Researcher ORCID Identifier
https://orcid.org/0000-0001-5674-9983
Date Degree Awarded
Winter 12-18-2020
Degree Type
Restricted to Claremont Colleges Dissertation
Degree Name
PHD in Applied Life Sciences
First Thesis/Dissertation Advisor
Cameron Bardliving, PhD
Second Thesis/Dissertation Advisor
Parviz Shamlou, PhD
Third Thesis/Dissertation Advisor
Hu Zhang, PhD
Terms of Use & License Information
Abstract
The need for production processes of large biotherapeutic particles, such as virus-based particles and extracellular vesicles, has risen due to increased demand in the development of vaccinations, gene therapies, and cancer treatments. Liquid chromatography plays a significant role in the purification process and is routinely used with therapeutic protein production. However, performance with larger macromolecules is often inconsistent, and parameter estimation for process development can be extremely time- and resource-intensive. This thesis aimed to utilize advances in computational fluid dynamic (CFD) modeling to generate a first-principle model of the chromatographic process while minimizing model parameter estimation's physical resource demand. Specifically, I utilized explicit geometric rendering to develop a CFD steady-state model to simulate fluid flow through and around a perfusive porous resin in a pseudo packed bed flow-cell to predicted fluid velocities and shear stress. I generated different explicit geometries, and compared the velocity profiles of steady-state simulations against reported literature values of commercially available resin's intraparticle convective flow. I then developed a two-part transient CFD discrete phase model to model a tracer protein's capture and release from a resin. Particle age distribution functions were calculated to characterize the macromixing in the model and compared them with existing single parameter models. These models exhibited similar distribution profiles and provided additional information about the shear forces acting on the particles. These preliminary studies revealed that shear is relatively low shear at process operating conditions, and the low yield of large biotherapeutic particles in chromatography is likely not due to shear forces.
Rights Information
© 2020 Kevin C Vehar
Recommended Citation
Vehar, Kevin. (2020). Modeling Residence Time Distribution of Chromatographic Perfusion Resin for Large Biopharmaceutical Molecules: A Computational Fluid Dynamic Study. KGI Theses and Dissertations, 18. https://scholarship.claremont.edu/kgi__theses/18.
Included in
Biotechnology Commons, Complex Fluids Commons, Fluid Dynamics Commons, Geometry and Topology Commons, Other Biomedical Engineering and Bioengineering Commons
