This "contour profiler matrix plot" was created using the optimum settings for each of the three significant variables in
the PharmaCo example. This tool brings together three-dimensional response surface plots, each of which was originally created
in the modeling software. The X and Y axes are made up of the DoE variables, and the Z axis (the contour curves) represent
yield (the response variable). In the white regions, yield is in specification, and in the shaded regions it falls outside
specification.
Each of the cells is a three-dimensional plot in which the Y variable is to the left of the cell, the X variable is below
the cell, and the Z variable is the measured response, yield. For example, the top left cell has raw material attribute 1
as the Y axis and raw material attribute 2 as the X axis; the surface depicted in the plot represents yield.
The goal is to find an available Design Space in which the variables can produce in-specification yield. That is: what values
for the variables offer the optimum path to achieving the desired yield? The "contour matrix profiler" shows the areas (in
white) in which you can operate successfully and those (shaded) in which you cannot. Those white areas are the Design Space
for PharmaCo's fermentation process. In that Design Space, one can identify an area in the center as the operating space,
in which the process or outcome will be in control. By choosing this "sweet spot" for yield, and again for the other CQAs
as the operating space, they can be sure that the process won't drift into inoperable regions.
THE ADVANTAGES OF UNDERSTANDING DESIGN SPACE
Successfully defining the Design Space means you have achieved a full understanding of the various permutations of input variables
and process parameters that ensure an in-specification product. As a result, you gain far more flexibility in changing process
parameters and other variables. For example, when raw material batches vary, PharmaCo can make the necessary adjustments in
the manufacturing process to compensate for the effects of differing properties and be confident that the resulting product
will achieve the desired results.
Despite the significant operational and business benefits that the FDA's QbD initiative offers, only a small number of organizations
know how to take full advantage of it. Many continue to use one-variable-at-a-time analysis, even though critical pro- cesses
may depend on the complex interactions of several variables. The result: poorly understood processes, an inability to demonstrate
adequate control, costly delays in development, and many processes that are neither robust nor reliable. But with carefully
structured data analysis through statistical tools, biotech organizations can achieve the robust and reliable processes required
to streamline scale-up, technology transfer, and validation, and produce high-quality biologic products. At a time when the
pharmaceutical industry is under intense pressure to make safe products and reduce costs, these tools and approaches offer
tremendous value.
As illustrated in the PharmaCo example, it's never too late to apply these statistical tools to a process. In fact, it's a
straightforward process and can be accomplished in a relatively short period of time. It offers significant enhancement to
process understanding, improves scientific rigor, and typically results in significantly enhanced qualitative and quantitative
performance as well as cost savings.
Jason Kamm is managing consultant and Conrad J. Heilman, Jr., PhD, is senior vice president, both at Tunnell Consulting, King of Prussia, PA, 610.337.0820,
heilman@tunnellconsulting.com
REFERENCES
1. International Conference on Harmonization. Q8, Pharmaceutical development. Geneva, Switzerland; 2005.
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