Application of mechanistic modeling to predict impact of resin lot variabilities

Ion exchange chromatography (IEX) is one of the most applied unit operations in the downstream processing of complex biomolecules. The performance of such a purification process can be very sensitive to small deviations in process conditions. For processes in long-term execution, one significant and inevitable source of process variability is the differences between resin production lots. For IEX resins, these properties include porosity, particle size, and ionic capacity. Of these properties, the ionic capacity is known to have a substantial impact on separation performance.

In this work, we investigated the ability of mechanistic modeling to account for resin lot variability and to predict the resulting changes in an IEX process. We demonstrated that the colloidal particle adsorption (CPA) model was able to accurately predict the breakthrough and elution behavior on the other resin lots. This result enables important considerations in process development: Impact of lot variability on process performance can be precisely investigated with in silico methods and processes can be rapidly adapted to maximize performance on a different resin lot.

Speaker
Tomas Fuchs