7.3 Process Characterization for Upstream and Downstream Process Development

Janakiraman, Vijay Manikandan; Westoby, Matthew; Conley, Lynn

doi:10.1515/9783110278965.598

Cited by 2 publications

(4 citation statements)

References 33 publications

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“…In this study, we have shown the utility of the ambr ™ mini‐bioreactor system for scale‐down modeling followed by process characterization and control strategy development. The prerequisite for any process characterization study is the successful development and qualification of an appropriate scale‐down model . We have shown using statistical multivariate techniques that the scale‐down model developed in the ambr system is comparable to manufacturing scale as well as bench scale bioreactors.…”

Section: Discussionmentioning

confidence: 99%

“…The prerequisite for any process characterization study is the successful development and qualification of an appropriate scale-down model. 4,19,21,22 We have shown using statistical multivariate techniques that the scale-down model developed in the ambr system is comparable to manufacturing scale as well as bench scale bioreactors. Multivariate analysis (MVA) is considered as a holistic approach for rigorous statistical comparison of large datasets.…”

Section: Discussionmentioning

confidence: 99%

“…4 The dataset of selected parameters was compiled in Microsoft Excel and imported into SIMCA-P1 MVA software version 13.0 (Umetrics, Inc., CA) for a batch level principal component analysis (PCA) as described elsewhere. 19 Process data from 8 manufacturing batches across two campaigns and 5 pilot scale batches was used to build the base model. Including pilot scale data is an accepted practice when limited amount of manufacturing data is available at the time of scaledown modeling.…”

Section: Multivariate Analysis (Mva)mentioning

confidence: 99%

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Application of high‐throughput mini‐bioreactor system for systematic scale‐down modeling, process characterization, and control strategy development

Janakiraman

Kwiatkowski

Kshirsagar

et al. 2015

Biotechnology Progress

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High-throughput systems and processes have typically been targeted for process development and optimization in the bioprocessing industry. For process characterization, bench scale bioreactors have been the system of choice. Due to the need for performing different process conditions for multiple process parameters, the process characterization studies typically span several months and are considered time and resource intensive. In this study, we have shown the application of a high-throughput mini-bioreactor system viz. the Advanced Microscale Bioreactor (ambr15(TM) ), to perform process characterization in less than a month and develop an input control strategy. As a pre-requisite to process characterization, a scale-down model was first developed in the ambr system (15 mL) using statistical multivariate analysis techniques that showed comparability with both manufacturing scale (15,000 L) and bench scale (5 L). Volumetric sparge rates were matched between ambr and manufacturing scale, and the ambr process matched the pCO2 profiles as well as several other process and product quality parameters. The scale-down model was used to perform the process characterization DoE study and product quality results were generated. Upon comparison with DoE data from the bench scale bioreactors, similar effects of process parameters on process yield and product quality were identified between the two systems. We used the ambr data for setting action limits for the critical controlled parameters (CCPs), which were comparable to those from bench scale bioreactor data. In other words, the current work shows that the ambr15(TM) system is capable of replacing the bench scale bioreactor system for routine process development and process characterization.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Multivariate Analysis (Mva)mentioning

confidence: 99%

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Application of high‐throughput mini‐bioreactor system for systematic scale‐down modeling, process characterization, and control strategy development

Janakiraman

Kwiatkowski

Kshirsagar

et al. 2015

Biotechnology Progress

Self Cite

View full text Add to dashboard Cite

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“…The main objectives of performing MVA are dimensionality reduction (for data summarization and visualization), 2 clustering analysis (identifying groups of similar and dissimilar observations), 5 discrimination (investigation of differences between known classes or groups), 6 correlation analysis (assessing collinearities between input variables and relationships between input and output variables), 7 feature extraction (identification of important input parameters that greatly affect an output variable), 8 and prediction (of the class or the output variable for a new observation). 9 MVA has been shown to have its utility in multiple applications pertinent to cell culture including cell line development, 10 omics analysis, 11 process development, 12 raw material characterization, 13 understanding scale differences, [14][15][16][17][18] understanding process variability, 19,20 and multivariate monitoring of bioprocesses. 21 The assessment of goodness of a scale-down model is generally done by performing univariate analysis (e.g., t-test, 22 ANOVA, 22 and equivalence test 23 ) for multiple process performance and product quality attributes.…”

Section: Introductionmentioning

confidence: 99%

Application of multivariate analysis and mass transfer principles for refinement of a 3-L bioreactor scale-down model-when shake flasks mimic 15,000-L bioreactors better

Ahuja¹,

Jain²,

Ram³

2015

Biotechnol Progress

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Characterization of manufacturing processes is key to understanding the effects of process parameters on process performance and product quality. These studies are generally conducted using small-scale model systems. Because of the importance of the results derived from these studies, the small-scale model should be predictive of large scale. Typically, small-scale bioreactors, which are considered superior to shake flasks in simulating large-scale bioreactors, are used as the scale-down models for characterizing mammalian cell culture processes. In this article, we describe a case study where a cell culture unit operation in bioreactors using one-sided pH control and their satellites (small-scale runs conducted using the same post-inoculation cultures and nutrient feeds) in 3-L bioreactors and shake flasks indicated that shake flasks mimicked the large-scale performance better than 3-L bioreactors. We detail here how multivariate analysis was used to make the pertinent assessment and to generate the hypothesis for refining the existing 3-L scale-down model. Relevant statistical techniques such as principal component analysis, partial least square, orthogonal partial least square, and discriminant analysis were used to identify the outliers and to determine the discriminatory variables responsible for performance differences at different scales. The resulting analysis, in combination with mass transfer principles, led to the hypothesis that observed similarities between 15,000-L and shake flask runs, and differences between 15,000-L and 3-L runs, were due to pCO2 and pH values. This hypothesis was confirmed by changing the aeration strategy at 3-L scale. By reducing the initial sparge rate in 3-L bioreactor, process performance and product quality data moved closer to that of large scale.

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7.3 Process Characterization for Upstream and Downstream Process Development

Cited by 2 publications

References 33 publications

Application of high‐throughput mini‐bioreactor system for systematic scale‐down modeling, process characterization, and control strategy development

Application of high‐throughput mini‐bioreactor system for systematic scale‐down modeling, process characterization, and control strategy development

Application of multivariate analysis and mass transfer principles for refinement of a 3-L bioreactor scale-down model-when shake flasks mimic 15,000-L bioreactors better

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