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 basis of platform approaches. Optimal operating conditions for single units are found by altering only few process parameters with Design of Experiment (DoE) procedures or high throughput screenings.

Design of Experiment failed to identify a suitable process. An industrial separation problem of a polishing step using 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 based on a platform process, in combination with DoE failed to find any suitable process better than a 65% yield.

Choosing optimization parameters using mechanistic knowledge. Afterwards, the process was optimized using a model-based approach. For the initiation of the process optimization, optimization parameters and a respective objective function had to be chosen. In order to achieve this, an understanding of mechanistic processes was utilized: In the present case, antibody and its fragments co-eluted despite a difference in surface charge. Because of thermodynamic effects, the more highly concentrated antibody peak overtook the fragment peak while moving through the column. Given this background, the 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. ChromX enables varying process parameters and optimization objectives while saving time and costs. As a side effect, mechanistic process understanding was generated and successfully employed for process development.

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