On the Equivalence of the Biological Effect Induced by Irradiation of Clusters of Heavy Atom Nanoparticles and Homogeneous Heavy Atom-Water Mixtures

Abstract

Simple Summary The use of nanoparticles in radiotherapy has been studied widely for over a decade due to their ability to reduce the survival fraction of tumor cells while reducing the doses deposited in healthy cells. Mathematical models were successfully implemented to reproduce experimental results in the literature at the keV range, but discrepancies were found at the MV energy range, the latter range being most used in radiotherapy. The main finding of this work is the demonstration of an equivalence of the physically mediated component of the cell damage between a cluster of nanoparticles and a gold–water mixture in the MV energy range, which reduces the complexity of modeling the interactions of radiation with clusters of nanoparticles seen in real case scenarios. Abstract A multiscale local effect model (LEM)-based framework was implemented to study the cell damage caused by the irradiation of clusters of gold nanoparticles (GNPs) under clinically relevant conditions. The results were compared with those obtained by a homogeneous mixture of water and gold (MixNP) irradiated under similar conditions. To that end, Monte Carlo simulations were performed for the irradiation of GNP clusters of different sizes and MixNPs with a 6 MV Linac spectrum to calculate the dose enhancement factor in water. The capabilities of our framework for the prediction of cell damage trends are examined and discussed. We found that the difference of the main parameter driving the cell damage between a cluster of GNPs and the MixNP was less than 1.6% for all cluster sizes. Our results demonstrate for the first time a simple route to intuit the radiobiological effects of clusters of nanoparticles through the consideration of an equivalent homogenous gold/water mixture. Furthermore, the negligible difference on cell damage between a cluster of GNPs and MixNP simplifies the modelling for the complex geometries of nanoparticle aggregations and saves computational resources.