Chinese hamster ovary (CHO) cells were the protein production platform used in 89% of the biologics drugs approved between 2018 and 2022, and their use does not appear to be slowing. Manufacturers are turning to continuous cell culturing to meet demands, but that alone cannot create enough product to meet customer needs. Protein production using CHO cells, therefore, must become even more efficient.
“Optimizing not only viable cell density but also specific productivity is crucial to increasing volumetric productivity further,” according to scientists at Sweden’s Umeå University and Sartorius Stedim Biotech, writing in a recent paper.
To that end, a team led by Pär Jonsson, PhD, senior research scientist, Sartorius Corporate Research, developed a method that “overcomes the time dependence inherent in bioprocess data to highlight metabolites with a consistent relation to a process variable across most time points,” Jonsson tells GEN.
The scientists assessed the biological and environmental conditions behind variances in CHO cell clones. They found that the citrate cycle and the adjacent pyruvate, glyoxylate, and pantothenate pathways were particularly important for cell productivity and cell death, thus offering a means by which manufacturers may increase volumetric productivity. Importantly, the metabolites identified by this method “were significant, even after accounting for the time dependency inherent in bioprocess data,” Jonsson says.
In this study, Jonsson and colleagues analyzed the extracellular metabolome of 11 CHO clones during a 14-day culture period. For the analysis, they customized an existing hierarchical strategy developed by Masoumeh Alinaghi and colleagues at Sartorius Stedim. That approach identifies relationships across all batches and presents them within a principal component analysis (PCA) model as a single value for each point in time.
Jonsson’s approach went further, customizing Alinaghi’s strategy to address time dependency by using orthogonal partial least squares (OPLS®) regression (which links metabolites to process variables) to analyze metabolites at each time point separately.
The team replaced the PCA model with an OPLS-effect projection (EP) model to focus on similarities among metabolites during their stationary and death phases. This, they note, “reveal(ed) metabolites that correlated consistently with a response variable across most days, without the time dependence that global (all time points at once) OPLS modeling missed.” The scientists detected 109 metabolites, of which 28 were unique to the hierarchical method, compared to eight for the global method.
Therefore, they concluded, “The hierarchical approach more accurately reflected the metabolite-process variable relationship.”
Although the extracellular and intracellular metabolomes have limited relevance to each other, understanding both processes better “could provide a more holistic understanding of cellular metabolism during bioprocess production,” Jonsson and colleagues point out, thus improving bioprocesses so therapeutics become safer and more affordable.
The method is broadly applicable. As Jonsson explains, “Hierarchical modeling with OPLS-EP is agnostic to the protein expression platform. Neither is the method restricted to metabolomics—it could be applied to proteomics or transcriptomics as well. The only requirement is the data structure: a suite of biological molecules and a process variable of interest that have been measured in multiple batches at many time points.”
The post New Analytics ID Metabolite Variations Tied to CHO Cell Efficiency appeared first on GEN - Genetic Engineering and Biotechnology News.
“Optimizing not only viable cell density but also specific productivity is crucial to increasing volumetric productivity further,” according to scientists at Sweden’s Umeå University and Sartorius Stedim Biotech, writing in a recent paper.
To that end, a team led by Pär Jonsson, PhD, senior research scientist, Sartorius Corporate Research, developed a method that “overcomes the time dependence inherent in bioprocess data to highlight metabolites with a consistent relation to a process variable across most time points,” Jonsson tells GEN.
The scientists assessed the biological and environmental conditions behind variances in CHO cell clones. They found that the citrate cycle and the adjacent pyruvate, glyoxylate, and pantothenate pathways were particularly important for cell productivity and cell death, thus offering a means by which manufacturers may increase volumetric productivity. Importantly, the metabolites identified by this method “were significant, even after accounting for the time dependency inherent in bioprocess data,” Jonsson says.
In this study, Jonsson and colleagues analyzed the extracellular metabolome of 11 CHO clones during a 14-day culture period. For the analysis, they customized an existing hierarchical strategy developed by Masoumeh Alinaghi and colleagues at Sartorius Stedim. That approach identifies relationships across all batches and presents them within a principal component analysis (PCA) model as a single value for each point in time.
Jonsson’s approach went further, customizing Alinaghi’s strategy to address time dependency by using orthogonal partial least squares (OPLS®) regression (which links metabolites to process variables) to analyze metabolites at each time point separately.
Replaced the PCA model
The team replaced the PCA model with an OPLS-effect projection (EP) model to focus on similarities among metabolites during their stationary and death phases. This, they note, “reveal(ed) metabolites that correlated consistently with a response variable across most days, without the time dependence that global (all time points at once) OPLS modeling missed.” The scientists detected 109 metabolites, of which 28 were unique to the hierarchical method, compared to eight for the global method.
Therefore, they concluded, “The hierarchical approach more accurately reflected the metabolite-process variable relationship.”
Although the extracellular and intracellular metabolomes have limited relevance to each other, understanding both processes better “could provide a more holistic understanding of cellular metabolism during bioprocess production,” Jonsson and colleagues point out, thus improving bioprocesses so therapeutics become safer and more affordable.
The method is broadly applicable. As Jonsson explains, “Hierarchical modeling with OPLS-EP is agnostic to the protein expression platform. Neither is the method restricted to metabolomics—it could be applied to proteomics or transcriptomics as well. The only requirement is the data structure: a suite of biological molecules and a process variable of interest that have been measured in multiple batches at many time points.”
The post New Analytics ID Metabolite Variations Tied to CHO Cell Efficiency appeared first on GEN - Genetic Engineering and Biotechnology News.