Modeling scalability of impurity precipitation in downstream biomanufacturing
© 2024 American Institute of Chemical Engineers..
Precipitation during the viral inactivation, neutralization and depth filtration step of a monoclonal antibody (mAb) purification process can provide quantifiable and potentially significant impurity reduction. However, robust commercial implementation of this unit operation is limited due to the lack of a representative scale-down model to characterize the removal of impurities. The objective of this work is to compare isoelectric impurity precipitation behavior for a monoclonal antibody product across scales, from benchtop to pilot manufacturing. Scaling parameters such as agitation and vessel geometry were investigated, with the precipitate amount and particle size distribution (PSD) characterized via turbidity and flow imaging microscopy. Qualitative analysis of the data shows that maintaining a consistent energy dissipation rate (EDR) could be used for approximate scaling of vessel geometry and agitator speeds in the absence of more detailed simulation. For a more rigorous approach, however, agitation was simulated via computational fluid dynamics (CFD) and these results were applied alongside a population balance model to simulate the trajectory of the size distribution of precipitate. CFD results were analyzed within a framework of a two-compartment mixing model comprising regions of high- and low-energy agitation, with material exchange between the two. Rate terms accounting for particle formation, growth and breakage within each region were defined, accounting for dependence on turbulence. This bifurcated model was successful in capturing the variability in particle sizes over time across scales. Such an approach enhances the mechanistic understanding of impurity precipitation and provides additional tools for model-assisted prediction for process scaling.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Biotechnology progress - (2024) vom: 27. März, Seite e3454 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Guo, Jing [VerfasserIn] |
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| Links: |
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| Themen: |
Computational fluid dynamics |
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| Anmerkungen: |
Date Revised 28.03.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1002/btpr.3454 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370288041 |
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| 520 | |a © 2024 American Institute of Chemical Engineers. | ||
| 520 | |a Precipitation during the viral inactivation, neutralization and depth filtration step of a monoclonal antibody (mAb) purification process can provide quantifiable and potentially significant impurity reduction. However, robust commercial implementation of this unit operation is limited due to the lack of a representative scale-down model to characterize the removal of impurities. The objective of this work is to compare isoelectric impurity precipitation behavior for a monoclonal antibody product across scales, from benchtop to pilot manufacturing. Scaling parameters such as agitation and vessel geometry were investigated, with the precipitate amount and particle size distribution (PSD) characterized via turbidity and flow imaging microscopy. Qualitative analysis of the data shows that maintaining a consistent energy dissipation rate (EDR) could be used for approximate scaling of vessel geometry and agitator speeds in the absence of more detailed simulation. For a more rigorous approach, however, agitation was simulated via computational fluid dynamics (CFD) and these results were applied alongside a population balance model to simulate the trajectory of the size distribution of precipitate. CFD results were analyzed within a framework of a two-compartment mixing model comprising regions of high- and low-energy agitation, with material exchange between the two. Rate terms accounting for particle formation, growth and breakage within each region were defined, accounting for dependence on turbulence. This bifurcated model was successful in capturing the variability in particle sizes over time across scales. Such an approach enhances the mechanistic understanding of impurity precipitation and provides additional tools for model-assisted prediction for process scaling | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a computational fluid dynamics | |
| 650 | 4 | |a monoclonal antibody | |
| 650 | 4 | |a population balance | |
| 650 | 4 | |a precipitation | |
| 650 | 4 | |a viral inactivation and neutralization | |
| 700 | 1 | |a Traylor, Steven J |e verfasserin |4 aut | |
| 700 | 1 | |a Agoub, Mohamed |e verfasserin |4 aut | |
| 700 | 1 | |a Jin, Weixin |e verfasserin |4 aut | |
| 700 | 1 | |a Hua, Helen |e verfasserin |4 aut | |
| 700 | 1 | |a Diemer, R Bertrum |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Xuankuo |e verfasserin |4 aut | |
| 700 | 1 | |a Ghose, Sanchayita |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zheng Jian |e verfasserin |4 aut | |
| 700 | 1 | |a Lenhoff, Abraham M |e verfasserin |4 aut | |
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