Controlling tangential flow filtration in biomanufacturing processes via machine learning: A literature review
With the rapid growth of the biopharmaceutical sector in recent years, in conjunction with many recent successful developments in machine learning and artificial intelligence, the demand for the sector to shift to Industry 4.0 has emerged. Process Analytical Technology (PAT) makes it possible to mon...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Digital Chemical Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772508124000735 |
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| author | Bastian Oetomo Ling Luo Yiran Qu Michele Discepola Sandra E. Kentish Sally L. Gras |
| author_facet | Bastian Oetomo Ling Luo Yiran Qu Michele Discepola Sandra E. Kentish Sally L. Gras |
| author_sort | Bastian Oetomo |
| collection | DOAJ |
| description | With the rapid growth of the biopharmaceutical sector in recent years, in conjunction with many recent successful developments in machine learning and artificial intelligence, the demand for the sector to shift to Industry 4.0 has emerged. Process Analytical Technology (PAT) makes it possible to monitor and control the manufacturing processes of monoclonal antibodies (mAbs), both in upstream and downstream processing. Despite downstream processing being responsible for approximately 60% of the cost of biological drug production, most of the recent developments focus on its upstream counterpart. This paper investigates existing literature on the application of machine learning and/or process control in downstream processing, with an emphasis on ultrafiltration/diafiltration (UF/DF) via tangential flow filtration (TFF). Literature on the intersection between control systems and machine learning will also be explored. |
| format | Article |
| id | doaj-art-935cf1ae1247435b9ef0fa87b37f1430 |
| institution | Kabale University |
| issn | 2772-5081 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Digital Chemical Engineering |
| spelling | doaj-art-935cf1ae1247435b9ef0fa87b37f14302025-01-11T06:42:17ZengElsevierDigital Chemical Engineering2772-50812025-03-0114100211Controlling tangential flow filtration in biomanufacturing processes via machine learning: A literature reviewBastian Oetomo0Ling Luo1Yiran Qu2Michele Discepola3Sandra E. Kentish4Sally L. Gras5The ARC Digital Bioprocess Development Hub, The University of Melbourne, Australia; School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia; Corresponding author at: School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia.The ARC Digital Bioprocess Development Hub, The University of Melbourne, Australia; School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, AustraliaThe ARC Digital Bioprocess Development Hub, The University of Melbourne, Australia; Department of Chemical Engineering, The University of Melbourne, Parkville, Victoria 3010, AustraliaThe ARC Digital Bioprocess Development Hub, The University of Melbourne, Australia; Department of Chemical Engineering, The University of Melbourne, Parkville, Victoria 3010, AustraliaThe ARC Digital Bioprocess Development Hub, The University of Melbourne, Australia; Department of Chemical Engineering, The University of Melbourne, Parkville, Victoria 3010, AustraliaThe ARC Digital Bioprocess Development Hub, The University of Melbourne, Australia; Department of Chemical Engineering, The University of Melbourne, Parkville, Victoria 3010, Australia; Bio21 Institute of Molecular Science and Biotechnology, Melbourne, Victoria 3052, AustraliaWith the rapid growth of the biopharmaceutical sector in recent years, in conjunction with many recent successful developments in machine learning and artificial intelligence, the demand for the sector to shift to Industry 4.0 has emerged. Process Analytical Technology (PAT) makes it possible to monitor and control the manufacturing processes of monoclonal antibodies (mAbs), both in upstream and downstream processing. Despite downstream processing being responsible for approximately 60% of the cost of biological drug production, most of the recent developments focus on its upstream counterpart. This paper investigates existing literature on the application of machine learning and/or process control in downstream processing, with an emphasis on ultrafiltration/diafiltration (UF/DF) via tangential flow filtration (TFF). Literature on the intersection between control systems and machine learning will also be explored.http://www.sciencedirect.com/science/article/pii/S2772508124000735Machine learningDownstream processingTangential flow filtrationCross-flow filtrationUltrafiltrationDiafiltration |
| spellingShingle | Bastian Oetomo Ling Luo Yiran Qu Michele Discepola Sandra E. Kentish Sally L. Gras Controlling tangential flow filtration in biomanufacturing processes via machine learning: A literature review Digital Chemical Engineering Machine learning Downstream processing Tangential flow filtration Cross-flow filtration Ultrafiltration Diafiltration |
| title | Controlling tangential flow filtration in biomanufacturing processes via machine learning: A literature review |
| title_full | Controlling tangential flow filtration in biomanufacturing processes via machine learning: A literature review |
| title_fullStr | Controlling tangential flow filtration in biomanufacturing processes via machine learning: A literature review |
| title_full_unstemmed | Controlling tangential flow filtration in biomanufacturing processes via machine learning: A literature review |
| title_short | Controlling tangential flow filtration in biomanufacturing processes via machine learning: A literature review |
| title_sort | controlling tangential flow filtration in biomanufacturing processes via machine learning a literature review |
| topic | Machine learning Downstream processing Tangential flow filtration Cross-flow filtration Ultrafiltration Diafiltration |
| url | http://www.sciencedirect.com/science/article/pii/S2772508124000735 |
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