Regulatory support...economic pressures... the complexity of protein-based products. As we detailed in the previous installment
in this series, (BioPharm International, May 2008) all of those trends are coming together to bring Quality by Design (QbD) to biotech companies. Because QbD envisions
designing-in product and performance characteristics from the first rather than deriving them through testing after the fact,
it opens the way to a risk-based approach to quality. The key to ensuring an acceptably low risk of failing to achieve the
desired clinical attributes lies in determining the Design Space, defined in ICH Q8 as "the multi-dimensional combination
and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide
assurance of quality."1
But as we also pointed out previously, one of the biggest obstacles to adopting QbD is the industry's inconsistent understanding
and application of the tools and methods that are essential for accurately determining Design Space. In this article, we show
how those tools are typically used in describing the relationships between the manufacturing process inputs and the critical
quality attributes (CQAs). Used correctly, these tools help make it possible to map the multidimensional Design Space in which
quality is ensured, and to carve out an efficient operating space in the center of that Design Space where process robustness
THE OBJECTIVE: PROCESS UNDERSTANDING
The CQAs are the desired outputs of the manufacturing process. In mapping out Design Space, the goal is to understand the
relative impact on CQAs of input variables—process steps, process parameters, and raw materials. For example, in a large-scale
mammalian cell culture bioreactor, there are any number of potential CQAs, which could include buffer components, media components,
supplements (e.g., nutrients), dissolved gases, etc. One surrogate measure for these CQAs in a biologic operation is yield,
and the case study detailed below focuses on this surrogate measure. The Design Space encompasses numerous permutations of
the input variables in relation to each other that still produce the desired outputs. In other words, the goal is to achieve
a profound and comprehensive understanding of the process and then carefully monitor and control those critical elements.
In a traditional approach to process understanding, every parameter could potentially receive the same degree of scrutiny.
However, given all the possible permutations of process steps, process parameters, and raw material components, such an approach
is highly inefficient and sometimes impossible to achieve. Although manufacturers using one-factor-at-a-time analysis are
able to produce an in-specification product by locking down individual process parameters, such success is usually short-lived.
Differences in raw material batches and drift in other parameters can soon bring new problems. By contrast, QbD's risk-based
approach seeks to determine the critical parameters and their combinations and control for them in a flexible manner in the
Conrad J. Heilman, Jr., PhD
APPLYING THE STATISTICAL TOOLS TO HISTORICAL DATA
The first step in achieving process understanding is to make sure that you have gathered in one place all of the historical
data about the development of the product. Ideally, a database and all development reports to date will already exist. But
if the data are lacking or spotty, it's necessary to compile the data before trying to map the Design Space. Once all of the
historical data is on hand, you can then apply and sequence the appropriate tools.