Process Control: Strategy for Speeding Up the Work at Each Step
Of course, it is critical to balance speed and risk. An effective mantra is to be bold but not reckless. At each step in the
development process, be aware of what data will be needed at various times during process development and manufacturing operations.
The most important data to collect at any time are those needed to satisfy the short- and long-term objectives of the program.
Over time, all of the important data will be collected. Non-important, or low value, data and information may become too expensive
to collect, with low payout. The goal is to minimize risk at each point in time.
Winning Strategies Recognize the Experimental Environment
Experience has shown that effective experimental strategies are a function of the experimental environment at hand. The environment
defines the appropriate designs to be used to collect the data.6 A major goal is process understanding, which is a function of knowing the variables that have a major effect on the process.
There are three environments that are commonly encountered.
1. Little is Known About the Critical Process Variables
At the beginning of many studies, the critical variables are not known. In such cases, it is prudent to start with a screening
study to be followed by characterization and optimization experiments that develop more detailed data on the variables, with
the large effects identified in the screening experiment.6
The end product when using a prioritization matrix approach (i.e., a cause and effect matrix) is a ranked list of the variables
and actions to be taken, including performing studies using designed experiments, refining the measurement system, and an
assessment using a failure modes and effects analysis (FMEA).7 These are all building blocks of QbD.
2. A Major Core Relationship is Known
In many cases, the subject matter experts already know the core relationships that have effects so large that they swamp the
effects of the other factors. In such cases, the first step is to run a small set of experiments to understand the nature
of this core response relationship.
3. Generalizing the Core Relationship to Other Products
As we move up the development cycle, we may want to determine if the core relationship can be generalized across similar product
types. The question here is whether we can spot key parameters that allow us to be more efficient in quickly identifying the
experimental design space of interest, and understanding the nature of the underlying relationship. The objective is to be
even more efficient with new pipeline products.
Illustrative Example: Viral Inactivation
An example that illustrates many of the points made above is determining the operating range for low pH viral inactivation,
a critical step in downstream purification. The purpose of this process step is to achieve maximum viral inactivation at an
acidic pH condition that still maintains product quality. Based on prior knowledge, product quality can be significantly affected
by several process parameters in addition to pH, including temperature, concentration, and hold time. The objective is to
find the process design space of these process parameters for normal operation and the worst conditions.
A Commonly Used Experimental Strategy
A typical pH response curve is shown in Figure 3. A commonly used strategy is for a fixed set of process parameters to run
several pH points to identify the knee of the curve, which is the critical pH range for viral inactivation. After the knee
of the curve has been identified, the process parameters could be varied one factor at a time until the desired set of conditions
is identified. This process requires a large number of tests and takes considerable time. There is a better strategy.