A parameter sensitivity study for simulating DNA damage after proton irradiation using TOPAS-nBio

Estimation of the relative biological effectiveness for double strand break induction of clinical kilovoltage beams using Monte Carlo simulations

BACKGROUND

The Relative Biological Effectiveness (RBE) of kilovoltage photon beams has been previously investigated in vitro and in silico using analytical methods. The estimated values range from 1.03 to 1.82 depending on the methodology and beam energies examined.

PURPOSE

The focus of this work was to independently estimate RBE values for a range of clinically used kilovoltage beams (70-200 kVp) while investigating the suitability of using TOPAS-nBio for this task.

METHODS

Previously validated spectra of clinical beams were used to generate secondary electron spectra at several depths in a water tank phantom via TOPAS Monte Carlo (MC) simulations. Cell geometry was irradiated with the secondary electrons in TOPAS-nBio MC simulations. The deposited dose and the calculated number of DNA strand breaks were used to estimate RBE values.

RESULTS

Monoenergetic secondary electron simulations revealed the highest direct and indirect double strand break yield at approximately 20 keV. The average RBE value for the kilovoltage beams was calculated to be 1.14.

CONCLUSIONS

TOPAS-nBio was successfully used to estimate the RBE values for a range of clinical radiotherapy beams. The calculated value was in agreement with previous estimates, providing confidence in its clinical use in the future.

Mechanistic modelling of relative biological effectiveness of carbon ion beams and comparison with experiments

Objective.Relative biological effectiveness (RBE) plays a vital role in carbon ion radiotherapy, which is a promising treatment method for reducing toxic effects on normal tissues and improving treatment efficacy. It is important to have an effective and precise way of obtaining RBE values to support clinical decisions. A method of calculating RBE from a mechanistic perspective is reported.Approach.Ratio of dose to obtain the same number of double strand breaks (DSBs) between different radiation types was used to evaluate RBE. Package gMicroMC was used to simulate DSB yields. The DSB inductions were then analyzed to calculate RBE. The RBE values were compared with experimental results.Main results.Furusawa's experiment yielded RBE values of 1.27, 2.22, 3.00 and 3.37 for carbon ion beam with dose-averaged LET of 30.3 keVμm-1, 54.5 keVμm-1, 88 keVμm-1and 137 keVμm-1, respectively. RBE values computed from gMicroMC simulations were 1.75, 2.22, 2.87 and 2.97. When it came to a more sophisticated carbon ion beam with 6 cm spread-out Bragg peak, RBE values were 1.61, 1.63, 2.19 and 2.36 for proximal, middle, distal and distal end part, respectively. Values simulated by gMicroMC were 1.50, 1.87, 2.19 and 2.34. The simulated results were in reasonable agreement with the experimental data.Significance.As a mechanistic way for the evaluation of RBE for carbon ion radiotherapy by combining the macroscopic simulation of energy spectrum and microscopic simulation of DNA damages, this work provides a promising tool for RBE calculation supporting clinical applications such as treatment planning.

A Monte Carlo study on the impact of indirect action on neutron relative biological effectiveness

Recent Monte Carlo studies have linked the energy-dependent risk of neutron-induced stochastic effects to the relative biological effectiveness (RBE) of neutrons in inflicting difficult-to-repair clusters of lesions in nuclear deoxyribonucleic acid (DNA). However, an investigation on the damaging effects of indirect radiation action is missing from such studies. In this work, we extended our group's existing simulation pipeline by incorporating and validating a model for indirect action. Our updated simulation pipeline was used to study the impact of indirect action and estimate neutron RBE for inflicting clustered lesions in DNA. In our results, although indirect action significantly increased the average yield of DNA damage clusters, our neutron RBE values are lower in magnitude than previous estimates due to model limitations and the greater relative impact of indirect action in lower-linear energy transfer (LET) radiation than in higher-LET radiation.

Single-cell DNA sequencing-a potential dosimetric tool

We hypothesised that single-cell whole-genome sequencing has the potential to detect mutational differences in the genomes of the cells that are irradiated with different doses of radiation and we set out to test our hypothesis using in silico and in vitro experiments. In this manuscript, we present our findings from a Monte Carlo single-cell irradiation simulation performed in TOPAS-nBio using a custom-built geometric nuclear deoxyribonucleic acid (DNA) model, which predicts a significant dose dependence of the number of cluster damages per cell as a function of radiation dose. We also present preliminary experimental results, obtained from single-cell whole-genome DNA sequencing analysis performed on cells irradiated with different doses of radiation, showing promising agreement with the simulation results.

Monte Carlo simulation of gold nanoparticles for X-ray enhancement application

BACKGROUND

Gold nanoparticles (Au NPs) are regarded as potential agents that enhance the radiosensitivity of tumor cells for theranostic applications. To elucidate the biological mechanisms of radiation dose enhancement effects of Au NPs as well as DNA damage attributable to the inclusion of Au NPs, Monte Carlo (MC) simulations have been deployed in a number of studies.

SCOPE OF REVIEW

This review paper concisely collates and reviews the information reported in the simulation research in terms of MC simulation of radiosensitization and dose enhancement effects caused by the inclusion of Au NPs in tumor cells, simulation mechanisms, benefits and limitations.

MAJOR CONCLUSIONS

In this review, we first explore the recent advances in MC simulation on Au NPs radiosensitization. The MC methods, physical dose enhancement and enhanced chemical and biological effects is discussed, followed by some results regarding the prediction of dose enhancement. We then review Multi-scale MC simulations of Au NP-induced DNA damages for X-ray irradiation. Moreover, we explain and look at Multi-scale MC simulations of Au NP-induced DNA damages for X-ray irradiation.

GENERAL SIGNIFICANCE

Using advanced chemical module-implemented MC simulations, there is a need to assess the radiation-induced chemical radicals that contribute to the dose-enhancing and biological effects of multiple Au NPs.

Biophysical characterization of collimated and uncollimated fields in pencil beam scanning proton therapy

Objective. The lateral dose fall-off in proton pencil beam scanning (PBS) technique remains the preferred choice for sparing adjacent organs at risk as opposed to the distal edge due to the proton range uncertainties and potentially high relative biological effectiveness. However, because of the substantial spot size along with the scattering in the air and in the patient, the lateral penumbra in PBS can be degraded. Combining PBS with an aperture can result in a sharper dose fall-off, particularly for shallow targets.Approach. The aim of this work was to characterize the radiation fields produced by collimated and uncollimated 100 and 140 MeV proton beams, using Monte Carlo simulations and measurements with a MiniPIX-Timepix detector. The dose and the linear energy transfer (LET) were then coupled with publishedin silicobiophysical models to elucidate the potential biological effects of collimated and uncollimated fields.Main results. Combining an aperture with PBS reduced the absorbed dose in the lateral fall-off and out-of-field by 60%. However, the results also showed that the absolute frequency-averaged LET (LET_F) values increased by a maximum of 3.5 keVμm^-1in collimated relative to uncollimated fields, while the dose-averaged LET (LET_D) increased by a maximum of 7 keVμm^-1. Despite the higher LET values produced by collimated fields, the predicted DNA damage yields remained lower, owing to the large dose reduction.Significance. This work demonstrated the dosimetric advantages of combining an aperture with PBS coupled with lower DNA damage induction. A methodology for calculating dose in water derived from measurements with a silicon-based detector was also presented. This work is the first to demonstrate experimentally the increase in LET caused by combining PBS with aperture, and to assess the potential DNA damage which is the initial step in the cascade of events leading to the majority of radiation-induced biological effects.

Monte-Carlo techniques for radiotherapy applications II: equipment and source modelling, dose calculations and radiobiology

Introduction:

This is the second of two papers giving an overview of the use of Monte-Carlo techniques for radiotherapy applications.

Methods:

The first paper gave an introduction and introduced some of the codes that are available to the user wishing to model the different aspects of radiotherapy treatment. It also aims to serve as a useful companion to a curated collection of papers on Monte-Carlo that have been published in this journal.

Results and Conclusions:

This paper focuses on the application of Monte-Carlo to specific problems in radiotherapy. These include radiotherapy and imaging beam production, brachytherapy, phantom and patient dosimetry, detector modelling and track structure calculations for micro-dosimetry, nano-dosimetry and radiobiology.

Development and implementation of a metaphase DNA model for ionizing radiation induced DNA damage calculation

Objective. To develop a metaphase chromosome model representing the complete genome of a human lymphocyte cell to support microscopic Monte Carlo (MMC) simulation-based radiation-induced DNA damage studies.Approach. We first employed coarse-grained polymer physics simulation to obtain a rod-shaped chromatid segment of 730 nm in diameter and 460 nm in height to match Hi-C data. We then voxelized the segment with a voxel size of 11 nm per side and connected the chromatid with 30 types of pre-constructed nucleosomes and 6 types of linker DNAs in base pair (bp) resolutions. Afterward, we piled different numbers of voxelized chromatid segments to create 23 pairs of chromosomes of 1-5μm long. Finally, we arranged the chromosomes at the cell metaphase plate of 5.5μm in radius to create the complete set of metaphase chromosomes. We implemented the model in gMicroMC simulation by denoting the DNA structure in a four-level hierarchical tree: nucleotide pairs, nucleosomes and linker DNAs, chromatid segments, and chromosomes. We applied the model to compute DNA damage under different radiation conditions and compared the results to those obtained with G0/G1 model and experimental measurements. We also performed uncertainty analysis for relevant simulation parameters.Main results. The chromatid segment was successfully voxelized and connected in bps resolution, containing 26.8 mega bps (Mbps) of DNA. With 466 segments, we obtained the metaphase chromosome containing 12.5 Gbps of DNA. Applying it to compute the radiation-induced DNA damage, the obtained results were self-consistent and agreed with experimental measurements. Through the parameter uncertainty study, we found that the DNA damage ratio between metaphase and G0/G1 phase models was not sensitive to the chemical simulation time. The damage was also not sensitive to the specific parameter settings in the polymer physics simulation, as long as the produced metaphase model followed a similar contact map distribution.Significance. Experimental data reveal that ionizing radiation induced DNA damage is cell cycle dependent. Yet, DNA chromosome models, except for the G0/G1 phase, are not available in the state-of-the-art MMC simulation. For the first time, we successfully built a metaphase chromosome model and implemented it into MMC simulation for radiation-induced DNA damage computation.

Impact of DNA Geometry and Scoring on Monte Carlo Track-Structure Simulations of Initial Radiation-Induced Damage

Track structure Monte Carlo simulations are a useful tool to investigate the damage induced to DNA by ionizing radiation. These simulations usually rely on simplified geometrical representations of the DNA subcomponents. DNA damage is determined by the physical and physicochemical processes occurring within these volumes. In particular, damage to the DNA backbone is generally assumed to result in strand breaks. DNA damage can be categorized as direct (ionization of an atom part of the DNA molecule) or indirect (damage from reactive chemical species following water radiolysis). We also consider quasi-direct effects, i.e., damage originated by charge transfers after ionization of the hydration shell surrounding the DNA. DNA geometries are needed to account for the damage induced by ionizing radiation, and different geometry models can be used for speed or accuracy reasons. In this work, we use the Monte Carlo track structure tool TOPAS-nBio, built on top of Geant4-DNA, for simulation at the nanometer scale to evaluate differences among three DNA geometrical models in an entire cell nucleus, including a sphere/spheroid model specifically designed for this work. In addition to strand breaks, we explicitly consider the direct, quasi-direct, and indirect damage induced to DNA base moieties. We use results from the literature to determine the best values for the relevant parameters. For example, the proportion of hydroxyl radical reactions between base moieties was 80%, and between backbone, moieties was 20%, the proportion of radical attacks leading to a strand break was 11%, and the expected ratio of base damages and strand breaks was 2.5-3. Our results show that failure to update parameters for new geometric models can lead to significant differences in predicted damage yields.

Mechanistic modelling of oxygen enhancement ratio of radiation via Monte Carlo simulation-based DNA damage calculation

Objective.Oxygen plays an important role in affecting the cellular radio-sensitivity to ionizing radiation. The objective of this study is to build a mechanistic model to compute oxygen enhancement ratio (OER) using a GPU-based Monte Carlo (MC) simulation package gMicroMC for microscopic radiation transport simulation and DNA damage calculation.Approach.We first simulated the water radiolysis process in the presence of DNA and oxygen for 1 ns and recorded the produced DNA damages. In this process, chemical reactions among oxygen, water radiolysis free radicals and DNA molecules were considered. We then applied a probabilistic approach to model the reactions between oxygen and indirect DNA damages for a maximal reaction time oft0. Finally, we defined two parametersP0andP1, representing probabilities for DNA damages without and with oxygen fixation effect not being restored in the repair process, to compute the final DNA double strand breaks (DSBs). As cell survival fraction is mainly determined by the number of DSBs, we assumed that the same numbers of DSBs resulted in the same cell survival rates, which enabled us to compute the OER as the ratio of doses producing the same number of DSBs without and with oxygen. We determined the three parameters (t0,P0andP1) by fitting the OERs obtained in our computation to a set of published experimental data under x-ray irradiation. We then validated the model by performing OER studies under proton irradiation and studied model sensitivity to parameter values.Main results.We obtained the model parameters ast0= 3.8 ms,P0= 0.08, andP1= 0.28 with a mean difference of 3.8% between the OERs computed by our model and that obtained from experimental measurements under x-ray irradiation. Applying the established model to proton irradiation, we obtained OERs as functions of oxygen concentration, LET, and dose values, which generally agreed with published experimental data. The parameter sensitivity analysis revealed that the absolute magnitude of the OER curve relied on the values ofP0andP1, while the curve was subject to a horizontal shift when adjustingt0.Significance.This study developed a mechanistic model that fully relies on microscopic MC simulations to compute OER.

Evaluating Iodine-125 DNA Damage Benchmarks of Monte Carlo DNA Damage Models

Simple Summary

Simulation of initial radiation-induced DNA damage remains a major area of research, with numerous Monte Carlo models having been developed to model radiation effects on the cellular scale. While many models have been reasonably fit to a range of biological endpoints, there remains a lack of robust benchmarking data. Here, we investigate the application of a dataset on strand breaking in a single DNA strand through incorporation of radioactive Iodine-125 to distinguish between different Monte Carlo nanoscale physics models. We find that, while all models are able to effectively fit this data, they do so using significantly different best-fitting parameters and make substantially different predictions for other endpoints. These observations suggest that most nanoscale models broadly agree on the distribution of energy and can be made to fit to single datasets, but robust, multi-endpoint analysis is required to fully optimize and validate these approaches.

Abstract

A wide range of Monte Carlo models have been applied to predict yields of DNA damage based on nanoscale track structure calculations. While often similar on the macroscopic scale, these models frequently employ different assumptions which lead to significant differences in nanoscale dose deposition. However, the impact of these differences on key biological readouts remains unclear. A major challenge in this area is the lack of robust datasets which can be used to benchmark models, due to a lack of resolution at the base pair level required to deeply test nanoscale dose deposition. Studies investigating the distribution of strand breakage in short DNA strands following the decay of incorporated ¹²⁵I offer one of the few benchmarks for model predictions on this scale. In this work, we have used TOPAS-nBio to evaluate the performance of three Geant4-DNA physics models at predicting the distribution and yield of strand breaks in this irradiation scenario. For each model, energy and OH radical distributions were simulated and used to generate predictions of strand breakage, varying energy thresholds for strand breakage and OH interaction rates to fit to the experimental data. All three models could fit well to the observed data, although the best-fitting strand break energy thresholds ranged from 29.5 to 32.5 eV, significantly higher than previous studies. However, despite well describing the resulting DNA fragment distribution, these fit models differed significantly with other endpoints, such as the total yield of breaks, which varied by 70%. Limitations in the underlying data due to inherent normalisation mean it is not possible to distinguish clearly between the models in terms of total yield. This suggests that, while these physics models can effectively fit some biological data, they may not always generalise in the same way to other endpoints, requiring caution in their extrapolation to new systems and the use of multiple different data sources for robust model benchmarking.

Applications of nanodosimetry in particle therapy planning and beyond

This topical review summarizes underlying concepts of nanodosimetry. It describes the development and current status of nanodosimetric detector technology. It also gives an overview of Monte Carlo track structure simulations that can provide nanodosimetric parameters for treatment planning of proton and ion therapy. Classical and modern radiobiological assays that can be used to demonstrate the relationship between the frequency and complexity of DNA lesion clusters and nanodosimetric parameters are reviewed. At the end of the review, existing approaches of treatment planning based on relative biological effectiveness (RBE) models or dose-averaged linear energy transfer are contrasted with an RBE-independent approach based on nandosimetric parameters. Beyond treatment planning, nanodosimetry is also expected to have applications and give new insights into radiation protection dosimetry.

Comparison between the results of a recently-developed biological weighting function (V79-RBE10BWF) and thein vitroclonogenic survival RBE10of other repair-competent asynchronized normoxic mammalian cell lines and ions not used for the development of the model

728 simulated microdosimetric lineal energy spectra (26 different ions between 1H and 238U, 28 energy points from 1 to 1000 MeV/n) were used in combination with a recently-developed biological weighting function (Parisi et al., 2020) and 571 published in vitro clonogenic survival curves in order to: 1) assess prediction intervals for the in silico results by deriving an empirical indication of the experimental uncertainty from the dispersion in the in vitro hamster lung fibroblast (V79) data used for the development of the biophysical model; 2) explore the possibility of modeling the relative biological effectiveness (RBE) of the 10% clonogenic survival of asynchronized normoxic repair-competent mammalian cell lines other than the one used for the development of the model (V79); 3) investigate the predictive power of the model through a comparison between in silico results and in vitro data for 10 ions not used for the development of the model. At first, different strategies for the assessment of the in silico prediction intervals were compared. The possible sources of uncertainty responsible for the dispersion in the in vitro data were also shortly reviewed. Secondly, also because of the relevant scatter in the in vitro data, no statistically-relevant differences were found between the RBE10 of the investigated different asynchronized normoxic repair-competent mammalian cell lines. The only exception (Chinese Hamster peritoneal fibroblasts, B14FAF28), is likely due to the limited dataset (all in vitro ion data were extracted from a single publication), systematic differences in the linear energy transfer (LET) calculations for the employed very-heavy ions, and the use of reference photon survival curves extracted from a different publication. Finally, the in silico predictions for the 10 ions not used for the model development were in good agreement with the corresponding in vitro data.

Application of High-Z Gold Nanoparticles in Targeted Cancer Radiotherapy-Pharmacokinetic Modeling, Monte Carlo Simulation and Radiobiological Effect Modeling

High-Z gold nanoparticles (AuNPs) conjugated to a targeting antibody can help to improve tumor control in radiotherapy while simultaneously minimizing radiotoxicity to adjacent healthy tissue. This paper summarizes the main findings of a joint research program which applied AuNP-conjugates in preclinical modeling of radiotherapy at the Klinikum rechts der Isar, Technical University of Munich and Helmholtz Zentrum München. A pharmacokinetic model of superparamagnetic iron oxide nanoparticles was developed in preparation for a model simulating the uptake and distribution of AuNPs in mice. Multi-scale Monte Carlo simulations were performed on a single AuNP and multiple AuNPs in tumor cells at cellular and molecular levels to determine enhancements in the radiation dose and generation of chemical radicals in close proximity to AuNPs. A biologically based mathematical model was developed to predict the biological response of AuNPs in radiation enhancement. Although simulations of a single AuNP demonstrated a clear dose enhancement, simulations relating to the generation of chemical radicals and the induction of DNA strand breaks induced by multiple AuNPs showed only a minor dose enhancement. The differences in the simulated enhancements at molecular and cellular levels indicate that further investigations are necessary to better understand the impact of the physical, chemical, and biological parameters in preclinical experimental settings prior to a translation of these AuNPs models into targeted cancer radiotherapy.

Multi-scale Monte Carlo simulations of gold nanoparticle-induced DNA damages for kilovoltage X-ray irradiation in a xenograft mouse model using TOPAS-nBio

Background

Gold nanoparticles (AuNPs) are considered as promising agents to increase the radiosensitivity of tumor cells. However, the biological mechanisms of radiation enhancement effects of AuNPs are still not well understood. We present a multi-scale Monte Carlo simulation framework within TOPAS-nBio to investigate the increase of DNA damage due to the presence of AuNPs in mouse tumor models.

Methods

A tumor was placed inside a voxel mouse model and irradiated with either 100 kVp or 200 kVp x-ray beams. Phase spaces were employed to transfer particles from the macroscopic (voxel) scale to the microscopic scale, which consists of a cell geometry including a detailed mouse DNA model. Radiosensitizing effects were calculated in the presence and absence of hybrid nanoparticles with a Fe2O3 core surrounded by a gold layer (AuFeNPs). To simulate DNA damage even for very small energy tracks, Geant4-DNA physics and chemistry models were used on microscopic scale.

Results

An AuFeNP induced enhancement of both dose and DNA strand breaks has been established for different scenarios. Produced chemical radicals including hydroxyl molecules, which were assumed to be responsible for DNA damage through chemical reactions, were found to be significantly increased. We further observed a dependency of the results on the location of the cells within the tumor for 200 kVp x-ray beams.

Conclusions

Our multi-scale approach allows to study irradiation induced physical and chemical effects on cells. We showed a potential increase in cell radiosensitization caused by relatively small concentrations of AuFeNPs. Our new methodology allows the individual adjustment of parameters in each simulation step and therefore can be used for other studies investigating the radiosensitizing effects of AuFeNPs or AuNPs in living cells.

Towards the characterization of neutron carcinogenesis through direct action simulations of clustered DNA damage

Neutron exposure poses a unique radiation protection concern because neutrons have a large, energy-dependent relative biological effectiveness (RBE) for stochastic effects. Recent computational studies on the microdosimetric properties of neutron dose deposition have implicated clustered DNA damage as a likely contributor to this marked energy dependence. So far, publications have focused solely on neutron RBE for inducing clusters of DNA damage containing two or more DNA double strand breaks (DSBs). In this study, we have conducted a novel assessment of neutron RBE for inducing all types of clustered DNA damage that contain two or more lesions, stratified by whether the clusters contain DSBs (complex DSB clusters) or not (non-DSB clusters). This assessment was conducted for eighteen initial neutron energies between 1 eV and 10 MeV as well as a reference radiation of 250 keV x-rays. We also examined the energy dependence of cluster length and cluster complexity because these factors are believed to impact the DNA repair process. To carry out our investigation, we developed a user-friendly TOPAS-nBio application that includes a custom nuclear DNA model and a novel algorithm for recording clustered DNA damage. We found that neutron RBE for inducing complex DSB clusters exhibited similar energy dependence to the canonical neutron RBE for stochastic radiobiological effects, at multiple depths in human tissue. Qualitatively similar results were obtained for non-DSB clusters, although the quantitative agreement was lower. Additionally we identified a significant neutron energy dependence in the average length and complexity of clustered lesions. These results support the idea that many types of clustered DNA damage contribute to the energy dependence of neutron RBE for stochastic radiobiological effects and imply that the size and constituent lesions of individual clusters should be taken into account when modeling DNA repair. Our results were qualitatively consistent for (i) multiple radiation doses (including a low-dose 0.1 Gy irradiation), (ii) variations in the maximal lesion separation distance used to define a cluster, and (iii) two distinct collections of physics models used to govern particle transport. Our complete TOPAS-nBio application has been released under an open-source license to enable others to independently validate our work and to expand upon it.

A Geant4-DNA Evaluation of Radiation-Induced DNA Damage on a Human Fibroblast

Simple Summary

DNA damage caused by ionizing radiation in a human fibroblast cell evaluated by the Geant4-DNA Monte Carlo toolkit is presented. A validation study using a computational geometric human DNA model was then carried out, and the calculated DNA damage as a function of particle type and energy is presented. The results of this work showed a significant improvement on past work and were consistent with recent radiobiological experimental data, such as damage yields. This work and the developed methodology could impact a broad number of research fields in which the understanding of radiation effects is crucial, such as cancer radiotherapy, space science, and medical physics.

Abstract

Accurately modeling the radiobiological mechanisms responsible for the induction of DNA damage remains a major scientific challenge, particularly for understanding the effects of low doses of ionizing radiation on living beings, such as the induction of carcinogenesis. A computational approach based on the Monte Carlo technique to simulate track structures in a biological medium is currently the most reliable method for calculating the early effects induced by ionizing radiation on DNA, the primary cellular target of such effects. The Geant4-DNA Monte Carlo toolkit can simulate not only the physical, but also the physico-chemical and chemical stages of water radiolysis. These stages can be combined with simplified geometric models of biological targets, such as DNA, to assess direct and indirect early DNA damage. In this study, DNA damage induced in a human fibroblast cell was evaluated using Geant4-DNA as a function of incident particle type (gammas, protons, and alphas) and energy. The resulting double-strand break yields as a function of linear energy transfer closely reproduced recent experimental data. Other quantities, such as fragment length distribution, scavengeable damage fraction, and time evolution of damage within an analytical repair model also supported the plausibility of predicting DNA damage using Geant4-DNA.The complete simulation chain application “molecularDNA”, an example for users of Geant4-DNA, will soon be distributed through Geant4.

TOPAS-nBio validation for simulating water radiolysis and DNA damage under low-LET irradiation

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.

Investigating the feasibility of TOPAS-nBio for Monte Carlo track structure simulations by adapting GEANT4-DNA examples application

Purpose.The purpose of this work is to investigate the feasibility of TOPAS-nBio for track structure simulations using tuple scoring and ROOT/Python-based post-processing.Materials and methods.There are several example applications implemented in GEANT4-DNA demonstrating track structure simulations. These examples are not implemented by default in TOPAS-nBio. In this study, the tuple scorer was used to re-simulate these examples. The simulations contained investigations of different physics lists, calculation of energy-dependent range, stopping power, mean free path andW-value. Additionally, further applications of the TOPAS-nBio tool were investigated, focusing on physical interactions and deposited energies of electrons with initial energies in the range of 10-60 eV, not covered in the recently published GEANT4-DNA simulations. Low-energetic electrons are currently of great interest in the radiobiology research community due to their high effectiveness towards the induction of biological damage.Results.The quantities calculated with TOPAS-nBio show a good agreement with the simulations of GEANT4-DNA with deviations of 5% at maximum. Thus, we have presented a feasible way to implement the example applications included in GEANT4-DNA in TOPAS-nBio. With the extended simulations, an insight could be given, which further tracking information can be gained with the track structure code and how cross sections and physics models influence a particle's fate.Conclusion.With our results, we could show the potentials of applying the tuple scorer in TOPAS-nBio Monte Carlo track structure simulations. Using this scorer, a large amount of information about the track structure can be accessed, which can be analyzed as preferred after the simulation.

Development of a DNA damage model that accommodates different cellular oxygen concentrations and radiation qualities

PURPOSE

Research regarding cellular responses at different oxygen concentrations (OCs) is of immense interest within the field of radiobiology. Therefore, this study aimed to develop a mechanistic model to analyze cellular responses at different OCs.

METHODS

A DNA damage model (the different cell oxygen level DNA damage [DICOLDD] model) that examines the oxygen effect was developed based on the oxygen fixation hypothesis, which states that dissolved oxygen can modify the reaction kinetics of DNA-derived radicals generated by ionizing radiation. The generation of DNA-derived radicals was simulated using the Monte Carlo method. The decay of DNA-derived radicals due to the competing processes of chemical repair, oxygen fixation, and intrinsic damaging was described using differential equations. The DICOLDD model was fitted to the previous experimental data obtained under different irradiation configurations and validated by calculating the yields of DNA double-strand breaks (DSBs) after exposure to ¹³⁷ Cs as well as cell survival fractions (SFs) using a mechanistic model of cellular survival. Moreover, we used the DICOLDD model to calculate DNA DSB damage yields after irradiation with 0.5-50 MeV protons.

RESULTS

Generally, DSB yields calculated after exposure to ¹³⁷ Cs at different OCs correspond to statistical uncertainties of previous experimental results. Calculated SFs of CHO and V79 cells exposed to photons, protons, and alpha particles at different OCs generally concur with those obtained in previous studies. Our results demonstrated that the variation in DSB yields was less than 10% when the cellular OC decreased from 21% to 5%. Additionally, DSB yields changed drastically when OC dropped below 1%.

CONCLUSIONS

We developed a DNA damage model to evaluate the oxygen effect and provide evidence that a reaction-kinetic model of DNA-derived radicals induced by ionizing radiation suffices to explain the observed oxygen effects. Therefore, the DICOLDD model is a powerful tool for the analysis of cellular responses at different OCs after exposure to different types of radiation.

Recent Developments on gMicroMC: Transport Simulations of Proton and Heavy Ions and Concurrent Transport of Radicals and DNA

Mechanistic Monte Carlo (MC) simulation of radiation interaction with water and DNA is important for the understanding of biological responses induced by ionizing radiation. In our previous work, we employed the Graphical Processing Unit (GPU)-based parallel computing technique to develop a novel, highly efficient, and open-source MC simulation tool, gMicroMC, for simulating electron-induced DNA damages. In this work, we reported two new developments in gMicroMC: the transport simulation of protons and heavy ions and the concurrent transport of radicals in the presence of DNA. We modeled these transports based on electromagnetic interactions between charged particles and water molecules and the chemical reactions between radicals and DNA molecules. Various physical properties, such as Linear Energy Transfer (LET) and particle range, from our simulation agreed with data published by NIST or simulation results from other CPU-based MC packages. The simulation results of DNA damage under the concurrent transport of radicals and DNA agreed with those from nBio-Topas simulation in a comprehensive testing case. GPU parallel computing enabled high computational efficiency. It took 41 s to simultaneously transport 100 protons with an initial kinetic energy of 10 MeV in water and 470 s to transport 105 radicals up to 1 µs in the presence of DNA.

Effect of spatial distribution of boron and oxygen concentration on DNA damage induced from boron neutron capture therapy using Monte Carlo simulations

PURPOSE

This paper aims to investigate how the spatial distribution of boron in cells and oxygen concentration affect the DNA damage induced by charged particles in boron neutron capture therapy (BNCT) by Monte Carlo simulations, and further to evaluate the relative biological effectiveness (RBE) of DNA double-strand breaks (DSBs) induction.

MATERIALS AND METHODS

The kinetic energy spectra of α, ⁷Li particles in BNCT arriving at the nucleus surface were obtained from GEANT4 (Geant4 10.05.p01). The DNA damage caused by BNCT was then evaluated using MCDS (MCDS 3.10A).

RESULTS

When α or ⁷Li particles were distributed in the cytomembrane or cytoplasm, the difference in DNA damage of the same types was less than 0.5%. Taking the 137Cs photons as the reference radiation, when the oxygen concentration varied from 0% to 50%, the RBE of 0.54MeV protons and recoil protons varied from 5 to 2, whereas it decreased from 10 to 3 for α or ⁷Li particles.

CONCLUSION

The RBE of DSB induction all charged particles in BNCT decreased with the increase of oxygen concentration. This work indicated that the RBE of different radiation particles of BNCT might be affected by many factors, which should be paid attention to in theoretical research or clinical application.

Modeling the effect of oxygen on the chemical stage of water radiolysis using GPU-based microscopic Monte Carlo simulations, with an application in FLASH radiotherapy

Oxygen plays a critical role in determining the initial DNA damages induced by ionizing radiation. It is important to mechanistically model the oxygen effect in the water radiolysis process. However, due to the computational costs from the many body interaction problem, oxygen is often ignored or treated as a constant continuum radiolysis-scavenger background in the simulations using common microscopic Monte Carlo tools. In this work, we reported our recent progress on the modeling of the chemical stage of the water radiolysis with an explicit consideration of the oxygen effect, based upon our initial development of an open-source graphical processing unit (GPU)-based MC simulation tool, gMicroMC. The inclusion of oxygen mainly reduces the yields of [Formula: see text] and [Formula: see text] chemical radicals, turning them into highly toxic [Formula: see text] and [Formula: see text] species. To demonstrate the practical value of gMicroMC in large scale simulation problems, we applied the oxygen-simulation-enabled gMicroMC to compute the yields of chemical radicals under a high instantaneous dose rate [Formula: see text] to study the oxygen depletion hypothesis in FLASH radiotherapy. A decreased oxygen consumption rate (OCR) was found associated with a reduced initial oxygen concentration level due to reduced probabilities of reactions. With respect to dose rate, for the oxygen concentration of 21% and electron energy of 4.5 [Formula: see text], OCR remained approximately constant (∼0.22 [Formula: see text]) for [Formula: see text]'s of [Formula: see text], [Formula: see text] and reduced to 0.19 [Formula: see text] at [Formula: see text], because the increased dose rate improved the mutual reaction frequencies among radicals, hence reducing their reactions with oxygen. We computed the time evolution of oxygen concentration under the FLASH irradiation setups. At the dose rate of [Formula: see text] and initial oxygen concentrations from 0.01% to 21%, the oxygen is unlikely to be fully depleted with an accumulative dose of 30 Gy, which is a typical dose used in FLASH experiments. The computational efficiency of gMicroMC when considering oxygen molecules in the chemical stage was evaluated through benchmark work to GEANT4-DNA with simulating an equivalent number of radicals. With an initial oxygen concentration of 3% (∼10⁵ molecules), a speedup factor of 1228 was achieved for gMicroMC on a single GPU card when comparing with GEANT4-DNA on a single CPU.

Fully integrated Monte Carlo simulation for evaluating radiation induced DNA damage and subsequent repair using Geant4-DNA

Ionising radiation induced DNA damage and subsequent biological responses to it depend on the radiation’s track-structure and its energy loss distribution pattern. To investigate the underlying biological mechanisms involved in such complex system, there is need of predicting biological response by integrated Monte Carlo (MC) simulations across physics, chemistry and biology. Hence, in this work, we have developed an application using the open source Geant4-DNA toolkit to propose a realistic “fully integrated” MC simulation to calculate both early DNA damage and subsequent biological responses with time. We had previously developed an application allowing simulations of radiation induced early DNA damage on a naked cell nucleus model. In the new version presented in this work, we have developed three additional important features: (1) modeling of a realistic cell geometry, (2) inclusion of a biological repair model, (3) refinement of DNA damage parameters for direct damage and indirect damage scoring. The simulation results are validated with experimental data in terms of Single Strand Break (SSB) yields for plasmid and Double Strand Break (DSB) yields for plasmid/human cell. In addition, the yields of indirect DSBs are compatible with the experimental scavengeable damage fraction. The simulation application also demonstrates agreement with experimental data of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}γ-H2AX yields for gamma ray irradiation. Using this application, it is now possible to predict biological response along time through track-structure MC simulations.

Impact of a 1.5 T magnetic field on DNA damage in MRI-guided HDR brachytherapy

PURPOSE

Some studies have suggested that the presence of a static magnetic field (SMF) during irradiation alters biological damage. Since MRI-guided radiotherapy is becoming increasingly common, we constructed a DNA-based detector to assess the effect of a 1.5 T SMF on DNA damage during high dose rate (HDR) brachytherapy irradiation.

METHODS

Block phantoms containing a small cavity for the placement of plasmid DNA (pBR322) samples were 3-D printed with biocompatible tissue equivalent material. The phantom was CT scanned and an HDR brachytherapy treatment plan was designed to deliver 20 Gy and 30 Gy doses to the DNA samples in the presence and absence of a 1.5 T SMF. Relative yields of single- and double-strand breaks (SSBs and DSBs, respectively) were computed from gel electrophoresis images of the DNA band intensities and averaged over sample sizes ranging from 12 to 30. Radiation dose was also measured in the presence and absence of the 1.5 T SMF using GafChromic™ EBT3 film placed in the coronal, sagittal, and axial planes.

RESULTS

The average yield of DNA with SSBs and DSBs in the presence and absence of the SMF showed no statistically significant differences (all p ≥ 0.17). Differences in the net optical densities of the EBT3 films for each plane were within experimental uncertainty, suggesting no dose difference in the presence and absence of the SMF.

CONCLUSIONS

HDR irradiation in the presence of the 1.5 T SMF did not alter dose deposition to the DNA cavity nor change SSB and DSB DNA damage.