Biomedical Engineering Reference
In-Depth Information
development can be discovered; yet, it is also the time when the experimental tools like
DOE are abandoned. While application of QbD can minimize the number and severity of
surprises on transfer of a process to manufacturing, less attention is given to detect
variables once a process is in manufacturing, opening up a possibility for trouble later on.
Rawmaterials may need a different strategy—one that continues from development
into the manufacturing stage. The reason is that most time-dependent, lot-to-lot changes
in raw materials happen during the manufacturing stage. Unlike with particular
manufacturing steps, QbD applied to raw materials must provide strategies—from
supply chain to cell harvest—to detect and manage changes in raw materials that may
happen for the first time after the process is being run in manufacturing. So, rawmaterial
QbD could be viewed as an attempt to leverage learning derived from intentional
variability applied during the development of a process into the uncharted design space
mapped out by future rawmaterial variation. Precisely because rawmaterial changes can
take place after development when systems are not designed to detect or learn from
change, there may be a distinct role for QbD applied to raw materials.
Unlike physical process conditions, rawmaterials are not under the direct control of
the manufacturer. They are usually made by external vendors. In addition, raw materials
can vary in purity from lot to lot in ways that take a long time to become apparent
compared to the time needed to develop a manufacturing process. Because of this long
timescale associated with raw material variability, a QbD strategy for raw materials will
look different from QbD for a process step. This chapter examines some of the
characteristics of raw materials that make them distinct and provides an example to
explore how QbD concepts might apply to raw materials. The data in this chapter were
not generated as part of a QbD study, instead they are part of an investigation for a
manufacturing process. The data may help illustrate how raw materials and feed
streams could be incorporated into an overall QbD strategy. With the retrospective
data, specific benefits and challenges that could result from “QbD for rawmaterials” will
be explored.
11.2 BACKGROUND
The typical manufacturing process for a biopharmaceutical is a batch process with
upstream cell-culture production, downstream purification, and finally drug product
manufacture. Each stage has different critical aspects related to raw materials where a
change in a rawmaterial could change product or process consistency. While QbD could
be applied to any of these stages, this discussion will focus on the upstream, cell culture.
Media used for cell culture has so many chemical components that, in some ways, it can
mimic the complexity of a cell itself. It is an extreme case for raw material control,
needing both simplicity and more understanding about detailed composition. The
nutrient formulations used in media for cell culture, even when simplified, can contain
more than 40 compounds. In addition, reactions can occur between compounds in the
media generating even more chemical complexity. When this potpourri of chemicals is
used to grow cells, which add their own degree of complexity, the result can be a system
that is operationally sensitive and prone to variability from run to run. Without a
