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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2510.08340 |
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| _version_ | 1866912639603441664 |
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| author | Cipriani, Beatrice Lopez, Hender |
| author_facet | Cipriani, Beatrice Lopez, Hender |
| contents | Nanoparticles (NPs) demonstrate considerable potential in medical applications, including targeted drug delivery and diagnostic probes. However, their efficacy depends on their ability to navigate through the complex biological environments inside living organisms. In such environments, NPs interact with a dense mixture of biomolecules, which can reduce their mobility and hinder diffusion. Understanding the factors influencing NP diffusion in these environments is key to improving nanomedicine design and predicting toxicological effects. In this study, we propose a computational approach to model NP diffusion in crowded environments. We introduce a mesoscale model that accounts for the combined effects of the Protein Corona (PC) and the crowded medium on NP movement. By including volume-exclusion interactions and modelling the PC both explicitly and implicitly, we identify key macromolecular descriptors that affect NP diffusion. Our results show that the morphology of the PC can significantly affect the diffusion of NPs, and the role of the occupied volume fraction and the size ratio between tracers and crowders are analysed. The results also show that approximating large macromolecular assemblies with a hydrodynamic single-sphere model leads to inexact diffusion estimates. To overcome the limitations of single-sphere representations, a strategy for an accurate parametrization of NP-PC systems using a single-sphere model is presented. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08340 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Impact of protein corona morphology on nanoparticle diffusion in biological fluids: insights from a mesoscale approach Cipriani, Beatrice Lopez, Hender Soft Condensed Matter Nanoparticles (NPs) demonstrate considerable potential in medical applications, including targeted drug delivery and diagnostic probes. However, their efficacy depends on their ability to navigate through the complex biological environments inside living organisms. In such environments, NPs interact with a dense mixture of biomolecules, which can reduce their mobility and hinder diffusion. Understanding the factors influencing NP diffusion in these environments is key to improving nanomedicine design and predicting toxicological effects. In this study, we propose a computational approach to model NP diffusion in crowded environments. We introduce a mesoscale model that accounts for the combined effects of the Protein Corona (PC) and the crowded medium on NP movement. By including volume-exclusion interactions and modelling the PC both explicitly and implicitly, we identify key macromolecular descriptors that affect NP diffusion. Our results show that the morphology of the PC can significantly affect the diffusion of NPs, and the role of the occupied volume fraction and the size ratio between tracers and crowders are analysed. The results also show that approximating large macromolecular assemblies with a hydrodynamic single-sphere model leads to inexact diffusion estimates. To overcome the limitations of single-sphere representations, a strategy for an accurate parametrization of NP-PC systems using a single-sphere model is presented. |
| title | Impact of protein corona morphology on nanoparticle diffusion in biological fluids: insights from a mesoscale approach |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2510.08340 |