COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning.

Rational vaccine design, especially vaccine antigen identification and optimization, is critical to successful and efficient vaccine development against various infectious diseases including coronavirus disease 2019 (COVID-19). In general, computational vaccine design includes three major stages: (i) identification and annotation of experimentally verified gold standard protective antigens through literature mining, (ii) rational vaccine design using reverse vaccinology (RV) and structural vaccinology (SV) and (iii) post-licensure vaccine success and adverse event surveillance and its usage for vaccine design. Protegen is a database of experimentally verified protective antigens, which can be used as gold standard data for rational vaccine design. RV predicts protective antigen targets primarily from genome sequence analysis. SV refines antigens through structural engineering. Recently, RV and SV approaches, with the support of various machine learning methods, have been applied to COVID-19 vaccine design. The analysis of post-licensure vaccine adverse event report data also provides valuable results in terms of vaccine safety and how vaccines should be used or paused. Ontology standardizes and incorporates heterogeneous data and knowledge in a human- and computer-interpretable manner, further supporting machine learning and vaccine design. Future directions on rational vaccine design are discussed.

Inhalable mRNA vaccines for respiratory diseases: a roadmap.

Global implementation of messenger RNA (mRNA) vaccines represents an enormous advance with far-reaching implications for respiratory disease treatment. mRNA vaccines offer exceptional efficacy and versatile capacity to be adapted to new viruses and variants; however, critical questions remain regarding immune persistence and formulation stability. This represents a significant opportunity for developing next-generation, inhaled mRNA vaccines with the ability to drive long-lasting, tissue-specific memory responses needed for rapid recall and immediate local protection. Advances in pulmonary delivery technologies offer potential to overcome translational challenges including design of aerosol-stable and lung-stable formulations, navigation of pulmonary biological barriers, and a lack of predictive models and measurement techniques. We highlight recent advances in each of these challenge areas to illuminate the path to translation.

Designing the Next Generation of Vaccines: Relevance for Future Pandemics.

The development of vaccines is one of the greatest medical interventions in the history of global infectious diseases and has contributed to the annual saving of at least 2 to 3 million lives worldwide. However, many diseases are not preventable through currently available vaccines, and the potential of modulating the immune response during vaccination has not been fully exploited. The first golden age of vaccines was based on the germ theory and the use of live, attenuated, inactivated pathogens or toxins. New strategies and formulations (e.g., adjuvants) with an immunomodulatory capacity to enhance the protective qualities and duration of vaccines have been incompletely exploited. These strategies can prevent disease and improve protection against infectious diseases, modulate the course of some noncommunicable diseases, and increase the immune responses of patients at a high risk of infection, such as the elderly or immunocompromised patients. In this minireview, we focus on how metabolic and epigenetic modulators can amplify and enhance the function of immunity in a given vaccine. We propose the term "amplifier" for such additives, and we pose that future vaccines will have three components: antigen, adjuvant, and amplifier.