A comprehensive analytical model for predicting drug absorption in the olfactory region: Application to nose-to-brain...

Nose-to-brain drug delivery offers a promising route for administering pharmaceutical substances directly to the brain. However, this pathway faces significant challenges, such as mucociliary clearance and enzymatic activity in the nasal cavity, which limit drug absorption. Although several formulation strategies exist to enhance drug solubility, diffusion across the airway surface liquid, and protection from enzymatic degradation, there is a lack of tools to systematically evaluate which strategy is best suited for specific molecules. To address this, we developed an analytical model of the olfactory region that integrates drug dissolution, diffusion through the mucus and periciliary layers, advection by mucociliary clearance, and enzymatic degradation. This analytical model allows to estimate the fraction of a drug, formulated as a powder and deposited in the olfactory region, that is indeed dissolved in the mucus and absorbed by the cells, and to identify the most influential physicochemical properties in the absorption process. To demonstrate the model's utility, we 3D-printed custom-made diffusion cells to measure experimentally the diffusion coefficients of caffeine, levodopa, and paliperidone palmitate, incorporating these values into our simulations. Based on these data, we predicted drug absorption levels and proposed strategies to optimise them. By applying the model to an existing formulation, we demonstrated that careful adjustments to a drug's properties can significantly enhance the fraction absorbed in the olfactory region. Additionally, we explored how the site of drug deposition, influenced by the delivery device, impacts absorption by affecting residence time in the olfactory region. Overall, our analytical model serves as a valuable tool for the development of effective nose-to-brain formulations and the selection of optimal administration devices, streamlining the process and reducing the need for extensive in vitro and in vivo testing.