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Published on: January 17, 2015
Antigen prioritization and selection for vaccine design and development: A comprehensive pipeline
Ehsan Mofidi Chelan1, Mohammad M Pourseif2, Amir Zarebkohan3
1Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran; Department of Medical Nanotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran.
Abstract:
Antigen selection is the earliest and most consequential decision in vaccine design, yet it remains fragmented across disciplines and disease areas. Here we present a comprehensive, disease-agnostic pipeline intended as a practical resource for prioritizing and selecting candidate antigens, the foundational step on which downstream efficacy, safety, and manufacturability ultimately depend. Rather than categorization delivery platforms, we develop a mechanism-driven decision framework that aligns each candidate with the correlate of protection and the effector program its biological context demands. We first distinguish upstream public-health prioritization from downstream molecular selection, then apply a shared set of criteria, biological essentiality, evolutionary conservation, structural accessibility, natural processing and presentation, host-population coverage, and safety relative to the self-proteome, across a broad range of indications: pathogen-driven infectious diseases, zoonoses within a One Health framework, infection-associated and non-infection-derived cancers, autoimmune disorders, severe allergy, and transplantation. For each setting we examine the condition-specific considerations that separate antibody- and cell-mediated protection, cytotoxic T-cell-based tumor control, and deletion-, anergy-, or regulatory-based tolerance, while foregrounding the principles they hold in common. Throughout, biological rationale is coupled to developability, showing how conformational stabilization, modality-specific constraints, and manufacturability decide whether computational candidates become viable products. Finally, we consider how deep learning, generative artificial intelligence, and digital-twin modeling can forecast immune escape and accelerate preparedness against emerging pathogens ("Disease X"). The result is a unifying, translationally grounded guide to rational antigen selection applicable regardless of the target disease.
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