Phase 2 Downstream Processing Modeling

Depth Filter Multi-Scale Hybrid Modeling

Industry Need

There is a need for modeling infrastructure for simulating the performance of one of the two initial downstream steps in the manufacture of monoclonal antibodies (mAbs), namely Depth Filtration. Being able to describe and predict the values of key outputs that are routinely measured and assayed in process development and manufacturing applications of these operations are very helpful. There is a need for readily usable models by industry practitioners and the models being adaptable by them, using well-defined procedures for determining model parameters, for application to systems using different mAbs, process streams and separations media.

Approach

The primary purpose of depth filtration in harvest is clarification – removal of particulates, especially cells, before HCCF is fed to the ProA capture column. In that context the key inputs are the particle size distribution (PSD) of the feed, the chemical, structural and hence filtration properties of the depth filter, and the flow rate or pressure drop for the feed to the filter. A key measured output is the temporal profile of the pressure drop (at constant flow rate, the customary approach) or flow rate (at constant pressure drop), but the more critical information for subsequent steps is the PSD in the effluent, usually characterized in lumped form by turbidity, and its complement, the solids loading retained on the filter. These elements represent the focus of the depth filtration study. optimizing filtration process performance. A model that fully captures filtration behavior can be used in seeking alternative feed strategies to the usual constant-flow approach, either in open-loop fashion or even implementing a model predictive control approach.

Impacts

The model has potential uses in optimizing filtration process performance. A model that fully captures filtration behavior can be used in seeking alternative feed strategies to the usual constant-flow approach, either in open-loop fashion or even implementing a model predictive control approach.

The long-term goal of using the depth filter models as part of an impurity control strategy will depend on the effectiveness with which impurity clearance can be described

Value Statement/Outcomes

This project enabled depth filtration modeling that improves clarification efficiency for drug companies, resulting in cost and quality benefits across harvest operations.

Outputs/Deliverables

1 Publication Draft Mechanistic modeling framework for primary depth filtration Myers, T., Valiya Parambathu, A., & Lenhoff, A.M.

Publication Draft Performance characterization and microstructural analysis of multi-layer primary depth filters Becker, M. L., Valiya Parambathu, A., Wang, Y., & Lenhoff, A. M.

Publication Draft Mechanistic modeling of primary depth filtration in CHO cell culture harvest Valiya Parambathu, A., Becker, M. L., Wang, Y., & Lenhoff, A. M.

Presentation Abstract submitted Mechanisms and modeling of primary depth filtration of CHO cell culture fluid Becker, M. L., Valiya Parambathu, A., Wang, Y., & Lenhoff, A. M. American Chemical Society National Meeting, Spring 2025

Presentation Scheduled for presentation Mechanisms and modeling of depth filtration in biopharma Valiya Parambathu, A., Becker, M. L., Myers, T., Wang, Y., & Lenhoff, A. M. Bioprocess International West, 2025

Presentations

Becker, M., Presenter, Deposition mechanisms in primary depth filtration via imaging and mechanistic modeling, PREP Symposium, Philadelphia PA, May 30, 2024.

Becker, M., Presenter, Mechanisms and modeling of primary depth filtration of CHO cell culture fluid, American Chemical Society National Meeting, San Diego CA, March 25, 2025.

Parambathu, V., Presenter, Mechanisms and modeling of primary depth filtration, Bioprocessing Summit, 2024, Boston MA, August 20, 2024.