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Laboratory Scale Production and Purification of a Therapeutic Antibody
Published on: January 24, 2017
Smart manufacturing for continuous downstream processing of monoclonal antibodies at the lab-scale
Andreas W Rebmann1, Jacinta Okpanum1, Sambit Ghosh1
1Chemical and Biological Engineering, Rensselaer Polytechnic Institute, 110 8th St, Troy, 12180, NY, United States of America.
None:
Continuous bioprocessing has shown promise for efficiently scaling production of monoclonal antibodies for biotherapeutic production. Integrating process analytical technologies at the lab-scale stage improves the reliability of results during development and the translation of the system during scale-up. Smart Manufacturing provides a framework for implementing process analytical technologies at all process scales by leveraging modern hardware, software, and techniques. Demonstration of scaling and retrofitting a legacy system into a smart manufacturing system for the continuous downstream processing of monoclonal antibodies at the lab-scale was achieved through modular and flexible controller hardware, Industrial Internet-of-Things architecture, interoperable OPC-UA servers, and cloud computing on the Smart Manufacturing Interoperability Platform. The system consisted of lab-scale equipment connected to a legacy control device with limited operation and I/O. A new system of hardware was implemented that interfaced with the legacy equipment while also providing scalability, reconfigurability, and controllability. Peristaltic pumps, weigh-scales, single-use pressure sensors, and optical sensors were connected to edge devices which bridged the hardware and software used for process monitoring and control. Lab-scale implementation and operation challenges such as connecting multiple vendor systems and inconsistent pump flowrates were addressed. Water-based pump flowrate control tests were performed on the legacy equipment with readiness for deployment onto the smart manufacturing system. The techniques used include a Kalman Filter, steady-state data reconciliation, and closed-loop control based on scale measurements.
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