DEPTH FILTERS IN BIOPROCESSING: PERFORMANCE AND SCALE-UP
Restricted (Penn State Only)
- Author:
- Nejatishahidein, Negin
- Graduate Program:
- Chemical Engineering (PHD)
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- December 01, 2021
- Committee Members:
- Andrew Zydney, Chair & Dissertation Advisor
William Hancock, Outside Unit & Field Member
Ali Borhan, Major Field Member
Stephanie Velegol, Major Field Member
Seong H. Kim, Professor in Charge/Director of Graduate Studies - Keywords:
- CFD
clarification
depth filtration
flow distribution
lenticular stack
scale‐up
cell culture fluid
host cell proteins
protein binding
monoclonal antibodies
flow structure - Abstract:
- Significant increases in cell density and product titer have led to renewed interest in the application of depth filtration for initial clarification of cell culture fluid in antibody production. Depth filtration is particularly attractive as part of single-use multi-product manufacturing facilities, which minimize the risk of cross-contamination while eliminating cleaning steps. In addition, specially designed depth filters can be used for pretreatment of in-process streams to remove key foulants, e.g., host cell proteins (HCP) and DNA, thereby improving the performance of subsequent chromatography or filtration steps. The overall objective of this thesis was to obtain a more fundamental understanding of the factors controlling the performance of depth filters specifically designed for bioprocessing applications, including issues associated with protein / DNA binding and scale-up. In Chapter 2 of this thesis, the effectiveness of a model depth filter containing diatomaceous earth was evaluated for HCP removal. Experiments were performed with both cell culture fluid (CCF) and a series of model proteins with defined pI, molecular weight, and hydrophobicity chosen to match the range of typical HCP. Data show the importance of both electrostatic and hydrophobic interactions in determining the overall protein removal properties. In addition to the filter chemistry, the performance characteristics of these depth filters can also be strongly influenced by the local flow and pressure distribution within the filter capsule. Chapter 3 examines the flow distribution and pressure profiles within a small-scale commercial depth filter using a combination of computational fluid dynamics (CFD), residence time distribution (RTD), and dye binding studies. Chapter 4 extends the developed model to predict the protein breakthrough curves in two small-scale depth filter modules, showing that the complex flow field in commercial depth filter modules can result in very diffuse breakthrough curves. These results provide important insights into the factors controlling protein binding/breakthrough in different depth filter modules. Although the performance of depth filters depends strongly on the local pressure and velocity distributions within the filter capsule, these are very difficult to probe experimentally, leading to challenges in both process design and scale-up. The main objective of Chapter 5 was to extend the modeling framework developed in Chapters 3 and 4 to examine the performance of pilot-scale (StaxTM) depth filter modules. The combination of experimental and computational studies provides important insights into the factors controlling the performance and scale-up of these commercially important depth filters, as well as a framework that can be broadly applied to develop more effective depth filters and depth filtration processes. Chapter 6 presents results on the clarification properties of different commercially available depth filters with different binding media and geometries. Data were obtained for different feed streams to highlight the very different binding characteristics and to explore how the fouling behavior affects the performance of these depth filters. In total, the results presented in this thesis provide important insights into the performance of depth filters used in bioprocessing, including the role of the media, the module geometry, and the underlying flow structure across different scales of operation.