I have an example that I hope is helpful for you. We are using a PCR assay to detection Plasmodium falciparum parasitemia in a human challenge model (see https://force-dsc.my.site.com/ddt/s/ddt-project?ddtprojectid=91). Although the clinical validation established in the biomarker qualification program, each assay needs an analytical application to use in this manner.
Within this application, there are a few possibilities on how to use the data. For our use case, it is a positive or negative categorical response. In this context, the concept of precision and accuracy across a calibrator curve range is not particularly relevant. Instead the analytical validation focuses on sensitivity, sample stability within a small window since testing real-time, cross-reactivity with other malaria species and other pathogens with similar sequence in the target gene, and false positive/negative results. Because we are initiated treatment with a positive result, we are not comparing how positive a sample is, only that it is positive.
In contrast, if our goal was to monitor levels of parasitemia after an interventional treatment, we would need to add elements of quantitation across the range of expected samples including precision and accuracy. If we were planning to bank samples and test at the end of the study, we would need additional stability studies.
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Joleen White Ph.D.
AAPS 2024 Global Health Community Chair
Bioanalytical 101 Course Development
Head of Bioassay Development
Gates Medical Research Institute
Cambridge MA
[email protected]Disclaimer: Opinions expressed are solely my own and do not express the views or opinions of my employer.
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Original Message:
Sent: 08-28-2023 01:08
From: Anonymous Member
Subject: Determining fit-for-purpose nature based on the COU of a biomarker assay
This message was posted by a user wishing to remain anonymous
Hello,
While I understand the concepts of fit-for-purpose validation in the context of biomarkers and the surrounding literature, I'm facing challenges in practically ensuring that a specific assay meets this requirement. I'm particularly seeking concrete examples of how a biomarker assay can effectively detect meaningful changes for the biomarker in question.
If we have a grasp of analytical, within-subject, and between-subject variability, as well as knowledge of the expected magnitude of change, how can we regularly utilize this data to ensure a fit for purpose assay? Do bioanalytical scientists rely primarily on visually inspecting data, or do they employ routine statistical analyses to determine an assay's detection capabilities? If so, which are appropriate? Is there common reliance on statisticians for data analysis? This becomes more pertinent when collaborating with Contract Research Organizations (CROs) which may have their own validation procedures – do CROs independently assess assay capabilities, or are sponsors providing support in this aspect?
I'm eager to learn from those with hands-on experience and would greatly appreciate the sharing of specific examples.
Thank you.