Rapid Development and Optimization of Cell Culture Media

January 1, 2008
Terrell Johnson

,
Min Zhang

,
Avril Lawshé

,
Kerry Koskie

,
James S. Ross

,
Matthew V. Caple

BioPharm International, BioPharm International-01-01-2008, Volume 21, Issue 1

Chinese hamster ovary (CHO) cells are used extensively in the biopharmaceutical industry to produce recombinant proteins that require post-translation modification for full biological functionality. Optimization of culture conditions for recombinant CHO cell lines presents challenges in light of the diverse nutritional requirements observed with different clonally derived cell lines.

ABSTRACT

Chinese hamster ovary (CHO) cells are used extensively in the biopharmaceutical industry to produce recombinant proteins that require post-translation modification for full biological functionality. Optimization of culture conditions for recombinant CHO cell lines presents challenges in light of the diverse nutritional requirements observed with different clonally derived cell lines. To address this challenge we have taken advantage of new technologies (high throughput screening and state-of-the-art cell line engineering), advances in medium development (a library of diverse CHO media formulations containing both animal-component free and chemically defined formulations), and the application of sophisticated Design of Experiment (DOE) experimental design and analysis techniques to develop a new strategic approach to medium development. This article describes this new strategic process and documents its use to develop several optimized formulations for a humanized IgG-producing CHO cell line.

Cell culture medium development is a complex process, involving the adjustment of numerous interacting components to their optimal concentrations with a goal of strong consistent support of cell growth and protein productivity. When optimizing medium for a specific CHO clone, the traditional approach of testing one medium component at a time to determine its optimal level is frequently used.1 While keeping all other components constant at their original levels, this method uses a large series of titrations of each individual medium component. At the end of the first round of titration, the working concentration exhibiting the highest activity is set as the optimal concentration. A second round of titration is performed for the next component, while keeping the concentration of the first component at its previously identified optimum. After selecting the working optimum concentration of the second medium component, subsequent rounds of titration are performed for the other medium components. This strategy is labor intensive, costly, time consuming, and fails to recognize synergistic interactions of components. To reduce costs and decrease media development time, a new streamlined strategic approach has been created to accentuate media development by applied DOE experimental techniques for screening cells for growth in a variety of media formulations (CHO Media Library), media mixing screening, and factorial designs combined with spent medium analysis to improve efficiency.

SAFC Biosciences

While the use of DOE techniques can improve the efficiency of media screening and subsequent optimization starting with any appropriate collection of media formulations, one of the cornerstones to this new approach is the availability of the proprietary CHO Media Library. The CHO Media Library was constructed from more than 30 different animal-component free (ACF) or chemically defined (CD) CHO formulations that were screened for cell growth and protein productivity using multiple CHO cell lines (Table 1). The top 18 ACF formulations (including eight CD formulations) have been ranked and put into the Library. To increase the high throughput capacity, a scaled down culture system based on 50-mL culture vessels with 25-mL working volumes was used to perform media screening. In developing a new medium formulation for a specific CHO cell line, the cell line is first screened with the formulations contained in the collection of media formulations selected for screening (e.g., CHO Media Library). The top three to five formulations are selected for further analysis using DOE mixing designs to identify the best formulation mixtures supporting this cell line, followed by spent medium analysis and DOE-based optimization of individual components using factorial designs. This strategy of combining medium mixing with individual component optimization allows multiple criteria to be analyzed simultaneously to identify synergistic responses and accelerate the desired user outcomes. Additionally, cell culture conditions can be optimized with the use of feeding strategies and modification of culture parameters (e.g., temperature shift) to best support recombinant protein production. The overall approach of optimization of medium and cell culture conditions can significantly improve pharmaceutical protein production in an efficient manner. Meanwhile, continued analysis of new media formulations and information from ongoing state-of-the-art studies in genomics/proteomics/metabolomics allows enhancement of the contents of the CHO Media Library (Figure 1).

Figure 1.

METHODS AND MATERIALS

The following methods and measurements were used to perform the analyses.

Cell Lines and Media

Table 1 shows the CHO cell lines tested as part of the development of the CHO Media Library. The media used were all animal-component free media and some were chemically-defined media without hydrolysates. Stock cultures for the different cell lines were pre-adapted to the test formulations or directly seeded into the test formulations without adaptation. All formulations and the two control media were supplemented with 6 mM L-glutamine unless otherwise specified. The CHO cell lines were cultured in the 125-mL shake flasks or 50-mL TPP tissue culture tubes in a Multitron incubator shaker (ATR Biotech). The cells were counted with a Cedex cell counter (Flownamics, Inc.).

Table 1. CHO cell line models for CHO Media Library study

Alkaline Phosphatase Activity

Alkaline phosphatase activity was measured with an Alkaline Phosphatase Fluorescence Detection Kit (Sigma-Aldrich, AP-F) according to the manufacturer's protocol, but with some adjustments. Briefly, 20 uL of cell supernatant sample (diluted with deionized water) was added to each well of a 96-well plate. Then, 180 uL of the bulk buffer solution consisting of dilution buffer and fluorescent assay buffer at a 1:8 ratio was added to each well. Four uL of 2.5 mM substrate (4-methylumbelliferyl phosphate disodium salt) was added to each well and mixed. The enzymatic activity was measured in a fluorometer at 360 nm excitation and 440 emission using the kinetic reading mode.

Human hGH ELISA

hGH productivity was quantified with an hGH ELISA Detection Kit (Roche Product # 1585878) according to the manufacturer's protocol.

Human IgG HPLC Assay

IgG concentration was measured with a Protein G affinity chromatography as described.2

Metabolite Profiling

Metabolites were quantified with a BioProfile 400 Analyzer (Nova Biomedical) according to the manufacturer's instruction.

Temperature Shift

The effect of shifting culture temperature was examined by changing the culture temperature from 37 °C to 31 °C on cell culture day six, a point at which cell growth had decreased such that the viable cell density increased <50% from the previous day.

Statistical Analysis

As an efficient statistical methodology, DOE has been used in different areas, including drug discovery and cell culture media development.3–6 Screening and mixing experimental designs (Figure 2) were developed and the data were analyzed using Design-Expert Version 7.0.1 (Stat-Ease).

Figure 2.

RESULTS AND DISCUSSION ON CASE STUDY

CHO Media Library Screening

To illustrate the process of medium development using this approach, we report the results of the development of a new medium formulation for an IgG producing cell line (CL1). Initially, the CL1 cell line was screened without cell preadaptation using shake flasks in the various formulations contained in the CHO Media Library and two additional control media formulations (CM1 and CM2) used by the customer before receipt of the cell line by SAFCB as controls. Cell growth and IgG productivity were monitored for all of the media formulations. Using these criteria, it was determined that approximately one-half of the media formulations in the library supported improved cell growth and IgG production compared to the two control media. Based on the cell growth, IgG productivity, and diversification of the formulation components, four media were chosen for the DOE mixture screening (Figure 3A).

Figure 3.

DOE Pyramid Design and DOE Mixture Screening

In the DOE media mixture study, a three-component mixing design (triangle design) has often been applied to find the best performing medium based on the top three media selected from the basal media screening. The best medium might be a single medium, or a mixture of any proportion of the original three single media tested. Additionally, the comparison of a blended and single medium can lead to the identification of effecter components more rapidly than traditional matrix experimentation.

Here, the idea of the pyramid DOE design for cell culture formulation mixture analysis is introduced (Figure 2). Instead of analyzing three components, the four best media are selected from the basal media screening (Figure 2A) for mixture analysis. To simplify the experimental design, only the combinations on the surfaces (a~d) of the pyramid have been evaluated in this study (Figure 2A) and 30 mixtures were evaluated with the CL1 cell line (Figure 2B). Based on the information from the surface design, if it is necessary, the second-round design with the combinations inside of the 3D-model could be performed to further identify the best cell culture medium.

All of the tested mixtures exhibited significantly higher IgG productivity compared to CM1 and three of the mixtures had twice the productivity observed in the control medium CM1 (Figure 3B). Mix #25 exhibited the highest IgG productivity and was selected for further investigation.

DOE Statistical Analysis

The data from the pyramid design was examined using statistical analysis and modeling to generate theoretical optimized formulations based on different user-defined performance criteria and with the results reported as contour plots. Independent analyses were performed to determine optimized formulations where either cell growth (ICA: specific cell growth) or IgG productivity were the primary driving criteria. The optimized formulations are indicated in the contour plot (Figure 4). In the plots, increasing desirability is indicated by the progression from blue to red coloring. When the optimization was based on the cell growth (ICA), the most desirable mixing ratio was 59.9% of medium C and 40.1% of medium E (Figure 4A). Alternatively, when the optimization was based on the IgG productivity, the most desirable mixing ratio was 56.2% of medium P, 39.3% of medium E, and 4.5% of medium D (Figure 4B). Based on these observations, the advantage of the pyramid design compared to the conventional triangle design can be seen: using traditional three-component DOE analysis, the best mixture for supporting the cell growth or the IgG productivity could be missed. Use of the pyramid model allows a more comprehensive analysis of mixture interactions and the identification of the best mixture to maximize the desired criteria, cell growth, or protein production. Additionally, if desired, the model could be further optimized based on investigating combinations in the interior of the pyramid model. However, the increased number of mixtures will significantly complicate the study design and careful consideration should be put into the selection of media with different good performance characteristics (e.g., growth or productivity) and dissimilar medium compositions to maximize the potential benefit of such analysis.

Figure 4.

Cell Culture Condition Optimization

Based on the results obtained with Mix #25, the customer elected to pursue further increases in performance through optimization of culture parameters rather than additional optimization of the medium. First, the culture temperature was shifted from 37 °C to 31 °C on cell culture day six, a point at which the rate of cell growth had decreased such that the viable cell density increased <50% from the previous day. Examination of the growth profile of CL1 cultured in Mix #25 or CM1 control medium subject to culture temperature shift (Figure 5A) illustrates that the low temperature significantly extends cell growth with minimum influence on the maximum viable cell density to both control and optimized medium. Metabolite analysis showed that there was no concentration difference between the high temperature and low temperature with the tested metabolites with the exception of D-Glucose. Culture in both Mix #25 and CM1 with temperature shift had much lower rates of D-Glucose consumption than those in the normal temperature (Figure 5B). For example, the D-Glucose level on day nine in cultures at 31 °C were about 10-fold higher than cultures at 37 °C (Figure 5B, inset). This finding strongly suggests that the lower rate of D-Glucose consumption might be associated with the improved cell growth.

Figure 5.

To confirm the positive effect of higher levels of D-Glucose, glucose feeding and temperature shift were performed on CL1 cells in Mix #25. As expected, D-Glucose feeding significantly extended cell growth and the combination of D-Glucose feeding and low temperature shift together further extended the cell growth at least seven days longer compared to the control condition (Figure 5C). Analysis of IgG productivity showed that D-Glucose feeding or low temperature also improved the IgG production compared to control conditions, and the combination of the D-Glucose feeding and temperature shift further improved the maximum IgG production by >30%, from ~800 mg/L to ~1,100 mg/L (Figure 5D).

CONCLUSIONS

A new cell culture medium development platform, using the high-throughput screening, diverse selections of CHO cell culture media formulations (CHO Media Library), cell line engineering, and DOE experimental design has been introduced to accentuate medium development. Compared to traditional medium development approaches, this platform dramatically reduces costs and development time (usually less than five months). Additionally, in this study the concept of a four-component DOE mixing analysis (pyramid model) provides a more informative and efficient approach than conventional three-component DOE analysis (triangle model). Using an IgG-producing CHO clone (CL1), this new strategic platform was used to develop a well-performing formulation supporting increased IgG production from ~400 mg/L to 800 mg/L (~100%). Optimization of the cell culture conditions (low-temperature shift and D-Glucose feeding) further improved IgG productivity by an additional 30%, further demonstrating the efficacy of this approach.

ACKNOWLEDGMENTS

The authors would like to thank Mark Angeles, from Sigma-Aldrich Analytical R&D, for IgG sample analysis. We also kindly acknowledge Mark Tizzard, Mason Williams, and Susan Bridges, from SAFC Biosciences, for their review of the manuscript and informative suggestions.

Min Zhang is a senior scientist, Avril Lawshé is a scientist, Kerry Koskie is an associate scientist, Terrell Johnson is a principal scientist, Matthew V. Caple is the director, and James S. Ross is an R&D manager, all at Cell Sciences & Development, SAFC Biosciences, St. Louis, MO, 314.771.5765, ext. 3390, min.zhang@sial.com

REFERENCES

1. McKeehan W, McKeehan K, Ham R. The relationship between defined low-molecular-weight substances and undefined serum-derived factors in the multiplication of untransformed fibroblasts. The Growth Requirements of Vertebrate Cells In Vitro, Chapter 15 (Waymouth C, Ham R, Chapple P eds.), Cambridge Univ Press (New York). 1981;223–243.

2. Cassulis P, Magasic MV, DeBari VA. Ligand affinity chromatographic separation of serum IgG on recombinant protein G-silica Clin Chem. 1991;37:882–886.

3. Montgomery D. Design and analysis of experiments. Fourth Edition. John Wiley and Sons, New York; 1997.

4. Cornell J. How to run mixture experiments for product quality. The ASQC basic references in quality control: statistical techniques (Shapiro S, Mykytka E eds.) Vol. 5. Quality Press, Milwaukee, WI; 1990.

5. Castro P, Hayter PM, Ison AP, Bull AT. Application of statistical design to the optimization of culture medium for recombinant interferon-gamma production by Chinese Hamster Ovary cells. Appl Microbiol Biotechnol. 1992(38):84–90.

6. Moran EB, et al. A systematic approach to the validation of process control parameters for monoclonal antibody production in fed-batch culture of a murine myeloma. Biotechnol Bioeng. 2000(69):242–255.

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