Establishing a Process Control Strategy
Figure 3: Parameter diagram for cell production process (inputs are blue, outputs are red).
Once the initial design is in place, a process control strategy will be developed to ensure the process delivers a product
that meets the CQAs.
First, a P-Diagram (Figure 3) is generated per unit operation to identify all parameter inputs and outputs for each step. This tool is a good visual aid
for process understanding and is also used to populate a customized risk-assessment tool called the Risk Assessment Mitigation
Matrix (RAMM), as shown in Figure 4. The RAMM tool is specifically designed to identify CPPs (3). This tool allows evaluation of what input parameters contribute
the most variation to individual outputs. The outputs are scored on their potential to impact the CQAs. Scores are based upon
three values, as shown in Figure 4 (1=green, 3=yellow, 9=red) to obtain separation and clearly identify criticality. The result of the analysis for each unit
operation is a list of potential CPPs. The scores in each row are totaled to understand CPPs, and the scores in each column
are totaled to understand an output's influence on CQAs. Additional development experiments are then prioritized to complete
the design space.
Figure 4: Risk assessment mitigation matrix (RAMM) tool for evaluating criticality of cell-culture production process. Inputs
are evaluated in rows against the outputs in columns; outputs are scored on their potential to impact critical quality attributes
(CQAs) (1=green, 3=yellow, 9=red).
A demonstrated understanding of the effects of CPPs and their interactions on CQAs is a requirement for an approved design
space. Small-scale characterization experiments with appropriate experimental designs allow for a multivariate analysis including
interactions, efficient use of data, and statistical modeling. Process characterization is often an iterative process involving
parameter screening, range identification and response surface mapping. This method enables revisiting the original process
development plans with the client to ensure that information gathered during early DOEs is understood and leveraged for future
experiments. This is how risk is reduced and commercial timelines can be compressed. It also allows for iterations of the
The following example shows an experiment designed to measure impact of seed density (SD), temperature shift degree, and day
of temperature shift on an important CQA of a complex biologic. An on-face central composite design was employed in this experiment.
The results are shown in Figure 5 along with a visual representation of the design space.
Figure 5: Potential critical process parameters (CPPs) to achieve optimal value for a critical quality attribute (CQA-1) of
a complex biologic (T = temperature). The combination of variables that produces product of high quality is defined as the
Following the CMC QbD Development Program allows definition of a design space earlier in the development program. This means
that even the earliest development experiments can be built upon and leveraged as the product advances towards commercialization.
Table II contrasts the two different approaches and demonstrates the advantages of the QbD approach to development. The deliverables
associated with the product lifecycle are more robust and timely. Additionally, the long-term support through the CPV program
is optimal and flexible. As shown in Figure 1, the advantages can also represent a more robust timeline.
Table II: Traditional vs. quality by design (QbD) case study results.
The QbD development program engages the client in a strategy to address critical issues pertaining to the product quality
and the process early on. This necessitates a formal plan at the start of the development program, and both the CMO and the
client need the commitment to do the work upfront. This strategy enables a seamless transition when the client and CMO push
together for commercialization of the product.
Clinton Weber is associate director of BioProcess Sciences, Ashok Kumar is principal scientist, Lisa Joslin is process validation manager, and Roland Ashton, James Schmid, and Michael Larson are development associates, all in the Process Development Group at CMC Biologics,
1. FDA, Draft Guidance for Industry: Target Product Profile—A Strategic Development Process Tool (Rockville, MD, Mar. 2007).
2. ICH, Q8 (R2) Pharmaceutical Development, Step 4 version (2009).
3. A. Brindle, et al., Pharm. Eng. 32 (1) 26, 28-33 (2012).