Robust Experimental Strategies for Improving Upstream Productivity - Identify the best experimentation methods for the data you need. - BioPharm International

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Robust Experimental Strategies for Improving Upstream Productivity
Identify the best experimentation methods for the data you need.


BioPharm International
Volume 23, Issue 7

REDUCING THE EFFECTS OF ENVIRONMENTAL FACTORS

It is important to discuss how the effects of environmental factors are included in the experimental strategy. Part of diagnosing the experimental environment is identifying environmental variables, which often are overlooked. Even the best strategy can be defeated if the effects of environmental variables are not properly taken into account. Environmental variables include factors such as bioreactors, raw material lots, operating teams, ambient temperature, and humidity. Recognizing the effects of variation from environmental factors, can go a long way in ensuring that the resulting data are not biased. Special experimental strategies also are needed to reduce the effects of extraneous variables that creep in when the experimental program is conducted over a long time period.

In one case, a laboratory was investigating the effects of upstream variables using two identical bioreactors. As an after-thought, the scientists decided to use both bioreactors in the same experiment. There was some concern that using both reactors would be a waste of time and resources because they were identical. Data analysis showed, however, that there was a big difference between the results from the two bioreactors. These differences were taken into account in future experiments.

In another situation, an experiment was designed using DOE procedures to study the effects of five upstream process variables. The data analysis produced some confusing results and a poor fit of the process model to the data (R2 values were low). An analysis of the model residuals showed that one or more variables, not controlled during the experiment, had changed during the study, leading to the poor model fit and confusing results. An investigation showed that the experiment had been conducted over an eight-month period. It is very difficult to hold the experimental environment constant for such a long time.

Heilman and Kamm report a study in which the biggest effect was raw material lot–variation introduced by different lots of media.8 This effect was present during the production of more than 50 batches before it was discovered.

In each of these cases, it is appropriate to use "blocking" techniques when conducting upstream experiments.9 Blocking accounts for the effects of extraneous factors, such as raw material lots, bioreactors, and time trends. The experimentation is divided into blocks of runs in which the experimental variation within the block is minimized. In the first case above, the blocking factor was the bioreactor. In the second case, the blocking factor was a time unit (e.g., months). The effects of the blocking factors are considered in the data analysis, and the effects of the variables being studied are not biased.

POOR MEASUREMENT PROCEDURES SLOW DOWN EXPERIMENTATION

Another problem that can reduce the speed and quality of process development is the measurement systems used to collect the data, i.e, availability of good methods and the efficiency with which the analytical laboratory is operated. Measurements enable us to see the effects of the variables driving the process and build models that are used to develop the design space.

The role of good measurements often is overlooked. Poor laboratory performance can slow down development if:

  • methods are not available when needed or have been developed poorly, producing misleading test results that slow the development of process understanding
  • laboratory testing procedures and scheduling are inefficient, producing delays in getting the results back from the laboratory. The value of good analytical methods is lessened if it takes a long time to get the samples analyzed.

Poor quality measurements and ineffective and inefficient laboratory procedures result in long timelines and misleading results. Measurement procedures must produce high quality, repeatable, and reproducible measurements and have stable measurement procedures and methods that are robust to small deviations from the method standard operating procedure.11 An example of improving the flow of samples through an analytical laboratory is discussed below.


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