INTRODUCTION

Chinese hamster ovary (CHO) cells are one of the main host systems used by the biopharmaceutical industry to manufacture biologics such as monoclonal antibodies (mAbs). While advances in process development, cell line engineering and clonal selection have largely improved recombinant mAb yields, ensuring consistent product quality still remains a major challenge (Hong et al., 2018). A key product quality attribute is N-linked glycosylation, which has been reported to significantly impact the biological activity of mAbs (Jennewein and Alter, 2017). For example, the absence of fucose in the terminal N-glycan of the fragment crystallizable (Fc) region of mAbs is reported to increase antibody-dependent cell-mediated cytotoxicity up to 50-fold (Shields et al., 2002), while N-glycan structures with high mannose content lead to decreased in vivo half-life (Goetze et al., 2011). In addition to N-glycosylation, protein aggregation is another key determinant of mAb quality (Paul et al., 2018). Particularly, high molecular weight (HMW) aggregates have been shown to enhance the risk of undesirable immunogenicity (Vázquez-Rey and Lang, 2011). Variations in protein charge can also adversely affect the pharmacokinetics of the mAb (Xie et al., 2016), especially decreases in net positive charge (leading to formation of acidic variants) can decrease tissue retention and increase whole body clearance of antibody (Boswell et al., 2010). Therefore, to ensure cells are in desired metabolic state for consistent quality profiles of mAbs, cell culture processes need to be well controlled by maintaining process parameters within an acceptable range.
Several cell culture process parameters including media components, feeding strategy, temperature, pH and osmolality are known to influence the quality profiles of mAbs. Among these, pH is a critical parameter which has been shown to greatly affect N-glycosylation profiles (Aghamohseni et al., 2014; Ivarsson et al., 2014; Jiang et al., 2018), aggregation levels (Paul et al., 2018) and charge variant species (Xie et al., 2016) of the mAb product. Current understanding of how process conditions affect quality profiles has stemmed from empirical understanding and experience within each organization without detailed scientific discussion in literature. With the greater emphasis placed by regulatory agencies on adopting the quality by design (QbD) approach in process understanding and product quality control, a more detailed characterization of cellular mechanisms arising from changes to process conditions, in part, facilitates the development of rational control strategies (Sommeregger et al., 2017). In this regard, a knowledge-driven approach based on the combined use of systems modeling and high-throughput “-omics” data profiling offers immense potential to link process conditions and/or clonal traits with product quality in a systematic manner (Lakshmanan et al., 2019; Yusufi et al., 2017). Only a handful of studies have adopted high-throughput “-omics” methodologies to understand the biological mechanisms in cell culture conditions that affect productivity and/or quality profiles.
Despite the enormous potential, significant resource demand has limited the ability of organizations to take on these omics approaches. Most efforts have utilized only a single omics or at most two different omics approaches for their investigations since substantial resources and knowledge are required to extend it further for multi-omics analyses. Such examples include the application of transcriptomics and proteomics profiling to identify the pathways that are associated with improved recombinant protein titers in low-temperature CHO cultures grown under batch or perfusion modes (Baik et al., 2006; Kaufmann et al., 1999; Tossolini et al., 2018). Similarly, a combination of metabolomics and proteomics detected higher reactive oxygen species (ROS) and glutathione peroxidase expression levels during the CHO inoculum scale-up step, indicating hypoxia as the cause of lower productivity (Gao et al., 2016). More recently, proteomics was used to assess the effect of pCO2, media hold duration, and media manganese content on mAb N-glycosylation structures in CHO fed-batch cultures (Nguyen Dang et al., 2019). However, to the best of our knowledge, no in-depth investigation has combined more than two “-omics” datasets to discern the complex underlying mechanisms between changes in process conditions and their impact on the product titer and/or quality profiles.
In this study, we obtained comprehensive time-based transcriptomics, proteomics and metabolomics data from CHO fed-batch cell cultures as a result of varying pH set points to understand the cellular effect of pH controls on the biology of CHO cells. Relevant statistical analyses of multi-omics datasets were carried out to investigate the variations in both mAb titer and quality, including N-glycosylation, protein aggregation and charge variants, as a consequence of pH variation. Our study pinpointed the influences of pH and cell culture time on mAb quality attributes through varying responses of several pathways including N-glycosylation, intracellular vesicular trafficking, and response to ER and oxidative stress. Specifically, we noted culture pH set points impacted organellar homeostasis, N-glycosylation, and vesicular trafficking efficiency, thus resulting in differences in cell productivity and product quality.