Prior population immunity reduces the expected impact of CTL-inducing vaccines for pandemic influenza control

Seasonality as a driver of pH1N12009 influenza vaccination campaign impact

Although the most recent respiratory virus pandemic was triggered by a Coronavirus, sustained and elevated prevalence of highly pathogenic avian influenza viruses able to infect mammalian hosts highlight the continued threat of pandemics of influenza A virus (IAV) to global health. Retrospective analysis of pandemic outcomes, including comparative investigation of intervention efficacy in different regions, provide important contributions to the evidence base for future pandemic planning. The swine-origin IAV pandemic of 2009 exhibited regional variation in onset, infection dynamics and annual infection attack rates (IARs). For example, the UK experienced three severe peaks of infection over two influenza seasons, whilst Australia experienced a single severe wave. We adopt a seasonally forced 2-subtype model for the transmission of pH1N12009 and seasonal H3N2 to examine the role vaccination campaigns may play in explaining differences in pandemic trajectories in temperate regions. Our model differentiates between the nature of vaccine- and infection-acquired immunity. In particular, we assume that immunity triggered by infection elicits heterologous cross-protection against viral shedding in addition to long-lasting neutralising antibody, whereas vaccination induces imperfect reduction in susceptibility. We employ an Approximate Bayesian Computation (ABC) framework to calibrate the model using data for pH1N12009 seroprevalence, relative subtype dominance, and annual IARs for Australia and the UK. Heterologous cross-protection substantially suppressed the pandemic IAR over the posterior, with the strength of protection against onward transmission inversely correlated with the initial reproduction number. We show that IAV pandemic timing relative to the usual seasonal influenza cycle influenced the size of the initial waves of pH1N12009 in temperate regions and the impact of vaccination campaigns.

Universally Immune: How Infection Permissive Next Generation Influenza Vaccines May Affect Population Immunity and Viral Spread

Next generation influenza vaccines that target conserved epitopes are becoming a clinical reality but still have challenges to overcome. Universal next generation vaccines are considered a vital tool to combat future pandemic viruses and have the potential to vastly improve long-term protection against seasonal influenza viruses. Key vaccine strategies include HA-stem and T cell activating vaccines; however, they could have unintended effects for virus adaptation as they recognise the virus after cell entry and do not directly block infection. This may lead to immune pressure on residual viruses. The potential for immune escape is already evident, for both the HA stem and T cell epitopes, and mosaic approaches for pre-emptive immune priming may be needed to circumvent key variants. Live attenuated influenza vaccines have not been immunogenic enough to boost T cells in adults with established prior immunity. Therefore, viral vectors or peptide approaches are key to harnessing T cell responses. A plethora of viral vector vaccines and routes of administration may be needed for next generation vaccine strategies that require repeated long-term administration to overcome vector immunity and increase our arsenal against diverse influenza viruses.

The Hurdles From Bench to Bedside in the Realization and Implementation of a Universal Influenza Vaccine

Influenza viruses circulate worldwide causing annual epidemics that have a substantial impact on public health. This is despite vaccines being in use for over 70 years and currently being administered to around 500 million people each year. Improvements in vaccine design are needed to increase the strength, breadth, and duration of immunity against diverse strains that circulate during regular epidemics, occasional pandemics, and from animal reservoirs. Universal vaccine strategies that target more conserved regions of the virus, such as the hemagglutinin (HA)-stalk, or recruit other cellular responses, such as T cells and NK cells, have the potential to provide broader immunity. Many pre-pandemic vaccines in clinical development do not utilize new vaccine platforms but use "tried and true" recombinant HA protein or inactivated virus strategies despite substantial leaps in fundamental research on universal vaccines. Significant hurdles exist for universal vaccine development from bench to bedside, so that promising preclinical data is not yet translating to human clinical trials. Few studies have assessed immune correlates derived from asymptomatic influenza virus infections, due to the scale of a study required to identity these cases. The realization and implementation of a universal influenza vaccine requires identification and standardization of set points of protective immune correlates, and consideration of dosage schedule to maximize vaccine uptake.

Harnessing the Power of T Cells: The Promising Hope for a Universal Influenza Vaccine

Next-generation vaccines that utilize T cells could potentially overcome the limitations of current influenza vaccines that rely on antibodies to provide narrow subtype-specific protection and are prone to antigenic mismatch with circulating strains. Evidence from animal models shows that T cells can provide heterosubtypic protection and are crucial for immune control of influenza virus infections. This has provided hope for the design of a universal vaccine able to prime against diverse influenza virus strains and subtypes. However, multiple hurdles exist for the realisation of a universal T cell vaccine. Overall primary concerns are: extrapolating human clinical studies, seeding durable effective T cell resident memory (Trm), population human leucocyte antigen (HLA) coverage, and the potential for T cell-mediated immune escape. Further comprehensive human clinical data is needed during natural infection to validate the protective role T cells play during infection in the absence of antibodies. Furthermore, fundamental questions still exist regarding the site, longevity and duration, quantity, and phenotype of T cells needed for optimal protection. Standardised experimental methods, and eventually simplified commercial assays, to assess peripheral influenza-specific T cell responses are needed for larger-scale clinical studies of T cells as a correlate of protection against influenza infection. The design and implementation of a T cell-inducing vaccine will require a consensus on the level of protection acceptable in the community, which may not provide sterilizing immunity but could protect the individual from severe disease, reduce the length of infection, and potentially reduce transmission in the community. Therefore, increasing the standard of care potentially offered by T cell vaccines should be considered in the context of pandemic preparedness and zoonotic infections, and in combination with improved antibody vaccine targeting methods. Current pandemic vaccine preparedness measures and ongoing clinical trials under-utilise T cell-inducing vaccines, reflecting the myriad questions that remain about how, when, where, and which T cells are needed to fight influenza virus infection. This review aims to bring together basic fundamentals of T cell biology with human clinical data, which need to be considered for the implementation of a universal vaccine against influenza that harnesses the power of T cells.

The case test-negative design for studies of the effectiveness of influenza vaccine in inpatient settings

Background: The test-negative design (TND) to evaluate influenza vaccine effectiveness is based on patients seeking care for acute respiratory infection, with those who test positive for influenza as cases and the test-negatives serving as controls. This design has not been validated for the inpatient setting where selection bias might be different from an outpatient setting.

Methods: We derived mathematical expressions for vaccine effectiveness (VE) against laboratory-confirmed influenza hospitalizations and used numerical simulations to verify theoretical results exploring expected biases under various scenarios. We explored meaningful interpretations of VE estimates from inpatient TND studies.

Results: VE estimates from inpatient TND studies capture the vaccine-mediated protection of the source population against laboratory-confirmed influenza hospitalizations. If vaccination does not modify disease severity, these estimates are equivalent to VE against influenza virus infection. If chronic cardiopulmonary individuals are enrolled because of non-infectious exacerbation, biased VE estimates (too high) will result. If chronic cardiopulmonary disease status is adjusted for accurately, the VE estimates will be unbiased. If chronic cardiopulmonary illness cannot be adequately be characterized, excluding these individuals may provide unbiased VE estimates.

Conclusions: The inpatient TND offers logistic advantages and can provide valid estimates of influenza VE. If highly vaccinated patients with respiratory exacerbation of chronic cardiopulmonary conditions are eligible for study inclusion, biased VE estimates will result unless this group is well characterized and the analysis can adequately adjust for it. Otherwise, such groups of subjects should be excluded from the analysis.

On the Role of CD8+ T Cells in Determining Recovery Time from Influenza Virus Infection

Myriad experiments have identified an important role for CD8⁺ T cell response mechanisms in determining recovery from influenza A virus infection. Animal models of influenza infection further implicate multiple elements of the immune response in defining the dynamical characteristics of viral infection. To date, influenza virus models, while capturing particular aspects of the natural infection history, have been unable to reproduce the full gamut of observed viral kinetic behavior in a single coherent framework. Here, we introduce a mathematical model of influenza viral dynamics incorporating innate, humoral, and cellular immune components and explore its properties with a particular emphasis on the role of cellular immunity. Calibrated against a range of murine data, our model is capable of recapitulating observed viral kinetics from a multitude of experiments. Importantly, the model predicts a robust exponential relationship between the level of effector CD8⁺ T cells and recovery time, whereby recovery time rapidly decreases to a fixed minimum recovery time with an increasing level of effector CD8⁺ T cells. We find support for this relationship in recent clinical data from influenza A (H7N9) hospitalized patients. The exponential relationship implies that people with a lower level of naive CD8⁺ T cells may receive significantly more benefit from induction of additional effector CD8⁺ T cells arising from immunological memory, itself established through either previous viral infection or T cell-based vaccines.

Pandemic Risk Assessment Model (PRAM): a mathematical modeling approach to pandemic influenza planning

The Pandemic Risk Assessment Model (PRAM) is a mathematical model developed to analyse two pandemic influenza control measures available to public health: antiviral treatment and immunization. PRAM is parameterized using surveillance data from Alberta, Canada during pandemic H1N1. Age structure and risk level are incorporated in the compartmental, deterministic model through a contact matrix. The model characterizes pandemic influenza scenarios by transmissibility and severity properties. Simulating a worst-case scenario similar to the 1918 pandemic with immediate stockpile release, antiviral demand is 20·3% of the population. With concurrent, effective and timely immunization strategies, antiviral demand would be significantly less. PRAM will be useful in informing policy decisions such as the size of the Alberta antiviral stockpile and can contribute to other pandemic influenza planning activities and scenario analyses.

Antigenic variability: Obstacles on the road to vaccines against traditionally difficult targets

Despite the impressive impact of vaccines on public health, the success of vaccines targeting many important pathogens and cancers has to date been limited. The burden of infectious diseases today is mainly caused by antigenically variable pathogens (AVPs), which escape immune responses induced by prior infection or vaccination through changes in molecular structures recognized by antibodies or T cells. Extensive genetic and antigenic variability is the major obstacle for the development of new or improved vaccines against "difficult" targets. Alternative, qualitatively new approaches leading to the generation of disease- and patient-specific vaccine immunogens that incorporate complex permanently changing epitope landscapes of intended targets accompanied by appropriate immunomodulators are urgently needed. In this review, we highlight some of the most critical common issues related to the development of vaccines against many pathogens and cancers that escape protective immune responses owing to antigenic variation, and discuss recent efforts to overcome the obstacles by applying alternative approaches for the rational design of new types of immunogens.

Modeling Influenza Virus Infection: A Roadmap for Influenza Research

Influenza A virus (IAV) infection represents a global threat causing seasonal outbreaks and pandemics. Additionally, secondary bacterial infections, caused mainly by Streptococcus pneumoniae, are one of the main complications and responsible for the enhanced morbidity and mortality associated with IAV infections. In spite of the significant advances in our knowledge of IAV infections, holistic comprehension of the interplay between IAV and the host immune response (IR) remains largely fragmented. During the last decade, mathematical modeling has been instrumental to explain and quantify IAV dynamics. In this paper, we review not only the state of the art of mathematical models of IAV infection but also the methodologies exploited for parameter estimation. We focus on the adaptive IR control of IAV infection and the possible mechanisms that could promote a secondary bacterial coinfection. To exemplify IAV dynamics and identifiability issues, a mathematical model to explain the interactions between adaptive IR and IAV infection is considered. Furthermore, in this paper we propose a roadmap for future influenza research. The development of a mathematical modeling framework with a secondary bacterial coinfection, immunosenescence, host genetic factors and responsiveness to vaccination will be pivotal to advance IAV infection understanding and treatment optimization.