Establishing a Control Strategy for Acidic Species
The two-round DOE study indicated that acidic species level was not sensitive to pH, DO, nutrient feed rate, or cell density
at the initiation of feeding within the studied range (knowledge space). Therefore, the control strategy focused on the three
significant parameters: temperature, seeding cell density, and process duration. The effect of these three parameters is cumulative.
If the three parameters were investigated separately in single-variable studies, a design space similar to the one illustrated
in Figure 9a would be established. However, the multivariate study showed that a small portion of this space would result
in unacceptable product. We evaluated two options to define the design space, a structured one (Figure 9b) and a truncated
one (Figure 9c). Although the structured space is easier to implement, the operation ranges are narrow, especially for temperature
(35.5–37.0 °C). For this limitation, we chose the truncated space, which allowed us to expand operating ranges without compromising
product quality. Specifically, we removed the combination of high seeding density (>4 x 105 viable cells/mL) and long process duration (>11.5 days).
An Integrated Approach to Defining the Design Space
One of the key challenges in defining a design space based on results from these multifaceted studies is that they are not
amenable to the comparatively simple "contour plots" that typically are used when defining the design space. For example,
in this simplified approach, the functional relationship (obtained through an empirical statistical model) between the process
parameters and quality attributes is used to predict the expected range of quality attributes for a given level of process
parameters. The parameter ranges that provide acceptable levels of all the quality attributes (the common areas) are then
described as the design space for the process parameters for that step.
Although this approach is acceptable for simple focus areas that are essentially independent, it is of limited use when applied
to more complex focus areas (as is often the case in bioprocesses). MAb manufacturing processes invariably involve process
parameters that are outputs or attributes of prior unit operations. As an example, we can look at the acetate ion and tris
ion concentrations that are process parameters for the anion exchange chromatography (AEX) and cation exchange chromatography
(CEX) steps, which are the unit operations that typically follow the low pH inactivation step in a MAb process. The AEX and
CEX chromatography steps are affected by changes in the inactivation pH, which is an input parameter for the low pH inactivation
step. Figure 10 shows the effect of the change in inactivation pH on acetate and tris ion concentrations. To identify the
design space that is acceptable for the entire process, the effect of the parameters affecting performance in subsequent steps
must be understood.
Therefore, in addition to using outputs (quality attributes) from one focus area as inputs (parameters) in other focus areas,
we also used challenge studies to evaluate the interrelationships between the several focus areas, with the ultimate goal
of ensuring product quality and process performance. An example of an experimental design for a challenge study is provided
in Figure 11. The results from these studies are then used to further refine the design space. For example, if the CEX step
is able to clear high levels of impurities generated under the suboptimal conditions, that provides flexibility for defining
the design space for the previous steps.
Another aspect of process integration involves evaluating risk by combining the understanding of the effect of parameters
on quality attributes with operational considerations (e.g., facility, equipment, procedures, controls). In this approach,
the severity score of the failures modes and effects analysis (FMEA) is derived from the process understanding studies and
combined with the occurrence and detectability scores to provide an overall risk assessment, which can then be used to assess
parameter criticality. This approach makes it possible to establish the design space and the associated control strategies
while considering the process as the whole, thus enabling Quality by Design.
Pfizer has developed a systematic strategy for implementing Quality by Design (QbD) principles for process understanding and
design. This strategy was used to develop and characterize a monoclonal antibody production process. Combined with design
of experiments (DOE), especially multivariate analyses, this strategy allowed us to focus on high-risk process parameters
and study these parameters as integral components of the process. The effective characterization of a scale-down bioreactor
model also aided in this process. This QbD process design enriches process understanding, boosts manufacturing process robustness
to consistently deliver safe and efficacious product, spurs technological innovation and new approaches to process design
and validation, and helps minimize and mitigate risk.
Amit Banerjee, PhD, is a research fellow at Pfizer Global Research & Development, St. Louis, MO, 636.247.5516, email@example.com
. NingNing Ma and Natarajan Ramasubramanyan also contributed to this article.
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