Using vaccine Immunostimulation/Immunodynamic modelling methods to inform vaccine dose decision-making

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.

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.

Prediction of long-term humoral response induced by the two-dose heterologous Ad26.ZEBOV, MVA-BN-Filo vaccine against Ebola

The persistence of the long-term immune response induced by the heterologous Ad26.ZEBOV, MVA-BN-Filo two-dose vaccination regimen against Ebola has been investigated in several clinical trials. Longitudinal data on IgG-binding antibody concentrations were analyzed from 487 participants enrolled in six Phase I and Phase II clinical trials conducted by the EBOVAC1 and EBOVAC2 consortia. A model based on ordinary differential equations describing the dynamics of antibodies and short- and long-lived antibody-secreting cells (ASCs) was used to model the humoral response from 7 days after the second vaccination to a follow-up period of 2 years. Using a population-based approach, we first assessed the robustness of the model, which was originally estimated based on Phase I data, against all data. Then we assessed the longevity of the humoral response and identified factors that influence these dynamics. We estimated a half-life of the long-lived ASC of at least 15 years and found an influence of geographic region, sex, and age on the humoral response dynamics, with longer antibody persistence in Europeans and women and higher production of antibodies in younger participants.

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

INTRODUCTION

Identifying optimal COVID-19 vaccine dose is essential for maximizing their impact. However, COVID-19 vaccine dose-finding has been an empirical process, limited by short development timeframes, and therefore potentially not thoroughly investigated. Mathematical IS/ID modelling is a novel method for predicting optimal vaccine dose which could inform future COVID-19 vaccine dose decision making.

METHODS

Published clinical data on COVID-19 vaccine dose-response was identified and extracted. Mathematical models were calibrated to the dose-response data stratified by subpopulation, where possible to predict optimal dose. Predicted optimal doses were summarised across vaccine type and compared to chosen dose for the primary series of COVID-19 vaccines to identify vaccine doses that may benefit from re-evaluation.

RESULTS

30 clinical dose-response datasets in adults and elderly population were extracted for four vaccine types and optimal doses predicted using the models. Results suggest that, if re-assessed for dose, COVID-19 vaccines Ad26.cov, ChadOx1 n-Cov19, BNT162b2, Coronavac, and NVX-CoV2373 could benefit from increased dose in adults and mRNA-1273 and Coronavac, could benefit from increased and decreased dose for the elderly population, respectively.

DISCUSSION

Future iterations of COVID-19 vaccines could benefit from re-evaluating dose to ensure most effective use of the vaccine and mathematical modelling can support this.

Modelling the response to vaccine in non-human primates to define SARS-CoV-2 mechanistic correlates of protection

The definition of correlates of protection is critical for the development of next-generation SARS-CoV-2 vaccine platforms. Here, we propose a model-based approach for identifying mechanistic correlates of protection based on mathematical modelling of viral dynamics and data mining of immunological markers. The application to three different studies in non-human primates evaluating SARS-CoV-2 vaccines based on CD40-targeting, two-component spike nanoparticle and mRNA 1273 identifies and quantifies two main mechanisms that are a decrease of rate of cell infection and an increase in clearance of infected cells. Inhibition of RBD binding to ACE2 appears to be a robust mechanistic correlate of protection across the three vaccine platforms although not capturing the whole biological vaccine effect. The model shows that RBD/ACE2 binding inhibition represents a strong mechanism of protection which required significant reduction in blocking potency to effectively compromise the control of viral replication.

Blueprint for pandemic response: Focus on translational medicine, clinical pharmacology and pharmacometrics

Perhaps the most important lesson learned from the COVID-19 pandemic is that of preparedness. Enhanced surveillance systems for early threat detection will be crucial to maximizing response time for implementation of public health measures and mobilization of resources in containing an emerging pandemic. Recent outbreaks have been dominated by viral pathogens, with RNA respiratory viruses being the most likely to have pandemic potential. These should therefore be a preparedness priority. Tools in the areas of virology, drug discovery, clinical pharmacology, translational medicine and pharmacometrics should be considered key components in the rapid identification and development of existing and novel interventions for a pandemic response. Prioritization of therapeutics should be based on in vitro activity, likelihood of achieving effective drug concentrations at the site of action, and safety profile at the doses that will be required for clinical efficacy. Deployment strategies must be tailored to the epidemiology of the disease, and the adequacy of the response should be re-evaluated in view of evolving epidemiological factors. An interdisciplinary framework integrating drug pharmacology, viral kinetics, epidemiology and health economics could help optimize the deployment strategy by improving decision-making around who to treat, when to treat, and with what type of intervention for optimal outcomes. Lastly, while an effective vaccine will ultimately end a pandemic, antiviral drug intervention guided by clinical pharmacology principles will continue to play a critical role in any pandemic response.

Current and prospective computational approaches and challenges for developing COVID-19 vaccines

SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.

Optimising Vaccine Dose in Inoculation against SARS-CoV-2, a Multi-Factor Optimisation Modelling Study to Maximise Vaccine Safety and Efficacy

Developing a vaccine against the global pandemic SARS-CoV-2 is a critical area of active research. Modelling can be used to identify optimal vaccine dosing; maximising vaccine efficacy and safety and minimising cost. We calibrated statistical models to published dose-dependent seroconversion and adverse event data of a recombinant adenovirus type-5 (Ad5) SARS-CoV-2 vaccine given at doses 5.0 × 10¹⁰, 1.0 × 10¹¹ and 1.5 × 10¹¹ viral particles. We estimated the optimal dose for three objectives, finding: (A) the minimum dose that may induce herd immunity, (B) the dose that maximises immunogenicity and safety and (C) the dose that maximises immunogenicity and safety whilst minimising cost. Results suggest optimal dose [95% confidence interval] in viral particles per person was (A) 1.3 × 10¹¹ [0.8–7.9 × 10¹¹], (B) 1.5 × 10¹¹ [0.3–5.0 × 10¹¹] and (C) 1.1 × 10¹¹ [0.2–1.5 × 10¹¹]. Optimal dose exceeded 5.0 × 10¹⁰ viral particles only if the cost of delivery exceeded £0.65 or cost per 10¹¹ viral particles was less than £6.23. Optimal dose may differ depending on the objectives of developers and policy-makers, but further research is required to improve the accuracy of optimal-dose estimates.

Uncovering drivers of dose-dependence and individual variation in malaria infection outcomes

To understand why some hosts get sicker than others from the same type of infection, it is essential to explain how key processes, such as host responses to infection and parasite growth, are influenced by various biotic and abiotic factors. In many disease systems, the initial infection dose impacts host morbidity and mortality. To explore drivers of dose-dependence and individual variation in infection outcomes, we devised a mathematical model of malaria infection that allowed host and parasite traits to be linear functions (reaction norms) of the initial dose. We fitted the model, using a hierarchical Bayesian approach, to experimental time-series data of acute Plasmodium chabaudi infection across doses spanning seven orders of magnitude. We found evidence for both dose-dependent facilitation and debilitation of host responses. Most importantly, increasing dose reduced the strength of activation of indiscriminate host clearance of red blood cells while increasing the half-life of that response, leading to the maximal response at an intermediate dose. We also explored the causes of diverse infection outcomes across replicate mice receiving the same dose. Besides random noise in the injected dose, we found variation in peak parasite load was due to unobserved individual variation in host responses to clear infected cells. Individual variation in anaemia was likely driven by random variation in parasite burst size, which is linked to the rate of host cells lost to malaria infection. General host vigour in the absence of infection was also correlated with host health during malaria infection. Our work demonstrates that the reaction norm approach provides a useful quantitative framework for examining the impact of a continuous external factor on within-host infection processes.

Personalized Cancer Vaccines: Clinical Landscape, Challenges, and Opportunities

Tremendous innovation is underway among a rapidly expanding repertoire of promising personalized immune-based treatments. Therapeutic cancer vaccines (TCVs) are attractive systemic immunotherapies that activate and expand antigen-specific CD8⁺ and CD4⁺ T cells to enhance anti-tumor immunity. Our review highlights key issues impacting TCVs in clinical practice and reports on progress in development. We review the mechanism of action, immune-monitoring, dosing strategies, combinations, obstacles, and regulation of cancer vaccines. Most trials of personalized TCVs are ongoing and represent diverse platforms with predominantly early investigations of mRNA, DNA, or peptide-based targeting strategies against neoantigens in solid tumors, with many in combination immunotherapies. Multiple delivery systems, routes of administration, and dosing strategies are used. Intravenous or intramuscular administration is common, including delivery by lipid nanoparticles. Absorption and biodistribution impact antigen uptake, expression, and presentation, affecting the strength, speed, and duration of immune response. The emerging trials illustrate the complexity of developing this class of innovative immunotherapies. Methodical testing of the multiple potential factors influencing immune responses, as well as refined quantitative methodologies to facilitate optimal dosing strategies, could help resolve uncertainty of therapeutic approaches. To increase the likelihood of success in bringing these medicines to patients, several unique development challenges must be overcome.

Response Type and Host Species may be Sufficient to Predict Dose-Response Curve Shape for Adenoviral Vector Vaccines

Vaccine dose-response curves can follow both saturating and peaking shapes. Dose-response curves for adenoviral vector vaccines have not been systematically described. In this paper, we explore the dose-response shape of published adenoviral animal and human studies. Where data were informative, dose-response was approximately five times more likely to be peaking than saturating. There was evidence that host species and response type may be sufficient for prediction of dose-response curve shape. Dose-response curve shape prediction could decrease clinical trial costs, accelerating the development of life-saving vaccines.

Immunologic Dose-Response to Adenovirus-Vectored Vaccines in Animals and Humans: A Systematic Review of Dose-Response Studies of Replication Incompetent Adenoviral Vaccine Vectors when Given via an Intramuscular or Subcutaneous Route

Optimal vaccine dosing is important to ensure the greatest protection and safety. Analysis of dose-response data, from previous studies, may inform future studies to determine the optimal dose. Implementing more quantitative modelling approaches in vaccine dose finding have been recently suggested to accelerate vaccine development. Adenoviral vectored vaccines are in advanced stage of development for a variety of prophylactic and therapeutic indications, however dose-response has not yet been systematically determined. To further inform adenoviral vectored vaccines dose identification, historical dose-response data should be systematically reviewed. A systematic literature review was conducted to collate and describe the available dose-response studies for adenovirus vectored vaccines. Of 2787 papers identified by Medline search strategy, 35 were found to conform to pre-defined criteria. The majority of studies were in mice or humans and studied adenovirus serotype 5. Dose-response data were available for 12 different immunological responses. The majority of papers evaluated three dose levels, only two evaluated more than five dose levels. The most common dosing range was 107-1010 viral particles in mouse studies and 108-1011 viral particles in human studies. Data were available on adenovirus vaccine dose-response, primarily on adenovirus serotype 5 backbones and in mice and humans. These data could be used for quantitative adenoviral vectored vaccine dose optimisation analysis.

Dose finding for new vaccines: The role for immunostimulation/immunodynamic modelling

Current methods to optimize vaccine dose are purely empirically based, whereas in the drug development field, dosing determinations use far more advanced quantitative methodology to accelerate decision-making. Applying these established methods in the field of vaccine development may reduce the currently large clinical trial sample sizes, long time frames, high costs, and ultimately have a better potential to save lives. We propose the field of immunostimulation/immunodynamic (IS/ID) modelling, which aims to translate mathematical frameworks used for drug dosing towards optimizing vaccine dose decision-making. Analogous to Pharmacokinetic/Pharmacodynamic (PK/PD) modelling, the mathematical description of drug distribution (PK) and effect (PD) in host, IS/ID modelling approaches apply mathematical models to describe the underlying mechanisms by which the immune response is stimulated by vaccination (IS) and the resulting measured immune response dynamics (ID). To move IS/ID modelling forward, existing datasets and further data on vaccine allometry and dose-dependent dynamics need to be generated and collate, requiring a collaborative environment with input from academia, industry, regulators, governmental and non-governmental agencies to share modelling expertise, and connect modellers to vaccine data.