Be part of launching a new scientific field - immunostimulation/ immunodynamic (IS/ID) mathematical modelling to accelerate vaccine development

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Vaccines, once developed, are one of the most effective ways of saving human lives. But, developing a new vaccine can take decades, and cost around a billion dollars. The current scientific methods used to identify the right amount of vaccine dose are empirical and antiquated. In contrast, mathematical model-based drug development (PK/PD) is regularly used to predict the best size and schedule of drug dose to use. Pharmacokinetic models are used to model the concentration of drug in the body over time and generally consider the 4 stages of absorption, distribution, metabolism and excretion of the drug. Pharmacodynamic models focus on the effects that a drug can have on the body between absorption and excretion. These tools can be used to calculate dosage to maintain the substance at an effective concentration without reaching harmful levels. In addition, there may be cases where the area under curve is important, meaning that these body is not only affected by this present concentration but is influenced by the entire history of dosing. Dependant on various factors a different model and varying patient effects may be relevant in drug models, and vaccine dosage models would likely need to consider similar issues. Adenovirus(Ad) vector vaccines are a novel approach to vaccine antigen delivery. The effectiveness of Ad as a vector comes primarily due to the production of T-cell and antibody responses, and the efficacy of such vaccines have been clinically trialed for various diseases. Adenoviral constructs alongside modified vaccinia Ankara, two viral vectors, are often used in combination to induce T cells responses. However, minimal work has been carried out to understand the best doses of vaccines when used in combination, and there is a poor understanding of the timing of vaccination, and if one or more primes or boosts should be given. The main focus should be on modelling techniques from drug development to adenovirus vaccine development. If successful this could launch a new scientific field - immunostimulation/ immunodynamic (IS/ID) modelling. Novel statistical and mechanistic models will be created, parameterised using empirical data from Vaccitech mouse vaccine experiments and the literature, to help design new pre-clinical and clinical studies performed at Vaccitech. This project has several research aims that will coincide with a collection of Vaccitech placements. Aim 1: First, I will need to continue to adapt my mathematical knowledge towards pharmacokinetic and pharmacodynamic models and establish a greater understanding of previous PK/PD work and of the immune response. This will be primarily achieved through literature surrounding the topic.Aim 2: Second, we will use existing experimental data to create and parameterise novel statistical/mechanistic models to make predictions for the vaccine dose/immunological response relationship in mice. Aim 2: Third, we will improve the statistical/mechanistic model dose-response predictions, by making predictions for the best design of new empirical mouse experiments, to maximise the dose-response information gained and minimise the number of mice used. Aim 3: Fourth, using an allometric scaling assumption and statistical/mechanistic modelling, we will make initial predictions for the most immunogenic dose-response relationship in humans, based on the mouse data. These predictions will be evaluated in new empirical clinical experiments carried out at Vaccitech. Aim 4: Within the period of this PhD, the modelling evidence we will create will be used to improve the design of a human trial of the Vaccitech adenoviral constructs and Modified vaccinia Ankara viral vectors. In addition to the creation of new knowledge, the project has a route to impact on vaccine development policy alongside wider economic and societal impact. If this field proves promising, it could greatly reduce the financial and time burdens involved in developing vaccines.