Advances and challenges in reverse vaccinology: From genomics to next-generation vaccines

Reverse vaccinology has emerged as a transformative paradigm in vaccine science, harnessing genomic data, computational pipelines, and artificial intelligence to rationally identify and prioritize antigen candidates — bypassing the need for pathogen culture and dramatically compressing development timelines. From its foundational application to Neisseria meningitidis to its expanding use across human, veterinary, and zoonotic pathogens, this approach has reshaped how researchers design vaccines against targets that were previously intractable, including pathogens with high biosafety risks, highly variable surface antigens, or proteins that mimic host structures. Yet as the field matures, it faces an increasingly complex set of challenges: translating computationally predicted candidates into clinically validated vaccines, ensuring broad population coverage across diverse immune landscapes, addressing antigen variability in rapidly evolving pathogens, and navigating regulatory and equity barriers that limit real-world deployment. At the frontier of these advances and challenges lies an urgent need for consolidated knowledge, standardized workflows, and cross-disciplinary dialogue — the driving motivation behind this Research Topic.This Research Topic aims to consolidate advances and foster deeper understanding of the principles and applications of reverse vaccinology. The main objectives are to explore its foundational platforms and pipelines, the integration of artificial intelligence (AI) and machine learning (ML), and innovative computational and experimental approaches for vaccine candidate design. Further, this research topic seeks to examine strategies to assess and optimize population coverage, immunogenicity, and real-world efficacy of reverse vaccinology-based vaccines. Additionally, it invites discussion of ethical, biosafety, and regulatory considerations, as well as challenges related to equitable access, standardization, and translation from discovery to deployment, supporting the broader vision of vaccinomics and global public health.This Research Topic broadly encompasses themes at the intersection of computational predictions and practical vaccine evaluation, with emphasis on translational challenges and global relevance. Its scope includes but is not limited to the following areas:o Reverse Vaccinology: past, present, and future contributions to vaccine scienceo Integration of artificial intelligence and machine learning, including immunoinformatics, epitope discovery, and assessment of population coverageo Codon-pair deoptimization and other genetic attenuation strategies for vaccine optimizationo CRISPR/Cas9 and genome editing tools in reverse vaccinology pipelineso Application of reverse vaccinology to human, veterinary, and zoonotic viruses (e.g., FeLV, Zika virus, ASFV, avian influenza)o Addressing antimicrobial resistance (AMR) through rational vaccine designo Development of broad-spectrum (e.g., pan-coronavirus, universal influenza) and multi-epitope vaccineso Standardized workflows, benchmarks, and validation pipelines for antigen prediction and vaccine assessmento Innovations in thermostable vaccine constructs suitable for low- and middle-income countries and outbreak settingso Overcoming computational, experimental, translational, and regulatory hurdles in reverse vaccinologyo Navigating regulatory pathways and approvals for reverse vaccinology-derived vaccinesWe welcome Original Research, Reviews, Mini-Reviews, and Perspectives related to these themes. Topic editors: Rekha Khandia, Tiratha Raj Singh, Mohit Kamthania, Brigitte VANNIER, Pedro A Reche Source: https://www.frontiersin.org/research-topics/77763/advances-and-challenges-in-reverse-vaccinology-from-genomics-to-next-generation-vaccines