Predicting in situ nanoparticle behavior using multiple particle tracking and artificial neural networks.

Area of Science:

• Biomedical Engineering
• Nanotechnology
• Computational Biology

Background:

• Predictive models are crucial for designing nanotherapeutics capable of crossing biological barriers and achieving targeted delivery.
• Understanding nanoparticle interactions within the brain microenvironment is essential for developing effective central nervous system therapies.

Purpose of the Study:

• To demonstrate the efficacy of artificial neural networks (ANNs) in predicting nanoparticle properties and brain microenvironment characteristics.
• To evaluate ANNs against traditional models for predicting nanoparticle behavior using large trajectory datasets.

Main Methods:

• Utilized multiple particle tracking to collect extensive trajectory datasets (>100,000) in in vitro brain gel models and organotypic brain slices.
• Developed and applied artificial neural networks to analyze trajectory data for predicting nanoparticle size, protein adsorption, cell internalization, viscosity, and brain region.
• Compared ANN performance against obstruction scaling and Stokes-Einstein models for specific predictive tasks.

Main Results:

• ANNs achieved higher recall scores than traditional models for predicting gel viscosity (0.75 vs. 0.49) and in situ nanoparticle size (0.90 vs. 0.83).
• ANNs successfully distinguished between different nanoparticle sizes in complex mixtures (recall score up to 0.85) and identified particle populations based on protein adhesion (recall score of 0.89), even where mathematical models are unavailable.
• Demonstrated ANNs' capability to predict various aspects of the particle-microenvironment interaction space.

Conclusions:

• Artificial neural networks, powered by large trajectory datasets, offer a robust method for characterizing nanoparticle-microenvironment interactions.
• This machine learning approach significantly advances the potential for designing and optimizing nanotherapeutic platforms for localized delivery.
• The findings highlight a powerful combination of data-driven methods and experimental tracking for understanding complex biological transport phenomena.