Improving outcomes in intensified processing via optimization of the cell line development workflow.

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Title: Improving outcomes in intensified processing via optimization of the cell line development workflow.
Authors: Balassi, Vincent1 (AUTHOR), Otto, Mary1 (AUTHOR), Kretzmer, Corey1 (AUTHOR), Petersen, Amber1 (AUTHOR), McLaurin, Channing2 (AUTHOR), Mahadevan, Jana2 (AUTHOR), Gustin, Jason1 (AUTHOR), Borgschulte, Trissa1 (AUTHOR), Razafsky, David1 (AUTHOR) David.Razafsky@MilliporeSigma.com
Source: Biotechnology Progress. May/Jun2025, Vol. 41 Issue 3, p1-19. 19p.
Subjects: Cell lines, Continuous processing, Cell growth, Models & modelmaking, Workflow
Abstract: As the industry continues to explore the benefits of continuous and intensified manufacturing, it is important to assure that the cell line development (CLD) workflows in practice today are well suited to generate clones that meet the unique challenges associated with these processes. Most cell lines used in intensified processes are currently developed using traditional fed‐batch CLD workflows followed by adaptation of these cell lines to perfusion processes. This method maybe suboptimal as fed‐batch CLD workflows select clones which produce high volumetric titers irrespective of cell growth rate and specific productivity (qP). Although sufficient for fed‐batch processes, performance of cells derived from this traditional CLD workflow may not be maintained in perfusion processes, where an intricate balance of performance parameters is needed. Until now, a thorough investigation into the effect of the CLD workflow on top clone performance in perfusion processes has not been conducted. Here, we show how the CLD workflow impacts cell performance in both fed‐batch and perfusion processes, emphasizing the advantages of adopting a perfusion‐specific CLD workflow which includes the use of medium specially designed for expansion and production in a perfusion setting, scale‐down models which more accurately simulate perfusion process, and the adoption of perfusion‐specific cell line selection criteria. Together, this results in the development of more efficient cell lines, fit for continuous and intensified processing. [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: Improving outcomes in intensified processing via optimization of the cell line development workflow.
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  Data: <searchLink fieldCode="AR" term="%22Balassi%2C+Vincent%22">Balassi, Vincent</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Otto%2C+Mary%22">Otto, Mary</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kretzmer%2C+Corey%22">Kretzmer, Corey</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Petersen%2C+Amber%22">Petersen, Amber</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22McLaurin%2C+Channing%22">McLaurin, Channing</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mahadevan%2C+Jana%22">Mahadevan, Jana</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gustin%2C+Jason%22">Gustin, Jason</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Borgschulte%2C+Trissa%22">Borgschulte, Trissa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Razafsky%2C+David%22">Razafsky, David</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> David.Razafsky@MilliporeSigma.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Biotechnology+Progress%22">Biotechnology Progress</searchLink>. May/Jun2025, Vol. 41 Issue 3, p1-19. 19p.
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  Data: As the industry continues to explore the benefits of continuous and intensified manufacturing, it is important to assure that the cell line development (CLD) workflows in practice today are well suited to generate clones that meet the unique challenges associated with these processes. Most cell lines used in intensified processes are currently developed using traditional fed‐batch CLD workflows followed by adaptation of these cell lines to perfusion processes. This method maybe suboptimal as fed‐batch CLD workflows select clones which produce high volumetric titers irrespective of cell growth rate and specific productivity (qP). Although sufficient for fed‐batch processes, performance of cells derived from this traditional CLD workflow may not be maintained in perfusion processes, where an intricate balance of performance parameters is needed. Until now, a thorough investigation into the effect of the CLD workflow on top clone performance in perfusion processes has not been conducted. Here, we show how the CLD workflow impacts cell performance in both fed‐batch and perfusion processes, emphasizing the advantages of adopting a perfusion‐specific CLD workflow which includes the use of medium specially designed for expansion and production in a perfusion setting, scale‐down models which more accurately simulate perfusion process, and the adoption of perfusion‐specific cell line selection criteria. Together, this results in the development of more efficient cell lines, fit for continuous and intensified processing. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  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.70003
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        Text: English
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        PageCount: 19
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      – SubjectFull: Cell lines
        Type: general
      – SubjectFull: Continuous processing
        Type: general
      – SubjectFull: Cell growth
        Type: general
      – SubjectFull: Models & modelmaking
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      – SubjectFull: Workflow
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            – D: 01
              M: 05
              Text: May/Jun2025
              Type: published
              Y: 2025
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