From Design to Bedside: Brain-Targeted Engineered Nanoparticles and Predictive Modeling for Neurodegenerative Diseases Therapeutics
- 1J. Mike Walker ‘66 Department of Mechanical Engineering, Texas A&M University, College Station, TX 77840 United States
- 2Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843 United States
- 3Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843 United States
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.
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BibTeX
@article{nguyen2026from,
title = {{From Design to Bedside: Brain-Targeted Engineered Nanoparticles and Predictive Modeling for Neurodegenerative Diseases Therapeutics}},
author = {Nguyen Nguyen and Muzhaozi Yuan and Ya Wang},
journal = {Pharmaceutical Research},
year = {2026},
month = oct,
doi = {10.1007/s11095-026-04201-5},
url = {https://doi.org/10.1007/s11095-026-04201-5},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - From Design to Bedside: Brain-Targeted Engineered Nanoparticles and Predictive Modeling for Neurodegenerative Diseases Therapeutics
AU - Nguyen Nguyen
AU - Muzhaozi Yuan
AU - Ya Wang
PY - 2026
DA - 2026/10/01
JO - Pharmaceutical Research
DO - 10.1007/s11095-026-04201-5
UR - https://doi.org/10.1007/s11095-026-04201-5
AB - 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.
SN - 0724-8741
SN - 1573-904X
ER -