Revisiting cell-particle association in vitro: A quantitative method to compare particle performance

Area of Science:

• Nanomedicine
• Biomaterials Science
• Computational Biology

Background:

• Nanoengineering offers potential for advanced drug delivery systems with high efficacy and selectivity.
• Accurate determination of cell-nanoparticle affinity is crucial for comparing particle performance and predicting in vivo behavior.
• Current methods for assessing particle performance are often qualitative or prone to experimental artifacts, limiting study reusability.

Purpose of the Study:

• To develop a mathematical model-based approach for quantifying cell-particle affinity.
• To provide a reliable metric for evaluating nanoengineered particle performance (stealth, fouling, targeting) in vitro, independent of experimental artifacts.
• To establish a robust method for comparing cell-particle interactions across different studies and particle types.

Main Methods:

• Development and application of a novel mathematical model to quantify cell-particle affinity.
• Creation of an in vitro dataset using poly(methacrylic acid) particles of varying sizes (100-1000 nm) and three cell lines (HeLa, THP-1, RAW 264.7).
• Validation and expansion of the dataset with previously published data to identify the best-fitting mathematical model for cell-particle association.

Main Results:

• The mathematical model successfully quantifies cell-particle affinity, mitigating confounding factors like settling effects and protocol variability.
• Analysis revealed a complex relationship between cell-particle association and particle size, challenging simplistic interpretations.
• The model provides a quantitative metric for stealth, fouling, and targeting performance of nanoengineered particles.

Conclusions:

• A new mathematical modeling approach enables artifact-independent quantification of cell-nanoparticle affinity.
• This method offers a reliable tool for comparing nanoengineered particle performance in vitro.
• An online tool is available for researchers to apply this quantitative analysis to their experimental data.