Validation of the radiobiology toolkit TOPAS-nBio in simple DNA geometries

Dosimetric and biological impact of activity extravasation of radiopharmaceuticals in PET imaging

BACKGROUND

The increasing use of nuclear medicine and PET imaging has intensified scrutiny of radiotracer extravasation. To our knowledge, this topic is understudied but holds great potential for enhancing our understanding of extravasation in clinical PET imaging.

PURPOSE

This work aims to (1) quantify the absorbed doses from radiotracer extravasation in PET imaging, both locally at the site of extravasation and with the extravasation location as a source of exposure to bodily organs and (2) assess the biological ramifications within the injection site at the cellular level.

METHODS

A radiation dosimetry simulation was performed using a whole-body 4D Extended Cardiac-Torso (XCAT) phantom embedded in the GATE Monte Carlo platform. A 10-mCi dose of 18F-FDG was chosen to simulate a typical clinical PET scan scenario, with 10% of the activity extravasated in the antecubital fossa of the right arm of the phantom. The extravasation volume was modeled as a 5.5 mL rectangle in the hypodermal layer of skin. Absorbed dose contributions were calculated for the first two half-lives, assuming biological clearance thereafter. Dose calculations were performed as absorbed doses at the organ and skin levels. Energy deposition was simulated both at the local extravasation site and in multiple organs of interest and converted to absorbed doses based on their respective masses. Each simulation was repeated ten times to estimate Monte Carlo uncertainties. Biological impacts on cells within the extravasated volume were evaluated by randomizing cells and exposing them to a uniform radiation source of 18F and 68Ga. Particle types, their energies, and direction cosines were recorded in phase space files using a separate Geant4 simulation to characterize their entry into the nucleus of the cellular volume. Subsequently, the phase space files were imported into the TOPAS-nBio simulation to assess the extent of DNA damage, including double-strand breaks (DSBs) and single-strand breaks (SSBs).

RESULTS

Organ-level dosimetric estimations are presented for 18F and 68Ga radionuclides in various organs of interest. With 10% extravasation, the hypodermal layer of the skin received the highest absorbed dose of 1.32 ± 0.01 Gy for 18F and 0.99 ± 0.01 Gy for 68Ga. The epidermal and dermal layers received absorbed doses of 0.07 ± 0.01 Gy and 0.13 ± 0.01 Gy for 18F, and 0.14 ± 0.01 Gy and 0.29 ± 0.01 Gy for 68Ga, respectively. In the extravasated volume, 18F caused an average absorbed dose per nucleus of 0.17 ± 0.01 Gy, estimated to result in 10.58 ± 0.50 DSBs and 268.11 ± 12.43 SSBs per nucleus. For 68Ga, the absorbed dose per nucleus was 0.11 ± 0.01 Gy, leading to an estimated 6.49 ± 0.34 DSBs and 161.24 ± 8.12 SSBs per nucleus. Absorbed doses in other organs were on the order of micro-gray (µGy).

CONCLUSION

The likelihood of epidermal erythema resulting from extravasation during PET imaging is low, as the simulated absorbed doses to the epidermis remain below the thresholds that trigger such effects. Moreover, the organ-level absorbed doses were found to be clinically insignificant across various simulated organs. The minimal DNA damage at the extravasation site suggests that long-term harm, such as radiation-induced carcinogenesis, is highly unlikely.

Comparison of in vitro cell survival predictions using Monte Carlo methods for proton irradiation

PURPOSE

It is possible to combine theoretical models with Monte Carlo simulations to investigate the relationship between radiation-induced initial DNA damage and cell survival. Several combinations of models have been proposed in recent years, sparking interest in comparing their predictions in view of future clinical applications.

METHODS

Two in silico methods for calculating cell survival fractions were optimized for proton irradiation of the Chinese hamster V79 cell line, for LET values ranging from 3.40 and 100 keV/μm. These methods, based on different Monte Carlo codes and theoretical models, were benchmarked against published V79 cell survival data to identify the sources of discrepancies.

RESULTS

The predictive capacities of the methods were evaluated for several proton LET values using an external dataset. After recalibrating model parameters, multiple methods were assessed. This approach helped identify sources of variation, the main one being the simulated number of DSBs, which differed by a factor up to 3 between the two Monte Carlo codes. In this process a new method was defined, that, in all but one case, allows for a reduction in prediction error of up to 56%. Additionally, a freely available GUI for computing cell survival was refined, to facilitate further comparison of diverse theoretical models.

CONCLUSION

The systematic comparison of two predictive chains, characterized by distinct applicability ranges and features, was conducted. Optimization and analysis of various combinations were undertaken to elucidate differences. Addressing and minimizing such discrepancies will be crucial for further enhancing the reliability of predictive models of cell survival, aiming for biologically informed treatment planning.

High-complexity of DNA double-strand breaks is key for alternative end-joining choice

The repair of DNA double-strand breaks (DSBs) through alternative non-homologous end-joining (alt-NHEJ) pathway significantly contributes to genetic instability. However, the mechanism governing alt-NHEJ pathway choice, particularly its association with DSB complexity, remains elusive due to the absence of a suitable reporter system. In this study, we established a unique Escherichia coli reporter system for detecting complex DSB-initiated alternative end-joining (A-EJ), an alt-NHEJ-like pathway. By utilizing various types of ionizing radiation to generate DSBs with varying degrees of complexity, we discovered that high complexity of DSBs might be a determinant for A-EJ choice. To facilitate efficient repair of high-complexity DSBs, A-EJ employs distinct molecular patterns such as longer micro-homologous junctions and non-templated nucleotide addition. Furthermore, the A-EJ choice is modulated by the degree of homology near DSB loci, competing with homologous recombination machinery. These findings further enhance the understanding of A-EJ/alt-NHEJ pathway choice.

Clustered DNA Damage and its Complexity: Tracking the History

The concept of radiation-induced clustered damage in DNA has grown over the past several decades to become a topic of considerable interest across the scientific disciplines involved in studies of the biological effects of ionizing radiation. This paper, prepared for the 70th anniversary issue of Radiation Research, traces historical development of the three main threads of physics, chemistry, and biochemical/cellular responses that led to the hypothesis and demonstration that a key component of the biological effectiveness of ionizing radiation is its characteristic of producing clustered DNA damage of varying complexities. The physics thread has roots that started as early as the 1920s, grew to identify critical nanometre-scale clusterings of ionizations relevant to biological effectiveness, and then, by the turn of the century, had produced an extensive array of quantitative predictions on the complexity of clustered DNA damage from different radiations. Monte Carlo track structure simulation techniques played a key role through these developments, and they are now incorporated into many recent and ongoing studies modelling the effects of radiation. The chemistry thread was seeded by water-radiolysis descriptions of events in water as radical-containing "spurs," demonstration of the important role of the hydroxyl radical in radiation-inactivation of cells and the difficulty of protection by radical scavengers. This led to the concept and description of locally multiply damaged sites (LMDS) for DNA double-strand breaks and other combinations of DNA base damage and strand breakage that could arise from a spur overlapping, or created in very close proximity to, the DNA. In these ways, both the physics and the chemistry threads, largely in parallel, put out the challenge to the experimental research community to verify these predictions of clustered DNA damage from ionizing radiations and to investigate their relevance to DNA repair and subsequent cellular effects. The third thread, biochemical and cell-based research, responded strongly to the challenge by demonstrating the existence and biological importance of clustered DNA damage. Investigations have included repair of a wide variety of defined constructs of clustered damage, evaluation of mutagenic consequences, identification of clustered base-damage within irradiated cells, and identification of co-localization of repair complexes indicative of complex clustered damage after high-LET irradiation, as well as extensive studies of the repair pathways involved in repair of simple double-strand breaks. There remains, however, a great deal more to be learned because of the diversity of clustered DNA damage and of the biological responses.

Primary study of the relative and compound biological effectiveness model for boron neutron capture therapy based on nanodosimetry

BACKGROUND

The current radiobiological model employed for boron neutron capture therapy (BNCT) treatment planning, which relies on microdosimetry, fails to provide an accurate representation the biological effects of BNCT. The precision in calculating the relative biological effectiveness (RBE) and compound biological effectiveness (CBE) plays a pivotal role in determining the therapeutic efficacy of BNCT. Therefore, this study focuses on how to improve the accuracy of the biological effects of BNCT.

PURPOSE

The purpose of this study is to propose new radiation biology models based on nanodosimetry to accurately assess RBE and CBE for BNCT.

METHODS

Nanodosimetry, rooted in ionization cluster size distributions (ICSD), introduces a novel approach to characterize radiation quality by effectively delineating RBE through the ion track structure at the nanoscale. In the context of prior research, this study presents a computational model for the nanoscale assessment of RBE and CBE. We establish a simplified model of DNA chromatin fiber using the Monte Carlo code TOPAS-nBio to evaluate the applicability of ICSD to BNCT and compute nanodosimetric parameters.

RESULTS

Our investigation reveals that both homogeneous and heterogeneous nanodosimetric parameters, as well as the corresponding biological model coefficients α and β, along with RBE values, exhibit variations in response to varying intracellular 10B concentrations. Notably, the nanodosimetric parameterM 1 C 2 $M_1^{{{\mathrm{C}}}_2}$ effectively captures the fluctuations in model coefficients α and RBE.

CONCLUSION

Our model facilitates a nanoscale analysis of BNCT, enabling predictions of nanodosimetric quantities for secondary ions as well as RBE, CBE, and other essential biological metrics related to the distribution of boron. This contribution significantly enhances the precision of RBE calculations and holds substantial promise for future applications in treatment planning.

Comparison of two methods simulating inter-track interactions using the radiobiological Monte Carlo toolkit TOPAS-nBio

Objective.To compare two independently developed methods that enable modelling inter-track interactions in TOPAS-nBio by examining the yield of radiolytic species in radiobiological Monte Carlo track structure simulations. One method uses a phase space file to assign more than one primary to one event, allowing for inter-track interaction between these primary particles. This method has previously been developed by this working group and published earlier. Using the other method, chemical reactions are simulated based on a new version of the independent reaction time approach to allow inter-track interactions.Approach.G-values were calculated and compared using both methods for different numbers of tracks able to undergo inter-track interactions.Main results.Differences in theG-values simulated with the two methods strongly depend on the molecule type, and deviations can range up to 3.9% (H2O2), although, on average, the deviations are smaller than 1.5%.Significance.Both methods seem to be suitable for simulating inter-track interactions, as they provide comparableG-values even though both techniques were developed independently of each other.

Effects of Differing Underlying Assumptions in In Silico Models on Predictions of DNA Damage and Repair

The induction and repair of DNA double-strand breaks (DSBs) are critical factors in the treatment of cancer by radiotherapy. To investigate the relationship between incident radiation and cell death through DSB induction many in silico models have been developed. These models produce and use custom formats of data, specific to the investigative aims of the researchers, and often focus on particular pairings of damage and repair models. In this work we use a standard format for reporting DNA damage to evaluate combinations of different, independently developed, models. We demonstrate the capacity of such inter-comparison to determine the sensitivity of models to both known and implicit assumptions. Specifically, we report on the impact of differences in assumptions regarding patterns of DNA damage induction on predicted initial DSB yield, and the subsequent effects this has on derived DNA repair models. The observed differences highlight the importance of considering initial DNA damage on the scale of nanometres rather than micrometres. We show that the differences in DNA damage models result in subsequent repair models assuming significantly different rates of random DSB end diffusion to compensate. This in turn leads to disagreement on the mechanisms responsible for different biological endpoints, particularly when different damage and repair models are combined, demonstrating the importance of inter-model comparisons to explore underlying model assumptions.

Estimation of relative biological effectiveness of 225Ac compared to 177Lu during [225Ac]Ac-PSMA and [177Lu]Lu-PSMA radiopharmaceutical therapy using TOPAS/TOPAS-nBio/MEDRAS

AIM

Over recent years, [225Ac]Ac-PSMA and [177Lu]Lu-PSMA radiopharmaceutical therapy have evolved as a promising treatment option for advanced prostate cancer. Especially for alpha particle emitter treatments, there is still a need for improving dosimetry, which requires accurate values of relative biological effectiveness (RBE). To achieve that, consideration of DNA damages in the cell nucleus and knowledge of the energy deposition in the location of the DNA at the nanometer scale are required. Monte Carlo particle track structure simulations provide access to interactions at this level. The aim of this study was to estimate the RBE of 225Ac compared to 177Lu. The initial damage distribution after radionuclide decay and the residual damage after DNA repair were considered.

METHODS

This study employed the TOol for PArtcile Simulation (TOPAS) based on the Geant4 simulation toolkit. Simulation of the nuclear DNA and damage scoring were performed using the TOPAS-nBio extension of TOPAS. DNA repair was modeled utilizing the Python-based program MEDRAS (Mechanistic DNA Repair and Survival). Five different cell geometries of equal volume and two radionuclide internalization assumptions as well as two cell arrangement scenarios were investigated. The radionuclide activity (number of source points) was adopted based on SPECT images of patients undergoing the above-mentioned therapies.

RESULTS

Based on the simulated dose-effect curves, the RBE of 225Ac compared to 177Lu was determined in a wide range of absorbed doses to the nucleus. In the case of spherical geometry, 3D cell arrangement and full radionuclide internalization, the RBE based on the initial damage had a constant value of approximately 2.14. Accounting for damage repair resulted in RBE values ranging between 9.38 and 1.46 for 225Ac absorbed doses to the nucleus between 0 and 50 Gy, respectively.

CONCLUSION

In this work, the consideration of DNA repair of the damage from [225Ac]Ac-PSMA and [177Lu]Lu-PSMA revealed a dose dependency of the RBE. Hence, this work suggested that DNA repair is an important aspect to understand response to different radiation qualities.

Calculation of the DNA damage yield and relative biological effectiveness in boron neutron capture therapy via the Monte Carlo track structure simulation

Objective.Boron neutron capture therapy (BNCT) is an advanced cellular-level hadron therapy that has exhibited remarkable therapeutic efficacy in the treatment of locally invasive malignancies. Despite its clinical success, the intricate nature of relative biological effectiveness (RBE) and mechanisms responsible for DNA damage remains elusive. This work aims to quantify the RBE of compound particles (i.e. alpha and lithium) in BNCT based on the calculation of DNA damage yields via the Monte Carlo track structure (MCTS) simulation.Approach. The TOPAS-nBio toolkit was employed to conduct MCTS simulations. The calculations encompassed four steps: determination of the angle and energy spectra on the nuclear membrane, quantification of the database containing DNA damage yields for ions with specific angle and energy, accumulation of the database and spectra to obtain the DNA damage yields of compound particles, and calculation of the RBE by comparison yields of double-strand break (DSB) with the reference gamma-ray. Furthermore, the impact of cell size and microscopic boron distribution was thoroughly discussed.Main results. The DSB yields induced by compound particles in three types of spherical cells (radius equal to 10, 8, and 6μm) were found to be 13.28, 17.34, 22.15 Gy Gbp-1for boronophenylalanine (BPA), and 1.07, 3.45, 8.32 Gy Gbp-1for sodium borocaptate (BSH). The corresponding DSB-based RBE values were determined to be 1.90, 2.48, 3.16 for BPA and 0.15, 0.49, 1.19 for BSH. The calculated DSB-based RBE showed agreement with experimentally values of compound biological effectiveness for melanoma and gliosarcoma. Besides, the DNA damage yield and DSB-based RBE value exhibited an increasing trend as the cell radius decreased. The impact of the boron concentration ratio on RBE diminished once the drug enrichment surpasses a certain threshold.Significance. This work is potential to provide valuable guidance for accurate biological-weighted dose evaluation in BNCT.

Simulation of DNA damage using Geant4-DNA: an overview of the "molecularDNA" example application

Purpose

The scientific community shows great interest in the study of DNA damage induction, DNA damage repair, and the biological effects on cells and cellular systems after exposure to ionizing radiation. Several in silico methods have been proposed so far to study these mechanisms using Monte Carlo simulations. This study outlines a Geant4-DNA example application, named "molecularDNA", publicly released in the 11.1 version of Geant4 (December 2022).

Methods

It was developed for novice Geant4 users and requires only a basic understanding of scripting languages to get started. The example includes two different DNA-scale geometries of biological targets, namely "cylinders" and "human cell". This public version is based on a previous prototype and includes new features, such as: the adoption of a new approach for the modeling of the chemical stage, the use of the standard DNA damage format to describe radiation-induced DNA damage, and upgraded computational tools to estimate DNA damage response.

Results

Simulation data in terms of single-strand break and double-strand break yields were produced using each of the available geometries. The results were compared with the literature, to validate the example, producing less than 5% difference in all cases. Conclusion: "molecularDNA" is a prototype tool that can be applied in a wide variety of radiobiology studies, providing the scientific community with an open-access base for DNA damage quantification calculations. New DNA and cell geometries for the "molecularDNA" example will be included in future versions of Geant4-DNA.

Effects of incoming particle energy and cluster size on the G-value of hydrated electrons

In hydrated electron (e^-_aq) dosimetry, absorbed radiation dose to water is measured by monitoring the concentration of radiation-induced e^-_aq. However, to obtain accurate dose, the radiation chemical yield of e^-_aq, G(e^-_aq), is needed for the radiation quality/setup under investigation. The aim of this study was to investigate the time-evolution of the G-values for the main generated reactive species during water radiolysis using GEANT4-DNA. The effects of cluster size and linear energy transfer (LET) on G(e^-_aq) were examined. Validity of GEANT4-DNA for calculation of G(e^-_aq) for clinically relevant energies was studied. Three scenarios were investigated with different phantom sizes and incoming electron energies (1 keV to 1 MeV). The time evolution of G(e^-_aq) was in good agreement with published data and did not change with decreasing phantom size. The time-evolution of the G-values increases with increasing LET for all radiolytic species. The particle tracks formed with high-energy electrons are separated and the resulting reactive species develop independently in time. With decreasing energy, the mean separation distance between reactive species decreases. The particle tracks might not initially overlap but will overlap shortly thereafter due to diffusion of reactive species, increasing the probability of e^-_aq recombination with other species. This also explains the decrease of G(e^-_aq) with cluster size and LET. Finally, if all factors are kept constant, as the incoming electron energy increases to clinically relevant energies, G(e^-_aq) remains similar to its value at 1 MeV, hence GEANT4-DNA can be used for clinically relevant energies.

MINAS TIRITH: a new tool for simulating radiation-induced DNA damage at the cell population level

Objective. The mechanisms of radiation-induced DNA damage can be understood via the fundamental acquisition of knowledge through a combination of experiments and modeling. Currently, most biological experiments are performed by irradiating an entire cell population, whereas modeling of radiation-induced effects is usually performed via Monte Carlo simulations with track structure codes coupled to realistic DNA geometries of a single-cell nucleus. However, the difference in scale between the two methods hinders a direct comparison because the dose distribution in the cell population is not necessarily uniform owing to the stochastic nature of the energy deposition. Thus, this study proposed the MINAS TIRITH tool to model the distribution of radiation-induced DNA damage in a cell population.Approach. The proposed method is based on precomputed databases of microdosimetric parameters and DNA damage distributions generated using the Geant4-DNA Monte Carlo Toolkit. First, a specific energyzwas assigned to each cell of an irradiated population for a particular absorbed doseDabs,following microdosimetric formalism. Then, each cell was assigned a realistic number of DNA damage events according to the specific energyz,respecting the stochastic character of its occurrence.Main results. This study validated the MINAS TIRITH tool by comparing its results with those obtained using the Geant4-DNA track structure code and a Geant4-DNA based simulation chain for DNA damage calculation. The different elements of comparison indicated consistency between MINAS TIRITH and the Monte Carlo simulation in case of the dose distribution in the population and the calculation of the amount of DNA damage.Significance. MINAS TIRITH is a new approach for the calculation of radiation-induced DNA damage at the cell population level that facilitates reasonable simulation times compared to those obtained with track structure codes. Moreover, this tool enables a more direct comparison between modeling and biological experimentation.

Monte-Carlo techniques for radiotherapy applications I: introduction and overview of the different Monte-Carlo codes

Introduction:

The dose calculation plays a crucial role in many aspects of contemporary clinical radiotherapy treatment planning process. It therefore goes without saying that the accuracy of the dose calculation is of very high importance. The gold standard for absorbed dose calculation is the Monte-Carlo algorithm.

Methods:

This first of two papers gives an overview of the main openly available and supported codes that have been widely used for radiotherapy simulations.

Results:

The paper aims to provide an overview of Monte-Carlo in the field of radiotherapy and point the reader in the right direction of work that could help them get started or develop their existing understanding and use of Monte-Carlo algorithms in their practice.

Conclusions:

It also serves as a useful companion to a curated collection of papers on Monte-Carlo that have been published in this journal.

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.

Nanoscale Calculation of Proton-Induced DNA Damage Using a Chromatin Geometry Model with Geant4-DNA

Monte Carlo simulations can quantify various types of DNA damage to evaluate the biological effects of ionizing radiation at the nanometer scale. This work presents a study simulating the DNA target response after proton irradiation. A chromatin fiber model and new physics constructors with the ELastic Scattering of Electrons and Positrons by neutral Atoms (ELSEPA) model were used to describe the DNA geometry and the physical stage of water radiolysis with the Geant4-DNA toolkit, respectively. Three key parameters (the energy threshold model for strand breaks, the physics model and the maximum distance to distinguish DSB clusters) of scoring DNA damage were studied to investigate the impact on the uncertainties of DNA damage. On the basis of comparison of our results with experimental data and published findings, we were able to accurately predict the yield of various types of DNA damage. Our results indicated that the difference in physics constructor can cause up to 56.4% in the DNA double-strand break (DSB) yields. The DSB yields were quite sensitive to the energy threshold for strand breaks (SB) and the maximum distance to classify the DSB clusters, which were even more than 100 times and four times than the default configurations, respectively.

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

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.

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.

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.

The relation between microdosimetry and induction of direct damage to DNA by alpha particles

In radiopharmaceutical treatmentsα-particles are employed to treat tumor cells. However, the mechanism that drives the biological effect induced is not well known. Being ionizing radiation,α-particles can affect biological organisms by producing damage to the DNA, either directly or indirectly. Following the principle that microdosimetry theory accounts for the stochastic way in which radiation deposits energy in sub-cellular sized volumes via physical collisions, we postulate that microdosimetry represents a reasonable framework to characterize the statistical nature of direct damage induction byα-particles to DNA. We used the TOPAS-nBio Monte Carlo package to simulate direct damage produced by monoenergetic alpha particles to different DNA structures. In separate simulations, we obtained the frequency-mean lineal energy (yF) and dose-mean lineal energy (yD) of microdosimetric distributions sampled with spherical sites of different sizes. The total number of DNA strand breaks, double strand breaks (DSBs) and complex strand breaks per track were quantified and presented as a function of eitheryForyD.The probability of interaction between a track and the DNA depends on how the base pairs are compacted. To characterize this variability on compactness, spherical sites of different size were used to match these probabilities of interaction, correlating the size-dependent specific energy (z) with the damage induced. The total number of DNA strand breaks per track was found to linearly correlate withyFandzFwhen using what we defined an effective volume as microdosimetric site, while the yield of DSB per unit dose linearly correlated withyDorzD,being larger for compacted than for unfolded DNA structures. The yield of complex breaks per unit dose exhibited a quadratic behavior with respect toyDand a greater difference among DNA compactness levels. Microdosimetric quantities correlate with the direct damage imparted on 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.

IDDRRA: A novel platform, based on Geant4-DNA to quantify DNA damage by ionizing radiation

PURPOSE

This study proposes a novel computational platform that we refer to as IDDRRA (DNA Damage Response to Ionizing RAdiation), which uses Monte Carlo (MC) simulations to score radiation induced DNA damage. MC simulations provide results of high accuracy on the interaction of radiation with matter while scoring the energy deposition based on state-of-the-art physics and chemistry models and probabilistic methods.

METHODS

The IDDRRA software is based on the Geant4-DNA toolkit together with new tools that were developed for the purpose of this study, including a new algorithm that was developed in Python for the design of the DNA molecules. New classes were developed in C++ to integrate the GUI and produce the simulation's output in text format. An algorithm was also developed to analyze the simulation's output in terms of energy deposition, Single Strand Breaks (SSB), Double Strand Breaks (DSB) and Cluster Damage Sites (CDS). Finally, a new tool was developed to implement probabilistic SSB and DSB repair models using MC techniques.

RESULTS

This article provides the first benchmarks that the user of the IDDRRA tool can use to validate the functionality of the software as well as to provide a starting point to produce different types of DNA simulations. These benchmarks incorporate different kind of particles (e-, e+, protons, electron spectrum) and DNA molecules.

CONCLUSION

We have developed the IDDRRA tool and demonstrated its use to study various aspects of the modeling and simulation of a DNA irradiation experiment. The tool is expandable and can be expanded by other users with new benchmarks and applications based on the user's needs and experience. New functionality will be added over time, including the quantification of the indirect damage.

Microdosimetric investigation of the radiation quality of low-medium energy electrons using Geant4-DNA

The increasing clinical use of low-energy photon and electron sources (below few tens of keV) has raised concerns on the adequacy of the existing approximation of an energy-independent radiobiological effectiveness. In this work, the variation of the quality factor (Q) and relative biological effectiveness (RBE) of electrons over the low-medium energy range (0.1 keV-1 MeV) is examined using several microdosimetry-based Monte Carlo methodologies with input data obtained from Geant4-DNA track-structure simulations. The sensitivity of the results to the different methodologies, Geant4-DNA physics models, and target sizes is examined. Calculations of Q and RBE are based on the ICRU Report 40 recommendations, the Kellerer-Hahn approximation, the site version of the theory of dual radiation action (TDRA), the microdosimetric kinetic model (MKM) of cell survival, and the calculated yield of DNA double strand breaks (DSB). The stochastic energy deposition spectra needed as input in the above approaches have been calculated for nanometer spherical volumes using the different electron physics models of Geant4-DNA. Results are normalized at 100 keV electrons which is here considered the reference radiation. It is shown that in the energy range ~50 keV-1 MeV, the calculated Q and RBE are approximately unity (to within 1-2%) irrespective of the methodology, Geant4-DNA physics model, and target size. At lower energies, Q and RBE become energy-dependent reaching a maximum value of ~1.5-2.5 between ~200 and 700 eV. The detailed variation of Q and RBE at low energies depends mostly upon the adopted methodology and target size, and less so upon the Geant4-DNA physics model. Overall, the DSB yield predicts the highest RBE values (with RBE_max≈2.5) whereas the MKM the lowest RBE values (with RBE_max≈1.5). The ICRU Report 40, Kellerer-Hahn, and TDRA methods are in excellent agreement (to within 1-2%) over the whole energy range predicting a Q_max≈2. In conclusion, the approximation Q=RBE=1 was found to be valid only above ~50 keV whereas at lower energies both Q and RBE become strongly energy-dependent. It is envisioned that the present work will contribute towards establishing robust methodologies to determine theoretically the energy-dependence of radiation quality of individual electrons which may then be used in subsequent calculations involving practical electron and photon radiation sources.

Evaluating very high energy electron RBE from nanodosimetric pBR322 plasmid DNA damage

This paper presents the first plasmid DNA irradiations carried out with Very High Energy Electrons (VHEE) over 100-200 MeV at the CLEAR user facility at CERN to determine the Relative Biological Effectiveness (RBE) of VHEE. DNA damage yields were measured in dry and aqueous environments to determine that ~ 99% of total DNA breaks were caused by indirect effects, consistent with other published measurements for protons and photons. Double-Strand Break (DSB) yield was used as the biological endpoint for RBE calculation, with values found to be consistent with established radiotherapy modalities. Similarities in physical damage between VHEE and conventional modalities gives confidence that biological effects of VHEE will also be similar-key for clinical implementation. Damage yields were used as a baseline for track structure simulations of VHEE plasmid irradiation using GEANT4-DNA. Current models for DSB yield have shown reasonable agreement with experimental values. The growing interest in FLASH radiotherapy motivated a study into DSB yield variation with dose rate following VHEE irradiation. No significant variations were observed between conventional and FLASH dose rate irradiations, indicating that no FLASH effect is seen under these conditions.

Cellular Response to Proton Irradiation: A Simulation Study with TOPAS-nBio

The cellular response to ionizing radiation continues to be of significant research interest in cancer radiotherapy, and DNA is recognized as the critical target for most of the biologic effects of radiation. Incident particles can cause initial DNA damages through physical and chemical interactions within a short time scale. Initial DNA damages can undergo repair via different pathways available at different stages of the cell cycle. The misrepair of DNA damage results in genomic rearrangement and causes mutations and chromosome aberrations, which are drivers of cell death. This work presents an integrated study of simulating cell response after proton irradiation with energies of 0.5-500 MeV (LET of 60-0.2 keV/µm). A model of a whole nucleus with fractal DNA geometry was implemented in TOPAS-nBio for initial DNA damage simulations. The default physics and chemistry models in TOPAS-nBio were used to describe interactions of primary particles, secondary particles, and radiolysis products within the nucleus. The initial DNA double-strand break (DSB) yield was found to increase from 6.5 DSB/Gy/Gbp at low-linear energy transfer (LET) of 0.2 keV/µm to 21.2 DSB/Gy/Gbp at high LET of 60 keV/µm. A mechanistic repair model was applied to predict the characteristics of DNA damage repair and dose response of chromosome aberrations. It was found that more than 95% of the DSBs are repaired within the first 24 h and the misrepaired DSB fraction increases rapidly with LET and reaches 15.8% at 60 keV/µm with an estimated chromosome aberration detection threshold of 3 Mbp. The dicentric and acentric fragment yields and the dose response of micronuclei formation after proton irradiation were calculated and compared with experimental results.

Hi-C implementation of genome structure for in silico models of radiation-induced DNA damage

Developments in the genome organisation field has resulted in the recent methodology to infer spatial conformations of the genome directly from experimentally measured genome contacts (Hi-C data). This provides a detailed description of both intra- and inter-chromosomal arrangements. Chromosomal intermingling is an important driver for radiation-induced DNA mis-repair. Which is a key biological endpoint of relevance to the fields of cancer therapy (radiotherapy), public health (biodosimetry) and space travel. For the first time, we leverage these methods of inferring genome organisation and couple them to nano-dosimetric radiation track structure modelling to predict quantities and distribution of DNA damage within cell-type specific geometries. These nano-dosimetric simulations are highly dependent on geometry and are benefited from the inclusion of experimentally driven chromosome conformations. We show how the changes in Hi-C contract maps impact the inferred geometries resulting in significant differences in chromosomal intermingling. We demonstrate how these differences propagate through to significant changes in the distribution of DNA damage throughout the cell nucleus, suggesting implications for DNA repair fidelity and subsequent cell fate. We suggest that differences in the geometric clustering for the chromosomes between the cell-types are a plausible factor leading to changes in cellular radiosensitivity. Furthermore, we investigate changes in cell shape, such as flattening, and show that this greatly impacts the distribution of DNA damage. This should be considered when comparing in vitro results to in vivo systems. The effect may be especially important when attempting to translate radiosensitivity measurements at the experimental in vitro level to the patient or human level.

Simulation of Proton-Induced DNA Damage Patterns Using an Improved Clustering Algorithm

Simulations of deoxyribonucleic acid (DNA) molecular damage use the traversal algorithm that has the disadvantages of being time-consuming, slowly converging, and requiring high-performance computer clusters. This work presents an improved version of the algorithm, "density-based spatial clustering of applications with noise" (DBSCAN), using a KD-tree approach to find neighbors of each point for calculating clustered DNA damage. The resulting algorithm considers the spatial distributions for sites of energy deposition and hydroxyl radical attack, yielding the statistical probability of (single and double) DNA strand breaks. This work achieves high accuracy and high speed at calculating clustered DNA damage that has been induced by proton treatment at the molecular level while running on an i7 quad-core CPU. The simulations focus on the indirect effect generated by hydroxyl radical attack on DNA. The obtained results are consistent with those of other published experiments and simulations. Due to the array of chemical processes triggered by proton treatment, it is possible to predict the effects that different track structures of various energy protons produce on eliciting direct and indirect damage of DNA.

Low energy (6-18 eV) electron scattering from condensed thymidine (dT) III: absolute electronic excitation cross sections

Absolute cross sections (CSs) for electronic excitation by low-energy electron (LEE) scattering, from condensed thymidine (dT) in the 6-18 eV incident energy range, were measured by high-resolution electron energy loss spectroscopy (HREELS). Various electron energy loss (EEL) spectra were acquired using 1 ML of dT condensed on a multilayer film of Ar held at about 20 K under ultra-high vacuum (∼1 × 10-11 Torr). dT is one of the most complex DNA constituents to be studied by HREELS and these spectra provide the first LEE energy-loss data for electronic excitation of a nucleoside. CSs for transitions to the states 13A', 13A'', 23A', 21A', 33A', 23A'', 43A', 33A'', 53A' and 51A' of dT were extracted from the EEL spectra. These states correlate to those previously measured for the thymine moiety. Two broad resonances are observed in the energy dependence of the CSs at around 8 and 10 eV; these energies are close to those found in earlier gas- and solid-phase studies on the interaction of LEEs with dT, thymine and related molecules. A quantitative comparison between the electronic CSs of dT and those of thymine and tetrahydrofuran indicates that no variation is induced in the electronic CSs of thymine upon chemically binding to a deoxyribose group.

Ionizing Radiation and Complex DNA Damage: Quantifying the Radiobiological Damage Using Monte Carlo Simulations

Ionizing radiation is a common tool in medical procedures. Monte Carlo (MC) techniques are widely used when dosimetry is the matter of investigation. The scientific community has invested, over the last 20 years, a lot of effort into improving the knowledge of radiation biology. The present article aims to summarize the understanding of the field of DNA damage response (DDR) to ionizing radiation by providing an overview on MC simulation studies that try to explain several aspects of radiation biology. The need for accurate techniques for the quantification of DNA damage is crucial, as it becomes a clinical need to evaluate the outcome of various applications including both low- and high-energy radiation medical procedures. Understanding DNA repair processes would improve radiation therapy procedures. Monte Carlo simulations are a promising tool in radiobiology studies, as there are clear prospects for more advanced tools that could be used in multidisciplinary studies, in the fields of physics, medicine, biology and chemistry. Still, lot of effort is needed to evolve MC simulation tools and apply them in multiscale studies starting from small DNA segments and reaching a population of cells.

Microdosimetric calculations of the direct DNA damage induced by low energy electrons using the Geant4-DNA Monte Carlo code

To calculate the yield of direct DNA damage induced by low energy electrons using Monte Carlo generated microdosimetric spectra at the nanometer scale and examine the influence of various simulation inputs. The potential of classical microdosimetry to offer a viable and simpler alternative to more elaborate mechanistic approaches for practical applications is discussed. Track-structure simulations with the Geant4-DNA low-energy extension of the Geant4 Monte Carlo toolkit were used for calculating lineal energy spectra in spherical volumes with dimensions relevant to double-strand-break (DSB) induction. The microdosimetric spectra were then used to calculate the yield of simple and clustered DSB based on literature values of the threshold energy of DNA damage. The influence of the different implementations of the dielectric function of liquid water available in Geant4-DNA (Option 2 and Option 4 constructors), as well as the effect of particle tracking cutoff energy and target size are examined. Frequency- and dose-mean lineal energies in liquid-water spheres of 2, 2.3, 2.6, and 3.4 nm diameter, as well as, number of simple and clustered DSB/Gy/cell are presented for electrons over the 100 eV to 100 keV energy range. Results are presented for both the 'default' (Option 2) and 'Ioannina' (Option 4) physics models of Geant4-DNA applying several commonly used tracking cutoff energies (10, 20, 50, 100 eV). Overall, the choice of the physics model and target diameter has a moderate effect (up to ~10%-30%) on the DSB yield whereas the effect of the tracking cutoff energy may be significant (>100%). Importantly, the yield of both simple and clustered DSB was found to vary significantly (by a factor of 2 or more) with electron energy over the examined range. The yields of electron-induced simple and clustered DSB exhibit a strong energy dependence over the 100 eV-100 keV range with implications to radiation quality issues. It is shown that a classical microdosimetry approach for the calculation of DNA damage based on lineal energy spectra in nanometer-size targets predicts comparable results to computationally intensive mechanistic approaches which use detailed atomistic DNA geometries, thus, offering a relatively simple and robust alternative for some practical applications.

Intercomparison of dose enhancement ratio and secondary electron spectra for gold nanoparticles irradiated by X-rays calculated using multiple Monte Carlo simulation codes

PURPOSE

Targeted radiation therapy has seen an increased interest in the past decade. In vitro and in vivo experiments showed enhanced radiation doses due to gold nanoparticles (GNPs) to tumors in mice and demonstrated a high potential for clinical application. However, finding a functionalized molecular formulation for actively targeting GNPs in tumor cells is challenging. Furthermore, the enhanced energy deposition by secondary electrons around GNPs, particularly by short-ranged Auger electrons is difficult to measure. Computational models, such as Monte Carlo (MC) radiation transport codes, have been used to estimate the physical quantities and effects of GNPs. However, as these codes differ from one to another, the reliability of physical and dosimetric quantities needs to be established at cellular and molecular levels, so that the subsequent biological effects can be assessed quantitatively.

METHODS

In this work, irradiation of single GNPs of 50 nm and 100 nm diameter by X-ray spectra generated by 50 and 100 peak kilovoltages was simulated for a defined geometry setup, by applying multiple MC codes in the EURADOS framework.

RESULTS

The mean dose enhancement ratio of the first 10 nm-thick water shell around a 100 nm GNP ranges from 400 for 100 kVp X-rays to 600 for 50 kVp X-rays with large uncertainty factors up to 2.3.

CONCLUSIONS

It is concluded that the absolute dose enhancement effects have large uncertainties and need an inter-code intercomparison for a high quality assurance; relative properties may be a better measure until more experimental data is available to constrain the models.

Low energy (1-19 eV) electron scattering from condensed thymidine (dT) I: absolute vibrational excitation cross sections

Absolute cross sections (CSs) for vibrational excitation by electrons of energy between 1-19 eV scattering from condensed thymidine (dT) were measured by means of high-resolution electron energy loss spectroscopy (HREELS). The CSs were extracted from electron energy loss spectra of dT condensed on multilayers film of Ar held at about 20 K under ultra-high vacuum (∼1 × 10-11 Torr). dT is one of the most complex molecules to be studied in condensed phase by HREELS. The magnitudes of the vibrational CSs lie within the 10-17 cm2 range. Structures observed in the energy dependence of the vibrational CSs under 3 eV and around 4 eV were compared with previous results of gas- and solid-phase studies on dT and related molecules (e.g., thymine and tetrahydrofuran). These structures were attributed to the formation of shape resonances.

Nanodosimetric quantities and RBE of a clinically relevant carbon-ion beam

PURPOSE

Although carbon-ion therapy is becoming increasingly attractive to the treatment of tumors, details about the ionization pattern formed by therapeutic carbon-ion beam in tissue have not been fully investigated. In this work, systematic calculations for the nanodosimetric quantities and relative biological effectiveness (RBE) of a clinically relevant carbon-ion beam were studied for the first time.

METHODS

The method combining both track structure and condensed history Monte Carlo (MC) simulations was adopted to calculate the nanodosimetric quantities. Fragments and energy spectra at different positions of the radiation field of a clinically relevant carbon-ion pencil beam were generated by means of MC simulations in water. Nanodosimetric quantities such as mean ionization cluster size ( M 1 ), the first moment of conditional cluster size ( M 1 C 2 ), cumulative probability ( F 2 ), and conditional cumulative probability ( F 3 C 2 ) at these positions were then acquired based on the spectra and the pre-calculated nanodosimetric database created by track structure MC simulations. What's more, a novel approach to calculate RBE based on the said nanodosimetric quantities was introduced. The RBE calculations were then conducted for the carbon-ion beam at different water-equivalent depths.

RESULTS

Lateral distributions at various water-equivalent depths of both the nanodosimetric quantities and RBE values were obtained. The values of M 1 , M 1 C 2 , F 2 , and F 3 C 2 were 1.49, 2.67, 0.30, and 0.38 at the plateau at the beam central axis and maximized at 2.79, 5.69, 0.47, and 0.68 at the depths around the Bragg peak, respectively. At a given depth, M 1 and F 2 decreased laterally with increasing the distance to the beam central axis while M 1 C 2 and F 3 C 2 remained nearly unchanged at first and then decreased except for M 1 C 2 at the rising edge of the Bragg peak. The calculated RBE values were 1.07 at the plateau and 3.13 around the Bragg peak. Good agreement between the calculated RBE values and experimental data was obtained.

CONCLUSIONS

Different nanodosimetric quantities feature the track structure of therapeutic carbon-ion beam in different manners. Detailed ionization patterns generated by carbon-ion beam could be characterized by nanodosimetric quantities. Moreover the combined method adopted in this work to calculate nanodosimetric quantities is not only valid but also convenient. Nanodosimetric quantities are significantly helpful for the RBE calculations in carbon-ion therapy.

Radio-enhancement by gold nanoparticles and their impact on water radiolysis for x-ray, proton and carbon-ion beams

Gold nanoparticle (GNP) radio-enhancement is a promising technique to increase the dose deposition in a tumor while sparing neighboring healthy tissue. Previous experimental studies showed effects on cell survival and tumor control for keV x-rays but surprisingly also for MV-photons, proton and carbon-ion beams. In a systematic study, we use the Monte Carlo simulation tool TOPAS-nBio to model the GNP radio-enhancement within a cell as a function of GNP concentration, size and clustering for a wide range of energies for photons, protons and, for the first time, carbon-ions. Moreover, we include water radiolysis, which has been recognized as a major pathway of GNP mediated radio-enhancement. At a GNP concentration of 0.5% and a GNP diameter of 10 nm, the dose enhancement ratio was highest for 50 keV x-rays (1.36) and decreased in the orthovoltage (1.04 at 250 keV) and megavoltage range (1.01 at 1 MeV). The dose enhancement linearly increased with GNP concentration and decreased with GNP size and degree of clustering for all radiation modalities. While the highest physical dose enhancement at 5% concentrations was only 1.003 for 10 MeV protons and 1.004 for 100 MeV carbon-ions, we find the number of hydroxyl ([Formula: see text]) altered by 23% and 3% after 1 [Formula: see text]s at low, clinically-relevant concentrations. For the same concentration and proton-impact, the G-value is most sensitive to the nanoparticle size with 46 times more radical interactions at GNPs for 2 nm than for 50 nm GNP diameter within 1 [Formula: see text]s. Nanoparticle clustering was found to decrease the number of interactions at GNPs, e.g. for a cluster of 25 GNPs by a factor of 3.4. The changes in G-value correlate to the average distance between the chemical species and the GNPs. While the radiochemistry of GNP-loaded water has yet to be fully understood, this work offers a first relative quantification of radiolysis products for a broad parameter-set.

Mechanistic Modelling of Radiation Responses

Radiobiological modelling has been a key part of radiation biology and therapy for many decades, and many aspects of clinical practice are guided by tools such as the linear-quadratic model. However, most of the models in regular clinical use are abstract and empirical, and do not provide significant scope for mechanistic interpretation or making predictions in novel cell lines or therapies. In this review, we will discuss the key areas of ongoing mechanistic research in radiation biology, including physical, chemical, and biological steps, and review a range of mechanistic modelling approaches which are being applied in each area, highlighting the possible opportunities and challenges presented by these techniques.

Radiobiological quantities in proton-therapy: Estimation and validation using Geant4-based Monte Carlo simulations

PURPOSE

The Geant4 Monte Carlo simulation toolkit was used to reproduce radiobiological parameters measured by irradiating three different cancerous cell lines with monochromatic and clinical proton beams.

METHODS

The experimental set-up adopted for irradiations was fully simulated with a dedicated open-source Geant4 application. Cells survival fractions was calculated coupling the Geant4 simulations with two analytical radiobiological models: one based on the LEM (Local Effect Model) approach and the other on a semi-empirical parameterisation. Results was evaluated and compared with experimental data.

RESULTS AND CONCLUSIONS

The results demonstrated the Geant4 ability to reproduce radiobiological quantities for different cell lines.

TOPAS-nBio: An Extension to the TOPAS Simulation Toolkit for Cellular and Sub-cellular Radiobiology

The TOPAS Monte Carlo (MC) system is used in radiation therapy and medical imaging research, having played a significant role in making Monte Carlo simulations widely available for proton therapy related research. While TOPAS provides detailed simulations of patient scale properties, the fundamental unit of the biological response to radiation is a cell. Thus, our goal was to develop TOPAS-nBio, an extension of TOPAS dedicated to advance understanding of radiobiological effects at the (sub-)cellular, (i.e., the cellular and sub-cellular) scale. TOPAS-nBio was designed as a set of open source classes that extends TOPAS to model radiobiological experiments. TOPAS-nBio is based on and extends Geant4-DNA, which extends the Geant4 toolkit, the basis of TOPAS, to include very low-energy interactions of particles down to vibrational energies, explicitly simulates every particle interaction (i.e., without using condensed histories) and propagates radiolysis products. To further facilitate the use of TOPAS-nBio, a graphical user interface was developed. TOPAS-nBio offers full track-structure Monte Carlo simulations, integration of chemical reactions within the first millisecond, an extensive catalogue of specialized cell geometries as well as sub-cellular structures such as DNA and mitochondria, and interfaces to mechanistic models of DNA repair kinetics. We compared TOPAS-nBio simulations to measured and published data of energy deposition patterns and chemical reaction rates (G values). Our simulations agreed well within the experimental uncertainties. Additionally, we expanded the chemical reactions and species provided in Geant4-DNA and developed a new method based on independent reaction times (IRT), including a total of 72 reactions classified into 6 types between neutral and charged species. Chemical stage simulations using IRT were a factor of 145 faster than with step-by-step tracking. Finally, we applied the geometric/chemical modeling to obtain initial yields of double-strand breaks (DSBs) in DNA fibers for proton irradiations of 3 and 50 MeV and compared the effect of including chemical reactions on the number and complexity of DSB induction. Over half of the DSBs were found to include chemical reactions with approximately 5% of DSBs caused only by chemical reactions. In conclusion, the TOPAS-nBio extension to the TOPAS MC application offers access to accurate and detailed multiscale simulations, from a macroscopic description of the radiation field to microscopic description of biological outcome for selected cells. TOPAS-nBio offers detailed physics and chemistry simulations of radiobiological experiments on cells simulating the initially induced damage and links to models of DNA repair kinetics.

Quantification of DNA double-strand breaks using Geant4-DNA

PURPOSE

This study aims to standardize the simulation procedure in measuring DNA double-strand breaks (DSBs), by using advanced Monte Carlo toolkits, and newly introduced experimental methods for DNA DSB measurement.

METHODS

For the experimental quantification of DNA DSB, an innovative DNA dosimeter was used to produce experimental data. GATE in combination with Geant4-DNA toolkit were exploited to simulate the experimental environment. The PDB4DNA example of Geant4-DNA was upgraded and investigated. Parameters of the simulation such energy threshold (ET) for a strand break and base pair threshold (BPT) for a DSB were evaluated, depending on the dose.

RESULTS

Simulations resulted to minimum differentiation in comparison to experimental data for ET = 19 ± 1 eV and BPT = 10 bp, and high differentiation for ET<17.5 eV or ET>22.5 eV and BPT = 10 bp. There was also small differentiation for ET = 17.5 eV and BPT = 6 bp. Uncertainty has been kept lower than 3%.

CONCLUSIONS

This study includes first results on the quantification of DNA double-strand breaks. The energy spectrum of a LINAC was simulated and used for the first time to irradiate DNA molecules. Simulation outcome was validated on experimental data that were produced by a prototype DNA dosimeter.

Clinically relevant nanodosimetric simulation of DNA damage complexity from photons and protons

Relative Biological Effectiveness (RBE), the ratio of doses between radiation modalities to produce the same biological endpoint, is a controversial and important topic in proton therapy. A number of phenomenological models incorporate variable RBE as a function of Linear Energy Transfer (LET), though a lack of mechanistic description limits their applicability. In this work we take a different approach, using a track structure model employing fundamental physics and chemistry to make predictions of proton and photon induced DNA damage, the first step in the mechanism of radiation-induced cell death. We apply this model to a proton therapy clinical case showing, for the first time, predictions of DNA damage on a patient treatment plan. Our model predictions are for an idealised cell and are applied to an ependymoma case, at this stage without any cell specific parameters. By comparing to similar predictions for photons, we present a voxel-wise RBE of DNA damage complexity. This RBE of damage complexity shows similar trends to the expected RBE for cell kill, implying that damage complexity is an important factor in DNA repair and therefore biological effect.

Relative Biological Effectiveness (RBE) is a controversial and important topic in proton therapy. This work uses Monte Carlo simulations of DNA damage for protons and photons to probe this phenomenon, providing a plausible mechanistic understanding.

Report of a National Cancer Institute special panel: Characterization of the physical parameters of particle beams for biological research

PURPOSE

To define the physical parameters needed to characterize a particle beam in order to allow intercomparison of different experiments performed using different ions at the same facility and using the same ion at different facilities.

METHODS

At the request of the National Cancer Institute (NCI), a special panel was convened to review the current status of the field and to provide suggested metrics for reporting the physical parameters of particle beams to be used for biological research. A set of physical parameters and measurements that should be performed by facilities and understood and reported by researchers supported by NCI to perform pre-clinical radiobiology and medical physics of heavy ions were generated.

RESULTS

Standard measures such as radiation delivery technique, beam modifiers used, nominal energy, field size, physical dose and dose rate should all be reported. However, more advanced physical measurements, including detailed characterization of beam quality by microdosimetric spectrum and fragmentation spectra, should also be established and reported. Details regarding how such data should be incorporated into Monte Carlo simulations and the proper reporting of simulation details are also discussed.

CONCLUSIONS

In order to allow for a clear relation of physical parameters to biological effects, facilities and researchers should establish and report detailed physical characteristics of the irradiation beams utilized including both standard and advanced measures. Biological researchers are encouraged to actively engage facility staff and physicists in the design and conduct of experiments. Modeling individual experimental setups will allow for the reporting of the uncertainties in the measurement or calculation of physical parameters which should be routinely reported.

A New Standard DNA Damage (SDD) Data Format

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.

Geometrical structures for radiation biology research as implemented in the TOPAS-nBio toolkit

Computational simulations, such as Monte Carlo track structure simulations, offer a powerful tool for quantitatively investigating radiation interactions within cells. The modelling of the spatial distribution of energy deposition events as well as diffusion of chemical free radical species, within realistic biological geometries, can help provide a comprehensive understanding of the effects of radiation on cells. Track structure simulations, however, generally require advanced computing skills to implement. The TOPAS-nBio toolkit, an extension to TOPAS (TOol for PArticle Simulation), aims to provide users with a comprehensive framework for radiobiology simulations, without the need for advanced computing skills. This includes providing users with an extensive library of advanced, realistic, biological geometries ranging from the micrometer scale (e.g. cells and organelles) down to the nanometer scale (e.g. DNA molecules and proteins). Here we present the geometries available in TOPAS-nBio.

Computational Modeling and Clonogenic Assay for Radioenhancement of Gold Nanoparticles Using 3D Live Cell Images

Radioenhancement of gold nanoparticles (GNPs) has shown great potential for increasing the therapeutic efficiency of radiotherapy. Here we report on a computational model of radiation response, which was developed to predict the survival curves of breast cancer cells incubated with GNPs. The amount of GNP uptake was estimated using inductively coupled plasma-mass spectroscopy, and the three-dimensional (3D) intracellular distribution of GNPs was obtained using optical diffraction tomography. The developed computational model utilized the 3D live cell imaging and recent Monte Carlo techniques to calculate microscopic dose distributions within the cell. Clonogenic assays with and without GNPs were performed to estimate the radioenhancement for 150 kVp X rays in terms of cell survival fractions. Measured cell survival fractions were comparable with the computational model.

Energy optimization in gold nanoparticle enhanced radiation therapy

Gold nanoparticles (GNPs) have been demonstrated as radiation dose enhancing agents. Kilovoltage external photon beams have been shown to yield the largest enhancement due to the high interaction probability with gold. While orthovoltage irradiations are feasible and promising, they suffer from a reduced tissue penetrating power. This study quantifies the effect of varying photon beam energies on various beam arrangements, body, tumor, and cellular GNP uptake geometries. Cell survival was modeled based on our previously developed GNP-local effect model with radial doses calculated using the TOPAS-nBio Monte Carlo code. Cell survival curves calculated for tumor sites with GNPs were used to calculate the relative biological effectiveness (RBE)-weighted dose. In order to evaluate the plan quality, the ratio of the mean dose between the tumor and normal tissue for 50-250 kVp beams with GNPs was compared to the standard of care using 6 MV photon beams without GNPs for breast and brain tumors. For breast using a single photon beam, kV  +  GNP was found to yield up to 2.73 times higher mean RBE-weighted dose to the tumor than two tangential megavoltage beams while delivering the same dose to healthy tissue. For irradiation of brain tumors using multiple photon beams, the GNP dose enhancement was found to be effective for energies above 50 keV. A small tumor at shallow depths was found to be the most effective treatment conditions for GNP enhanced radiation therapy. GNP uptake distributions in the cell (with or without nuclear uptake) and the beam arrangement were found to be important factors in determining the optimal photon beam energy.