An effective way to maximize the likelihood of success while significantly reducing the number of required trial enrollees is to pre-stratify potential participants, ensuring that those most likely to respond to the drug are included in the study.
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In addition to significantly reducing costs and improving the efficiency of the clinical trial process, this aligns with the principles of precision medicine, which involves tailoring medications to specific patient groups.
Protein biomarkers that can stratify patients by multiple disease endotypes or prognostic pathways, or even predict responses to a specific treatment, have immense promise for pre-stratification before enrollment.
One notable recent example is a study of interstitial lung disease (ILD). The progressive fibrosing form of ILD (PF-ILD) is a devastating and often fatal illness that can result from any of several ILD forms. Previously, there were no predictive indicators to identify high-risk ILD patients, complicating any clinical trials for preventative medicine.
A team from the University of California, Davis used Olink to assess ∼350 plasma proteins in ILD patients. They employed machine learning algorithms to identify a 12-protein signature with strong predictive value for PF-ILD, which was verified in an independent patient cohort.
With a negative predictive value of 0.91, the scientists projected that pre-stratifying patients with this protein signature before participation in a clinical trial for a possible PF-ILD treatment would reduce the required trial cohort size by 80 %, potentially saving more than $26 million.
References and further reading
- Bowman WS et al. Proteomic Biomarkers of Progressive Fibrosing Interstitial Lung Disease. The Lancet Respiratory Medicine, 2022. Direct URL (open access via PubMed Central):https://pmc.ncbi.nlm.nih.gov/articles/PMC9177713/
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