A Self-Consistent, High-Fidelity Adsorption Model for Chromatographic Process Predictions: Low-to-High Load Density and Charge Variants in a Preparative Cation Exchanger
The development of ion exchange chromatography to polish biopharmaceuticals requires extensive experimental benchmarking. As part of the Design of Experiments (DoE), statistical models increased efficiency somewhat and are still state of the art; however, the capability to predict process conditions...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Separations |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2297-8739/12/6/147 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The development of ion exchange chromatography to polish biopharmaceuticals requires extensive experimental benchmarking. As part of the Design of Experiments (DoE), statistical models increased efficiency somewhat and are still state of the art; however, the capability to predict process conditions is limited due to their nature as interpolating models. Applying the DoE still requires numerous experiments and is constrained to the design space, posing a risk of missing the potential optimum. To make a leap in model-based process development, applying extrapolating models can tremendously extend the design space and also allow for process understanding and knowledge transfer. While existing chromatography modeling software explains experimental data, it often lacks predictive power for new conditions. In academic–industrial cooperation, we demonstrate a new high-fidelity model based on biophysics for developing ion-exchange chromatography in biomanufacturing, making it a general tool in rationalizing process development for the present demand of recombinant proteins and monoclonal antibodies and the emerging demand of new modalities. Using the new computational tool, we achieved predictability and attained high accuracy; with minimal experimental effort to calibrate the system, the mathematical model predicted sensitive process conditions, and even described product-related impurities, antibody charge variants. Thus, the computational tool can be deployed for process-by-design and material-by-design approaches. |
|---|---|
| ISSN: | 2297-8739 |