The Time Value of Information in Designing Downstream Purification Processes - To speed up the downstream process, you must get the right data, in the right amount, at the right time. Here's how. - Bi

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The Time Value of Information in Designing Downstream Purification Processes
To speed up the downstream process, you must get the right data, in the right amount, at the right time. Here's how.


BioPharm International Supplements


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


Figure 3
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.


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