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Vaccination is one of the most successful public health interventions in human history. A key component of subunit-vaccines are adjuvants; immunostimulants that shape and enhance the immune response to antigens. Despite the importance of adjuvants, the mechanisms of how adjuvants promote immune responses are not fully understood, limiting detailed appli- cation of modelling and simulation to determine optimal vaccine formulations, dosing and timings of prime and boost. To rationally accelerate next-generation vaccines while promot- ing transparency, it is critical to foster a detailed understanding of the mechanism of action of efficacious adjuvants. AS01 is a patented adjuvant used in clinically approved Malaria (RTS,S/AS01) and Herpes-Zoster vaccines (Shingrix). In this thesis, the first detailed the- oretical model of AS01's mechanism of action from injection site to generation of antibody response is presented. From this non-executable description of AS01 biology, mathematical models were developed. These mathematical models permitted analysis of the interplay of cytokines (IFNγ, IL12 and IL18) and cells (macrophages, dendritic cells and monocytes) in early events based on data from a murine iliac draining lymph node. Model analysis led to the novel hypothesis that dendritic cells are the predominate cell type responsible for early IL12 production. IL12 is known to synergise with macrophage-derived IL18 to initiate early IFNγ. We provide evidence that AS01-mediated loss of subcapsular macrophages has a role in regulating the duration of IFNγ production acting as an endogenous inhibitory mechanism. Model analysis suggests that this cell loss affects the minimal temporal spacing of AS01-based vaccination required to obtain canonical IFN responses. This mathematical model was extended to capture CD4+ T cell priming dynamics. By analysing two simula- tions that permit exploring different hypotheses of AS01-mediated CD4+ T cell priming, we observed that our hypothesis of how IFN regulates CD4+ T cell priming was likely to be incomplete, which led to a refined hypothesis for IFNγ's role in this process. We observed that 'inflammation-induced' na ̈ıve CD4+ T cell recruitment may be a key factor regulating the magnitude of CD4+ T cell priming. Thus, physiological factors including time of the day and spacing of consecutive jabs may have a key role in AS01 vaccine efficacy.

The aim of this research was to explore the factors that influence the implementation of pharmacist prescribing in secondary care. A two-phased, mixed methods study was conducted. The first phase comprised a small qualitative study to investigate the views of clinicians, patients and NH5 managers and a survey of doctors, nurses and pharmacists to quantify those views. It was conducted in one NHS trust before pharmacist prescribing had been introduced. In the second phase, which used documentary analysis, focus groups and interviews, case studies of non-medical prescribing implementation were carried out in five hospital trusts. Factors identified in the first phase qualitative study, included the attitudes of clinical staff and patients, and adequacy of resources. Pharmacists were perceived by some stakeholders to be "drug experts" but there were reservations about their diagnostic skills and not "knowing" a patient in the same way as doctors and nurses do. The survey showed different levels of support for pharmacist prescribing from doctors (58%), nurses (68%) and pharmacists (96%). Nurses (40%) were more supportive of pharmacist independent prescribing than doctors (18%) but were more likely to support prescribing roles for themselves than pharmacists. The case studies found that experiences from 'doing' pharmacist prescribing became influential in shaping professional views of whether and how implementation should proceed. Views from the two phases overlapped but some differences emerged between the more theoretical considerations in the first phase of the research and experiential learning in the second, concerning, for example, the relative merits of supplementary and independent prescribing. A model by Greenhalgh et al (2004) of adoption of innovations proved useful in understanding the complexity of how and why there was mediation of the effects of the different factors. How factors influenced implementation varied according to culture/context/policy at a micro- and a macro- level.