Nose-to-Brain Drug Delivery and Physico-Chemical Properties of Nanosystems: Analysis and Correlation Studies of Data from Scientific Literature

Abstract

Angela Bonaccorso,1,2,* Alessandro Ortis,3,* Teresa Musumeci,1,2 Claudia Carbone,1,2 Mazhar Hussain,3 Valentina Di Salvatore,1 Sebastiano Battiato,3 Francesco Pappalardo,1,2 Rosario Pignatello1,2 1Department of Drug and Health Sciences, University of Catania, Catania, Italy; 2NANOMED–Research Centre for Nanomedicine and Pharmaceutical Nanotechnology, University of Catania, Catania, 95125, Italy; 3Department of Mathematics and Computer Science, University of Catania, Catania, Italy*These authors contributed equally to this workCorrespondence: Teresa Musumeci, Email [email protected]: In the last few decades, nose-to-brain delivery has been investigated as an alternative route to deliver molecules to the Central Nervous System (CNS), bypassing the Blood–Brain Barrier. The use of nanotechnological carriers to promote drug transfer via this route has been widely explored. The exact mechanisms of transport remain unclear because different pathways (systemic or axonal) may be involved. Despite the large number of studies in this field, various aspects still need to be addressed. For example, what physicochemical properties should a suitable carrier possess in order to achieve this goal? To determine the correlation between carrier features (eg, particle size and surface charge) and drug targeting efficiency percentage (DTE%) and direct transport percentage (DTP%), correlation studies were performed using machine learning.Methods: Detailed analysis of the literature from 2010 to 2021 was performed on Pubmed in order to build “NANOSE” database. Regression analyses have been applied to exploit machine-learning technology.Results: A total of 64 research articles were considered for building the NANOSE database (102 formulations). Particle-based formulations were characterized by an average size between 150– 200 nm and presented a negative zeta potential (ZP) from − 10 to − 25 mV. The most general-purpose model for the regression of DTP/DTE values is represented by Decision Tree regression, followed by K-Nearest Neighbors Regressor (KNeighbor regression).Conclusion: A literature review revealed that nose-to-brain delivery has been widely investigated in neurodegenerative diseases. Correlation studies between the physicochemical properties of nanosystems (mean size and ZP) and DTE/DTP parameters suggest that ZP may be more significant than particle size for DTP/DTE predictability.Keywords: nanomedicine, intranasal administration, pharmacokinetic, DTE, DTP, machine learning

Keywords

• nanomedicine
• intranasal administration
• pharmacokinetic
• dte
• dtp
• machine learning