Modeling Direct and Indirect Action on Cell Survival After Photon Irradiation under Normoxia and Hypoxia.

Hans Liew,Stewart Mein,Jürgen Debus,Ivana Dokic,Andrea Mairani

doi:10.3390/ijms21103471

Abstract

The demand for personalized medicine in radiotherapy has been met by a surge of mechanistic models offering predictions of the biological effect of ionizing radiation under consideration of a growing number of parameters. We present an extension of our existing model of cell survival after photon irradiation to explicitly differentiate between the damage inflicted by the direct and indirect (radicals-mediated) action of ionizing radiation. Within our approach, we assume that the oxygenation status affects the indirect action. The effect of different concentrations of dimethyl sulfoxide (DMSO), an effective radical scavenger, has been simulated at different dose levels in normoxic and hypoxic conditions for various cell lines. Our model is found to accurately predict experimental data available in literature, validating the assumptions made in our approach. The presented extension adds further flexibility to our model and could act as basis for further developments of our model.

Highlights

50% of cancer patients are treated with some form of radiation therapy during the course of the disease [1] with recent trends shifting towards more personalized planning and delivery
The fraction of indirect damage quenched by dimethyl sulfoxide (DMSO) was tuned for each DMSO concentration measured by Chapman et al [16] and Hirayama et al [12] so that our model would reproduce the observed survival trends under normoxia
An improved fitting could be achieved increasing the number of free parameters of fDMSO parametrization; in this study we opted for model simplicity (2 free parameters), which itself was capable of replicating the general trends of the fDMSO

Summary

Introduction

50% of cancer patients are treated with some form of radiation therapy during the course of the disease [1] with recent trends shifting towards more personalized planning and delivery. Innovative treatments, require development, validation and clinical translation of highly detailed and accurate physical and biological models for normal tissue and tumor response, considering various bio-factors based on both measurable quantities, discovered mechanisms and theory [2]. In this manuscript, we present an extension of the “UNIfied and VERSatile bio response Engine” (UNIVERSE) biological modeling environment [3], which is progressively extended by mechanistic implementations of biological processes relevant for the ultimate radiation response of cells. We obtain an improved understanding and predictability of the effect of hypoxia, which is known to be highly relevant to treatment outcomes in radiation clinics [8,9,10]

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Results

Conclusion