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Biomarkers have the potential to play a vital role as catalysts for the clinical testing of immune therapies for T1D, by enabling the design of more effective clinical trials on the path to their approval.
In type 1 diabetes (T1D), T cells play a key role in the destruction of insulin-producing beta cells.
Now for Context of Use– The COU elements, are clearly laid out in the FDA guidance and provide an informative, structured exercise for identifying the biomarker’s utility.
A few COU elements are key: 1) What aspect of the biomarker is measured and in what form; 2) What is its purpose in drug development; 3) What is its interpretation, and decision/action based on a finding.
The potential utility of biomarkers in clinical studies, investigating either new treatments or new strategies of clinical management, is capitalising on recent advances in technology, from molecular sciences to powerful imaging, bearing the promise of expediting the discovery of new treatments.
In the active search for new biomarkers, many potential candidates can be considered side by side, allowing many failures but a few great winners.
As such, they have been widely studied, both to better understand the disease and to evaluate responses to immune therapies in subjects with or at risk of T1D.
The T cell biomarker community has actively pursued two categories of T cell biomarkers; antigen-specific and non-antigen-specific.
In one memorable session, an outspoken colleague described developing biomarkers with FDA was like playing football for the first time on a very foggy day.
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Run down the field, the ball drops from the sky, continue to run in the fog until you see an upright. Similarly, “we’ll tell you when you have sent enough data.” Understandably, FDA has a strong reluctance to rely on unproven biomarkers in their review because of the risk of false results.
In my meetings with FDA (both public and private) they repeatedly cite the CAST study as great example of a false positive. In many cases collaboration between industry and academia is crucial to provide the large amount of patient data to qualify a biomarker.