A computational model for predicting nanoparticle accumulation in tumor vasculature

Area of Science:

• Biomedical Engineering
• Computational Biology
• Nanotechnology

Background:

• Systemic nanoparticle (NP) delivery targets malignant tissues for imaging and therapy.
• Tumor neovasculature development influences NP accumulation and distribution.
• Predicting NP-vasculature interactions is crucial for effective treatment.

Purpose of the Study:

• To develop a computational model predicting tumor neovasculature development and NP vascular accumulation.
• To analyze the impact of NP properties and tumor stage on NP deposition and distribution.
• To identify optimal NP characteristics for enhanced tumor targeting.

Main Methods:

• Integrated a multidimensional tumor-growth model with a mesoscale formulation for NP adhesion.
• Computed NP deposition fraction and spatial distribution based on tumor stage (0-24 days).
• Investigated NP vascular affinity, size, ligand density, and receptor expression.

Main Results:

• Tumor neovasculature blood flow and shear rates increase with tumor growth.
• NP accumulation is sensitive to vascular affinity: high affinity leads to inlet accumulation, low affinity to uniform but low doses.
• An optimal NP vascular affinity balances accumulation dose and spatial distribution, dependent on tumor stage and NP properties.

Conclusions:

• A computational tool can select optimal NP formulations for high accumulation and uniform intra-tumor distribution.
• NP delivery is compromised in underdeveloped vascular networks, bypassing the tumor.
• Optimizing NP-vasculature interactions is key for effective nanomedicine in oncology.