(697a) Process Modelling, Simulation and Optimisation for Continuous Biopharmaceutical Manufacturing | AIChE

(697a) Process Modelling, Simulation and Optimisation for Continuous Biopharmaceutical Manufacturing


Diab, S. - Presenter, University of Edinburgh
Shirahata, H., The University of Tokyo
Sugiyama, H., The University of Tokyo
Gerogiorgis, D. I., University of Edinburgh
Batch processing is the traditional mode of manufacturing for a wide range of products by the pharmaceutical industry. While batch methods have advantages such as flexible equipment usage and the ability for specific batch recall, issues with product quality variations and process reliability necessitate technological innovation to maintain profitability and sustainability. Continuous pharmaceutical manufacturing (CPM) is a promising alternative to the batch paradigm [1], offering remarkable operating and economic benefits [2], attracting strong interest from academia, industry and regulatory bodies [3]. Continuous biopharmaceutical manufacturing also shows promise for technoeconomic benefits [4]; the elucidation of end-to-end continuous biopharmaceutical manufacturing campaigns, including essential continuous purification and separation processes, is essential for successful implementation.

Process systems engineering (PSE) methodologies are valid alternatives to laborious experimental campaigns at the early stages of process development, allowing screening for promising candidate process configurations prior to further development and scale-up [5], as well as in retrofitting [6]. For sterile filling of biopharmaceuticals, a systematic decision-making procedure has been developed to support the selection of equipment technology, in order to choose between conventional multi-use or novel single-use options [7]. Process modelling and nonlinear optimisation studies have also been used to establish cost optimal operating configurations for the CPM of pharmaceutical [8] and biopharmaceutical products [9]. However, plantwide modelling, simulation and technoeconomic optimisation of a continuous biopharmaceutical manufacturing plant has yet to be implemented to establish such cost optimal operating configurations.

This paper presents the nonlinear optimisation for total cost minimisation of the upstream continuous manufacturing of a biopharmaceutical product. Kinetic parameter estimation for biopharmaceutical synthesis modelling has been performed on the basis of available literature data for a batch culture of hybridoma cell line [10] and for a continuous culture of budding yeasts [11]. Plantwide models for both upstream synthesis and downstream separation process models are then discussed, and the formulation of the objective function (plant cost) for total cost minimisation is presented, along with the illustration of minima for various process configurations, followed by a critical discussion of the implemented methodology. This work illustrates potential advantages attainable via end-to-end continuous biopharmaceutical manufacturing, and the importance of process modelling and optimisation studies at the early stages of design.


[1] B. Gutmann, D. Cantillo, and C. O. Kappe, “Continuous-Flow Technology: A Tool for the Safe Manufacturing of Active Pharmaceutical Ingredients,” Angew. Chemie-International Ed. 54(23): 6688–6728 (2015).

[2] D. Dallinger and C. O. Kappe, “Why flow means green – Evaluating the merits of continuous processing in the context of sustainability,” Curr. Opin. Green Sustain. Chem.7: 6–12 (2017).

[3] S. L. Lee, T. F. O’Connor, X. Yang, C. N. Cruz, S. Chatterjee, R. D. Madurawe, C. M. V. Moore, L. X. Yu, and J. Woodcock, “Modernizing Pharmaceutical Manufacturing: from Batch to Continuous Production,” J. Pharm. Innov. 10(1): 191–199 (2015).

[4] M. S. Croughan, K. B. Konstantinov, and C. Cooney, “The future of industrial bioprocessing: Batch or continuous?,” Biotechnol. Bioeng. 112(4): 648–651, (2015).

[5] S. K. Teoh, C. Rathi, and P. Sharratt, “Practical Assessment Methodology for Converting Fine Chemicals Processes from Batch to Continuous,” Org. Process Res. Dev. 20(2) 414–431 (2015).

[6] A. Segawa, S. Yoshikawa, T. Toyama, H. Nakanishi, E. Kikuchi-Uehara, M. Hirao, and H. Sugiyama, “Method for reducing environmental, health, and safety risks in active pharmaceutical ingredient manufacturing based on multiobjective evaluation,” Process Saf. Environ. Prot. 104: 304–313 (2016).

[7] H. Shirahata, M. Hirao, and H. Sugiyama, “Decision-Support Method for the Choice Between Single-Use and Multi-Use Technologies in Sterile Drug Product Manufacturing,” J. Pharm. Innov. 12(1): 1–13 (2017).

[8] H.G. Jolliffe, S. Diab, and D. I. Gerogiorgis, “Nonlinear optimisation via explicit NRTL model solubility prediction for antisolvent mixture selection in artemisinin crystallisation,” Org. Process Res. Dev. 22(1): 40–53 (2018).

[9] P. Kroll, A. Hofer, S. Ulonska, J. Kager, and C. Herwig, “Model-Based Methods in the Biopharmaceutical Process Lifecycle,” Pharm. Res. 34(12): 2596–2613 (2017).

[10] C. Kontoravdi, E. N. Pistikopoulos, and A. Mantalaris, “Systematic development of predictive mathematical models for animal cell cultures,” Comput. Chem. Eng. 34(8): 1192–1198 (2010).

[11] H. N. Chang, N. J. Kim, J. Kang, C. M. Jeong, J. D. R. Choi, Q. Fei, B. J. Kim, S. Kwon, S. Y. Lee, and J. Kim, “Multi-stage high cell continuous fermentation for high productivity and titer,” Bioprocess Biosyst. Eng. 34(4): 419–431 (2011).