Due to duration of a study, kit shelf life, and other factors, it is not unusual for samples for quantification of a biomarker study to be run on different lots of immunoassay kits in support of both preclinical and clinical studies. Yet for a variety of reasons—going up and down the supply chain of critical reagents that go into them—“there is quite a bit of variability in terms of the quality of these commercial kits,” laments Afshin Safavi, Ph.D., senior vp of BioAgilytix Labs.
Even home-brewed assays rely heavily on the critical reagents, he pointed out. “We don’t call it a kit, but in essence we are developing a kit in our shop internally.”
Like it or not, the researcher needs to shoulder some of the burden of making sure the kits perform the same way lot-to-lot, year-to-year—and if not, to come up with systems to bridge the data that are generated by different lots.
To do this, Dr. Safavi recommended at a minimum running a series of quality controls and samples in both old and new lots. For larger studies “our practice is actually to repeat the lot-bridging process over three consecutive days, and that way you generate a larger number of data points that are more statistically significant,” he said. “And then we come up with a correction factor…to normalize that data to the previous lot.”
With the trend to multiplex assays, the bridging process becomes more critical than ever because the panel of proteins included in different biomarker kits comprising a multiplex assay do not always vary in the same way as each other from lot to lot, Dr. Safavi observed.
When applying a correction factor, it is important also to know what kind of tolerance there is for differences, and this can depend upon the intended use of the kit, the disease area being supported, as well as the changes you may predict, he says. If a 500% change is expected due to drug treatment, 40% variability is not going to have much effect on the decisions likely to be made based on the test, but if a 20% change is expected due to drug treatment even a 5% change may affect the quality of the data generated and impact the decision process.
Dr. Safavi was quick to emphasize that his comments applied only to research kits used in biomarker studies. “When it comes to pharmacokinetics or immunogenicity assays, they have their own sets of processes for qualifying and bridging reagents and assays and lots.”