Lipid-Based Nanocarriers for Brain Drug Delivery

Abstract

Efficient delivery of therapeutic agents across the blood–brain barrier (BBB) remains one of the greatest challenges in treating neurological disorders. Although lipid-based nanocarriers have demonstrated promising capabilities for improving drug transport into the central nervous system, current formulation development largely depends on iterative laboratory optimization, making translation slow and resource-intensive. A critical research gap exists in integrating explainable artificial intelligence (AI) with lipid nanocarrier engineering to predict BBB penetration while simultaneously optimizing formulation characteristics for individual therapeutic molecules. This study proposes an AI-assisted framework that combines physicochemical descriptors, lipid composition, and biological transport parameters to improve formulation selection before experimental validation. The proposed strategy emphasizes interpretable machine learning rather than black-box prediction, enabling researchers to understand the contribution of formulation variables to delivery efficiency. Recent advances in digital pharmaceutics and predictive analytics are incorporated to establish a computationally guided development pipeline. The framework is intended to reduce experimental burden while improving formulation precision for brain-targeted therapies