Optimal Resin Selection for Integrated Chromatographic Separations in High-Throughput Screening

Abstract

Chromatography is an essential step of biopharmaceutical production. High-throughput screening (HTS) is widely used in the biopharmaceutical industry to identify optimal conditions for chromatography processes. At the early stage of the purification process development, different resins are tested under various operating conditions, resulting in the generation of large amounts of data. To facilitate the decision-making process, we develop an optimisation-based decision support approach to process data generated from microscale experiments in order to identify the best resins to maximise key performance metrics of the biopharmaceutical manufacturing process. A multiobjective mixed integer nonlinear programming (MINLP) model is developed, for optimal resin selection for chromatographic sequence used for protein purification. The proposed model is solved using the e-constraint method and applied to an industrially-relevant example with two chromatography steps. The computational results show that the developed approach is an efficient way to identify the best resins.