Over the years, much has been learned about the downstream purification of monoclonal antibodies (MAbs). Standard processes are well established and there is extensive literature that describes methods and approaches. At the same time, there is a need to improve the process. In recent years, the US Food and Drug Administration has been influential in steering process improvement efforts through its 21st Century, Quality by Design (QbD), and process analytical technology (PAT) initiatives. This has led to industry reports such as, "A-MAb: A Case Study in Bioprocess Development," and novel methods for defining the design space.
Speeding up Knowledge Development
QbD and Downstream Process Design
Along the way, four critical aspects of the downstream development process must be addressed:
The development of appropriate measurement systems is central to the QbD building blocks. Measurement can be thought of as part of the process modeling work shown in Figure 2, which cannot be done effectively without good inputs. QbD clearly is more than just creating the design space.
Process Understanding: How Do We Know When We Have Sufficient Data?
A commonly asked question is, "How much data?" It can't be said early on whether QbD will require more or fewer data; the answer is situational and depends on the approach currently being used. In the early stages of QbD implementation, you will likely collect more data than needed. Also, regulatory agencies are reviewing QbD approaches and will become more comfortable with data collected in ways different than in past filings. Over time, the amount of data required will decrease as we learn how to effectively target the most critical data. The FDA, and indeed good science, stress that to produce quality products on a sustainable basis, you need to understand your process. Process understanding exists when you can accurately predict the performance of your process. This leads to the conclusion that there are sufficient data when performance can be accurately predicted. The required accuracy (quality) and precision of the prediction vary over the life of the development process; it is higher at the end of development than at the beginning.