liu, Xuping; Wang, Haibin; Tan, Wen‐Song

doi:10.1007/s10295-019-02248-5

Cited by 9 publications

(7 citation statements)

References 22 publications

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“…Optimizing the critical feed composition and the concentrations of components are crucial to process development (Zou et al 2020 ). In fed-batch industry, Raman spectroscopy with partial least squares regression (PLSR) or multiple linear regression (MLR) are applied to monitor and control the concentrations of glucose and amino acids (Kozma et al 2018 ).…”

Section: Feed Composition In Fed-batch Culturementioning

confidence: 99%

Progress in fed-batch culture for recombinant protein production in CHO cells

Lin

et al. 2023

Appl Microbiol Biotechnol

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Nearly 80% of the approved human therapeutic antibodies are produced by Chinese Hamster Ovary (CHO) cells. To achieve better cell growth and high-yield recombinant protein, fed-batch culture is typically used for recombinant protein production in CHO cells. According to the demand of nutrients consumption, feed medium containing multiple components in cell culture can affect the characteristics of cell growth and improve the yield and quality of recombinant protein. Fed-batch optimization should have a connection with comprehensive factors such as culture environmental parameters, feed composition, and feeding strategy. At present, process intensification (PI) is explored to maintain production flexible and meet forthcoming demands of biotherapeutics process. Here, CHO cell culture, feed composition in fed-batch culture, fed-batch culture environmental parameters, feeding strategies, metabolic byproducts in fed-batch culture, chemostat cultivation, and the intensified fed-batch are reviewed. Key points • Fed-batch culture in CHO cells is reviewed. • Fed-batch has become a common technology for recombinant protein production. • Fed batch culture promotes recombinant protein production in CHO cells.

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Section: Feed Composition In Fed-batch Culturementioning

confidence: 99%

Progress in fed-batch culture for recombinant protein production in CHO cells

Lin

et al. 2023

Appl Microbiol Biotechnol

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“…An example of a FS method known as nonparametric regression with Gaussian kernel (NRGK) was previously used to improve product titer. 65 In this work, the authors also proved that the NRGK selection (an ML approach) of the CPPs was superior to PLSR.…”

Section: The Art Of Developing a Successful ML Model To Advance Qbdmentioning

confidence: 52%

Harnessing the potential of machine learning for advancing “Quality by Design” in biomanufacturing

Walsh

Myint

Nguyen-Khuong

et al. 2022

mAbs

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Ensuring consistent high yields and product quality are key challenges in biomanufacturing. Even minor deviations in critical process parameters (CPPs) such as media and feed compositions can significantly affect product critical quality attributes (CQAs). To identify CPPs and their interdependencies with product yield and CQAs, design of experiments, and multivariate statistical approaches are typically used in industry. Although these models can predict the effect of CPPs on product yield, there is room to improve CQA prediction performance by capturing the complex relationships in high-dimensional data. In this regard, machine learning (ML) approaches offer immense potential in handling non-linear datasets and thus are able to identify new CPPs that could effectively predict the CQAs. ML techniques can also be synergized with mechanistic models as a ‘hybrid ML’ or ‘white box ML’ to identify how CPPs affect the product yield and quality mechanistically, thus enabling rational design and control of the bioprocess. In this review, we describe the role of statistical modeling in Quality by Design (QbD) for biomanufacturing, and provide a generic outline on how relevant ML can be used to meaningfully analyze bioprocessing datasets. We then offer our perspectives on how relevant use of ML can accelerate the implementation of systematic QbD within the biopharma 4.0 paradigm.

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“…The use of robots can further promote sequential optimizations through DOE planning, subsequent cell culture, cell characterization by omics analysis and/or cell sorting, and validation of candidate optimal protocols without human intervention. Those acquired data should be particularly suitable for further in silico modeling of cell culture processes [ 128 , 129 , 130 ].…”

Section: Discussionmentioning

confidence: 99%

Clever Experimental Designs: Shortcuts for Better iPSC Differentiation

Yasui

Sekine

Taniguchi

2021

Cells

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For practical use of pluripotent stem cells (PSCs) for disease modelling, drug screening, and regenerative medicine, the cell differentiation process needs to be properly refined to generate end products with consistent and high quality. To construct and optimize a robust cell-induction process, a myriad of cell culture conditions should be considered. In contrast to inefficient brute-force screening, statistical design of experiments (DOE) approaches, such as factorial design, orthogonal array design, response surface methodology (RSM), definitive screening design (DSD), and mixture design, enable efficient and strategic screening of conditions in smaller experimental runs through multifactorial screening and/or quantitative modeling. Although DOE has become routinely utilized in the bioengineering and pharmaceutical fields, the imminent need of more detailed cell-lineage specification, complex organoid construction, and a stable supply of qualified cell-derived material requires expedition of DOE utilization in stem cell bioprocessing. This review summarizes DOE-based cell culture optimizations of PSCs, mesenchymal stem cells (MSCs), hematopoietic stem cells (HSCs), and Chinese hamster ovary (CHO) cells, which guide effective research and development of PSC-derived materials for academic and industrial applications.

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A novel method based on nonparametric regression with a Gaussian kernel algorithm identifies the critical components in CHO media and feed optimization

Cited by 9 publications

References 22 publications

Progress in fed-batch culture for recombinant protein production in CHO cells

Progress in fed-batch culture for recombinant protein production in CHO cells

Harnessing the potential of machine learning for advancing “Quality by Design” in biomanufacturing

Clever Experimental Designs: Shortcuts for Better iPSC Differentiation

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