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Use of live digital twin (shadow) soft sensor to monitor membrane degradation in continuous manufacturing single pass tangential flow filtration.

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Title: Use of live digital twin (shadow) soft sensor to monitor membrane degradation in continuous manufacturing single pass tangential flow filtration.
Authors: Taylor, Robert1 (AUTHOR), Mandur, Jasdeep2 (AUTHOR), Amira, Umme1 (AUTHOR), Al‐Inati, Natalie1 (AUTHOR), Marin‐Celis, Juan1 (AUTHOR), Hatch, Scott3 (AUTHOR), Cerezo, Lara Fernandez4 (AUTHOR), Pinto, Nuno4 (AUTHOR), Metsi‐Guckel, Efimia5 (AUTHOR), Matos, Tiago1 (AUTHOR) tiago.matos@merck.com, Brower, Mark4 (AUTHOR), Mehta, Krunal1 (AUTHOR), Sarkar, Avik5 (AUTHOR)
Source: Biotechnology Progress. Nov/Dec2025, Vol. 41 Issue 6, p1-16. 16p.
Subjects: Digital twin, Continuous processing, Microfiltration, Process control systems, Biotechnological process monitoring, Siemens AG, Monoclonal antibodies, Biochemical engineering, Intelligent sensors
Abstract: Digital twins (DT) are sophisticated mathematical models representing real‐world physical processes, equipped with predictive capabilities that adapt alongside the physical system. The successful implementation of DT in bioprocessing offers numerous advantages, including enhanced understanding of processes, accelerated overall development timelines, and effective monitoring of critical process parameters (CPPs). A comprehensive end‐to‐end DT can facilitate informed control decisions and forecast how disturbances within the process may affect the final output, accelerating the overall development timelines while optimizing process efficiency and productivity. Tangential flow filtration (TFF) is a standard methodology in bioprocessing, commonly employed to concentrate and exchange buffers for bioproducts. The advancement of continuous process technologies has led to the emergence of alternative TFF methods, notably single‐pass tangential flow filtration (SPTFF), which streamlines the process by eliminating the need for stream recirculation. Here, we present the development of a live DT of the SPTFF concentration step within the downstream continuous manufacturing line for a monoclonal antibody (mAb) process. A live DT, equipped with a state estimation tool, was implemented via the Siemens' gPROMS Digital Applications (gDAP) platform. The DT demonstrated the ability to monitor changes in membrane resistance, a typical process parameter that is not directly measured. This parameter is crucial for SPTFF control, as it allows for the constant setting of the concentration factor (CF) by adjusting the retentate flow rate based on the measured resistance and calculated transmembrane pressure (TMP). This achievement illustrates the potential of DT as effective tools for accurately tracking the complete state of the bioprocess. [ABSTRACT FROM AUTHOR]
Copyright of Biotechnology Progress is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Use of live digital twin (shadow) soft sensor to monitor membrane degradation in continuous manufacturing single pass tangential flow filtration.
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  Data: <searchLink fieldCode="AR" term="%22Taylor%2C+Robert%22">Taylor, Robert</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mandur%2C+Jasdeep%22">Mandur, Jasdeep</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Amira%2C+Umme%22">Amira, Umme</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Al‐Inati%2C+Natalie%22">Al‐Inati, Natalie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Marin‐Celis%2C+Juan%22">Marin‐Celis, Juan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hatch%2C+Scott%22">Hatch, Scott</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cerezo%2C+Lara+Fernandez%22">Cerezo, Lara Fernandez</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pinto%2C+Nuno%22">Pinto, Nuno</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Metsi‐Guckel%2C+Efimia%22">Metsi‐Guckel, Efimia</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Matos%2C+Tiago%22">Matos, Tiago</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tiago.matos@merck.com</i><br /><searchLink fieldCode="AR" term="%22Brower%2C+Mark%22">Brower, Mark</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mehta%2C+Krunal%22">Mehta, Krunal</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sarkar%2C+Avik%22">Sarkar, Avik</searchLink><relatesTo>5</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Biotechnology+Progress%22">Biotechnology Progress</searchLink>. Nov/Dec2025, Vol. 41 Issue 6, p1-16. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink><br /><searchLink fieldCode="DE" term="%22Continuous+processing%22">Continuous processing</searchLink><br /><searchLink fieldCode="DE" term="%22Microfiltration%22">Microfiltration</searchLink><br /><searchLink fieldCode="DE" term="%22Process+control+systems%22">Process control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Biotechnological+process+monitoring%22">Biotechnological process monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Siemens+AG%22">Siemens AG</searchLink><br /><searchLink fieldCode="DE" term="%22Monoclonal+antibodies%22">Monoclonal antibodies</searchLink><br /><searchLink fieldCode="DE" term="%22Biochemical+engineering%22">Biochemical engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+sensors%22">Intelligent sensors</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Digital twins (DT) are sophisticated mathematical models representing real‐world physical processes, equipped with predictive capabilities that adapt alongside the physical system. The successful implementation of DT in bioprocessing offers numerous advantages, including enhanced understanding of processes, accelerated overall development timelines, and effective monitoring of critical process parameters (CPPs). A comprehensive end‐to‐end DT can facilitate informed control decisions and forecast how disturbances within the process may affect the final output, accelerating the overall development timelines while optimizing process efficiency and productivity. Tangential flow filtration (TFF) is a standard methodology in bioprocessing, commonly employed to concentrate and exchange buffers for bioproducts. The advancement of continuous process technologies has led to the emergence of alternative TFF methods, notably single‐pass tangential flow filtration (SPTFF), which streamlines the process by eliminating the need for stream recirculation. Here, we present the development of a live DT of the SPTFF concentration step within the downstream continuous manufacturing line for a monoclonal antibody (mAb) process. A live DT, equipped with a state estimation tool, was implemented via the Siemens' gPROMS Digital Applications (gDAP) platform. The DT demonstrated the ability to monitor changes in membrane resistance, a typical process parameter that is not directly measured. This parameter is crucial for SPTFF control, as it allows for the constant setting of the concentration factor (CF) by adjusting the retentate flow rate based on the measured resistance and calculated transmembrane pressure (TMP). This achievement illustrates the potential of DT as effective tools for accurately tracking the complete state of the bioprocess. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Biotechnology Progress is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/btpr.70058
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      – Code: eng
        Text: English
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        PageCount: 16
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      – SubjectFull: Digital twin
        Type: general
      – SubjectFull: Continuous processing
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      – SubjectFull: Microfiltration
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      – SubjectFull: Process control systems
        Type: general
      – SubjectFull: Biotechnological process monitoring
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      – SubjectFull: Siemens AG
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      – SubjectFull: Monoclonal antibodies
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      – SubjectFull: Biochemical engineering
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      – SubjectFull: Intelligent sensors
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      – TitleFull: Use of live digital twin (shadow) soft sensor to monitor membrane degradation in continuous manufacturing single pass tangential flow filtration.
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              Text: Nov/Dec2025
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