Multiobjective evolutionary optimization in antibody purification process design

Abstract

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. (C) 2014 Published by Elsevier B.V.