Handling the FMEA

In the previous posts, JMP was used to illustrate how a fishbone diagram could be used to sketch the how the different elements of a chromatography process could affect the product quality and how there needed to be sufficient quality systems in place to enable efficient conversion of this data into knowledge.

The FMEA should enable the reviewer to see a clear connection between the process parameters and the critical quality attributes (CQAs) of the product.  The next step is a bit trickier.  The FMEA should also provide a roadmap for developing the process knowledge relating the effect of these process parameters to the CQAs.  What I really liked about the paper by Xu, et al was the use of a Pareto chart to establish the number of factors that would be studied in their process characterization work.  The Pareto plot allows the visualization of the risk priority number (RPN) as well as the cumulative effect of the number of factors.  By setting the bar at 90% of the cumulative effect, the argument may be made to the regulators that the majority of the risk to the product quality has been accounted for in the process characterization work.

Pareto Plot of RPN Score by Operating Parameter (Adapted from Xu, et al)

Setting the bar at 90% means there will be seven factors that need to be studied for the process characterization work.  These can be readily handled with a screening design to identify which have the most significant effect and then augment the design for refinement of the operating space.  A Plackett-Burman would need only 12 experiments to find the main effects!  Nice.  Before tackling these experiments, a justifiable scale-down model has to be in place for comparison with the manufacturing scale.  In the next entry, I'll be talking about strategies for justification of a scale down model.