The chemical stage of the Monte Carlo track-structure (MCTS) code Geant4-DNA was extended for its use in DNA strand break (SB) simulations and compared against published experimental data. Geant4-DNA simulations were performed using pUC19 plasmids (2686 base pairs) in a buffered solution of DMSO irradiated by60Co or137Csγ-rays. A comprehensive evaluation of SSB yields was performed considering DMSO, DNA concentration, dose and plasmid supercoiling. The latter was measured using the super helix density value used in a Brownian dynamics plasmid generation algorithm. The Geant4-DNA implementation of the independent reaction times method (IRT), developed to simulate the reaction kinetics of radiochemical species, allowed to score the fraction of supercoiled, relaxed and linearized plasmid fractions as a function of the absorbed dose. The percentage of the number of SB after •OH + DNA and H• + DNA reactions, referred as SSB efficiency, obtained using MCTS were 13.77% and 0.74% respectively. This is in reasonable agreement with published values of 12% and 0.8%. The SSB yields as a function of DMSO concentration, DNA concentration and super helix density recreated the expected published experimental behaviors within 5%, one standard deviation. The dose response of SSB and DSB yields agreed with published measurements within 5%, one standard deviation. We demonstrated that the developed extension of IRT in Geant4-DNA, facilitated the reproduction of experimental conditions. Furthermore, its calculations were strongly in agreement with experimental data. These two facts will facilitate the use of this extension in future radiobiological applications, aiding the study of DNA damage mechanisms with a high level of detail.
The chemical stage of the Monte Carlo track-structure simulation code Geant4-DNA has been revised and validated. The root-mean-square (RMS) empirical parameter that dictates the displacement of water molecules after an ionization and excitation event in Geant4-DNA has been shortened to better fit experimental data. The pre-defined dissociation channels and branching ratios were not modified, but the reaction rate coefficients for simulating the chemical stage of water radiolysis were updated. The evaluation of Geant4-DNA was accomplished with TOPAS-nBio. For that, we compared predicted time-dependentGvalues in pure liquid water for·OH, e-aq, and H2with published experimental data. For H2O2and H·, simulation of added scavengers at different concentrations resulted in better agreement with measurements. In addition, DNA geometry information was integrated with chemistry simulation in TOPAS-nBio to realize reactions between radiolytic chemical species and DNA. This was used in the estimation of the yield of single-strand breaks (SSB) induced by137Csγ-ray radiolysis of supercoiled pUC18 plasmids dissolved in aerated solutions containing DMSO. The efficiency of SSB induction by reaction between radiolytic species and DNA used in the simulation was chosen to provide the best agreement with published measurements. An RMS displacement of 1.24 nm provided agreement with measured data within experimental uncertainties for time-dependentGvalues and under the presence of scavengers. SSB efficiencies of 24% and 0.5% for·OH and H·, respectively, led to an overall agreement of TOPAS-nBio results within experimental uncertainties. The efficiencies obtained agreed with values obtained with published non-homogeneous kinetic model and step-by-step Monte Carlo simulations but disagreed by 12% with published direct measurements. Improvement of the spatial resolution of the DNA damage model might mitigate such disagreement. In conclusion, with these improvements, Geant4-DNA/TOPAS-nBio provides a fast, accurate, and user-friendly tool for simulating DNA damage under low linear energy transfer irradiation.
Our understanding of radiation-induced cellular damage has greatly improved over the past few decades. Despite this progress, there are still many obstacles to fully understand how radiation interacts with biologically relevant cellular components, such as DNA, to cause observable end points such as cell killing. Damage in DNA is identified as a major route of cell killing. One hurdle when modeling biological effects is the difficulty in directly comparing results generated by members of different research groups. Multiple Monte Carlo codes have been developed to simulate damage induction at the DNA scale, while at the same time various groups have developed models that describe DNA repair processes with varying levels of detail. These repair models are intrinsically linked to the damage model employed in their development, making it difficult to disentangle systematic effects in either part of the modeling chain. These modeling chains typically consist of track-structure Monte Carlo simulations of the physical interactions creating direct damages to DNA, followed by simulations of the production and initial reactions of chemical species causing so-called "indirect" damages. After the induction of DNA damage, DNA repair models combine the simulated damage patterns with biological models to determine the biological consequences of the damage. To date, the effect of the environment, such as molecular oxygen (normoxic vs. hypoxic), has been poorly considered. We propose a new standard DNA damage (SDD) data format to unify the interface between the simulation of damage induction in DNA and the biological modeling of DNA repair processes, and introduce the effect of the environment (molecular oxygen or other compounds) as a flexible parameter. Such a standard greatly facilitates inter-model comparisons, providing an ideal environment to tease out model assumptions and identify persistent, underlying mechanisms. Through inter-model comparisons, this unified standard has the potential to greatly advance our understanding of the underlying mechanisms of radiation-induced DNA damage and the resulting observable biological effects when radiation parameters and/or environmental conditions change.
