Statistical Modelling of the Formulation Variables in Non-Viral Gene Delivery Systems

Abstract

Traditionally, optimisation of a gene delivery formulation utilises a study design that involves altering only one formulation variable at any one time whilst keeping the other variables constant. As gene delivery formulations become more complex, e.g. to include multiple cellular and sub-cellular targeting elements, there will be an increasing requirement to generate and analyse data more efficiently and allow examination of the interaction between variables. This study aims to demonstrate the utility of multifactorial design, specifically a Central Composite Design, in modelling the responses size, zeta potential and in vitro transfection efficiency of some prototypic non-viral gene delivery vectors, i.e. cationic liposome-pDNA complexes, and extending the application of the design strategy to more complex vectors, i.e. tri-component lipid:polycation:DNA (LPD). The modelled predictions of how the above responses change as a function of formulation show consistency with an extensive literature base of data obtained using more traditional approaches, and highlight the robustness and utility of the Central Composite Design in examining key formulation variables in non-viral gene delivery systems. The approach should be further developed to maximise the predictive impact of data across the full range of pharmaceutical sciences.