Abstract
BACKGROUNDThis paper considers a real-world optimization problem involving the identification of cost-effective equipment sizing strategies for the sequence of chromatography steps employed to purify biopharmaceuticals. Tackling this problem requires solving a combinatorial optimization problem subject to multiple constraints, uncertain parameters, and time-consuming fitness evaluations.RESULTSAn industrially-relevant case study is used to illustrate that evolutionary algorithms can identify chromatography sizing strategies with significant improvements in performance criteria related to process cost, time and product waste over the base case. The results demonstrate also that evolutionary algorithms perform best when infeasible solutions are repaired intelligently, the population size is set appropriately, and elitism is combined with a low number of Monte Carlo trials (needed to account for uncertainty). Adopting this setup turns out to be more important for scenarios where less time is available for the purification process. Finally, a data-visualization tool is employed to illustrate how user preferences can be accounted for when it comes to selecting a sizing strategy to be implemented in a real industrial setting.CONCLUSIONThis work demonstrates that closed-loop evolutionary optimization, when tuned properly and combined with a detailed manufacturing cost model, acts as a powerful decisional tool for the identification of cost-effective purification strategies. © 2013 The Authors. Journal of Chemical Technology & Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
Highlights
Monoclonal antibodies represent the fastest growing category of therapeutic biopharmaceutical drugs due to their unique binding specificity to targets
This study investigates the application of evolutionary algorithms (EAs) for the identification of chromatographycolumnsizingstrategies – defined here by the diameter and bed height of a column, the number of columns used in parallel, and the number of cycles a column is run for – that are cost-effective in terms of cost of goods (COG) per gram (COG/g) of product manufactured
The industrial case study was applied to monoclonal antibodies which represent the fastest growing segment of the pharmaceutical industry where a significant focus is on the need for more cost-effective and robust purification processes for different facility configurations
Summary
Monoclonal antibodies (mAbs) represent the fastest growing category of therapeutic biopharmaceutical drugs due to their unique binding specificity to targets. While alternatives to traditional column chromatography platforms are emerging, industry practitioners are still reluctant to perform major process changes.[1,2] At the same time, it is important to determine how best to use existing production facilities for mAbs.[3,4] This is challenging given the significant improvements in USP productivities that have been accomplished over the past decade with higher mAb concentrations (titers) being achieved in cell culture These improvements have not been matched in purification capacities, leading to concerns over purification bottlenecks and the desire to continuously optimize the design and operation of existing chromatography steps. Tackling this problem requires solving a combinatorial optimization problem subject to multiple constraints, uncertain parameters, and timeconsuming fitness evaluations
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