2021 Annual Meeting
(293c) Biomanufacturing and Testbed Development for the Continuous Production of Monoclonal Antibodies
Authors
We have built a continuous testbed for the manufacturing of mAbs, which is being used to experimentally validate data analytics-based modeling tools and control design methods for the manufacturing of biopharmaceuticals [Refs. 1 and 2 and citations therein]. The testbed consists of 4 parallel upstream systems including 4 perfusion devices (Repligen ATF2), with one reactor assembly integrated within a fully continuous downstream system, including Protein A chromatography, in-house designed viral inactivation, and ion exchange chromatography. The testbed is equipped with instrumentation to fully characterize the process, including in-reactor probes for Raman spectroscopy (Kaiser RamanRXN2), viable cell density (Aber Futura) and optical density (Optek). To provide further at-line process and product characterization, each upstream assembly is equipped with two MAST Sample Pilots for automated sampling of both the reactor contents and the perfusate. The MAST system delivers samples to a Nova FLEX2 cell culture analyzer for key metabolite quantification as well as the verification of in-reactor sensors, including pH. For at-line characterization of critical quality attributes (CQAs), the cell-free perfusate samples are collected in a Gilson GX-271 liquid handler, purified using at-line Protein A chromatography, and delivered automatically via MAST to either an Agilent 1260 Bio-Inert HPLC for assessment of aggregation and titer or to an Agilent 6545XT LC/QTOF for characterization of glycosylation profiles utilizing mass spectrometry.
This continuous biomanufacturing testbed is being used to obtain process data and to develop analytical methods for determination of mAbâs critical quality attributes (CQAs). The presentation discusses our team effort in continuous process development as well as the impact of processing modes on the product CQAs. Results obtained in the continuous biomanufacturing testbed provide an opportunity to test and validate our analytical methods as well as to develop control strategies that will benefit future biomanufacturing.
References
- W. Sun and R. D. Braatz. Smart process analytics for predictive modeling. Computers & Chemical Engineering, 144, 107134, 2021.
- M. S. Hong, K. A. Severson, M. Jiang, A. E. Lu, J. C. Love, and R. D. Braatz. Challenges and opportunities of biopharmaceutical manufacturing control. Computers & Chemical Engineering, 110, 106-114, 2018.