The derivation of bioprocess understanding from mechanistic models of chromatography

This thesis, completed in collaboration with Purification Process Development of Pfizer Biotherapeutics, is concerned with how mechanistic models of chromatographic bioseparations can be applied in industry to accelerate development and increase robustness of industrial protein purification processes, whilst also realising the benefits of a systematic development approach based on fundamental process and product understanding. The first results chapter considers the application of mechanistic models to provide a link between high throughput screening (HTS) and scouting runs conducted during early process development. The chapter focuses on an anion exchange (AEX) weak partitioning chromatography (WPC) polishing step in a platform monoclonal antibody purification process. Adsorption isotherms are formulated from experimental multicomponent batch adsorption studies of monomer – aggregate. A new approach is taken where the adsorption equilibria is characterised as a function of the product partition coefficient, enabling the model to be applied to new candidate monoclonal antibodies without additional experimental effort. Stochastic simulations conducted at an early stage of process development identify promising operating parameter ranges for challenging separations, directs optimal performance, and reduces development times. A detailed analysis of model predictions increases fundamental knowledge and understanding of the complex WPC multidimensional design space, which enables better informed process development at Pfizer. Resin fouling over a chromatography columns lifetime can cause significant (undesired) changes in process performance. A lack of fundamental knowledge and mechanistic understanding of fouling in industrial bioseparations limits the application of mechanistic models in industry. An experimental investigation was conducted into fouling of the AEX WPC considered in the first results chapter. Analysis of fouled resin samples by batch uptake experiments, scanning electron microscopy, confocal laser scanning microscopy and scale down column studies, indicated significant blockage of the pores at the resin surface occurred that after successive batch cycles. Mass transport into resin particles was severely hindered, but saturation capacity was not affected. The increased understanding of resin fouling can direct future efforts to mitigate this detrimental phenomenon and maintain process performance, whilst providing a basis for the development of new fouling models. The third results chapter considers an industrial hydrophobic interaction chromatography (HIC) separation at a late stage of process development. Resin lot variability, combined with a variable feed stream, had resulted in serious performance issues during the purification of a therapeutic protein from crude feed material. The traditional approach to tackling this type of problem involves defining a design space based on an extensive experimental effort directed by factorial design of experiments conducted at great cost. The result is a fixed, inflexible manufacturing process, with a control strategy based on reproducibility rather than robustness, and little fundamental understanding of the source of the issue. In the third results chapter, the application of mechanistic models to identify robust operating conditions for the HIC is considered. A model is developed, validated experimentally, and used to generate probabilistic design spaces accounting the historical variability in the resin lots and load material. The stochastic simulation approach is extended to explore the impact of reducing variability in the load material on the design space. With historical process variability, no operating condition was found where the probability of meeting product quality specifications remained > 0.95 for all resin lots. Model simulations indicated that adopting an adaptive design space, where operating conditions are changed according to which resin lot is in use, is favorable for ensuring process robustness, which is a step change concept for bioprocessing. The conclusions and outcomes resulting from the application of mechanistic models to the two industrial systems in this thesis, provides a basis for the next generation purification process development platform.