The biopharmaceutical industry of the future will need highly flexible processes that are adaptable to new and increasingly broad product lines. Reliable real-time monitoring and control of critical quality attributes (CQAs) is central to providing this process flexibility, and adaptive control strategies will be needed to achieve this control. Effective, automated control over critical quality attributes is an important step in realizing “lights out” end-to-end (E2E) biopharmaceutical processes that will enhance patient access to safe and efficacious therapeutics. Enable robust control of CQAs, especially in response to variability in raw materials.
The test bed will be equipped to grow Chinese hamster ovary cells (CHO) for production and purification of a monoclonal antibody (mAb). The main process will consist of two jacketed 5 L bioreactors (that can be operated in parallel) for cell growth, a depth filtration system for clarification of cell culture harvest, and a protein A affinity purification step (using an existing AKTA avant chromatography system). The system is an expansion of a previous NIIMBL test bed for automation workforce development.
Demonstrated proof-of-concept for how to deploy advanced PAT and MPC for feedback control of CQA’s.
Provided framework for the biomanufacturing industry regarding data interoperability
Barriers and hurdles encountered have led to further industry engagement/NIIMBL initiatives
This project validated adaptive control modeling that reduces variability-driven failures for drug companies, resulting in risk and quality benefits across bioprocess control.
Commissioned automation testbed for proof-of-concept demonstration of adaptive control.
Integrated advanced at-line PAT for the purpose of monitoring and feedback control of CQA’s.
Ingested data from multiple sources with a hybrid model used predict and control CQA’s
Deployed adaptive model predictive control in 5 L scale culture
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North Carolina State University
