Biotechnology advances · 2026
Antigen prioritization and selection for vaccine design and development: A comprehensive pipeline.
СтатьяОбзорНазвание и аннотация: Перевод в очереди
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Chelan Ehsan Mofidi, Pourseif Mohammad M, Zarebkohan Amir, Moghaddam Hossein Ghasemi, Kiani Jafar, Tohidkia Mohammad-Reza, Almasian Bijan, Parhizkar Aram, Omidi Yadollah
Аннотация
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.