Development of a dissolvable microneedle influenza vaccine patch

Delivery of large molecular weight biological molecules to the epidermis and dermis is constrained by the tough outer layer of the epidermis, the stratum corneum (sc). Microneedle technologies attempt to overcome this physical barrier using sharp micron-size projections to penetrate the sc. Dissolvable microneedles (DMN), are a particular microneedle design whereby the needle structure is composed of a soluble matrix that upon application to the skin, dissolves releasing the vaccine load into skin. This thesis examines (1) the formulation and processing considerations around DMN fabrication, (2) the immunogenicity of DMN containing trivalent influenza vaccine (TIV) in pre-clinical mouse and pig models and (3) the thermostability of these DMN formulations during storage. The results demonstrate the importance of formulation for microneedle formation and mechanical strength. Trehalose and polyvinylalcohol based formulations produced optimal microneedle structures and were amenable to piezoelectric dispensing; allowing for precise multi-layered DMN to be fabricated. The effect of drying conditions was assessed and found to be critical for DMN mechanical strength and skin penetration. The antibody responses to TIV generated by DMN-mediated vaccination were comparable or greater to those induced by immunization with a commercial TIV via the IM route in mice. DMN mediated immunisation resulted in a significantly broader humoral response to heterotypic influenza viruses compared to IM delivery. Stored at 40°C, a licensed seasonal influenza vaccine incorporated into DMN array was thermostable for at least 6 month as determined by Single Radial Immunodiffusion and immunogenicity in mice. The thesis advances the field of DMN influenza vaccination by elucidating important processing and formulation considerations in the fabrication of highly reproducible DMN. It also demonstrated that DMN can induce broader, larger humoral responses than conventional IM administration while demonstrating enhanced accelerated stability. Crucially, this works advances an automated fabrication system that will allow for clinical translation of DMN.

Dynamic classifiers for neonatal brain monitoring

Brain injury due to lack of oxygen or impaired blood flow around the time of birth, may cause long term neurological dysfunction or death in severe cases. The treatments need to be initiated as soon as possible and tailored according to the nature of the injury to achieve best outcomes. The Electroencephalogram (EEG) currently provides the best insight into neurological activities. However, its interpretation presents formidable challenge for the neurophsiologists. Moreover, such expertise is not widely available particularly around the clock in a typical busy Neonatal Intensive Care Unit (NICU). Therefore, an automated computerized system for detecting and grading the severity of brain injuries could be of great help for medical staff to diagnose and then initiate on-time treatments. In this study, automated systems for detection of neonatal seizures and grading the severity of Hypoxic-Ischemic Encephalopathy (HIE) using EEG and Heart Rate (HR) signals are presented. It is well known that there is a lot of contextual and temporal information present in the EEG and HR signals if examined at longer time scale. The systems developed in the past, exploited this information either at very early stage of the system without any intelligent block or at very later stage where presence of such information is much reduced. This work has particularly focused on the development of a system that can incorporate the contextual information at the middle (classifier) level. This is achieved by using dynamic classifiers that are able to process the sequences of feature vectors rather than only one feature vector at a time.