Metabolomics process modeling: A systems biology approach to understand variability in commercial biologics cell culture processes

The biopharmaceutical industry strives to develop and operate efficient, robust, reproducible commercial biologics processes. A major challenge of industrial biologics processes is optimization of cell culture conditions to increase productivity while maintaining consistent product quality. The cell culture operations, which involve the use of live cell hosts, have historically introduced significant variability to the overall process. Technological improvements which include the implementation of advanced cell line engineering, chemically defined media, quality by design (QbD) development approaches, and in-line and at-line monitoring, have significantly reduced process variability. Nonetheless, performance variability remains a challenge for many commercial programs. This variability in turn can impact both product yields and product quality. Even small performance differences can become significant in low-yield processes with large campaign sizes, or processes manufactured at multiple sites. The ability to understand and eliminate sources of variability is greatly enhanced by augmenting the quality and quantity of data available from commercial campaigns. Metabolomics Process Monitoring (MPM) is a data-driven approach to understand sources of manufacturing variability on a cellular level. Here we present a case study of MPM implementation in a legacy commercial biologics program. First, we describe how the MPM workflow was successfully integrated into a commercial manufacturing process. Second, we discuss novel data normalization techniques developed to enable long term trending. Third, we describe the selection of an orthogonal projections to latent structures (OPLS) model to link systems biology and process data. Finally, we share key mechanistic insights obtained from the case study, and provide a vision for how MPM can enhance commercial biologics capabilities going forward

Understanding and controlling sialyation in a CHO fusion protein at lab and manufacturing scale using targeted omics techniques

Biologics, including antibodies, hormones and cytokines, represent an increasingly important class of therapeutics, with 7 of the 10 top selling drugs from 2013 in this class. The glycosylation distribution of these proteins is an important characteristic that can impact biological activity, circulatory half-life, and immunogenicity. One property that affects glycoproteins is the terminal addition of N-acetylneuraminic acid (sialic acid) to glycosylation chains. Despite the importance of glycosylation in many therapeutic proteins, limited information is available to date linking process parameters to changes in glycosylation distribution. The majority of the work that has been done is limited to a small number of proteins (such as interferon gamma) and small scale systems (shake flasks and bench top bioreactors). Although this work represents a useful starting point, glycosylation is a parameter that is known to be influenced by production scale. Here we examine a glycosylated CHO fusion protein for which sialyation level is known to impact protein quality. Variation in this parameter was observed across pilot and manufacturing scale batches. In order to better understand and control the biological source of the variation in the process, we employed metabolomic and transcriptomic methods, and successfully identified metabolic biomarkers, such as extracellular mannose, for sialylation level. Additional studies demonstrated that changes to sugar metabolism were contributing to a build-up of intermediates and inhibition of glycan sialyation, thereby identifying the biological source of variation in the process. As a result of these studies, we evaluated the impact of process modifications including feed composition and gassing to enable consistent control of sialyation profiles. This work represents a novel contribution to the field. We examine sialyation control of a CHO fusion protein at laboratory and manufacturing scale. Furthermore, we combine ‘Omics techniques with bioprocess and analytical data to achieve a more detailed understanding of cell expression and metabolism [1], and leverage this understanding to refine the process and control a quality attribute. Finally, this approach can be generalized beyond this specific process and applied to additional cell lines where undesired process variation is observed

Understanding and Controlling Sialylation in a CHO Fc-Fusion Process.

A Chinese hamster ovary (CHO) bioprocess, where the product is a sialylated Fc-fusion protein, was operated at pilot and manufacturing scale and significant variation of sialylation level was observed. In order to more tightly control glycosylation profiles, we sought to identify the cause of variability. Untargeted metabolomics and transcriptomics methods were applied to select samples from the large scale runs. Lower sialylation was correlated with elevated mannose levels, a shift in glucose metabolism, and increased oxidative stress response. Using a 5-L scale model operated with a reduced dissolved oxygen set point, we were able to reproduce the phenotypic profiles observed at manufacturing scale including lower sialylation, higher lactate and lower ammonia levels. Targeted transcriptomics and metabolomics confirmed that reduced oxygen levels resulted in increased mannose levels, a shift towards glycolysis, and increased oxidative stress response similar to the manufacturing scale. Finally, we propose a biological mechanism linking large scale operation and sialylation variation. Oxidative stress results from gas transfer limitations at large scale and the presence of oxygen dead-zones inducing upregulation of glycolysis and mannose biosynthesis, and downregulation of hexosamine biosynthesis and acetyl-CoA formation. The lower flux through the hexosamine pathway and reduced intracellular pools of acetyl-CoA led to reduced formation of N-acetylglucosamine and N-acetylneuraminic acid, both key building blocks of N-glycan structures. This study reports for the first time a link between oxidative stress and mammalian protein sialyation. In this study, process, analytical, metabolomic, and transcriptomic data at manufacturing, pilot, and laboratory scales were taken together to develop a systems level understanding of the process and identify oxygen limitation as the root cause of glycosylation variability

N-Glycan distribution for 5-L treatments.

<p>N-Glycan distribution for 5-L treatments.</p

N-Glycan Structures.

<p>The N-glycan assay method quantifies the percent distribution of N-glycan species. Five key structures were quantified in samples generated from 5-L bioreactors were operated under normal (50%) and low (15%) DO conditions. G0F, G1F and G2F are unsialylated, S1G1F is monosialylated, and S2G2F is disialylated.</p

Links between glucose metabolism and NANA.

<p>Untargeted metabolomics & glucose consumption calculations were performed on samples ranging from 50-L to 5000-L scale. A. Relative mannose levels, a metabolite not provided in the cell culture media, were compared across runs with normal (red) and low (blue) NANA levels. Error bars represent the mannose range associated with each time point. B. Maximum specific glucose consumption was calculated for each run and found to be significantly inversely correlated to day 10 NANA (p<0.001). C. Residuals of maximum specific glucose consumption were calculated for each run and found to be significantly inversely correlated to day 10 NANA (p<0.0001).</p

Proposed biological mechanism for manufacturing and low oxygen laboratory scale bioreactors.

<p>Reduced oxygen levels trigger an oxidative stress response and shift in glucose metabolism, resulting in upregulation of glycolysis and mannose synthesis, and downregulation of the hexosamine pathway and acetyl-CoA formation. This metabolic shift results in reduced GlcNAc levels, as well as levels of metabolites formed from GlcNAc including UDP-GlcNAc and CMP-NANA, triggering a reduction in terminal sialylation of N-glycans. Metabolite or gene expression levels directly measures as increased (green arrows) or decreased (red arrows) are shown. Abbreviations are as follows: CMP, cytidine monophosphate; CMP-SAT, CMP sialic acid transporter; CoA, coenzyme A; CTP, cytidine triphosphate; Frc-6-P, fructose-6-phosphate; Glc-6-P, glucose-6-phosphate; GlcN, glucosamine; GlcNAc, N-acetyl glucosamine; Gln, glutamine; Glu, glutamate; GTP, guanosine triphosphate; Man, mannose; Man-6-P, mannose-6-phosphate; ManNAc, N-acetyl mannosamine; MK, mannosekinase; NANA, N-acetylneuraminic acid; NH₃, ammonia; PDH, pyruvate dehydrogenase; PDK, pyruvate dehydrogenase kinase; PFK, phosphofructokinase; PMI, mannose phosphate isomerase; UDP, uridine diphosphate; UMP, uridine monophosphate; UTP, uridine triphosphate.</p

Reduced DO level impacts 5-L cell culture performance and sialylation.

<p>5-L bioreactors were operated under control (50% DO, blue circle) and low DO (20% grey triangle, 15% yellow square, 10% red diamond, and 10% shifted to 20% DO on day 5 green diamond) conditions and compared to 5000-L operation (50% DO, orange circle). Viability (A), lactate (B) and ammonia (C) profiles were established for up to 14 days of bioreactor operation. D. Day 10 titer, day 12 NANA and NANA slope values were normalized to the 5-L control (50% DO) condition. Replicate bioreactors were used for control (n = 6), 5000-L (n = 8) and 15% DO (n = 4) conditions. Statistical differences were determined using a student t-test, * indicates p<0.05 and ** indicates p<0.01.</p

Intracellular metabolites impacted by oxygen treatments.

<p>Intracellular metabolites impacted by oxygen treatments.</p