Rapid Development and Optimization of Cell Culture Media - - BioPharm International

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Rapid Development and Optimization of Cell Culture Media


BioPharm International
Volume 21, Issue 1

RESULTS AND DISCUSSION ON CASE STUDY

CHO Media Library Screening


Figure 3.
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).

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


Figure 4.
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.


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