Multiobjective evolutionary optimization in antibody purification process design

To contribute towards designing more cost-efficient, robust and flexible downstream processes for the manufacture of monoclonal antibodies (mAbs), a framework consisting of an evolutionary multiobjective optimization algorithm (EMOA) linked to a biomanufacturing process economics model is presented. The EMOA is tuned to discover sequences of chromatographic purification steps and column sizing strategies that provide the best trade-off with respect to multiple objectives including cost of goods per gram (COG/g), robustness in COG/g, and impurity removal capabilities. Additional complexities accounted for by the framework include uncertainties and constraints. The framework is validated on industrially relevant case studies varying in upstream and downstream processing train ratios, annual demands, and impurity loads. Results obtained by the framework are presented using a range of visualization tools, and indicate that the performance impact of uncertainty is a function of both the level of uncertainty and the objective being optimized, and that uncertainty can cause otherwise optimal processes to become suboptimal. The optimal purification processes discovered outperform the industrial standard with, e.g. savings in COG/g of up to 10%. Guidelines are provided for choosing an optimal purification process as a function of the objectives being optimized and impurity levels present.