Alternative approaches for the characterisation of chromatographic separations from scale down experiments

Accurately characterising the design space of a process is critical for the development of robust and economic purification processes. However, this can require extensive experimentation which can be expensive and time consuming. Furthermore, this experimentation may require amounts of material that is not available during the early stages of process development, which can lead to delays in the development of the biopharmaceutical product. An approach that can ease the burden of characterisation is the use of scale-down methods and devices. Scale-down experiments require significantly smaller amounts of material and can be automated to improve throughput. Unfortunately various issues associated with scale-down experimentation limit their usefulness. One such issue is that various sources of variability can introduce noise into the results of these experiments. In this study Monte Carlo simulations were carried out in conjunction with a mechanistic general rate model of a typical ion exchange chromatographic purification to investigate the impact of variability on the results of these experiments. A comparison of alternative process modelling approaches was carried out to determine which was capable of producing the most accurate process models from noisy data. A Kriging algorithm was found to be the most capable technique. There are also various scaling effects that cause the scale down experiments to not be representative of the large scale process. Two approaches were developed that augmented extensive scale down experimentation with a small number of large scale experiments to produce representative models of the large scale design space. One of the approaches used derived transformation functions to transform the scale-down data into the large scale design space and the other used a Cokriging algorithm. Using the cation exchange chromatography purification of myoglobin from egg white proteins as a test platform it was shown that it was possible to produce accurate characterisations of the large scale process using only a fraction of the material that would be otherwise required for carrying out the experimentation at large scale. These approaches were further tested using a purification sequence consisting of a heat treatment step followed by a cation exchange chromatography step for the purification of an antibody fragment from E. coli lysate. In this study fractionation diagrams were adapted to describe the elution profiles of the product and its various impurities to enable the multi-objective optimisation of the process. It was demonstrated that this approach could be used to produce detailed characterisations that revealed the relationships between the process variables and multiple responses across the whole process sequence