Serum exhibits a highly complex composition, which poses challenges for discovering novel pharmacodynamic biomarkers through serum proteomics for disease prediction and diagnosis. Recent studies have shown that nanoparticles can effectively decrease the abundance of high-concentration proteins while enriching low-abundance proteins in serum. In this study, we synthesized silica-coated iron oxide nanoparticles and established a highly efficient and reproducible protein corona (PC)-based proteomic analysis approach to broaden the scope of serum proteome profiling. Using this PC-based strategy, we identified 1,070 proteins with a median coefficient of variation of 12.56%, representing twice the number of proteins detected by conventional direct digestion, along with enrichment in additional biological processes. We further applied this approach to detect pharmacodynamic biomarkers in a collagen-induced arthritis (CIA) rat model treated with methotrexate (MTX). Bioinformatic analysis revealed 485 differentially expressed proteins (DEPs) in CIA rats, among which 323 DEPs were restored to near-normal levels following MTX treatment. Overall, this strategy not only enhances the understanding of disease mechanisms and drug actions via serum proteomics but also offers a robust platform for identifying pharmacodynamic biomarkers relevant to disease prediction, diagnosis, and therapeutic evaluation.
