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Modeling scalability of impurity precipitation in downstream biomanufacturing
Jing Guo1,2, Steven J Traylor2, Mohamed Agoub1,2
1Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware, USA.
Biotechnology Progress
|March 28, 2024
Summary
Precipitation during monoclonal antibody (mAb) purification effectively reduces impurities. This study developed scale-down models using computational fluid dynamics (CFD) and population balance modeling to predict impurity precipitation across manufacturing scales.
Area of Science:
- Biopharmaceutical Manufacturing
- Chemical Engineering
- Process Development
Background:
- Precipitation is a key step in monoclonal antibody (mAb) purification for impurity removal.
- Commercial implementation is hindered by a lack of representative scale-down models for impurity precipitation.
- Understanding scale-up challenges is crucial for robust bioprocess design.
Purpose of the Study:
- To compare isoelectric impurity precipitation behavior of a mAb product across different manufacturing scales (benchtop to pilot).
- To investigate the impact of scaling parameters like agitation and vessel geometry on precipitation.
- To develop and validate predictive models for impurity precipitation during mAb purification scale-up.
Main Methods:
- Characterization of precipitate amount and particle size distribution (PSD) using turbidity and flow imaging microscopy.
- Investigation of scaling parameters including energy dissipation rate (EDR), agitation, and vessel geometry.
- Simulation of agitation using computational fluid dynamics (CFD) integrated with a population balance model.
- Development of a two-compartment mixing model to capture precipitation dynamics under varying turbulence.
Main Results:
- Consistent energy dissipation rate (EDR) provides an approximate method for scaling vessel geometry and agitator speeds.
- CFD and population balance modeling successfully simulated precipitate particle size distribution trajectories across scales.
- A bifurcated model accurately captured the temporal variability in particle sizes during precipitation.
- The developed models enhance mechanistic understanding and predictive capabilities for process scaling.
Conclusions:
- Scale-down models integrating CFD and population balance are effective for predicting impurity precipitation in mAb purification.
- These models facilitate mechanistic understanding and support model-assisted process scaling.
- The findings contribute to the robust commercial implementation of precipitation as a unit operation in biopharmaceutical manufacturing.
