Modeling scalability of impurity precipitation in downstream biomanufacturing
Date
2024-03-27
Journal Title
Journal ISSN
Volume Title
Publisher
Biotechnology Progress
Abstract
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.
Description
This is the peer reviewed version of the following article: Guo, Jing, Steven J. Traylor, Mohamed Agoub, Weixin Jin, Helen Hua, R. Bertrum Diemer, Xuankuo Xu, Sanchayita Ghose, Zheng Jian Li, and Abraham M. Lenhoff. “Modeling Scalability of Impurity Precipitation in Downstream Biomanufacturing.” Biotechnology Progress, March 27, 2024, e3454. https://doi.org/10.1002/btpr.3454, which has been published in final form at https://doi.org/10.1002/btpr.3454. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.© 2024 American Institute of Chemical Engineers. This article will be embargoed until 03/27/2025.
Keywords
computational fluid dynamics, monoclonal antibody, population balance, precipitation, viral inactivation and neutralization
Citation
Guo, Jing, Steven J. Traylor, Mohamed Agoub, Weixin Jin, Helen Hua, R. Bertrum Diemer, Xuankuo Xu, Sanchayita Ghose, Zheng Jian Li, and Abraham M. Lenhoff. “Modeling Scalability of Impurity Precipitation in Downstream Biomanufacturing.” Biotechnology Progress, March 27, 2024, e3454. https://doi.org/10.1002/btpr.3454.