LATENT VARIABLE METHODS IN PROTEIN DELIVERY APPLICATIONS

Abstract

While there has been explosive growth in the development of protein therapeutics, the many challenges associated with the delivery of proteins need to be overcome for achieving desired results. Among the various particle synthesis and encapsulation methods, ionic gelation has gained significant attention due to simplicity and the mild conditions of the process. Electrostatic interactions can not only drive the ionic gelation process with polysaccharide based systems, they also control the system dynamics due to complex formations between the polysaccharides and proteins. In this work, it was hypothesized that the electrostatic interactions between the charged polysaccharide network and the protein can be used as a means of controlling protein entrapment and release. This hypothesis was studied and further investigated using multivariate statistical analysis which offers a mathematical description of the correlations and therefore, provides a useful tool for optimizing delivery systems. Statistical analysis of a lysozyme/crosslinker-alginate complex system quantified the effects of the initial concentration of the compounds on complex composition and the influence of the crosslinker nature on complex degradation rates; the mathematical relationships developed were subsequently used for predicting complex properties (Chapter 2). The potential use of the complex systems as protein delivery systems, which would release the protein in response to changes in environmental conditions, was studied (Chapter 3). The statistical model showed high fitting capability (R2 values between 0.834 and 0.906) for the complex properties and also quantified the dependence of the release kinetics (ktn) on the ionic strength and pH of the release media. In addition to protein release from disintegration-controlled complex systems, the factors affecting diffusion-controlled protein release from calcium-alginate microparticles were investigated (Chapter 4). Multivariate analysis showed that while the parameter k was mainly influenced by protein properties (net charge and molecular weight), the parameter n was mostly affected by polymer and buffer properties. Overall, the multivariate statistical method provides a great platform for understanding the trends and predicting future patterns. By understanding the effect of different factors on the release, protein delivery systems from polysaccharide based systems have a great deal of potential to lead to effective protein therapeutics.