(531g) Using Metabolic Modeling and Metabolomics to Improve Cell Culture-Based Bioprocesses | AIChE

(531g) Using Metabolic Modeling and Metabolomics to Improve Cell Culture-Based Bioprocesses


Lee, K. - Presenter, Tufts University
The number of new recombinant protein therapeutics and other biologics have quadrupled since 2008. Biologics now account for more than a quarter of all new drug products approved by the FDA. Most biologics are manufactured using eukaryotic cell cultures to enable post-translational modifications, with Chinese hamster ovary (CHO) cells as the dominant production platform. Many years of advances have achieved increased volumetric productivity through very high density fed-batch cultures. However, these high density cultures have also presented new challenges in maintaining cellular viability and productivity over the duration of the culture. A large body of published studies suggests that addressing metabolic inefficiencies could further improve CHO cell-based production of biologics.

This presentation describes recent efforts in our laboratory to better understand key metabolic events that occur over the course of industrially relevant CHO cell fed-batch cultures, and to leverage this understanding to devise strategies for process improvement. Our laboratory has pursued both modeling and metabolomics as complementary approaches to gain insights into metabolic inefficiencies present in high density CHO cell cultures.

The first part of this presentation will discuss a dynamic model of CHO cell metabolism that accurately predicts the effects of both process- and cell-level modifications. Using this model, we explored ~104 combinations of process and cell modifications, and found that knockdowns in glycolysis can raise cell viability across a range of process situations. However, depending on the process conditions, such knockdowns could reduce, rather than increase, the titer. Our results highlight the benefits of considering process and cell modifications together.

The second part of this presentation will discuss applications of targeted and untargeted metabolomics, respectively, to quantify key fluxes and identify potential growth inhibitory byproducts of CHO cell metabolism. Targeted LC-MS experiments of isotopically labeled central carbon intermediates identified a bottleneck in the oxidation of glucose and amino acids that is linked with cytosolic fatty acid synthesis, which could potentially explain the high lactate phenotype often observed in high density CHO cell cultures. Untargeted LC-MS experiments were utilized to compare the global profile of metabolic byproducts of genetically similar cells lines grown under identical conditions yet exhibiting significantly different growth rates. A major technical challenge in untargeted metabolomics is metabolite identification. A typical LC-MS experiment generates 103~104 unique data features. Despite progress, only a small fraction of these features can be reliably annotated with metabolite identities. To address this challenge, we assembled a novel computational workflow that utilizes the biological context of a sample to annotate and interpret the metabolomics data. The accuracy of our workflow is illustrated through comparisons with other currently available metabolite identification tools. Applied to LC-MS data collected on the CHO cell lines with different growth rates, our workflow identified several previously uncharacterized metabolic byproducts that are significantly elevated in low growth cell lines. Subsequent dose response studies confirmed that at least one of these byproducts potently inhibits CHO cell growth.