Abstract
Abstract
Nanoparticle (NP)-based drug delivery systems offer promising solutions for neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and Huntington’s, which have no cure due to the brain’s structural complexity and the restrictive nature of the blood-brain barrier (BBB). Conventional therapeutics, despite strong
in vitro
efficacy, often fail clinically due to poor brain penetration. Recent advances in NP engineering, including optimization of size (10–100 nm), surface charge (-10 to -20 mV), and targeted ligand decoration, have improved BBB crossing and reduced premature clearance. In parallel, predictive in silico approaches, such as physiologically based pharmacokinetic (PBPK) modeling, computational fluid dynamics, and machine learning models, enable quantitative prediction of NP biodistribution, residence times, and region-specific uptake. By simulating key determinants, including blood flow dynamics, vasculature heterogeneity, permeability, and retention, these computational tools accelerate lead selection, reduce experimental burden, and inform design before
in vivo
testing. This review integrates recent progress in NP design with advanced computational modeling, highlighting how their convergence provides a rational framework for developing next-generation brain-targeted therapeutics. We further discuss regulatory and translational challenges, including safety, biocompatibility, and the need for humanized preclinical benchmarks. Together, these innovations offer a roadmap for advancing NP-based strategies toward clinical impact in neurodegenerative disease.