Designing in Quality: Approaches to Defining the Design Space for a Monoclonal Antibody Process - How to use risk assessment strategies to integrate operations. - BioPharm International
A typical monoclonal antibody (MAb) manufacturing process involves >20 distinct unit operations with >200 process parameters,
and more than 50 different raw materials, making the complexity level significantly higher than that of a small-molecule drug.
Therefore, a clear and efficient strategy is required to identify high-risk process parameters for process characterization.
Figure 3
A multidisciplinary team consisting of representatives from the quality, process development, regulatory, manufacturing, and
analytical groups actively participates in the risk-assessment process, using data and knowledge from various sources, including
previous development, platform process knowledge, manufacturing data from relevant bioprocesses, and literature information.
The inputs for risk assessments are summarized in Figure 2. The output from the risk-assessment exercise is captured in a
"process understanding plan."
Table 1. "Cause/Effect" matrix (abbreviated) for an upstream focus area. The scale is 1 to 10, with 10 being the most significant.
The process for risk assessment leading to experiment prioritization is illustrated in Figure 3. Based on equipment and operation
similarity, the process was segmented into various focus areas with defined boundaries. For each focus area, the relevant
quality attributes are ranked according to a predefined scale (1 to 10). Then, the effect of each process parameter on every
relevant quality attribute is assessed using a predefined scale (1 to 10). Based on this ranking, a cumulative score is calculated
for each parameter. This score represents the relative importance of the parameter for the focus area and is used to prioritize
experiments.
Table 2. "Cause/Effect" matrix (abbreviated) for a downstream focus area. The scale is 1 to 10, with 10 being the most significant.
Tables 1 and 2 provide two abbreviated examples of a "cause and effect matrix" for an upstream cell culture and a downstream
purification process. The examples include the rankings for quality attributes as well as the rankings of the impact of process
parameters on quality attributes. The parameters are ranked based on the calculated scores.
