Abstract
This work addresses the multi-objective optimisation of manufacturing strategies of monoclonal antibodies under uncertainty. The chromatography sequencing and column sizing strategies, including resin at each chromatography step, number of columns, column diameters and bed heights, and number of cycles per batch, are optimised. The objective functions simultaneously minimise the cost of goods per gram and maximise the impurity reduction ability of the purification process. Three parameters are treated as uncertainties, including bioreactor titre, and chromatography yield and capability to remove impurities. Using chance constraint programming techniques, a multi-objective mixed integer optimisation model is proposed. Adapting both ε-constraint method and Dinkelbach's algorithm, an iterative solution approach is developed for Pareto-optimal solutions. The proposed model and approach are applied to an industrially-relevant example, demonstrating the benefits of the proposed model through Monte Carlo simulation. The sensitivity analysis of the confidence levels used in the chance constraints of the proposed model is also conducted.
Highlights
The market of biopharmaceutical products is currently in a fastdevelopment stage, in which the sales of monoclonal antibodies products, important biopharmaceutical drugs for the treatment of cancer, autoimmune diseases, cardiovascular disease, etc., have grown rapidly
An optimisation framework with an evolutionary multi-objective optimisation algorithm was developed to consider multiple objectives, including cost of goods (COG)/g, robustness in COG/g, and impurity removal capabilities, in the optimisation of monoclonal antibodies (mAbs) manufacturing process (Allmendinger et al, 2014b) Another decision-making framework on rapid resin selection in biopharmaceutical purification process development considered both yield of purification process and purity of the target protein as objective functions, which were optimised by a mathematical programming model (Liu et al, 2017)
This work addressed the multi-objective optimisation of downstream processing of mAb products, to find the optimal chromatography sequencing and column sizing strategies
Summary
The market of biopharmaceutical products is currently in a fastdevelopment stage, in which the sales of monoclonal antibodies (mAbs) products, important biopharmaceutical drugs for the treatment of cancer, autoimmune diseases, cardiovascular disease, etc., have grown rapidly. An optimisation framework with an evolutionary multi-objective optimisation algorithm was developed to consider multiple objectives, including COG/g, robustness in COG/g, and impurity removal capabilities, in the optimisation of mAb manufacturing process (Allmendinger et al, 2014b) Another decision-making framework on rapid resin selection in biopharmaceutical purification process development considered both yield of purification process and purity of the target protein as objective functions, which were optimised by a mathematical programming model (Liu et al, 2017). A deterministic multi-objective optimisation model of a biopharmaceutical manufacturing process was developed to optimise both the cost and impurity removal capabilities of the purification process (Liu and Papageorgiou, 2018). Both chromatography sequencing and column sizing strategies of a mAb purification process are determined in order to achieve optimal COG/g and impurity removal capability at the DSP.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
