Data-Driven Yield Improvement in Upstream Bioprocessing of Monoclonal Antibodies: A Machine Learning Case Study
Αποθηκεύτηκε σε:
| Εκδόθηκε σε: | Processes vol. 13, no. 11 (2025), p. 3394-3412 |
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| Κύριος συγγραφέας: | |
| Άλλοι συγγραφείς: | , |
| Έκδοση: |
MDPI AG
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| Θέματα: | |
| Διαθέσιμο Online: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Strüssmann, Breno Renato |u Department of Civil and Mechanical Engineering, Technical University of Denmark, 2800 Copenhagen, Denmark; lahv@dtu.dk | |
| 245 | 1 | |a Data-Driven Yield Improvement in Upstream Bioprocessing of Monoclonal Antibodies: A Machine Learning Case Study | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The increasing demand for monoclonal antibody (mAb) therapeutics has intensified the need for more efficient and consistent biomanufacturing processes. We present a data-driven, machine-learning (ML) approach to exploring and predicting upstream yield behavior. Drawing on industrial-scale batch records for a single mAb product from a contract development and manufacturing organization, we applied regression models to identify key process parameters and estimate production outcomes. Random forest regression, gradient boosting machine, and support vector regression (SVR) were evaluated to predict three yield indicators: bioreactor final weight (BFW), harvest titer (HT), and packed cell volume (PCV). SVR outperformed other models for BFW prediction (R2 = 0.978), while HT and PCV were difficult to model accurately with the available data. Exploratory analysis using sequential least-squares programming suggested parameter combinations associated with improved yield estimates relative to historical data. Sensitivity analysis highlighted the most influential process parameters. While the findings demonstrate the potential of ML for predictive, data-driven yield improvement, the results should be interpreted as an exploratory proof of concept rather than a fully validated optimization framework. This study highlights the need to incorporate process constraints and control logic, along with interpretable or hybrid modeling frameworks, to enable practical deployment in regulated biomanufacturing environments. | |
| 653 | |a Monoclonal antibodies | ||
| 653 | |a Datasets | ||
| 653 | |a Bioprocessing | ||
| 653 | |a Sensitivity analysis | ||
| 653 | |a Parameter sensitivity | ||
| 653 | |a Regression analysis | ||
| 653 | |a Regression models | ||
| 653 | |a Data analysis | ||
| 653 | |a Machine learning | ||
| 653 | |a Manufacturing | ||
| 653 | |a Cell culture | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Biotechnology | ||
| 653 | |a Good Manufacturing Practice | ||
| 653 | |a Parameter identification | ||
| 653 | |a Parameter estimation | ||
| 653 | |a Support vector machines | ||
| 653 | |a Process controls | ||
| 653 | |a Bioreactors | ||
| 653 | |a Cell size | ||
| 653 | |a Process parameters | ||
| 700 | 1 | |a de Queiroz Anderson Rodrigo |u CCEE Department, NC State University, Raleigh, NC 27606, USA; ardequei@ncsu.edu | |
| 700 | 1 | |a Hvam Lars |u Department of Civil and Mechanical Engineering, Technical University of Denmark, 2800 Copenhagen, Denmark; lahv@dtu.dk | |
| 773 | 0 | |t Processes |g vol. 13, no. 11 (2025), p. 3394-3412 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275549596/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3275549596/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275549596/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |