Application of machine learning methods to pathogen safety evaluation in biological manufacturing processes
| Autor: | Shawn Liu, Ivan Cui, Shyam Panjwani, June X. Zou, Konstantinos Spetsieris, Oliver Hesse, Wensheng Wang, Michal Mleczko, Roger Canales, Mohammad Anwaruzzaman |
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| Rok vydání: | 2021 |
| Předmět: |
0106 biological sciences
Process (engineering) Computer science medicine.drug_class viruses CHO Cells Machine learning computer.software_genre Monoclonal antibody 01 natural sciences RESEARCH ARTICLES Machine Learning Cricetulus Cricetinae 010608 biotechnology medicine Animals Pathogen business.industry 010401 analytical chemistry pathogen safety Antibodies Monoclonal Wet laboratory Hydrogen-Ion Concentration Viral Inactivation Recombinant Proteins 0104 chemical sciences Bioseparations and Downstream Processing Antibody production Chromatographic separation biological manufacturing process monoclonal antibody low pH viral inactivation Viruses Virus Inactivation Artificial intelligence Safety Viral contamination business computer Filtration Research Article Biotechnology |
| Zdroj: | Biotechnology Progress |
| ISSN: | 1520-6033 8756-7938 |
| DOI: | 10.1002/btpr.3135 |
| Popis: | The production of recombinant therapeutic proteins from animal or human cell lines entails the risk of endogenous viral contamination from cell substrates and adventitious agents from raw materials and environment. One of the approaches to control such potential viral contamination is to ensure the manufacturing process can adequately clear the potential viral contaminants. Viral clearance for production of human monoclonal antibodies is achieved by dedicated unit operations, such as low pH inactivation, viral filtration, and chromatographic separation. The process development of each viral clearance step for a new antibody production requires significant effort and resources invested in wet laboratory experiments for process characterization studies. Machine learning methods have the potential to help streamline the development and optimization of viral clearance unit operations for new therapeutic antibodies. The current work focuses on evaluating the usefulness of machine learning methods for process understanding and predictive modeling for viral clearance via a case study on low pH viral inactivation. |
| Databáze: | OpenAIRE |
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