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...
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MDPI AG
2025-06-01
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| author | Gregor M. Essert Marko Tesanovic Sonja Berensmeier Isabell Hagemann Peter Schwan |
| author_facet | Gregor M. Essert Marko Tesanovic Sonja Berensmeier Isabell Hagemann Peter Schwan |
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| description | 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. |
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| institution | Kabale University |
| issn | 2297-8739 |
| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-c5ff7cc40cbb410f8f78e15fa34483f52025-08-20T03:26:52ZengMDPI AGSeparations2297-87392025-06-0112614710.3390/separations12060147A Self-Consistent, High-Fidelity Adsorption Model for Chromatographic Process Predictions: Low-to-High Load Density and Charge Variants in a Preparative Cation ExchangerGregor M. Essert0Marko Tesanovic1Sonja Berensmeier2Isabell Hagemann3Peter Schwan4Chair of Bioseparation Engineering, TUM School of Engineering and Design, Department of Energy and Process Engineering, Technical University of Munich, 85748 Garching, GermanyChair of Bioseparation Engineering, TUM School of Engineering and Design, Department of Energy and Process Engineering, Technical University of Munich, 85748 Garching, GermanyChair of Bioseparation Engineering, TUM School of Engineering and Design, Department of Energy and Process Engineering, Technical University of Munich, 85748 Garching, GermanyBayer AG, 51373 Leverkusen, GermanyBayer AG, 51373 Leverkusen, GermanyThe 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.https://www.mdpi.com/2297-8739/12/6/147proteinantibodymodel-based process developmentdownstream processingbiomanufacturingpurification |
| spellingShingle | Gregor M. Essert Marko Tesanovic Sonja Berensmeier Isabell Hagemann Peter Schwan A Self-Consistent, High-Fidelity Adsorption Model for Chromatographic Process Predictions: Low-to-High Load Density and Charge Variants in a Preparative Cation Exchanger Separations protein antibody model-based process development downstream processing biomanufacturing purification |
| title | A Self-Consistent, High-Fidelity Adsorption Model for Chromatographic Process Predictions: Low-to-High Load Density and Charge Variants in a Preparative Cation Exchanger |
| title_full | A Self-Consistent, High-Fidelity Adsorption Model for Chromatographic Process Predictions: Low-to-High Load Density and Charge Variants in a Preparative Cation Exchanger |
| title_fullStr | A Self-Consistent, High-Fidelity Adsorption Model for Chromatographic Process Predictions: Low-to-High Load Density and Charge Variants in a Preparative Cation Exchanger |
| title_full_unstemmed | A Self-Consistent, High-Fidelity Adsorption Model for Chromatographic Process Predictions: Low-to-High Load Density and Charge Variants in a Preparative Cation Exchanger |
| title_short | A Self-Consistent, High-Fidelity Adsorption Model for Chromatographic Process Predictions: Low-to-High Load Density and Charge Variants in a Preparative Cation Exchanger |
| title_sort | self consistent high fidelity adsorption model for chromatographic process predictions low to high load density and charge variants in a preparative cation exchanger |
| topic | protein antibody model-based process development downstream processing biomanufacturing purification |
| url | https://www.mdpi.com/2297-8739/12/6/147 |
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