Sensors and chemometrics in downstream processing

被引:8
|
作者
Duerauer, Astrid [1 ,4 ]
Jungbauer, Alois [1 ,2 ]
Scharl, Theresa [3 ]
机构
[1] Univ Nat Resources & Life Sci, Inst Bioproc Sci & Engn, Vienna, Austria
[2] Austrian Ctr Ind Biotechnol, Vienna, Austria
[3] Univ Nat Resources & Life Sci, Inst Stat, Vienna, Austria
[4] Inst Bioproc Sci & Engn, Muthgasse 18, A-1190 Vienna, Austria
关键词
continuous integrated biomanufacturing; machine learning; process control; real-time release; PROCESS ANALYTICAL TECHNOLOGY; NEURAL-NETWORKS; TRYPTOPHAN FLUORESCENCE; PROTEIN QUANTIFICATION; LIGHT-SCATTERING; SOFT SENSORS; CHROMATOGRAPHY; PAT; SPECTROSCOPY; MACHINE;
D O I
10.1002/bit.28499
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
The biopharmaceutical industry is still running in batch mode, mostly because it is highly regulated. In the past, sensors were not readily available and in-process control was mainly executed offline. The most important product parameters are quantity, purity, and potency, in addition to adventitious agents and bioburden. New concepts using disposable single-use technologies and integrated bioprocessing for manufacturing will dominate the future of bioprocessing. To ensure the quality of pharmaceuticals, initiatives such as Process Analytical Technologies, Quality by Design, and Continuous Integrated Manufacturing have been established. The aim is that these initiatives, together with technology development, will pave the way for process automation and autonomous bioprocessing without any human intervention. Then, real-time release would be realized, leading to a highly predictive and robust biomanufacturing system. The steps toward such automated and autonomous bioprocessing are reviewed in the context of monitoring and control. It is possible to integrate real-time monitoring gradually, and it should be considered from a soft sensor perspective. This concept has already been successfully implemented in other industries and requires relatively simple model training and the use of established statistical tools, such as multivariate statistics or neural networks. This review describes a scenario for integrating soft sensors and predictive chemometrics into modern process control. This is exemplified by selective downstream processing steps, such as chromatography and membrane filtration, the most common unit operations for separation of biopharmaceuticals.
引用
收藏
页码:2347 / 2364
页数:18
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