A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes.

ABSTRACT: As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusi

SUBMITTER: Downey B 

PROVIDER: S-EPMC5763414 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature