In Silico Modeling of Nanoparticle Transport across the Blood-Brain Barrier: A Systematic Review

Reliable prediction of nanoparticle (NP) transport across the blood-brain barrier (BBB) is essential for designing effective central nervous system-targeted drug delivery systems. The BBB protects the brain but severely restricts the entry of therapeutic compounds, and fewer than 5% of candidate drugs reach the brain in pharmacologically meaningful amounts. NP-based delivery systems have emerged as a promising approach to overcome this limitation by enhancing drug stability, circulation, and BBB penetration. Experimental in vivo animal models and in vitro BBB assays provide valuable mechanistic insights but are costly, time-consuming, and limited in translational efficiency. In silico methodologies offer a complementary strategy by enabling efficient screening of NP designs, supporting interpretation of experimental data, and reducing dependence on animal models. This paper presents a systematic review of 56 peer-reviewed publications that applied computational methods to study NP transport across the BBB. The included works fall into 5 main categories: (a) molecular simulations, (b) quantitative structure-activity/property relationship models, (c) machine learning and deep learning approaches, (d) pharmacokinetic and pharmacodynamic modeling, and (e) nanoinformatics frameworks. These approaches address key stages of NP transport, including protein corona formation, interactions with endothelial membranes, and translocation across the barrier. By comparing these diverse methods, this review highlights their complementary strengths and integration potential for improving permeability prediction. Together, they demonstrate how multiscale computational modeling enhances understanding of NP behavior at the BBB, supports more ethical and nonanimal research, and paves the way for artificial intelligence-guided design of brain-targeted nanomedicines.