Optimization-based framework for resin selection strategies in biopharmaceutical purification process development

This work addresses rapid resin selection for integrated chromatographic separations when conducted as part of a high‐throughput screening exercise during the early stages of purification process development. An optimization‐based decision support framework is proposed to process the data generated from microscale experiments to identify the best resins to maximize key performance metrics for a biopharmaceutical manufacturing process, such as yield and purity. A multiobjective mixed integer nonlinear programming model is developed and solved using the ε‐constraint method. Dinkelbach's algorithm is used to solve the resulting mixed integer linear fractional programming model. The proposed framework is successfully applied to an industrial case study of a process to purify recombinant Fc Fusion protein from low molecular weight and high molecular weight product related impurities, involving two chromatographic steps with eight and three candidate resins for each step, respectively. The computational results show the advantage of the proposed framework in terms of computational efficiency and flexibility. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1116–1126, 2017

Fouling of an anion exchange chromatography operation in a monoclonal antibody process: Visualization and kinetic studies

Fouling of chromatographic resins over their operational lifetimes can be a significant problem for commercial bioseparations. In this article, scanning electron microscopy (SEM), batch uptake experiments, confocal laser scanning microscopy (CLSM) and small-scale column studies were applied to characterize a case study where fouling had been observed during process development. The fouling was found to occur on an anion exchange (AEX) polishing step following a protein A affinity capture step in a process for the purification of a monoclonal antibody. Fouled resin samples analyzed by SEM and batch uptake experiments indicated that after successive batch cycles, significant blockage of the pores at the resin surface occurred, thereby decreasing the protein uptake rate. Further studies were performed using CLSM to allow temporal and spatial measurements of protein adsorption within the resin, for clean, partially fouled and extensively fouled resin samples. These samples were packed within a miniaturized flowcell and challenged with fluorescently labeled albumin that enabled in situ measurements. The results indicated that the foulant has a significant impact on the kinetics of adsorption, severely decreasing the protein uptake rate, but only results in a minimal decrease in saturation capacity. The impact of the foulant on the kinetics of adsorption was further investigated by loading BSA onto fouled resin over an extended range of flow rates. By decreasing the flow rate during BSA loading, the capacity of the resin was recovered. These data support the hypothesis that the foulant is located on the particle surface, only penetrating the particle to a limited degree. The increased understanding into the nature of the fouling can help in the continued process development of this industrial example.

Scanning electron microscopy (SEM), batch uptake experiments, confocal laser scanning microscopy (CLSM) and small-scale column experiments were applied to characterize a case study where fouling had been observed on an anion exchange chromatography in a monoclonal antibody process. The results suggest the foulant is located on the particle surface, resulting in a minimal decrease in saturation capacity, but having a significant impact on the kinetics of adsorption, severely decreasing protein uptake rate.

Modelling approaches for bio-manufacturing operations

Fast and cost-effective methods are needed to reduce the time and money needed for drug commercialisation and to determine the risks involved in adopting specific manufacturing strategies. Simulations offer one such approach for exploring design spaces before significant process development is carried out and can be used from the very earliest development stages through to scale-up and optimisation of operating conditions and resource deployment patterns both before and after plant start-up. The advantages this brings in terms of financial savings can be considerable, but to achieve these requires a full appreciation of the complexities of processes and how best to represent them mathematically within the context of in silico software. This chapter provides a summary of some of the work that has been carried out in the areas of mathematical modelling and discrete event simulations for production, recovery and purification operations when designing bio-pharmaceutical processes, looking at both financial and technical modelling.