Influenza viruses continue to undergo antigenic drift and shift, resulting in the need to update existing vaccines annually. Therefore, the development of a universal influenza vaccine has become an urgent global need. This paper reviews the functions of common antigenic targets of influenza vaccines and their advantages and disadvantages in universal vaccine design. We also summarize the common design strategies for universal influenza vaccines, which mainly include the immunofocusing strategy, multi-target combination strategy, T-cell strategy, computationally optimized broadly cross-reactive antigenic strategy (COBRA), and artificial intelligence strategy. In addition, we also sort out the latest research progress of universal influenza vaccines under different technological routes. This will help researchers better grasp the latest developments of universal influenza vaccines.

Monoclonal antibodies (mAbs), as potent therapeutic agents, have been widely applied in the treatment of various major diseases, including infectious diseases, autoimmune disorders, cancers, and neurodegenerative diseases. However, early-generation mAbs were limited by high immunogenicity, short half-life, and insufficient affinity, which compromised their therapeutic efficacy. With technological advancements, novel approaches such as high-throughput screening and glycosyl modification have been introduced to improve the performance of mAbs. Furthermore, computer-aided design techniques—including molecular docking, molecular dynamics simulations, and artificial intelligence -based methods—are increasingly being employed to accelerate the optimization process. This review summarizes recent progress in the optimization of therapeutic mAbs, with a focus on technological breakthroughs and applications in affinity enhancement, development of broad-spectrum mAbs, specificity modulation, immunogenicity reduction, and stability improvement. Additionally, it discusses current challenges and future directions in antibody optimization. This review aims to provide insights and references for the development and optimization of next-generation antibody drugs, ultimately promoting the clinical application of safer and more effective mAb-based therapies.

Norovirus is a major etiological agent of nonbacterial gastroenteritis around the world. Due to its in vitro culture complexity, high genome diversity, and the lack of cross-reactive immunity between genogroups, there is an unmet urgent need for polyvalent norovirus vaccines that provide broad-spectrum protection, and no vaccine has gained global approval to date. In this study, we constructed a bivalent norovirus vaccine, based on the highly attenuated poxvirus [strain VG9] vector, expressing the major capsid protein VP1 from genotypes GII.4 and GII.17. VG9-NOR exhibited a comparable replication ability to the authentic virus while preserving good safety. After the intramuscular and intranasal immunization of mice, VG9-NOR induced high IgG- and IgA-binding antibody (Ab) titers against GII.4 and GII.17, increased the secretion of GII.4 and GII.17-specific HGBA-blocking antibodies, and enhanced GII.17-specific mucosal immunity. Furthermore, VG9-NOR also induced a Th1-mediated cellular response. These results demonstrate that the polyvalent poxvirus vector vaccine expressing VP1 variants from different subtypes is able to elicit effective protection. Our study highlights the VG9 vector as a highly promising candidate for the development of polyvalent norovirus vaccines.

The high case fatality rate, cross-species transmission, and ongoing evolution of H5 avian influenza viruses pose an imminent threat of an influenza pandemic, particularly with the currently predominant clade 2.3.4.4b lineage. Existing seasonal influenza vaccines and licensed H5 vaccines provide limited cross-protection against H5 viruses, underscoring an urgent need for the development of broadly protective H5 vaccines. In this study, we analyzed all human-infected H5 hemagglutinin (HA) sequences using bioinformatics approaches and subsequently designed a novel H5 influenza vaccine through algorithm optimization. The predicted structure of this vaccine closely resembles that of the wild-type H5 HA trimer. In animal studies, the algorithm-optimized H5 mRNA vaccine not only induced high levels of neutralizing antibodies against multiple clade 2.3.4.4b H5 viruses but also elicited cross-neutralizing antibodies against clade 2.3.4.4 and clade 2.2.1 H5 viruses, as well as robust cellular immune responses. These findings highlight the potential of algorithm-based approaches in developing broadly protective vaccines against pandemic viruses and suggest that this vaccine candidate could serve as a strategic stockpile for preventing H5 influenza pandemics.