Automated Measurement and Monitoring of Bioprocesses: Key Elements of the M3C Strategy

Sonnleitner, Bernhard

doi:10.1007/10_2012_173

Cited by 20 publications

(30 citation statements)

References 116 publications

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“…At this time, most of the research is focused on two probing technologies, i.e. optical analysis [88][89][90] and spectroscopy [91]. Spectroscopy-based methods are mainly focused on the excretion of metabolites in the extracellular medium and do not take into account physiology-related parameters.…”

Section: Alternatives To Flow Cytometry For Monitoring Microbial Hetementioning

confidence: 99%

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

Delvigne

Goffin

2013

Biotechnology Journal

126

102

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Heterogeneity or segregation of microbial populations has been the subject of much research, but the real impact of this phenomenon on bioprocesses remains poorly understood. The main reason for this lack of knowledge is the difficulty in monitoring microbial population heterogeneity under dynamic process conditions. The main concepts resulting in microbial population heterogeneity in the context of bioprocesses have been summarized by two distinct hypotheses. The first involves the individual history of microbial cells or the "path" followed during their residence time inside the process equipment. The second hypothesis involves a coordinated response by the microbial population as a bet-hedging strategy, in order to cope with process-related stresses. The respective contribution of each hypothesis to microbial heterogeneity in bioprocesses is still unclear. This illustrates the fact that, although microbial phenotypic heterogeneity has been thoroughly investigated at a fundamental level, the implications of this phenomenon in the context of microbial bioprocesses are still subject to debate. At this time, automated flow cytometry is the best technique for investigating microbial heterogeneity under process conditions. However, dedicated software and relevant biomarkers are needed for the proper integration of flow cytometry as a bioprocess control tool.

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Section: Alternatives To Flow Cytometry For Monitoring Microbial Hetementioning

confidence: 99%

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

Delvigne

Goffin

2013

Biotechnology Journal

126

102

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“…The respiratory coefficient (RQ), defined as the carbon dioxide production rate divided by the oxygen consumption rate in units of mol mol −1 , can be calculated from the OUR and carbon dioxide production rate data. RQ allows one to make inferences about substrate utilization rates of the culture and has been proposed as a valuable parameter for monitoring and controlling cell cultures .…”

Section: Bioprocess Monitoring Systemsmentioning

confidence: 99%

Sensor systems for bioprocess monitoring

Biechele

Busse

Solle

et al. 2015

Engineering in Life Sciences

164

158

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The ability to measure all process variables is of great importance in the field of bioprocess monitoring and control, and continuous, real‐time measurements are highly desired. The more complete and real‐time the measurements are, the more stable, reproducible, and efficient the process can be, leading to reproducibly high‐quality products. This additional information allows the operator to better document the entire process. The process analytical technologies initiative of the US Food and Drug Administration is strongly related to the analysis and control of biopharmaceutical processes. The aim of the initiative is to create processes, generating products of ensured quality by measuring quality‐related process variables. The quality of the product is enhanced by a deep understanding of the process, which is enabled by an effective and suitable sensor system. The aim of this review is to provide an overview of current and emerging sensors for bioprocess monitoring. Sensors directly interfaced to bioreactors for measuring important variables from the gas phase, such as oxygen and carbon dioxide concentration, are discussed, as well as sensors for the monitoring of the biomass concentration and morphology and of the changing medium composition. Furthermore, sensor systems are discussed. These involve sensors (especially biosensors) that are not implemented directly inside the bioreactor but rather are used in conjunction with sample‐taking systems such as flow injection analysis. A major focus is given to spectroscopic sensors, which are noninvasive and offer interesting options for a simultaneous analysis of various compounds. Since data handling is extremely important for this kind of sensor, chemometrics are also included. Soft sensors are discussed as technology that allows a user to incorporate more process data as it become available. Finally, the current state of disposable sensor technology is presented. These sensors are needed for the growing area of disposable and continuous biomanufacturing.

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“…Several of the sensors are standard in existing process equipment . Partly, this can be ascribed to reliability, durability, stability, and the low price of these sensors.…”

Section: ‘Hardware Sensor’ Alternativesmentioning

confidence: 99%

Mini‐review: soft sensors as means for PAT in the manufacture of bio‐therapeutics

Mandenius

Gustavsson

2014

J of Chemical Tech & Biotech

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This mini‐review discusses how soft sensors can contribute to accomplish FDA's process analytical technology (PAT) ambitions in the manufacture of bio‐therapeutics. Focus is on applications with protein‐based drugs (proteins, antibodies), but also gene therapy vectors as well as cell cultures are considered where chemical and bio‐analytical as well as mathematical and statistical methods are used as tools. An overview of existing soft sensor alternatives and how these can be configured to meet typical industrial needs is provided. It is noted how several of these needs coincide with the PAT regulatory incentives but do also address process economic aspects of bio‐therapeutic manufacture. Evaluation of soft sensor alternatives is highlighted in relation to the production targets, quality attributes and the specification of these as well as shortcomings and needs for further improvements. © 2014 Society of Chemical Industry

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Automated Measurement and Monitoring of Bioprocesses: Key Elements of the M3C Strategy

Cited by 20 publications

References 116 publications

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

Sensor systems for bioprocess monitoring

Mini‐review: soft sensors as means for PAT in the manufacture of bio‐therapeutics

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