Thermodynamic prediction of active ingredient loading in polymeric microparticles

Abstract

The growing use of microparticles as a controlled-delivery system for pharmaceutical and non-pharmaceutical active ingredients (AIs) has prompted a costly trial-and-error development of new and effective microparticle systems. In order to facilitate a more rational design and optimization of AI loadings in microparticles, we have developed a molecular–thermodynamic theory to predict the loading of liquid AIs in polymeric microparticles that are manufactured by a solvent evaporation process. This process involves the emulsification of a liquid polymer solution (consisting of polymer and AI dissolved in a volatile solvent) in an aqueous surfactant solution. The theory describes the equilibrium distribution of the AI between the aqueous phase and the dispersed polymeric droplets. The universal functional activity coefficient (UNIFAC) and UNIFAC–Free Volume (FV) group-contribution methods are utilized to model the nonidealities in the water and polymeric droplet phases, respectively. The inputs to the theory are: (i) the chemical structures, densities and total masses of the manufacturing ingredients, (ii) the manufacturing temperature and (iii) the glass transition temperature of the polymer. Since surfactant concentrations exceeding the critical micellar concentration (CMC) are often required in order to stabilize the dispersed polymeric droplets during the emulsion manufacturing process, the theory also accounts for AI solubilization in surfactant micelles present in the manufacturing solution. To test the AI loading predictions, we compare theoretical predictions of AI loadings in poly(lactic acid), poly(methyl methacrylate) and polystyrene microparticles to experimentally measured ones for five model AIs with varying degrees of hydrophobicity (benzyl alcohol, n-octanol, geraniol, farnesol and galaxolide). We also demonstrate how the developed theory can be utilized to screen polymers with respect to their abilities to load a given AI, as well as to provide guidelines for manufacturing microparticles having the desired AI loading.