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

Area of Science:

• Biomedical Engineering
• Nanotechnology
• Computational Science

Background:

• The blood-brain barrier (BBB) limits drug entry into the central nervous system, hindering effective treatments.
• Nanoparticle (NP)-based delivery systems show promise for enhancing drug stability, circulation, and BBB penetration.
• Traditional experimental methods (in vivo, in vitro) are costly, time-consuming, and have limited translational efficiency.

Purpose of the Study:

• To systematically review computational methods used for studying nanoparticle transport across the BBB.
• To assess the strengths and integration potential of various in silico approaches for predicting NP permeability.
• To highlight the role of computational modeling in advancing brain-targeted nanomedicine design.

Main Methods:

• Systematic literature review of 56 peer-reviewed publications.
• Categorization of computational methods into molecular simulations, QSAR/QSPR models, machine learning/deep learning, PK/PD modeling, and nanoinformatics frameworks.
• Analysis of how these methods address key NP transport stages: protein corona formation, membrane interactions, and translocation.

Main Results:

• Computational methods effectively screen NP designs and support experimental data interpretation.
• Diverse in silico approaches address different stages of NP transport across the BBB.
• Integration of multiscale computational modeling enhances understanding of NP behavior at the BBB.

Conclusions:

• In silico methodologies provide a complementary strategy to experimental models, reducing reliance on animal testing.
• Multiscale computational modeling improves the prediction of nanoparticle permeability across the BBB.
• AI-guided design of brain-targeted nanomedicines is facilitated by these advanced computational approaches.