Modelling of reversible tissue electroporation and its thermal effects in drug delivery

TL;DR

This paper presents a mathematical model of reversible tissue electroporation that predicts drug delivery efficiency and thermal effects, optimizing parameters to maximize drug uptake while minimizing tissue damage.

Contribution

It introduces a novel time-dependent mass transfer coefficient model for reversible electroporation considering thermal effects, aiding clinical drug delivery optimization.

Findings

Model accurately predicts drug concentration changes under various parameters.

Optimal electroporation parameters enhance drug uptake without thermal damage.

Thermal effects are effectively integrated into the drug delivery model.

Abstract

Electroporation is a very useful tool for drug delivery into various diseased tissues of the human body. This technique helps to improve the clinical treatment by transferring drugs into the targeted cells rapidly. In electroporation, drug particles enter easily into the intracellular compartment through the temporarily permeabilized cell membrane due to the applied electric field. In this work, a mathematical model of drug delivery focusing on reversible tissue electroporation is presented. In addition, the thermal effects on the tissue, which is an outcome of Joule heating, are also considered. This model introduces a time-dependent mass transfer coefficient, which is significant to drug transport. Multiple pulses with low voltage are applied to reach sufficient drugs into the targeted cells. According to the physical circumstances, a set of differential equations are considered and solved. The changes in drug concentration with different parameters (e.g., diffusion coefficient, drug permeability, pulse length, and pulse number) are analyzed. The model optimizes the electroporation parameters to uptake drugs sufficiently into the cells with no thermal damage. This model can be used in clinical experiments to predict drug uptake into the infected cells by controlling the model parameters according to the nature of infections.