Robustness testing effects interpretation

The diligent analyst would develop a robust method with rigorous matrix effect tests on multiple lots, including hemolyzed and lipidemic samples. An initial test would be a spike-recovery evaluation on at least six individual lots. Samples should be spiked at or near the LLOQ, and at a high level near the ULOQ. If matrix interference were indicated by unacceptable relative error (RE) percentage in certain lots, the spiked sample of the unacceptable lots should be diluted with the standard calibrator matrix to estimate the minimum dilution requirement (MDR) at and above which the spike-recovery is acceptable. The spike-recovery test should then be repeated with the test samples diluted at the MDR. Note that this approach will increase the LLOQ for a less sensitive assay. If sensitivity is an issue, then other venues will be required to address the matrix effect problem. For example, the method can be modified to include sample clean-up, antibodies and/or assay conditions may be changed, or the study purpose may be tolerable to acknowledge that the method may not be selective for a few patients (whose data may require special interpretation). 

Although the influence of relevant biotic and abiotic variables on the fate and effects of chemicals can, to a certain extent, be explored through controlled experiments and observations of natural systems, the combinations of factors that can be tested in practice are very limited. Mesocosm and field studies are often expensive to perform, can be difficult to replicate sufficiently, and are frequently complicated to interpret. Because they typically represent one unique scenario (species composition and density, temperature, light, nutrient level, and timing of pesticide application in relation to the environmental conditions), questions are often raised about the generality and robustness of the results. Likewise, unexpected or uncontrollable events may occur (e.g., it may have been an unusually rainy, sunny, hot, or cool season), the influence of which on the estimate of risk can be difficult to assess. 

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