Characterizing a Chromatography Step

I'm back!  It was an epic vacation that required almost as much time to recover from!  That's a good vacation, if you ask me.  :-)

Previously, there were seven factors identified for characterization for a cation exchange step of a biosimilar.  Now, a full-factorial approach would require 128 runs for all 7 factors.  What a mess in terms of time and resources!  To reduce the design complexity, the authors take a cue from the literature where the buffer concentration and pH are wrapped up into one parameter for both the equilibration and elution buffers.  By combining these two parameters, the authors study the process in worst/best case situations for the equilibration and elution.  With 5 factors, a full factorial approach would take 32 experiments - still quite a bit of time and sample commitment.  In lieu of that, the authors apply a fractional factorial approach to track all the pairwise interactions that results in a reduction to 16 experiments.  Not too shabby!  I liked this strategy for its economy while maximizing the amount of knowledge.

The downside of the approach is the authors will miss the impact of the individual factors of buffer concentration and pH.  A Placket-Burman study with all 7 factors would take only 12 experiments.  Based upon the chemistry of the cation exchange resin, it's not a tremendous leap to imagine that load pH, elution pH, and aggregate amount in the load would be the dominant factors affecting the process performance.  Augmenting the original 12 experiments to evaluate these would require an additional 8 experiments (20 total).  For an additional four experiments, the authors could have obtained greater definition of the process space and reduce the number of process parameters necessary for including as part of their control strategy.

Sadly, the authors don't provide the data in their paper; however, they do provide the parameter estimates from their results that could be used to generate a simulation of the data (I may include that next time).  They found that the amount of aggregate in the load and the equilibration buffer were the driving process parameters for this antibody.  The downside of the result: the equil buffer is characterized by both the concentration of the buffer and the pH.  When the time comes to file these results, I'd bet the agencies are going to want to see a clear scientific picture of that the sponsor understands how the chemistry is driving the aggregate clearance and that the sponsor has the appropriate controls in place to ensure the product remains safe for patients.

I've grappled with how to define critical and non-critical process parameters - especially when there's limited raw material lots used in the early stages of a campaign.  The authors use the statistically significant approach to define critical and non-critical process parameters.  I like their approach; however, what happens when there's a statistically significant model but it is practically meaningless?  I'm going try to tackle that question in my next entry.