Discerning key parameters influencing high productivity and quality through recognition of patterns in process data

Lê, Hương Giang; Castro-Melchor, Marlene; Hakemeyer, Christian; Jung, Christine; B, Szperalski; Karypis, George; Hu, Wei‐Shou

doi:10.1186/1753-6561-5-s8-p91

Cited by 4 publications

(9 citation statements)

References 5 publications

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“…The culture could be kept in a metabolically highly interesting state for a duration of 14 days in a dynamic fed-batch process. The resulting Y L/G was, therefore, around zero most of the time and also assumed negative values, which are found to correlate with high titers in cell culture ( Figure 7 D) [ 5 , 64 ].…”

Section: Resultsmentioning

confidence: 99%

“…The lactic acid profile of a mammalian cell culture process affects the maximum achievable cell count and final product titer [ 2 , 3 ]. Manufacturing runs with a high and a low lactic acid profile have been linked with productivity of the process by using Multivariate Data Analysis (MVDA) methods [ 4 , 5 ]. It does not come as a big surprise that there is not only one but there are several approaches to decrease lactic acid.…”

Section: Introductionmentioning

confidence: 99%

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Metabolic Control in Mammalian Fed-Batch Cell Cultures for Reduced Lactic Acid Accumulation and Improved Process Robustness

et al. 2016

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Biomass and cell-specific metabolic rates usually change dynamically over time, making the “feed according to need” strategy difficult to realize in a commercial fed-batch process. We here demonstrate a novel feeding strategy which is designed to hold a particular metabolic state in a fed-batch process by adaptive feeding in real time. The feed rate is calculated with a transferable biomass model based on capacitance, which changes the nutrient flow stoichiometrically in real time. A limited glucose environment was used to confine the cell in a particular metabolic state. In order to cope with uncertainty, two strategies were tested to change the adaptive feed rate and prevent starvation while in limitation: (i) inline pH and online glucose concentration measurement or (ii) inline pH alone, which was shown to be sufficient for the problem statement. In this contribution, we achieved metabolic control within a defined target range. The direct benefit was two-fold: the lactic acid profile was improved and pH could be kept stable. Multivariate Data Analysis (MVDA) has shown that pH influenced lactic acid production or consumption in historical data sets. We demonstrate that a low pH (around 6.8) is not required for our strategy, as glucose availability is already limiting the flux. On the contrary, we boosted glycolytic flux in glucose limitation by setting the pH to 7.4. This new approach led to a yield of lactic acid/glucose (Y L/G) around zero for the whole process time and high titers in our labs. We hypothesize that a higher carbon flux, resulting from a higher pH, may lead to more cells which produce more product. The relevance of this work aims at feeding mammalian cell cultures safely in limitation with a desired metabolic flux range. This resulted in extremely stable, low glucose levels, very robust pH profiles without acid/base interventions and a metabolic state in which lactic acid was consumed instead of being produced from day 1. With this contribution, we wish to extend the basic repertoire of available process control strategies, which will open up new avenues in automation technology and radically improve process robustness in both process development and manufacturing.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Metabolic Control in Mammalian Fed-Batch Cell Cultures for Reduced Lactic Acid Accumulation and Improved Process Robustness

et al. 2016

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“…Le et al used the same approach in another study to identify patterns, which cause variation of the process outcome (final product concentration and a-galactosylated antibodies). 21 In modern biopharmaceutical industry, robustness of processes is tested at small scale in so-called process characterization (PC) studies. In contrast to the publications [19][20][21] where data from large scale manufacturing were used, we used the data generated during PC studies.…”

Section: Another Way To Exploit Commonly Gathered Off-linementioning

confidence: 99%

“…One part of the study consisted of the generation of PLS‐R and SVM models to forecast the final product concentration at different time points during cultivations. Le et al used the same approach in another study to identify patterns, which cause variation of the process outcome (final product concentration and a‐galactosylated antibodies) …”

Section: Introductionmentioning

confidence: 99%

Progress toward forecasting product quality and quantity of mammalian cell culture processes by performance‐based modeling

Schmidberger

Posch

Sasse

et al. 2015

Biotechnology Progress

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The production of biopharmaceuticals requires highly sophisticated, complex cell based processes. Once a process has been developed, acceptable ranges for various control parameters are typically defined based on process characterization studies often comprising several dozens of small scale bioreactor cultivations. A lot of data is generated during these studies and usually only the information needed to define acceptable ranges is processed in more detail. Making use of the wealth of information contained in such data sets, we present here a methodology that uses performance data (such as metabolite profiles) to forecast the product quality and quantity of mammalian cell culture processes based on a toolbox of advanced statistical methods. With this performance based modeling (PBM) the final product concentration and 12 quality attributes (QAs) for two different biopharmaceutical products were predicted in daily intervals throughout the main stage process. The best forecast was achieved for product concentration in a very early phase of the process. Furthermore, some glycan isoforms were predicted with good accuracy several days before the bioreactor was harvested. Overall, PBM clearly demonstrated its capability of early process endpoint prediction by only using commonly available data, even though it was not possible to predict all QAs with the desired accuracy. Knowing the product quality prior to the harvest allows the manufacturer to take counter measures in case the forecasted quality or quantity deviates from what is expected. This would be a big step towards real-time release, an important element of the FDA's PAT initiative.

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“…To meet this demand, a variety of strategies have been employed to develop efficient biopharmaceutical manufacturing processes. These strategies include: 1) maximizing the productivity of the manufacturing cell line by the judicious use of expression systems for optimal transcription and translation of the therapeutic protein [2]; 2) engineering the transfection host cell line for efficient post-translational modification and secretion [3]; and 3) improving the cell culture process including media optimization [4][5][6][7][8], developing advanced feeding strategies [9,10] which in turn increases the culture density. Additionally, robust and highly productive host cell lines can be deployed by increasing the efficiency of gene expression that regulates proliferation [11] survival and longevity [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Proteomic Analysis of Bioreactor Cultures of an Antibody Expressing CHOGS Cell Line that Promotes High Productivity

Dorai

Liu²,

Yao

et al. 2013

J Proteomics Bioinform

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Discerning key parameters influencing high productivity and quality through recognition of patterns in process data

Cited by 4 publications

References 5 publications

Metabolic Control in Mammalian Fed-Batch Cell Cultures for Reduced Lactic Acid Accumulation and Improved Process Robustness

Metabolic Control in Mammalian Fed-Batch Cell Cultures for Reduced Lactic Acid Accumulation and Improved Process Robustness

Progress toward forecasting product quality and quantity of mammalian cell culture processes by performance‐based modeling

Proteomic Analysis of Bioreactor Cultures of an Antibody Expressing CHOGS Cell Line that Promotes High Productivity

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