Reduced-order modeling of solute transport within physiologically realistic solid tumor microenvironment

Area of Science:

• Biomedical Engineering
• Computational Biology
• Fluid Dynamics

Background:

• Solid tumors feature dense extracellular matrices and poor vascularization, impeding transport.
• Elevated interstitial pressure in tumors hinders drug delivery and growth.
• Understanding intratumoral transport is crucial for effective cancer therapies.

Purpose of the Study:

• To develop an integrated computational framework for simulating plasma transport in solid tumors.
• To incorporate electrohydrodynamic (EHD) forces and multi-phase flow dynamics.
• To calibrate a reduced-order model for predicting plasma progression.

Main Methods:

• Coupled simulations of plasma, red blood cells (RBCs), and white blood cells (WBCs) with glycocalyx-patched vessels.
• Integration of electrohydrodynamic (EHD) forces into a reduced-order reverse advection-diffusion (RAD) model.
• Calibration of the RAD model using multiphase computational fluid dynamics (CFD) simulations.

Main Results:

• EHD significantly increased plasma intensity by 25.34% compared to non-EHD models.
• Simulations revealed two-stage kinetics in plasma perfusion, starting with an advection-dominated regime.
• The calibrated RAD model accurately reproduced observed plasma propagation patterns.

Conclusions:

• The developed framework bridges the gap between classical models and complex CFD simulations.
• This tool quantifies plasma progression in dense solid tumors.
• It establishes baseline transport capacity of the tumor extracellular matrix.