In today's blog post, Biological Assays presenter Dr. Todd Coffey, CMC Statistician, Seattle Genetics, share with us a little about his work in evaluate parallelism for bioassays. Here's what he had to share.One of the criticisms of the equivalence approach for assessing similarity is that development teams will inadvertently utilize compromised samples and implement a too-wide “zone of indifference”. How do you protect against this problem?
Compiling a set of historical data that includes the natural variability of parallel curves is not a trivial exercise. I offer three suggestions to protect inadvertently against including compromised samples: 1) Ensure the dataset is large enough to include all sources of assay variation. With an adequately large dataset, patterns can often be identified that may elucidate which samples are compromised and why they should not be included with the other parallel samples. 2) Carefully assess the data for unusual trends and patterns, both visually and with statistical analysis. 3) Compare the parallelism metric for degraded samples that are expected to be non-parallel to parallel samples that are suspected of being compromised.
The USP states that one can use the absolute difference of slopes or a ratio of slopes when using the equivalency approach for similarity. Do you have a preference and why?
Using ratios has the advantage of being generalizable across assays. However, calculating confidence intervals on the ratios is not always straight-forward because the ratio of two normally distributed variables is not normally distributed. Thus special care has to be used to correctly calculate confidence intervals on ratios. While not as generalizable across assays, calculating differences between standard and test is more straight-forward statistically and is also interpretable. For these reasons, I prefer to set equivalence limits using differences
How many reference vs. reference runs do you recommend using to establish a “zone of indifference”?
There are at least three issues to consider when discussing sample size. First, to be representative, the number of runs needs to include all sources of variation. Second, the sources of variation have a great impact on the relative value of measurements and the sample size is dependent on getting the right data. For example, if most of the assay variation comes from factors that vary between runs, then many measurements in the same run are of much less value than measurements from different assays. In this case, the sample size is dependent more on the number of times the assay is run after varying the factors that cause the variation. Finally, to provide accurate estimates of tolerance intervals, the sample size of independent measurements generally needs to be several dozen. When that sample size is not attainable, I recommend setting initial limits, monitoring the assay, and then modifying limits as new data emerge.
Many companies start with a difference approach to similarity during early product development when they have a small number of lots and the assay is still being developed. At what stage of development is it reasonable to implement an equivalency approach for similarity?
I recommend using the equivalence approach when there is an adequate set of representative historical data that contains all sources of variation. Sometimes this dataset is available during qualification or before the IND is submitted. If it is not available then, the next potential milestone may be when process characterization activities begin.
Dr. Coffey will be presenting Tips and Tricks for Implementing Equivalence Testing for the Evaluation of Parallelism this May 14-16, 2013 in Seattle, Washington at the Development, Validation and Maintenance of Biological Assays event. For more information on his session and the rest of the program, download the agenda. If you'd like to join him, as a reader of this blog when you register to join us and mention code IBA13JP and save 20% off the standard rate. Have any questions? Feel free to email Jennifer Pereira.
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