Phage Display Technology for Target-Specific Neuro-Immunology: A Comprehensive Methodological Analysis and Clinical Translation Framework
Abstract
Neuroinflammatory disorders represent one of the most complex therapeutic challenges in modern medicine due to the blood-brain barrier, molecular heterogeneity of diseased tissues, and the critical need for target specificity to avoid off-target effects. Phage display technology, originally developed by George P. Smith in 1985 and recognized with the Nobel Prize in Chemistry in 2018, offers a methodological platform for identifying peptide ligands with high affinity and specificity for molecular targets uniquely expressed on inflamed neural tissues. This article provides a comprehensive examination of the theoretical foundations, methodological frameworks, experimental validation protocols, and clinical translation pathways for phage display applications in neuroimmunology. Through detailed analysis of real-world case studies, including applications in multiple sclerosis, Alzheimer’s disease, and glioblastoma, we examine how phage-derived peptides can target the P2X7 purinergic receptor, NMDA receptor subunits, and other inflammation-specific epitopes. The discussion encompasses biopanning strategies, affinity maturation, mathematical modeling of binding kinetics, nanoparticle conjugation approaches, and regulatory considerations for clinical implementation. Data from peer-reviewed studies, including dissociation constant measurements, in vivo biodistribution analyses, and phase I clinical trial outcomes, are presented to establish evidence-based conclusions about the technology’s potential and limitations. This analysis aims to provide researchers, clinicians, and pharmaceutical developers with a rigorous understanding of phage display technology’s role in advancing neuroimmunological therapeutics.
Fields of Study & Subjects
Monoclonal and Polyclonal Antibodies Research
Neuroinflammation and Neurodegeneration Mechanisms
vaccines and immunoinformatics approaches
Publication details
- Journal / source
- Figshare
- Publication date
- 7 March 2026
- Language
- EN
- Document type
- Preprint