Nutrient Supplementation Strategies for Biopharmaceutical Production, Part 3

Richard Fike

January 1, 2010

14 Min Read

Scale-up studies are needed for assessing cell culture production system options and for testing nutrient supplementation techniques as well. With the many supplementation options available, choices need to be made as early in product development as possible because advantages can change with scale. One published fed-batch scale-up study testing from 3 L up to 2,500 L highlights items to be considered in addition to the nutrient supplementation process such as the impact of pH and CO2 control (1). That thorough study showcases epratuzumab biomanufacturing, which passed two global phase 2 clinical trials and met all specifications for process robustness, product consistency, and reproducibility.

As cultures increase in cell concentration and volume during scale-up into bioreactors, not only are nutrients depleted, but waste products can accumulate to sufficient levels to limit product formation. As a metabolic end product of glucose metabolism, lactate is usually considered to be detrimental to cultures, but after it is synthesized to quite high levels, a number of cell lines will begin to use it through the more efficient citric acid cycle (also known as the tricarboxylic acid cycle or TCA cycle). That happens especially under low-glucose feeding strategies, but only as long as sufficient oxygen is present.


So it’s important to define all physical and chemical parameters early into culture scale-up. Even the mode of oxygen delivery needs to be analyzed. Sparging with air or oxygen to maintain appropriate O2 levels can become problematic because of excessive cell sheer, which may lower growth rates or decrease viability. Very tiny bubbles can be especially detrimental to cells. And sparging can also cause significant foaming at the surface of the bioreactor, which may also decrease the number of viable cells, although various detergent-like solutions are available to reduce the effect. Oxygenation limitations can become significant in very high-density cultures (as in hollow-fiber bioreactors) even where bubble sheer is not an issue.

Scale-Up Modeling

The number of bioreactor runs that can realistically be performed on a production system is limited. Modeling has proven to be successful in defining appropriate nutrient supplementation schemes, especially when it’s validated through a limited number of scale-up cultures. Most examples given here represent relatively complicated models used with smaller-volume processes, so they may offer relevant options to be considered.

Selvarasu used available metabolic pathway maps for testing with hybridoma cultures producing monoclonal antibodies (MAbs) in fed-batch mode (2). Bioreactor sampling for major media components such as glucose, glutamine, and other amino acids (as well as ammonia and lactate waste products and MAbs) were compared with values predicted from a model. Results showed the model to be fairly accurate and useful for development of a nutrient supplementation protocol, especially when it was used in conjunction with smaller-scale bioreactor runs.

Henry et al. used logistic fit of simpler noninstrumented batch and semicontinuous cultures to compare cell growth kinetics, nutrient consumption, metabolite, and product formation data with those of more complex continuous and perfusion models (3). They found good correlation. Goudar et al. also found logistic modeling to be superior to more commonly used standard polynomial fitting or unstructured kinetic modeling in describing batch and fed-batch data (4).

In a different approach, Dhir et al. used “dynamic optimization” to arrive at a superior supplementation protocol (5). They developed a control algorithm that allowed on-line readjustment of supplementation depending on the differences in real-time parameters observed in a culture to those predicted by the model. Using their model to optimize hybridoma growth in a fed-batch bioreactor, the team obtained a 44% increase in cell density, resulting in a 31% productivity increase over a fixed, off-line optimization protocol. The model should be usable for other cell systems too.

Another modeling system — a macrokinetic model based on stoichiometric balance — was developed by Zhou et al. using a 653 myeloma (6). It describes dynamic balances among lactate, alanine, reduced nicotinamide adenine dinucleotide (NADH), and adenosine-5′-triphosphate (ATP) during metabolism of glucose, glutamine, and other amino acids. The model provides data to assist in estimation of specific substrate consumption rates and specific growth rates as well as oxygen uptake and acetyl–coenzyme-A (a-CoA) formation rates, which the team validated using batch and fed-batch culture experiments.

Scale-Down Systems

Two newer methods have become available recently and significantly improve developers’ ability to rapidly develop clone-specific nutrient supplements and test them in different feeding strategies. Using these new technologies can significantly reduce product development timelines while ensuring maximum cell productivity. The DASGIP system seen in Photo 1 ( supports up to 16 bench-scale (≤0.5-L) parallel bioreactors for testing of multiple parameters in the same run to reduce cell and process variability (7).

Photo 1:

Another recent advancement is the SimCell system from Seahorse Bioscience Inc. ( seen in Photo 2. It uses microbioreactor array (MBA) cassettes, each having six chambers to simulate six different cell cultures (8). Each chamber holds 700 µL of medium and has proven to be scalable to bench-top and manufacturing-scale bioreactors (Figure 1). The SimCell system contains four incubators, each holding 42 MBAs for a potential of 1,008 simultaneous cultures. In practice, cycle-time limitations somewhat reduce culture numbers, but results of a 420-condition run have been published (9). Combinations of different cell clones, basal media, nutrient supplements, temperature, pH, oxygen, and feeding strategies can be rapidly assessed in synchrony.

Photo 2:

Each modeling system has its advantages and disadvantages, but modeling approaches will assume a more important place in nutrient supplementation development as more data are compiled and as production processes come under greater regulatory scrutiny. Productivity efficiencies need to be increased in the interest of profitability as well.

Impact on Product Quality

Glycosylation: There are many indicators of protein quality. Consistency, reliability, robustness, and potency of biological products need to be confirmed and monitored closely during scale-up. Glycosylation of proteins is critical because it relates to in vivo performance through half-life and potency. It has been found to be particularly variable depending on a number of culture parameters.

One reason for using mammalian cell lines such as Chinese hamster ovary (CHO) cells for protein production is that they can add carbohydrate in the form of glycans to synthesized protein molecules (10). Types and locations of glycosylation patterns vary widely. In addition, significant variation occurs depending on a host of different conditions: e.g., culture age (10,11), nutritional status (12), oxygen stress (13), temperature reduction (11), and waste products such as ammonia (14). Emphasis needs to be placed on glycosylation early in scale- up because in vivo half-life and functionality are involved.

Wong et al. showed that glycosylation changes were related to depletion of components such as glucose and glutamine in CHO batch cultures producing IFN-γ(12). Although an on-line fed-batch supplementation protocol led to a 10-fold productivity improvement, when glutamine levels were too low sialylation decreased and the presence of minor glycan species and high-mannose types increased. The team also noted that at some point viability was affected, which further altered glycosylation. Levels with detrimental effects on glycosylation were 11).

Yang et al. noticed significant glycoform heterogeneity depending on concentrations of ammonia and confirmed the importance of continual product quality monitoring on scale-up (15). Because of ammonia build-up concerns, Nilsang et al. compared a stabilized form of glutamine from Invitrogen with glutamine for M2139 hybridoma cells. Use of the GlutaMAX-I product doubled the MAb concentration (15). Serrato et al. found that although heterogeneous dissolved oxygen tension (DOT) in a bioreactor did not affect productivity, it did affect glycosylation (13). That suggests that using growth or cell status as indicators of glycosylation integrity may not be a valid choice. The issue would be especially relevant in scale-up, when large tanks and potential lower mixing can cause oscillations of DOT, pointing to importance of early testing for product consistency.

Using NS0 cells secreting humanized IgG1, Hills et al. assessed the potential for assisting galactosylation and sialylation by adding 10-mM glucosamine or galactose into the culture medium (16). Intracellular increases in molecular intermediaries were noted, but their impact on secreted molecules was minimal. In another study, poor sialylation of interferon was seen in CHO batch and fed-batch cultures using Primatone RL tryptic meat digest (17). The authors thus questioned the use of an undefined product that could negatively affect product quality. Glycosylation differences have been observed by some authors depending on cell culture basal media, going from doubly glycosylated to mono- or nonglycosylated (18). Erythropoeitin (EPO) was found to be highly glycosylated but heterogeneous at the same time.

Glycosylation is critical to product performance. It is affected by essentially all variables that could exist within a culture. This suggests that using conditions to provide consistency if not culture homogeneity may be critical to the long-term success of any product. The impact of physical and chemical variables on glycosylation should be assessed as early as possible in scaling up.

Using Microarray Gene Analysis: Most nutrient supplementation guidelines come from biochemical and direct cell biology measurements such as specific nutrient depletion and cell expansion rates tested in different media prototypes with design of experiments (DoE) studies. Recent efforts in molecular biology show potential for elucidating how to increase productivity and maintain proper glycosylation patterns. Combining cell growth/productivity studies with others that look within cells may speed identification of beneficial cellular pathways.

Both transcriptional and proteomic data obtained under varying conditions should help cell culturists better define how to modify cell pathways for desired characteristics. In a HEK-293 system using a controlled, dynamic, low-glutamine feeding protocol, Lee et al. boosted cell yield fourfold and virus expression 10,000-fold (19). DNA transcriptional microarray analysis showed significant gene expression differences between batch and fed-batch modes. Using an 18,000-element human chip, the team profiled midexponential, late-exponential, and stationary stages of their cell culture system.

In another study, to identify the glycosylation gene expression impact of a toxic metabolite (ammonium), Chen et al. monitored 12 glycosylation-related genes in CHO using reverse-transcription polymerase chain reaction, RT-PCR (20). They attributed galactosylation and sialylation inhibition at high ammonium levels to decreased gene expression of galactosyltransferase, sialyltransferase, and CMP-sialic acid transporter (rather than sialidase). Ammonium effects on glycosylation genes associated with the endoplasmic reticulum and cytosol were less than on genes associated with the Golgi apparatus.

Another subject of active study is the impact of metabolic inducers such as butyrate on gene expression. Gatti et al. studied transciptional analysis of hybridoma and CHO cells undergoing butyrate induction (21). DNA- microarray–based transcriptome analysis revealed that both cell types responded similarly to butyrate. Effects the team noted were gene expression levels related to histone modification, chaperones, lipid metabolism, and protein processing.

Yee et al. also studied CHO cells undergoing butyrate induction (22). Genomic and proteomic analysis with DNA microarrays and two-dimensional gel electrophoresis showed effects on many genes involved in cell cycle and apoptosis. In addition, genes involved in protein processing, secretion, and oxidation reduction (redox) were upregulated, with glycoslylation processing machinery also affected. This is consistent with biological studies showing improved productivity after butyrate induction but potentially altered glycoslylation depending on cell type. And it points to potential benefits of combining biological, biochemical, and molecular approaches in nutrient supplementation investigations.

Other studies have discovered that low oxygen conditions led to increased transcription activity of specific hypoxia-related genes (23). DNA microarray and proteomic analyses are being used to study effects on gene expression of different chemical and physical conditions that may affect protein production. Other areas investigated include high osmolality (24), low temperature (25) and metabolic shift (26), with which significant numbers of impacts were observed.

As gene expression databases are compiled for specific cell types and conditions, much useful mining for improved culture supplementation protocols should result. Early gene profiling of cell clones in proper modeling systems may allow advanced warning for problems that would otherwise get discovered months later during manufacturing scale-up. As we study the molecular biology of each facet of cell-based protein production, an integrated approach may be developed for superior production levels with fully defined and consistent glycosylation patterns.

A Bright Future Ahead

As recombinant protein production assumes an ever increasing role in worldwide health care, maximizing productivity per bioreactor unit volume may parallel increased yearly efficiencies seen in other areas, such as computer memory. Both genetic manipulations and physical/ biochemical environments will mesh together to produce the optimal cellular production systems. Even at today’s early stage in cell optimization, great improvements have been accomplished using a number of protocols.

However, impacts on product quality must be continually assessed. Are product shelf life, in vivo clearance, or performance altered? HTS methods will yield increasingly accurate, in-depth cell biochemical understanding on a molecular level, which should lead to many new productivity-enhancing biochemicals and strategies (27). Continuing improvements in nutrient supplementation hold much promise for yielding significantly higher productivity levels than those seen today.


1.) Yang, J-D. 2007. Fed-Batch Bioreactor Process Scale-Up From 3-L to 2,500-L Scale for Monoclonal Antibody Production from Cell Culture. Biotechnol. Bioeng. 98:141-154.

2.) Selvarasu, S. 2009. Elucidation of Metabolism in Hybridoma Cells Grown in Fed-Batch Culture by Genome-Scale Modeling. Biotechnol. Bioeng. 102:1494-1504.

3.) Henry, O. 2008. Simpler Noninstrumented Batch and Semicontinuous Cultures Provide Mammalian Cell Kinetic Data Comparable to Continuous and Perfusion Cultures. Biotechnol. Prog. 24:921-931.

4.) Goudar, C. 2005. Logistic Equations Effectively Model Mammalian Cell Batch and Fed-Batch Kinetics by Logically Constraining the Fit. Biotechnol. Bioeng. 21:1109-1118.

5.) Dhir, S. 2000. Dynamic Optimization of Hybridoma Growth in a Fed-Batch Bioreactor. Biotechnol. Bioeng. 67:197-205.

6.) Zhou, F. 2007. A Macrokinetic Model for Myeloma Cell Culture Based on Stoichiometric Balance. Biotechnol. Appl. Biochem. 46:85-95.

7.) Bauwens, C. 2005. Development of a Perfusion Fed Bioreactor for Embryonic Stem Cell-Derived Cardiomyocyte Generation: Oxygen-Mediated Enhancement of Cardiomyocyte Output. Biotechnol. Bioeng. 90:452-461.

8.) Heath, C. 2007. Cell Culture Process Development: Advances in Process Engineering. Biotechnol. Prog. 23:46-51.

9.) Xiao, Z. 2009.. Rapid Fed-Batch Process Development in SimCell™.

10.) Butler, M. 2005. Animal Cell Cultures: Recent Achievements and Perspectives in the Production of Biopharmaceuticals. Appl. Microbiol. Biotechnol. 68:283-291.

11.) Andersen, D. 2000. Multiple Cell Culture Factors Can Affect the Glycosylation of Asn-184 in CHO-Produced Tissue-Type Plasminogen Activator. Biotechnol. Bioeng. 70:25-31.

12.) Wong, D. 2005. Impact of Dynamic Online Fed-Batch Strategies on Metabolism, Productivity and N-Glycosylation Quality in CHO Cell Cultures. Biotechnol. Bioeng. 89:164-177.

13.) Serrato, J. 2004. Heterogeneous Conditions in Dissolved Oxygen Affect N-Glycosylation But Not Productivity of a Monoclonal Antibody in Hybridoma Cultures. Biotechnol. Bioeng. 88:176-188.

14.) Nilsang, S. 2008. Three-Dimensional Culture for Monoclonal Antibody Production by Hybridoma Cells Immobilized in Macroporous Gel Particles. Biotechnol. Prog. 24:1122-1131.

15.) Yang, M, and M. Butler. 2002. Effects of Ammonia and Glucosamine on the Heterogeneity of Erythropoietin Glycoforms. Biotechnol. Prog. 18:129-138.

16.) Hills, A. 2001. Metabolic Control of Recombinant Monoclonal Antibody N-Glycosylation in GS-NSO Cells. Biotechnol. Bioeng. 75:239-251.

17.) Gu, X. 1997. Influence of Primatone RL Supplementation on Sialylation of Recombinant Human Interferon-γ Produced by Chinese Hamster Ovary Cell Culture Using Serum-Free Media. Biotechnol. Bioeng. 56:353-360.

18.) Kochanowski, N. 2008. Influence of Intracellular Nucleotide and Nucleotide Sugar Contents on Recombinant Interferon-γ Glycosylation During Batch and Fed-Batch Cultures of CHO Cells. Biotechnol. Bioeng. 100:721-733.

19.) Lee, Y. 2007. Transcriptional Profiling of Batch and Fed-Batch Protein-Free 293-HEK Cultures. Metab. Eng. 9:52-67.

20.) Chen, P. 2006. Effects of Elevated Ammonium on Glycosylation Gene Expression in CHO Cells. Metab. Eng. 8:123-132.

21.) Gatti, M. 2007. Comparative Transcriptional Analysis of Mouse Hybridoma and Recombinant Chinese Hamster Ovary Cells Undergoing Butyrate Treatment. J. Biosci. Bioeng. 103:82-91.

22.) Yee, J. 2008. Genomic and Proteomic Exploration of CHO and Hybridoma Cells Under Sodium Butyrate Treatment. Biotechnol. Bioeng. 99:1186-1204.

23.) Marx, F. 2004. How Cells Endure Low Oxygen. Science 303:1454-1456.

24.) Shen, D, and S. Sharfstein. 2006. Genome-Wide Analysis of the Transcriptional Response of Murine Hybridomas to Osmotic Shock. Biotechnol. Bioeng. 93:132-145.

25.) Baik, J.. 2006. Initial Transcriptome and Proteome Analyses of Low Culture Temperature-Induced Expression in CHO Cells Producing Erythropoietin. Biotechnol. Bioeng. 93:361-371.

26.) Korke, R. 2004. Large Scale Gene Expression Profiling of Metabolic Shift of Mammalian Cells in Culture. J. Biotechnol. 107:1-17.

27.) Carrier, T, and M. Gadgil. 2007.Chapter 4: Increasing Performance of Mammalian Expression PlatformsAdvances in Large–Scale Biopharmaceutical Manufacturing and Scale–Up Production, 2nd Edition, ASM Press, Washington.

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