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. 2022 Apr;39(4):767-781.
doi: 10.1007/s11095-022-03222-0. Epub 2022 Mar 21.

Effect of Particle Size and Surface Charge on Nanoparticles Diffusion in the Brain White Matter

Tian Yuan  1 , Ling Gao  2 , Wenbo Zhan  3 , Daniele Dini  4
Affiliations

Effect of Particle Size and Surface Charge on Nanoparticles Diffusion in the Brain White Matter

Tian Yuan et al. Pharm Res. 2022 Apr.

Abstract

Purpose: Brain disorders have become a serious problem for healthcare worldwide. Nanoparticle-based drugs are one of the emerging therapies and have shown great promise to treat brain diseases. Modifications on particle size and surface charge are two efficient ways to increase the transport efficiency of nanoparticles through brain-blood barrier; however, partly due to the high complexity of brain microstructure and limited visibility of Nanoparticles (NPs), our understanding of how these two modifications can affect the transport of NPs in the brain is insufficient.

Methods: In this study, a framework, which contains a stochastic geometric model of brain white matter (WM) and a mathematical particle tracing model, was developed to investigate the relationship between particle size/surface charge of the NPs and their effective diffusion coefficients (D) in WM.

Results: The predictive capabilities of this method have been validated using published experimental tests. For negatively charged NPs, both particle size and surface charge are positively correlated with D before reaching a size threshold. When Zeta potential (Zp) is less negative than -10 mV, the difference between NPs' D in WM and pure interstitial fluid (IF) is limited.

Conclusion: A deeper understanding on the relationships between particle size/surface charge of NPs and their D in WM has been obtained. The results from this study and the developed modelling framework provide important tools for the development of nano-drugs and nano-carriers to cure brain diseases.

Keywords: Brain diseases; Brain tissue; Diffusion coefficient; Extracellular space; Nanoparticles.

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Figures

Fig. 1
Fig. 1
Relationship between surface charge density and Zp of a negatively charged particle.
Fig. 2
Fig. 2
(A) Stochastic geometric model used in this study and the mesh density for simulations. The void circles represent the axons, and the grey domain stands for the ECS. (B) Simplified 3D geometry of WM used in mechanical or hydraulic simulations. The blue elements represent axons and the white space is ECS.
Fig. 3
Fig. 3
Typical time − R2 relationship of the NPs.
Fig. 4
Fig. 4
Method of calculating D and simulated results of experiments in Ref. [13, 63]. (A) Diffusion in dilute (0.3%) agarose. (B) Diffusion in WM.
Fig. 5
Fig. 5
Relationship between Zp and D.
Fig. 6
Fig. 6
Relationship between particle size and D. (A) Zp of the NPs was kept the same. (B) Surface charge of the NPs was kept the same.
Fig. 7
Fig. 7
Diffusion of NPs in IF. (A) Relationship between particle size and D. (B) Relationship between Zp and D.
Fig. 8
Fig. 8
D of the NPs in WM and IF.
See this image and copyright information in PMC

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