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Application of machine learning methods to pathogen safety evaluation in biological manufacturing processes
Author(s) -
Panjwani Shyam,
Cui Ivan,
Spetsieris Konstantinos,
Mleczko Michal,
Wang Wensheng,
Zou June X.,
Anwaruzzaman Mohammad,
Liu Shawn,
Canales Roger,
Hesse Oliver
Publication year - 2021
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1002/btpr.3135
Subject(s) - monoclonal antibody , biochemical engineering , process (computing) , computer science , contamination , filtration (mathematics) , antibody , chemistry , biology , engineering , immunology , mathematics , ecology , statistics , operating system
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.