(584a) Process Modeling and Control of Digital Biopharmaceutical Manufacturing

Recent trends in biopharmaceutical manufacturing provide opportunities for process systems engineering to make major advances in biomanufacturing: (1) process analytical technology (PAT) is providing on-line measurements of critical quality attributes (CQAs) for constructing first-principles and data-based models of each unit operation and enable advanced control, (2) a transition from batch to continuous operation provides process control needs to handle the propagation of impurities and other disturbances caused by tight integration of unit operations, and (3) the invention of new designs for downstream processes is creating new processes to control [1].

These trends ultimately lead biomanufacturing to digital manufacturing, which is an integrated approach to manufacturing centered around a computer system. Digital manufacturing is enhanced by using modern system engineering tools. Modeling and simulation provide increased mechanistic fidelity, better empirical approaches, and automated decision making. Process optimization enables drug- and patient- specific manufacturing and reduces experimental costs. Process control deals with process variations and enables fully automated systems to minimize operator error [2].

This presentation describes how modern system engineering tools are applied for digitalization of biomanufacturing systems through process modeling and control. The presentation describes mathematical models and optimal control methods for multiple bioreactor configurations including microbioreactor systems [3] and stirred-tank bioreactors [4]. The presentation describes laboratory unit operation systems constructed for downstream process including protein crystallization [5] and continuous viral inactivation. These systems are optimally designed and controlled based on first-principles models.

References:

[1] M. S. Hong, K. A. Severson, M. Jiang, A. E. Lu, J. C. Love, and R. D. Braatz, Challenges and opportunities in biopharmaceutical manufacturing control, Computers & Chemical Engineering, 110:106-114, 2018.

[2] M. S. Hong, W. Sun, A. E. Lu, and R. D. Braatz. Process analytical technology and digital biomanufacturing of monoclonal antibodies. American Pharmaceutical Review, 23(6):122-125, 2020.

[3] M. S. Hong and R. D. Braatz. Mechanistic modeling and parameter-adaptive nonlinear model predictive control of a microbioreactor. Computers & Chemical Engineering, 147:107255, 2021.

[4] M. S. Hong, M. L. Velez-Suberbie, A. J. Maloney, A. Biedermann, K. R. Love, J. C. Love, T. K. Mukhopadhyay, and R. D. Braatz. Macroscopic modeling of bioreactors for recombinant protein producing Pichia pastoris in defined medium. Biotechnology & Bioengineering, 118(3):1199-1212, 2021.

[5] M. S. Hong, K. Kaur, N. Sawant, S. B. Joshi, D. B. Volkin, and R. D. Braatz. Crystallization of a non-replicating rotavirus vaccine candidate. Biotechnology & Bioengineering, 118(4):1750-1756, 2021.