NIH grant: Transforming Autoimmune Diagnostic and Disease Monitoring with a Scalable IP-to-MS Platform for Autoantigen Profiling (1R43AI197429-01)

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NIH grant 1R43AI197429-01: Transforming Autoimmune Diagnostic and Disease Monitoring with a Scalable IP-to-MS Platform for Autoantigen Profiling
Institute: {'code': 'AI', 'abbreviation': 'NIAID', 'name': 'National Institute of Allergy and Infectious Diseases', 'admin_org_id': '12500003', 'admin_funding_url': ''}. PI: Robyn Therese Domsic. Award: $320863.
Start: 2026-08-01T00:00:00.
PROJECT SUMMARY
Autoimmune diseases, such as systemic sclerosis (SSc), pose a growing public health challenge, affecting 7-9% of the U.S. population, impacting 23-30 million individuals. Accurate and timely diagnosis remains a major barrier to effective treatment, as current clinical tests like immunoprecipitation (IP), enzyme-linked
immunosorbent assay (ELISA), and double-immunodiffusion (DID) are labor-intensive, low-throughput, and biased toward known autoantibodies. These limitations often result in false positives, missed diagnoses, and delayed care, particularly for patients whose conditions do not fit textbook presentations. The need for a more comprehensive, scalable, and unbiased diagnostic tool is critical to improving outcomes for patients with autoimmune diseases. Impact Proteomics has developed a novel immunoproteomics platform, IP-to-mass spectrometry (IP-to-MS), which leverages a patented ProMTag system (Patent: US-11655271-B2) to separate autoantigens from abundant contaminants like immunoglobulins. This workflow enables rapid, reproducible, and quantitative detection of both known and novel autoantibodies from as little as 10 ìL of patient serum. Unlike conventional assays, IP-to-MS provides high-resolution autoantibody profiles that can guide diagnosis, monitor disease progression, and stratify patients for personalized treatment. This Phase I SBIR project will establish the clinical and technical feasibility of IP-to-MS for autoimmune diagnostics. Aim 1 will define detection limits, signal thresholds, and quantitative ranges for key SSc biomarkers,
such as Topoisomerase I and RNA polymerase III. Aim 2 will apply IP-to-MS to a well-annotated cohort of 1,000 SSc patient samples to benchmark autoantibody profiles, stratify disease subtypes, and explore predictive machine learning models. By enabling more accurate, rapid, and comprehensive autoimmune testing, this project supports public health by improving diagnostic access, reducing healthcare disparities, and enhancing precision medicine for immune-mediated diseases. Success will position IP-to-MS for clinical adoption and further development toward FDA clerance in Phase II.

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