Multivariate statistical methods in bioprocess fault detection and performance forecasting

Ignova, M.; Glassey, J.; Ward, Alan C.; Montague, G.A.

doi:10.1177/014233129701900507

Cited by 20 publications

(14 citation statements)

References 28 publications

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“…However, the same statistical approach was not successful when applied to a more complex antibiotics process. 15 Other authors succeeded in forecasting the final product concentration such as Ignova et al for a penicillin G, 16 Lennox et al for an unspecified product 17 and Gunther et al for a recombinant protein. 18 Forecasting of the final product concentration was also published for a mammalian cell culture (CHO) process, where Le et al, built PLS-R and support vector machine (SVM) models to forecast product concentration based on 243 production cultivations (12000L scale), in order to find the reason for batches with lower product concentrations.…”

Section: Another Way To Exploit Commonly Gathered Off-linementioning

confidence: 99%

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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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Section: Another Way To Exploit Commonly Gathered Off-linementioning

confidence: 99%

“…However, the same statistical approach was not successful when applied to a more complex antibiotics process . Other authors succeeded in forecasting the final product concentration such as Ignova et al for a penicillin G, Lennox et al for an unspecified product and Gunther et al for a recombinant protein …”

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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“…Early work on soft sensors in the bioprocess industry concentrated on on‐line prediction of time trajectories of process variables that were traditionally difficult or time consuming to measure, such as biomass or product concentrations [10]. These sensors were typically using on‐line/in situ measurements of exit gas composition and physico‐chemical parameters, such as pH, temperature and dissolved oxygen [38]. They were frequently applied in various bioprocesses, ranging from microbial to cell culture cultivations, for monitoring, on‐line process control and fault detection with the aim of improving productivity and process efficiency [39–40].…”

Section: Industrial Needs and Opportunities For Soft Sensors In Bimentioning

confidence: 99%

Soft sensors in bioprocessing: A status report and recommendations

Luttmann

Bracewell

Cornelissen

et al. 2012

Biotechnology Journal

196

115

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The following report with recommendations is the result of an expert panel meeting on soft sensor applications in bioprocess engineering that was organized by the Measurement, Monitoring, Modelling and Control (M3C) Working Group of the European Federation of Biotechnology ‐ Section of Biochemical Engineering Science (ESBES). The aim of the panel was to provide an update on the present status of the subject and to identify critical needs and issues for the furthering of the successful development of soft sensor methods in bioprocess engineering research and for industrial applications, in particular with focus on biopharmaceutical applications. It concludes with a set of recommendations, which highlight current prospects for the extended use of soft sensors and those areas requiring development.

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“…Additionally, the identification of a seed quality prior to its transfer to the production vessel may have added benefits in terms of scheduling and process economics. The issue of fault diagnosis is also important throughout the production fermentation; thus extensive research has been undertaken in this particular area of bioprocess operation (Ignova et al, 1997). The complexity of the problem, the uncertainty in measurement and the importance of tight regulation have led to the use of RTKBSs.…”

Section: Knowledge-based Systems In Fermentation Monitoringmentioning

confidence: 99%

From process experts to a real‐time knowledge‐based system

Ranjan¹,

Glassey

Montague

et al. 2002

Expert Systems

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This paper describes the development and implementation of a real-time knowledge-based system (RTKBS) carried out in collaboration with Eli Lilly in the UK. The RTKBS was developed in order to provide consistent advice to the operators of a large-scale antibiotic production plant in real time. The ultimate objective was to reduce variability and improve the process yield. A wealth of process information exists in the experience of the process operators, engineers and scientists, and it was capitalized upon in this application. However, this information tends primarily to be qualitative in nature and its elicitation traditionally represents a bottleneck in developing an RTKBS. The paper demonstrates that this type of process knowledge can be effectively and rapidly captured and then coded within an RTKBS. The project was undertaken in two stages. During the knowledge elicitation stage the specific application area was identified in consultation with the industrial partner and qualitative knowledge in the selected area was extracted from experts/operators using KAT (a structured technique for knowledge elicitation). Subsequently this knowledge was converted into a rule base and implemented within G2, which then acted as an advisory/decision support system.

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Multivariate statistical methods in bioprocess fault detection and performance forecasting

Cited by 20 publications

References 28 publications

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

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

Soft sensors in bioprocessing: A status report and recommendations

From process experts to a real‐time knowledge‐based system

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