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


A Better Strategy


Figure 4
First, we must quantitatively understand the core relationship illustrated in Figure 3. This is similar to how the commonly used strategy begins. The first step is to run experiments for a small number of points along the range of the core response relationship, in this case pH and the quality attribute (QA) in the expected target setting of the other process parameters. Figure 4 shows this relationship. Knowing the nature of the core relationship, we can create the experimental design space in the region of interest.


Figure 5
In this case, the region of interest is where the derivative is changing, i.e., the "knee," where the curve is bending over. When the knee of the curve has been found, a designed experiment is set up in the region of interest to explore the effects and interactions of the factors of interest, as illustrated in Figure 4. The workhorse central composite design is centered at the knee. From a relatively small number of experiments we can gain a good quantitative calibration of the region of interest and can establish a reasonable design space (Figure 5).




We are now ready to establish the acceptable region of process operation. It has been found that a logit function models the relationship between pH and the QA very well. Knowing the non-linear model form, we can determine the optimal design points and the most precise estimate of the acceptance region using the following equation:

in which A is the asymptotic maximum, b is the point of inflection where the quality attribute (QA) is equal to A/2, and d is the steepness of the curve close to the inflection point.

The optimal design points to fit a logit model are known based on point of inflection and range. These are shown in Figure 6 for two different situations.


Figure 6
Different products have different points of inflection. Working from these points, a sensible design is found that takes into account the features of the nonlinear model and provides minimum error of prediction in the region of interest.

Generalize Across Products

Knowledge of the general relationship or model can be used to learn more efficiently with new products of the same type. Using historical data and data from some small studies, we determined that many products share the same rate of decline (parameter d in the logit model) but differ by the point of inflection (half point b = (A/2)). Identifying the location for different products is equivalent to sliding the curve along the pH axis.

Knowing that the general model holds for most new products allows the quick identification of an optimal experimental design scheme for new products after running only a few preliminary experimental points. Thus, the knowledge allows us to obtain new knowledge more quickly, thus reducing development time.

Automate the Design and Analysis

After we have the design procedure worked out, the speed can be accelerated further by automating and validating design and analysis software. An intranet-based tool is being developed to produce an optimal design series. The tool performs analysis with standard polynomial and non-linear model fits. This feature will allow those not skilled in the details of the method to make effective designs and analyses output.

A focused and risk-based control scheme also is being developed using the knowledge gained and will be used to develop the design space.


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