Early integration of Design of Experiment (DOE) and multivariate statistics identifies feeding regimens suitable for CHO cell line development and screening
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In Chinese Hamster Ovary (CHO) cell lines, the establishment of the ideal fed-batch regimen promotes metabolic conditions advantageous for the bioproduction of therapeutic molecules. A tailored, cell line-specific feeding scheme is typically defined during process development (PD) activities, through the incorporation of Design of Experiment (DOE) and late stage cell culture approaches. The feeding during early stage cell line development (CLD) was a simplified “one-fits-all” design, inherited from PD lab, that didn’t account for CLD needs of throughput and streamlined workflow. The “one-fits-all” efficiency was not routinely verified when novel technologies were incorporated in CLD and sub-optimal feeding carried the risk of not selecting the most desirable cell lines amenable to late stage PD. In our work we developed the DOE-feed method; a streamlined, three-stages framework for identifying efficient feeding schemes as the CLD technologies evolved. We combined early stage cell culture input data with late-stage techniques, such as statistical modelling, principal component analysis (PCA), DOE and Prediction Profiler. Novel in our DOE-feed work, we deliberately anticipated the application of statistics and approached the method development as an early-stage, continuously updated process, by building iterative datasets and statistically interpreting their responses. We capitalized on the statistical models defined by the DOE-feed methodology to study the influence of feeds on daily productivity and growth and to extrapolate feeding-schemes that improved the cell line screening. The DOE-feed became a methodology suited for CLD needs at AbbVie, and optimized the early stage screening, reduced the operational hands-on time and improved the overall workstream efficiency.
KeywordsChinese hamster ovary Cell Line Development Feed medium Design of Experiment Multivariate Data Analysis
The authors participated in the interpretation of data, review, and approval of the publication; all authors contributed to the development of the publication and maintained control over the final content. AM, BN, YY, SZ, and GC have or had a financial interest in AbbVie. BN, YY and GC are AbbVie employees. AM and SZ are former employees of AbbVie. SY serves as a PhD advisor to AM.
The design, study conduct, and financial support for the study were provided by AbbVie.
Compliance with ethical standards
Conflict of interest
All authors have no conflict of interest to declare.
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