(542e) First-Principles Dynamic Simulation of an Integrated Continuous Biomanufacturing Platform

Biopharmaceuticals, which are also widely known as biologics or biologic drugs, are products derived from biological organisms for treating or preventing diseases. The global sales of biopharmaceuticals have continually increased for many years, with hundreds of approved products on the market and over 7000 medicines in development. Monoclonal antibodies (mAbs) are the highest selling class of biopharmaceuticals due to their specific action and reduced immunogenicity. With continued growth in sales of existing mAb products and a growing pipeline of mAb product candidates being developed, the total sales of mAb products and all biopharmaceuticals will continue to increase in the coming years. The development of mAbs is expected to grow further as more diseases are understood at molecular and cellular levels [1].

Most biomanufacturing unit operations use empirical (black box) or semi-empirical (grey box) models based on experimental data because biomanufacturing processes are challenging to model using first principles. However, data-based models are not suitable for process prediction and optimization because they perform poorly on extrapolation from their original data set and are not based on any process understanding [2]. First-principles models, on the other hand, are based on understanding of fundamental physical phenomena such as conservation laws, thermodynamics, chemical kinetics, and transport phenomena. These models are useful for process development because less experimental data is needed. Integrated first-principles models for the entire biomanufacturing plant are needed to consider that individual unit operations do not operate in isolation, and changes in a unit operation can affect a process further downstream [3,4].

This presentation describes a software tool for carrying out high-fidelity dynamic simulations of integrated continuous mAb manufacturing plants. The simulations include first-principles models of individual biomanufacturing unit operations, including bioreactors, chromatography columns, crystallizers, viral inactivation units, and filtration units. Thoroughly validated models were not available for the quantitative prediction of some relationships. The lack of validated models was addressed by using the best models available in the literature and then validating them using experimental data collected for the individual unit operations from an automated integrated continuous manufacturing platform at MIT. These validated individual units are then combined into a plant-wide dynamic model that includes the effects of model parameter uncertainties and disturbances, which is then used to validate the integration of the individual models and to map the raw materials and operations to the critical quality attributes and other variables of interest anywhere in the system.

Plant-wide first-principles predictive models can be used to design, compare, and evaluate various control and real-time release testing strategies [5]. This approach enables evaluation of the impact of choosing the underlying manufacturing model and control strategy on process performance. The plant-wide simulation software is designed to make the replacement of individual models and unit operations seamless. This plug-and-play ability enables the simulation-based evaluation of multiple process options before equipment is swapped in and out of the real physical manufacturing plant, including for less established technology such as protein capture and purification via crystallization [1,6].

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 and Chemical Engineering, 110:106–114, 2018.

[2] D. B. Boedeker and D. J. Magnus. Opportunities and limitations of continuous processing and use of disposables. American Pharmaceutical Review, 20(1):66+, 2017.

[3] Z. Xing, B. M. Kenty, Z. J. Li, and S. S. Lee. Scale-up analysis for a CHO cell culture process in large-scale bioreactor. Biotechnology and Bioengineering, 103(4):733–746, 2009.

[4] R. P. Harrison, S. Ruck, N. Medcalf, and Q. A. Rafiq. Decentralized manufacturing of cell and gene therapies: Overcoming challenges and identifying opportunities. Cytotherapy, 19(10):1140–1151, 2017.

[5] M. Jiang, K. A. Severson, J. C. Love, H. Madden, P. Swann, L. Zang, and R. D. Braatz. Opportunities and challenges of real-time release testing in biopharmaceutical manufacturing. Biotechnology and Bioengineering, 114(11):2445-2456, 2017.

[6] B. Smejkal, N. J. Agrawal, B. Helk, H. Schulz, M. Giffard, M. Mechelke, F. Ortner, P. Heckmeier, B. L. Trout, and D. Hekmat. Fast and scalable purification of a therapeutic full‐length antibody based on process crystallization. Biotechnology and Bioengineering, 110(9):2452-2461, 2013.