(466d) Multi-Omics Approaches to Unravel Cellular Metabolism Towards Enhancing Process Robustness

Progress in process development, from cell line screening to online monitoring technologies have enabled the generation of highly productive processes, to meet the growing clinical demand for biological therapeutics. This has shifted the focus of development activities to guarantee process robustness and consistency in product quality attributes, as these processes are still susceptible to variability in raw materials or operational errors such as overfeeding. Advances in various omic’s methodologies enable a comprehensive systems biology approach into understanding cellular behavior in response to such process deviations and enable formulation of mitigation strategies.

In this work, a multi-omics approach was applied to a chemically defined CHO fed-batch process resulting in high peak cell densities and titers up to 10 g/L. Upon dosing a single supplement to this process, the cellular metabolism changed resulting in even higher peak cell densities, decreased ammonia generation and further increased productivity. However, at different doses corresponding product quality attributes such as glycosylation and protein aggregation were affected. A multi-omics approach encompassing transcriptomics, proteomics and metabolomics methods was used to investigate the underlying cause for differences in cellular phenotype from a global perspective. A total of 16000 transcripts, 7000 proteins, and 700 metabolites were evaluated for each condition. Differences in glycosylation were driven by a multitude of changes in metabolic pathways as confirmed by the complementary omics data. Among the key pathways affected were those involved in central carbon metabolism, amino acid metabolism and cellular redox homeostasis. Protein aggregation on the other hand was driven by an extracellular mechanism involving the supplement discussed above and was confirmed through independent cell free studies. This comprehensive analysis resulted in further media optimization and in establishing potential cell line engineering targets to improve process efficiency and robustness.