Predictive models of diffusive nanoparticle transport in 3-dimensional tumor cell spheroids

Area of Science:

• Nanotechnology
• Biomedical Engineering
• Pharmacokinetics

Background:

• Understanding nanoparticle (NP) transport in solid tumors is essential for effective nanomedicine development.
• Predicting NP diffusion and spatial distribution requires knowledge of NP properties and NP-cell interactions.

Purpose of the Study:

• To test the hypothesis that NP transport in tumors can be predicted using NP properties and NP-cell interaction parameters.
• To develop and validate a computational model for NP diffusion in 3D tumor systems.

Main Methods:

• Established mathematical models and equations for NP transport.
• Experimentally measured NP-cell interaction parameters for various NP formulations in 2D cell cultures.
• Simulated NP diffusion in 3D systems using the developed models and parameters.
• Validated model predictions against experimental NP concentration-depth profiles in 3D tumor spheroids.

Main Results:

• The model accurately predicted NP diffusion and distribution for negatively charged and near-neutral NPs in 3D spheroids (>90% agreement).
• Model performance was less accurate for positively charged liposomes, particularly those with fusogenic lipids.
• Demonstrated the utility of combining in vitro biointerface data with in silico modeling for predicting NP transport.

Conclusions:

• In vitro NP-cell interaction data can be effectively integrated with computational models to predict NP diffusive transport in 3D tumor systems.
• This approach offers a potential cost-effective method for optimizing NP delivery and residence time in solid tumors.
• Further development including convective transport could enhance predictive capabilities for nanomedicine applications.