Figure 4 and Table 6 show the consistency of the runs. The yield and purity were 66% ± 6% and 92% ± 2%, respectively. Those
are narrow ranges, particularly for a product in its first manufacturing campaign. The only differences visible in the chromatograms
in Figure 4 are the start of the elution and the peak height. The one output variable that showed high variance was resolution
of species B from the main product, which confirmed one of the main predictions of the bench-scale model. Figure 5 shows the
distribution of species during the elution gradient for one of the manufacturing runs, demonstrating the comparability of
the bench-scale model and manufacturing scale runs.
The key and critical parameters for the manufacturing runs were analyzed using JMP 6. The caveat is that the number of data
points and the breadth of the ranges of the data are insufficient to yield any statistically significant correlations. However,
the trends shown in Figure 6 seem to confirm some of the main points of the model derived from the DOE. Loading density and
conductivity, used here in place of ammonium sulfate concentration, appeared to result in earlier elution as predicted. Increasing
conductivity resulted in less resolution of species A from the main product, and more species A in the load had a negative
impact on yield.
A contract manufacturing organization must maximize efficiency during process development to gain the knowledge necessary
to design quality manufacturing processes and still meet timelines and budget. In this case study, the key and critical parameters
for Octyl chromatography and their ranges had to be determined for the first manufacturing campaign at the 2,000-L scale.
Several steps were taken to work within the limitations of the contract while extracting the most information possible to
start building the design space for the process. A fractional factorial design was used for the DOE, which required only 18
chromatography runs to identify the main effects of seven process variables. Upstream and downstream development overlapped
to condense the timeline. The resulting variability in impurities in the harvests revealed a critical parameter. Small-diameter
columns were used to minimize load material and raw material needs for the experiments. Efficiency was further increased by
automated methods for purification and analysis; running the instruments overnight reduced labor charges to the client. Use
of JMP software provided objective analysis according to accepted statistical concepts, and thus provided a scientific basis
for setting parameters.
The non-critical parameters that were identified were pH and flow rate or residence time. Key parameters were temperature,
loading density, and the concentration of ammonium sulfate in the load. The critical parameter for this case study was the
percent of species A present in the load. It affected yield and separation of product from impurities and was not well controlled
at that point in development. The Octyl step was designed to consistently remove amounts of species A between 7 and 12% by
controlling the key parameters. Meanwhile, upstream conditions were refined so that the percent of species A in the load during
the manufacturing runs was 9.5% ± 0.6%. Thus percent A was down-graded from a critical to a key parameter.
The results of the manufacturing runs showed that the process was robust when the key parameters identified in process development
were controlled. The small variation among the runs coincided with the results of the bench-scale experiments, demonstrating
the predictive power of the model.
This work shows how quality by design principles can be applied during process development in the CMO environment. Design
of experiments is a cost-effective method for developing robust processes and providing information for expanding the design
space. Both the CMO and the client reap the benefits of consistent yields and quality as well as the high success rate that
results from a well-characterized process for the production of biopharmaceuticals.