Scale-up analysis for a CHO cell culture process in large-scale bioreactors.
ABSTRACT Bioprocess scale-up is a fundamental component of process development in the biotechnology industry. When scaling up a mammalian cell culture process, it is important to consider factors such as mixing time, oxygen transfer, and carbon dioxide removal. In this study, cell-free mixing studies were performed in production scale 5,000-L bioreactors to evaluate scale-up issues. Using the current bioreactor configuration, the 5,000-L bioreactor had a lower oxygen transfer coefficient, longer mixing time, and lower carbon dioxide removal rate than that was observed in bench scale 5- and 20-L bioreactors. The oxygen transfer threshold analysis indicates that the current 5,000-L configuration can only support a maximum viable cell density of 7 x 10(6) cells mL(-1). Moreover, experiments using a dual probe technique demonstrated that pH and dissolved oxygen gradients may exist in 5,000-L bioreactors using the current configuration. Empirical equations were developed to predict mixing time, oxygen transfer coefficient, and carbon dioxide removal rate under different mixing-related engineering parameters in the 5,000-L bioreactors. These equations indicate that increasing bottom air sparging rate is more efficient than increasing power input in improving oxygen transfer and carbon dioxide removal. Furthermore, as the liquid volume increases in a production bioreactor operated in fed-batch mode, bulk mixing becomes a challenge. The mixing studies suggest that the engineering parameters related to bulk mixing and carbon dioxide removal in the 5,000-L bioreactors may need optimizing to mitigate the risk of different performance upon process scale-up.
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ABSTRACT: High recombinant protein productivity in mammalian cell lines is often associated with phenotypic changes in protein content, energy metabolism, and cell growth, but the key determinants that regulate productivity are still not clearly understood. The mammalian target of rapamycin (mTOR) signalling pathway has emerged as a central regulator for many cellular processes including cell growth, apoptosis, metabolism, and protein synthesis. This role of this pathway changes in response to diverse environmental cues and allows the upstream proteins that respond directly to extracellular signals (such as nutrient availability, energy status, and physical stresses) to communicate with downstream effectors which, in turn, regulate various essential cellular processes. In this study, we have performed a transcriptomic analysis using a pathway-focused polymerase chain reaction (PCR) array to compare the expression of 84 target genes related to the mTOR signalling in two recombinant CHO cell lines with a 17.4-fold difference in specific monoclonal antibody productivity (qp). Eight differentially expressed genes that exhibited more than a 1.5-fold change were identified. Pik3cd (encoding the Class 1A catalytic subunit of phosphatidylinositol 3-kinase [PI3K]) was the most differentially expressed gene having a 71.3-fold higher level of expression in the high producer cell line than in the low producer. The difference in the gene's transcription levels was confirmed at the protein level by examining expression of p110delta. Expression of p110delta correlated with specific productivity (qp) across six different CHO cell lines, with a range of expression levels from 3 to 51 pg/cell/day, suggesting that p110delta may be a key factor in regulating productivity in recombinant cell lines.BMC Biotechnology 02/2014; 14(1):15. · 2.17 Impact Factor
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ABSTRACT: Process transfer is associated with a considerable risk potential. The most critical equipment aspects in upstream operations are the type and scale of bioreactors. Single-use systems have the advantage of a relatively fixed bioreactor design where only few adaptations can be made, e.g. in stirrer geometry or type of submerse aeration. Here we describe the transfer of a CHO fed-batch process in the 1,000 L scale from a XDR™ to a S.U.B. bioreactor used for GMP compliant manufacturing of biologics. The transfer method, which was based on a preceding intensive characterization of both bioreactors, aimed either to keep the oxygen mass transfer or the power input constant. The transfer strategies were evaluated theoretically based on derived empirical correlations for the mass transfer coefficients, kLaO2 and kLaCO2. An operation boundary of 10 to 31 W m−3 for the S.U.B. bioreactor was defined, which is an approximately 35 % higher power input compared to that in the XDR™. The transfer strategy succeeded in maintaining essential biological parameters like cell concentration (± 5%), viability (± 2%) and product formation (± 3%) very similar. This is, to the authors’ knowledge, the first time that distinct process performance comparison in different 1,000 L single-use bioreactors is published.This article is protected by copyright. All rights reservedEngineering in Life Sciences 04/2014; · 1.63 Impact Factor
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ABSTRACT: In characterizing a cell culture process to support regulatory activities such as process validation and Quality by Design, developing a representative scale down model for design space definition is of great importance. The manufacturing bioreactor should ideally reproduce bench scale performance with respect to all measurable parameters. However, due to intrinsic geometric differences between scales, process performance at manufacturing scale often varies from bench scale performance, typically exhibiting differences in parameters such as cell growth, protein productivity, and/or dissolved carbon dioxide concentration. Here we describe a case study in which a bench scale cell culture process model is developed to mimic historical manufacturing scale performance for a late stage CHO-based monoclonal antibody program. Using multivariate analysis (MVA) as primary data analysis tool in addition to traditional univariate analysis techniques to identify gaps between scales, process adjustments were implemented at bench scale resulting in an improved scale down cell culture process model. Finally we propose an approach for small scale model qualification including three main aspects: multivariate analysis, comparison of key physiological rates, and comparison of product quality attributes. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 2013.Biotechnology Progress 10/2013; · 1.85 Impact Factor