Biomarker Selection and Analysis in Clinical Trials-Biomarker Selection and Analysis in Clinical Trials

Video Transcription

Video Summary

The webinar, sponsored by the AAI Clinical Immunology Committee, focused on how biomarkers are selected, validated, and used in clinical trials, with examples from rheumatoid arthritis and organ transplantation.<br /><br />Dr. Katherine Liao explained biomarker development in rheumatoid arthritis (RA), emphasizing the need to move beyond trial-and-error treatment selection. She distinguished observational studies from clinical trials and showed how biomarkers can be used for patient stratification, treatment selection, and surrogate endpoints. Her examples included high-sensitivity cardiac troponin as a potential biomarker to identify RA patients at underestimated cardiovascular risk, and synovial tissue biomarkers from biopsy-driven trials such as R4RA and AMP-AIM. She stressed that biomarker usefulness depends on disease stage, population, and whether the marker truly predicts clinically meaningful outcomes.<br /><br />Dr. Ellen Kirk discussed biomarker use in transplantation. He highlighted the challenges of low event rates, changing disease prevalence over time, and the need to measure outcomes that matter most, such as graft and patient survival rather than acute rejection alone. He reviewed established and emerging biomarkers including donor-specific antibodies, cell-free DNA, drug exposure measures, viral viremia, and composite scores like IBOX. He noted that many promising biomarkers are limited by cost, complexity, and lack of standardization, but surrogate endpoints may accelerate trial development.<br /><br />In the discussion, both speakers agreed that AI and machine learning can help analyze complex biomarker datasets, but clinical validation and trial design remain essential.

Keywords

biomarkers

clinical trials

rheumatoid arthritis

organ transplantation

biomarker validation

patient stratification

surrogate endpoints

donor-specific antibodies

cell-free DNA

machine learning