Modeling of drug diffusion in a solid tumor leading to tumor cell death

TL;DR

This paper introduces a mechanistic model for drug diffusion in spherical tumors that accounts for oxygen levels and exposure time, enabling detailed dose-response analysis with fewer parameters than traditional models.

Contribution

It presents a simplified, mechanistic tumor ablation model with only two free parameters, improving interpretability and alignment with biological processes.

Findings

Model accurately predicts dose-threshold-response surfaces.

Fewer parameters than traditional sigmoidal models.

Reproduces established relations like Haber's rule.

Abstract

It has been shown recently that changing the fluidic properties of a drug can improve its efficacy in ablating solid tumors. We develop a modeling framework for tumor ablation, and present the simplest possible model for drug diffusion in a spherical tumor with leaky boundaries and assuming cell death eventually leads to ablation of that cell effectively making the two quantities numerically equivalent. The death of a cell after a given exposure time depends on both the concentration of the drug and the amount of oxygen available to the cell. Higher oxygen availability leads to cell death at lower drug concentrations. It can be assumed that a minimum concentration is required for a cell to die, effectively connecting diffusion with efficacy. The concentration threshold decreases as exposure time increases, which allows us to compute dose-response curves. Furthermore, these curves can be plotted at much finer time intervals compared to that of experiments, which is used to produce a dose-threshold-response surface giving an observer a complete picture of the drug's efficacy for an individual. In addition, since the diffusion, leak coefficients, and the availability of oxygen is different for different individuals and tumors, we produce artificial replication data through bootstrapping to simulate error. While the usual data-driven model with Sigmoidal curves use 12 free parameters, our mechanistic model only has two free parameters, allowing it to be open to scrutiny rather than forcing agreement with data. Even so, the simplest model in our framework, derived here, shows close agreement with the bootstrapped curves, and reproduces well established relations, such as Haber's rule.