Nanotechnology for the delivery of vaccines

Liposomes offer an ideal platform for the delivery of subunit vaccines, due to their versatility and flexibility, which allows for antigen as well as immunostimulatory lipids and TLR agonists to become associated with these bilayered vesicles. Liposomes have the ability to protect vaccine antigen, as well as enhance delivery to antigen presenting cells, whilst the importance of cationic surface charge for delivery of TB subunit vaccines and formation of an ‘antigen depot’ may play a key role in boosting cell-mediated immunity and Th1 immune responses. The rational design of vaccine adjuvants requires the thorough investigation into the physicochemical characteristics that dictate the function of a liposomal adjuvant. Within this thesis, physicochemical characteristics were investigated in order to show any effects on the biodistribution profiles and the ensuing immune responses of these formulations. Initially the role of liposome charge within the formulation was investigated and subsequently their efficacy as vaccine adjuvants in combination with their biodistribution was measured to allow the role of formulation in vaccine function to be considered. These results showed that cationic surface charge, in combination with high loading of H56 vaccine antigen through electrostatic binding, was crucial in the promotion of the ‘depot-effect’ at the injection site which increases the initiation of Th1 cell-mediated immune responses that are required to offer protection against tuberculosis. To further investigate this, different methods of liposome production were also investigated where antigen incorporation within the vesicles as well as surface adsorption were adopted. Using the dehydration-rehydration (DRV) method (where liposomes are freeze-dried in the presence of antigen to promote antigen encapsulation) and the double emulsion (DE) method, a range of liposomes entrapping antigen were formulated. Variation in the liposome preparation method can lead to antigen entrapment within the delivery system which has been shown to be greater for DRV-formulated liposomes compared to their DE-counterparts. This resulted in no significant effect on the vaccine biodistribution profile, as well as not significantly altering the efficacy of cationic liposomal adjuvants. To further enhance the efficacy of these systems, the addition of TLR agonists either at the vesicle surface as well as within the delivery system has been displayed through variation in the preparation method. Anionic liposomal adjuvants have been formulated, which displayed rapid drainage from the injection site to the draining lymph nodes and displayed a reduction in measured Th1 immune responses. However, variation in the preparation method can alter the immune response profile for anionic liposomal adjuvants with a bias in immune response to Th2 responses being noted. Through the use of high shear mixing and stepwise incorporation, the efficient loading of TLR agonist within liposomes has been shown. However, interestingly the conjugation between lipid and non-electrostatically bound TLR agonist, followed by insertion into the bilayer of DDA/TDB resulted in localised agonist retention at the injection site and further stimulation of the Th1 immune response at the SOI, spleen and draining lymphatics as well as enhanced antibody titres.

Decoherence of charge qubit coupled to interacting background charges

The major contribution to decoherence of a double quantum dot or a Josephson-junction charge qubit comes from the electrostatic coupling to fluctuating background charges hybridized with the conduction electrons in the reservoir. However, estimations according to previously developed theories show that finding a sufficient number of effective fluctuators in a realistic experimental layout is quite improbable. We show that this paradox is resolved by allowing for a short-range Coulomb interaction of the fluctuators with the electrons in the reservoir. This dramatically enhances both the number of effective fluctuators and their contribution to decoherence, resulting in the most dangerous decoherence mechanism for charge qubits.

Liposome formulation of poorly water soluble drugs:optimisation of drug loading and ESEM analysis of stability

Liposomes due to their biphasic characteristic and diversity in design, composition and construction, offer a dynamic and adaptable technology for enhancing drug solubility. Starting with equimolar egg-phosphatidylcholine (PC)/cholesterol liposomes, the influence of the liposomal composition and surface charge on the incorporation and retention of a model poorly water soluble drug, ibuprofen was investigated. Both the incorporation and the release of ibuprofen were influenced by the lipid composition of the multi-lamellar vesicles (MLV) with inclusion of the long alkyl chain lipid (dilignoceroyl phosphatidylcholine (C 24PC)) resulting in enhanced ibuprofen incorporation efficiency and retention. The cholesterol content of the liposome bilayer was also shown to influence ibuprofen incorporation with maximum ibuprofen incorporation efficiency achieved when 4 μmol of cholesterol was present in the MLV formulation. Addition of anionic lipid dicetylphosphate (DCP) reduced ibuprofen drug loading presumably due to electrostatic repulsive forces between the carboxyl group of ibuprofen and the anionic head-group of DCP. In contrast, the addition of 2 μmol of the cationic lipid stearylamine (SA) to the liposome formulation (PC:Chol - 16 μmol:4 μmol) increased ibuprofen incorporation efficiency by approximately 8%. However further increases of the SA content to 4 μmol and above reduced incorporation by almost 50% compared to liposome formulations excluding the cationic lipid. Environmental scanning electron microscopy (ESEM) was used to dynamically follow the changes in liposome morphology during dehydration to provide an alternative assay of liposome stability. ESEM analysis clearly demonstrated that ibuprofen incorporation improved the stability of PC:Chol liposomes as evidenced by an increased resistance to coalescence during dehydration. These finding suggest a positive interaction between amphiphilic ibuprofen molecules and the bilayer structure of the liposome. © 2004 Elsevier B.V. All rights reserved.

The charge effect on the hindrance factors for diffusion and convection of a solute in pores II

The diffusion and convection of a solute suspended in a fluid across porous membranes are known to be reduced compared to those in a bulk solution, owing to the fluid mechanical interaction between the solute and the pore wall as well as steric restriction. If the solute and the pore wall are electrically charged, the electrostatic interaction between them could affect the hindrance to diffusion and convection. In this study, the transport of charged spherical solutes through charged circular cylindrical pores filled with an electrolyte solution containing small ions was studied numerically by using a fluid mechanical and electrostatic model. Based on a mean field theory, the electrostatic interaction energy between the solute and the pore wall was estimated from the Poisson-Boltzmann equation, and the charge effect on the solute transport was examined for the solute and pore wall of like charge. The results were compared with those obtained from the linearized form of the Poisson-Boltzmann equation, i.e.the Debye-Hückel equation. © 2012 The Japan Society of Fluid Mechanics and IOP Publishing Ltd.

Charge effects on hindrance factors for diffusion and convection of solute in pores I

The transport of a spherical solute through a long circular cylindrical pore filled with an electrolyte solution is studied numerically, in the presence of constant surface charge on the solute and the pore wall. Fluid dynamic analyses were carried out to calculate the flow field around the solute in the pore to evaluate the drag coefficients exerted on the solute. Electrical potentials around the solute in the electrolyte solution were computed based on a mean-field theory to provide the interaction energy between the charged solute and the pore wall. Combining the results of the fluid dynamic and electrostatic analyses, we estimated the rate of the diffusive and convective transport of the solute across the pore. Although the present estimates of the drag coefficients on the solute suggest more than 10% difference from existing studies, depending on the radius ratio of the solute relative to the pore and the radial position of the solute center in the pore, this difference leads to a minor effect on the hindrance factors. It was found that even at rather large ion concentrations, the repulsive electrostatic interaction between the charged solute and the pore wall of like charge could significantly reduce the transport rate of the solute.

Effects of electric charge on osmotic flow across periodically arranged circular cylinders

An electrostatic model is developed for osmotic flow across a layer consisting of identical circular cylinders with a fixed surface charge, aligned parallel to each other so as to form an ordered hexagonal arrangement. The expression of the osmotic reflection coefficient is derived for spherical solutes with a fixed surface charge suspended in an electrolyte, based on low-Reynolds-number hydrodynamics and a continuum, point-charge description of the electric double layers. The repulsive electrostatic interaction between the surface charges with the same sign on the solute and the cylinders is shown to increase the exclusion region of solute from the cylinder surface, which enhances the osmotic flow. Applying the present model to the study of osmotic flow across the endothelial surface glycocalyx of capillary walls has revealed that this electrostatic model could account well for the reflection coefficients measured for charged macromolecules, such as albumin, in the physiological range of charge density and ion concentration.

Electrical charge effect on osmotic flow through pores

An electrostatic model for osmotic flow through circular cylindrical pores is developed to describe the reflection coefficient for the membrane transport in the presence of surface charges on the pore wall and the solute. For a spherical solute placed at an arbitrary radial position in the pore, the electrical potential was computed by a spectral element method applied to the Poisson-Boltzmann equation together with the condition of electrical neutrality. The interaction energy between the surface charges was used to estimate the osmotic reflection coefficient. The proposed model predicts that even for a small Debye length compared to the pore radius, the repulsive electrostatic interaction between the surface charges could significantly increase the osmotic flow through the pore.

Charge effects on the hindered transport of macromolecules across the endothelial surface glycocalyx layer

A fluid mechanical and electrostatic model for the transport of solute molecules across the vascular endothelial surface glycocalyx layer (EGL) was developed to study the charge effect on the diffusive and convective transport of the solutes. The solute was assumed to be a spherical particle with a constant surface charge density, and the EGL was represented as an array of periodically arranged circular cylinders of like charge, with a constant surface charge density. By combining the fluid mechanical analyses for the flow around a solute suspended in an electrolyte solution and the electrostatic analyses for the free energy of the interaction between the solute and cylinders based on a mean field theory, we estimated the transport coefficients of the solute across the EGL. Both of diffusive and convective transports are reduced compared to those for an uncharged system, due to the stronger exclusion of the solute that results from the repulsive electrostatic interaction. The model prediction for the reflection coefficient for serum albumin agreed well with experimental observations if the charge density in the EGL is ranged from approximately -10 to -30 mEq/l.

A case-study investigating the physicochemical characteristics that dictate the function of a liposomal adjuvant

A range of particulate delivery systems have been considered as vaccine adjuvants. Of these systems, liposomes offer a range of advantages including versatility and flexibility in design format and their ability to incorporate a range of immunomodulators and antigens. Here we briefly outline research, from within our laboratories, which focused on the systematic evaluation of cationic liposomes as vaccines adjuvants. Our aim was to identify physicochemical characteristics that correlate with vaccine efficacy, with particular consideration of the interlink between depot-forming action and immune responses. A variety of parameters were investigated and over a range of studies we have confirmed that cationic liposomes, based on dimethyldioctadecylammonium bromide and trehalose 6,6'-dibehenate formed a depot at the injection site, which stimulates recruitment of antigen presenting cells to the injection site and promotes strong humoral and cell-mediated immune responses. Physicochemical factors which promote a strong vaccine depot include the combination of a high cationic charge and electrostatic binding of the antigen to the liposome system and the use of lipids with high transition temperatures, which form rigid bilayer vesicles. Reduction in vesicle size of cationic vesicles did not promote enhanced drainage from the injection site. However, reducing the cationic nature through substitution of the cationic lipid for a neutral lipid, or by masking of the charge using PEGylation, resulted in a reduced depot formation and reduced Th1-type immune responses, while Th2-type responses were less influenced. These studies confirm that the physicochemical characteristics of particulate-based adjuvants play a key role in the modulation of immune responses.

Multi-level visualisation using Gaussian process latent variable models

Projection of a high-dimensional dataset onto a two-dimensional space is a useful tool to visualise structures and relationships in the dataset. However, a single two-dimensional visualisation may not display all the intrinsic structure. Therefore, hierarchical/multi-level visualisation methods have been used to extract more detailed understanding of the data. Here we propose a multi-level Gaussian process latent variable model (MLGPLVM). MLGPLVM works by segmenting data (with e.g. K-means, Gaussian mixture model or interactive clustering) in the visualisation space and then fitting a visualisation model to each subset. To measure the quality of multi-level visualisation (with respect to parent and child models), metrics such as trustworthiness, continuity, mean relative rank errors, visualisation distance distortion and the negative log-likelihood per point are used. We evaluate the MLGPLVM approach on the ‘Oil Flow’ dataset and a dataset of protein electrostatic potentials for the ‘Major Histocompatibility Complex (MHC) class I’ of humans. In both cases, visual observation and the quantitative quality measures have shown better visualisation at lower levels.

Gold nanoparticles decorated with oligo(ethylene glycol) thiols:protein resistance and colloidal stability

The interactions between proteins and gold colloids functionalized with protein-resistant oligo(ethylene glycol) (OEG) thiol, HS(CH(2))(11) (OCH(2)CH(2))(6)OMe (EG(6)OMe), in aqueous solution have been studied by small-angle X-ray scattering (SAXS) and UV-vis spectroscopy. The mean size, 2R, and the size distribution of the decorated gold colloids have been characterized by SAXS. The monolayer-protected gold colloids have no correlations due to the low volume fraction in solution and are stable in a wide range of temperatures (5-70 degrees C, pH (1.3-12.4), and ionic strength (0-1.0 M). In contrast, protein (bovine serum albumin) solutions with concentrations in the range of 60-200 mg/mL (4.6-14.5 vol show a pronounced correlation peak in SAXS, which results from the repulsive electrostatic interaction between charged proteins. These protein interactions show significant dependence on ionic strength, as would be expected for an electrostatic interaction (Zhang et al. J. Phys. Chem. B 2007, 111, 251). For a mixture of proteins and gold colloids, the protein-protein interaction changes little upon mixing with OEG-decorated gold colloids. In contrast, the colloid-colloid interaction is found to be strongly dependent on the protein concentration and the size of the colloid itself. Adding protein to a colloidal solution results in an attractive depletion interaction between functionalized gold colloids, and above a critical protein concentration, c*, the colloids form aggregates and flocculate. Adding salt to such mixtures enhances the depletion effect and decreases the critical protein concentration. The aggregation is a reversible process (i.e., diluting the solution leads to dissolution of aggregates). The results also indicate that the charge of the OEG self-assembled monolayer at a curved interface has a rather limited effect on the colloidal stabilization and the repulsive interaction with proteins.

Statistical deconvolution of enthalpic energetic contributions to MHC-peptide binding affinity

Background - MHC Class I molecules present antigenic peptides to cytotoxic T cells, which forms an integral part of the adaptive immune response. Peptides are bound within a groove formed by the MHC heavy chain. Previous approaches to MHC Class I-peptide binding prediction have largely concentrated on the peptide anchor residues located at the P2 and C-terminus positions. Results - A large dataset comprising MHC-peptide structural complexes was created by re-modelling pre-determined x-ray crystallographic structures. Static energetic analysis, following energy minimisation, was performed on the dataset in order to characterise interactions between bound peptides and the MHC Class I molecule, partitioning the interactions within the groove into van der Waals, electrostatic and total non-bonded energy contributions. Conclusion - The QSAR techniques of Genetic Function Approximation (GFA) and Genetic Partial Least Squares (G/PLS) algorithms were used to identify key interactions between the two molecules by comparing the calculated energy values with experimentally-determined BL50 data. Although the peptide termini binding interactions help ensure the stability of the MHC Class I-peptide complex, the central region of the peptide is also important in defining the specificity of the interaction. As thermodynamic studies indicate that peptide association and dissociation may be driven entropically, it may be necessary to incorporate entropic contributions into future calculations.

Graphene-assisted microfiber for optical-power-based temperature sensor

Combined the large evanescent field of microfiber with the high thermal conductivity of graphene, a sensitive all-fiber temperature sensor based on graphene-assisted micro fiber is proposed and experimentally demonstrated. Microfiber can be easily attached with graphene due to the electrostatic 6 force, resulting in an effective interaction between graphene and the evanescent field of microfiber. The change of the ambient temperature has a great influence on the conductivity of graphene, leading to the variation of the effective refractive index of microfiber. Consequently, the optical power transmission will be changed. The temperature sensitivity of 0.1018 dB/°C in the heating process and 0.1052 dB/°C in the cooling process as well as a high resolution of 0.0098 °C is obtained in the experiment. The scheme may have great potential in sensing fields owing to the advantages of high sensitivity, compact size, and low cost.

A comparative molecular similarity indices (CoMSIA) study of peptide binding to the HLA-A3 superfamily

Epitope identification is the basis of modern vaccine design. The present paper studied the supermotif of the HLA-A3 superfamily, using comparative molecular similarity indices analysis (CoMSIA). Four alleles with high phenotype frequencies were used: A*1101, A*0301, A*3101 and A*6801. Five physicochemical properties—steric bulk, electrostatic potential, local hydro-phobicity, hydrogen-bond donor and acceptor abilities—were considered and ‘all fields’ models were produced for each of the alleles. The models have a moderate level of predictivity and there is a good correlation between the data. A revised HLA-A3 supermotif was defined based on the comparison of favoured and disfavoured properties for each position of the MHC bound peptide. The present study demonstrated that CoMSIA is an effective tool for studying peptide–MHC interactions.

Visualisation of bioinformatics datasets

Analysing the molecular polymorphism and interactions of DNA, RNA and proteins is of fundamental importance in biology. Predicting functions of polymorphic molecules is important in order to design more effective medicines. Analysing major histocompatibility complex (MHC) polymorphism is important for mate choice, epitope-based vaccine design and transplantation rejection etc. Most of the existing exploratory approaches cannot analyse these datasets because of the large number of molecules with a high number of descriptors per molecule. This thesis develops novel methods for data projection in order to explore high dimensional biological dataset by visualising them in a low-dimensional space. With increasing dimensionality, some existing data visualisation methods such as generative topographic mapping (GTM) become computationally intractable. We propose variants of these methods, where we use log-transformations at certain steps of expectation maximisation (EM) based parameter learning process, to make them tractable for high-dimensional datasets. We demonstrate these proposed variants both for synthetic and electrostatic potential dataset of MHC class-I. We also propose to extend a latent trait model (LTM), suitable for visualising high dimensional discrete data, to simultaneously estimate feature saliency as an integrated part of the parameter learning process of a visualisation model. This LTM variant not only gives better visualisation by modifying the project map based on feature relevance, but also helps users to assess the significance of each feature. Another problem which is not addressed much in the literature is the visualisation of mixed-type data. We propose to combine GTM and LTM in a principled way where appropriate noise models are used for each type of data in order to visualise mixed-type data in a single plot. We call this model a generalised GTM (GGTM). We also propose to extend GGTM model to estimate feature saliencies while training a visualisation model and this is called GGTM with feature saliency (GGTM-FS). We demonstrate effectiveness of these proposed models both for synthetic and real datasets. We evaluate visualisation quality using quality metrics such as distance distortion measure and rank based measures: trustworthiness, continuity, mean relative rank errors with respect to data space and latent space. In cases where the labels are known we also use quality metrics of KL divergence and nearest neighbour classifications error in order to determine the separation between classes. We demonstrate the efficacy of these proposed models both for synthetic and real biological datasets with a main focus on the MHC class-I dataset.