Optimization of an Antibody Polishing Step

What to do when process development with Design of Experiment fails?

Figure: ChromX graphical user interface of successful optimization with the objective of maximizing purity, yield and production rate. Product (black peak) is separated from critical component (green peak).

Antibody purification processes are typically developed on the base of platform approaches. Optimal operating conditions for single units are thereby found by altering only few process parameters with Design of Experiments (DoE) procedures or high throughput screenings.

Design of Experiment failed to identify a suitable process. An industrial separation problem of a polishing step using a two-step elution was considered. The challenge was to separate an antibody from a mixture from critical aggregates and fragments, while keeping adsorber material as well as maintaining high yield and purity. A process development approach basing on a platform process, in combination with DoE failed to find any suitable process better than 65% yield.

Choosing optimization parameters using mechanistic knowledge. The process was following optimized in a model-based approach. For the initiation of the process optimization, first optimization parameters and a respective objective function had to be chosen. Hereto, mechanistic process understanding was utilized: In the present case, antibody and its fragments co-eluted despite a difference in surface charge. Because of thermodynamic effects, the higher concentrated antibody front overtook the fragment peak while moving through the column. Given this background, volume of protein load as well as the column length were considered as additional optimization parameters.

Successful separation of target protein. With the optimized protein load and column length, the mixture could be separated successfully in a two-step elution. The achieved purity was now above 98% and yield was 95%. Apart from the given experimental results of the previous DoE, no additional calibration experiments were needed to achieve these results. With ChromX varying process parameters and optimization objectives is possible in a time-saving and economical way. As a side effect, mechanistic process understanding was generated and successfully employed for process development.

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