Identifying COVID-19 optimal vaccine dose using mathematical immunostimulation/immunodynamic modelling

Lymphatic distribution considerations for subunit vaccine design and development

Subunit vaccines are an important platform for controlling current and emerging infectious diseases. The lymph nodes are the primary site generating the humoral response and delivery of antigens to these sites is critical to effective immunization. Indeed, the duration of antigen exposure within the lymph node is correlated with the antibody response. While current licensed vaccines are typically given through the intramuscular route, injecting vaccines subcutaneously allows for direct access to lymphatic vessels and therefore can enhance the transfer of antigen to the lymph nodes. However, protein subunit antigen uptake into the lymph nodes is inefficient, and subunit vaccines require adjuvants to stimulate the initial immune response. Therefore, formulation strategies have been developed to enhance the exposure of subunit proteins and adjuvants to the lymph nodes by increasing lymphatic uptake or prolonging the retention at the injection site. Given that lymph node exposure is a crucial consideration in vaccine design, in depth analyses of the pharmacokinetics of antigens and adjuvants should be the focus of future preclinical and clinical studies. This review will provide an overview of formulation strategies for targeting the lymphatics and prolonging antigen exposure and will discuss pharmacokinetic evaluations which can be applied toward vaccine development.

Computational design of mRNA vaccines

mRNA technology has emerged as a successful vaccine platform that offered a swift response to the COVID-19 pandemic. Accumulating evidence shows that vaccine efficacy, thermostability, and other important properties, are largely impacted by intrinsic properties of the mRNA molecule, such as RNA sequence and structure, both of which can be optimized. Designing mRNA sequence for vaccines presents a combinatorial problem due to an extremely large selection space. For instance, due to the degeneracy of the genetic code, there are over 10632 possible mRNA sequences that could encode the spike protein, the COVID-19 vaccines' target. Moreover, designing different elements of the mRNA sequence simultaneously against multiple objectives such as translational efficiency, reduced reactogenicity, and improved stability requires an efficient and sophisticated optimization strategy. Recently, there has been a growing interest in utilizing computational tools to redesign mRNA sequences to improve vaccine characteristics and expedite discovery timelines. In this review, we explore important biophysical features of mRNA to be considered for vaccine design and discuss how computational approaches can be applied to rapidly design mRNA sequences with desirable characteristics.

The COVID-19 inflammation and high mortality mechanism trigger

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lasted from March 2020 to May 2023, infecting over 689 million and causing 6.9 million deaths globally. SARS-CoV-2 enters human cells via the spike protein binding to ACE2 receptors, leading to viral replication and an exaggerated immune response characterized by a "cytokine storm." This review analyzes the COVID-19 pathogenesis, strains, risk factors for severe disease, and vaccine types and effectiveness. A systematic literature search for 2020-2023 was conducted. Results show the cytokine storm underlies COVID-19 pathogenesis, causing multiorgan damage. Key viral strains include Alpha, Beta, Gamma, Delta, and Omicron, differing in transmissibility, disease severity, and vaccine escape. Risk factors for severe COVID-19 include older age, obesity, and comorbidities. mRNA, viral vector, and inactivated vaccines effectively prevent hospitalization and death, although new variants exhibit some vaccine escape. Ongoing monitoring of emerging strains and vaccine effectiveness is warranted. This review provides updated information on COVID-19 pathogenesis, viral variants, risk factors, and vaccines to inform public health strategies for containment and treatment.

A Quantitative Clinical Pharmacology-Based Framework For Model-Informed Vaccine Development

Historically, vaccine development and dose optimization have followed mostly empirical approaches without clinical pharmacology and model-informed approaches playing a major role, in contrast to conventional drug development. This is attributed to the complex cascade of immunobiological mechanisms associated with vaccines and a lack of quantitative frameworks for extracting dose-exposure-efficacy-toxicity relationships. However, the Covid-19 pandemic highlighted the lack of sufficient immunogenicity due to suboptimal vaccine dosing regimens and the need for well-designed, model-informed clinical trials which enhance the probability of selection of optimal vaccine dosing regimens. In this perspective, we attempt to develop a quantitative clinical pharmacology-based approach that integrates vaccine dose-efficacy-toxicity across various stages of vaccine development into a unified framework that we term as model-informed vaccine dose-optimization and development (MIVD). We highlight scenarios where the adoption of MIVD approaches may have a strategic advantage compared to conventional practices for vaccines.