Nano-scale simulation of neuronal damage by galactic cosmic rays.

The effects of realistic, deep space radiation environments on neuronal function remain largely unexplored.In silicomodeling studies of radiation-induced neuronal damage provide important quantitative information about physico-chemical processes that are not directly accessible through radiobiological experiments. Here, we present the first nano-scale computational analysis of broad-spectrum galactic cosmic ray irradiation in a realistic neuron geometry. We constructed thousands ofin silicorealizations of a CA1 pyramidal neuron, each with over 3500 stochastically generated dendritic spines. We simulated the entire 33 ion-energy beam spectrum currently in use at the NASA Space Radiation Laboratory galactic cosmic ray simulator (GCRSim) using the TOol for PArticle Simulation (TOPAS) and TOPAS-nBio Monte Carlo-based track structure simulation toolkits. We then assessed the resulting nano-scale dosimetry, physics processes, and fluence patterns. Additional comparisons were made to a simplified 6 ion-energy spectrum (SimGCRSim) also used in NASA experiments. For a neuronal absorbed dose of 0.5 Gy GCRSim, we report an average of 250 ± 10 ionizations per micrometer of dendritic length, and an additional 50 ± 10, 7 ± 2, and 4 ± 2 ionizations per mushroom, thin, and stubby spine, respectively. We show that neuronal energy deposition by proton andα-particle tracks declines approximately hyperbolically with increasing primary particle energy at mission-relevant energies. We demonstrate an inverted exponential relationship between dendritic segment irradiation probability and neuronal absorbed dose for each ion-energy beam. We also find that there are no significant differences in the average physical responses between the GCRSim and SimGCRSim spectra. To our knowledge, this is the first nano-scale simulation study of a realistic neuron geometry using the GCRSim and SimGCRSim spectra. These results may be used as inputs to theoretical models, aid in the interpretation of experimental results, and help guide future study designs.

Challenges in the quantification approach to a radiation relevant adverse outcome pathway for lung cancer.

PURPOSE: Adverse outcome pathways (AOPs) provide a modular framework for describing sequences of biological key events (KEs) and key event relationships (KERs) across levels of biological organization. Empirical evidence across KERs can support construction of quantified AOPs (qAOPs). Using an example AOP of energy deposition from ionizing radiation onto DNA leading to lung cancer incidence, we investigate the feasibility of quantifying data from KERs supported by all types of stressors. The merits and challenges of this process in the context of AOP construction are discussed. MATERIALS AND METHODS: Empirical evidence across studies of dose-response from four KERs of the AOP were compiled independently for quantification. Three upstream KERs comprised of evidence from various radiation types in line with AOP guidelines. For these three KERs, a focused analysis of data from alpha-particle studies was undertaken to better characterize the process to the adverse outcome (AO) for a radon gas stressor. Numerical information was extracted from tables and graphs to plot and tabulate the response of KEs. To complement areas of the AOP quantification process, Monte Carlo (MC) simulations in TOPAS-nBio were performed to model exposure conditions relevant to the AO for an example bronchial compartment of the lung with secretory cell nuclei targets. RESULTS: Quantification of AOP KERs highlighted the relevance of radiation types under the stressor-agnostic intent of AOP design, motivating a focus on specific types. For a given type, significant differences of KE response indicate meaningful data to derive linkages from the MIE to the AO is lacking and that better response-response focused studies are required. The MC study estimates the linear energy transfer (LET) of alpha-particles emitted by radon-222 and its progeny in the secretory cell nuclei of the example lung compartment to range from 94-5+5 to 192-18+15 keV/µm. CONCLUSION: Quantifying AOP components provides a means to assemble empirical evidence across different studies. This highlights challenges in the context of studies examining similar endpoints using different radiation types. Data linking KERs to a MIE of 'deposition of energy' is shown to be non-compatible with the stressor-agnostic principles of AOP design. Limiting data to that describing response-response relationships between adjacent KERs may better delineate studies relevant to the damage that drives a pathway to the next KE and still support an 'all hazards' approach. Such data remains limited and future investigations in the radiation field may consider this approach when designing experiments and reporting their results and outcomes.

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.

End-of-Range Radiobiological Effect on Rib Fractures in Patients Receiving Proton Therapy for Breast Cancer.

PURPOSE: A prospective trial of proton therapy for breast cancer revealed an increased rib fracture rate of 7%, which is higher than the expected rate based on the literature on photon therapies. We aim to evaluate the hypothesis that the increased relative biological effectiveness (RBE) at the distal edge of proton beams is the cause. METHODS AND MATERIALS: We combined the cohort from the prospective clinical trial and a retrospective cohort from a database. Monte Carlo simulations were performed to recalculate the physical dose and dose-averaged linear energy transfer (LETd). The first 10 ribs and fracture areas in patients with fractures were contoured and deformably registered. The LETd-weighted dose was used as a surrogate for biological effectiveness and compared with the conventional fixed RBE of 1.1. Dose to 0.5 cm3 of the ribs (D0.5) was selected to analyze the dose-response relationship using logistic regression. We chose an alpha/beta ratio of 3 to calculate the biological effective dose in Gy3(RBE). RESULTS: Thirteen of 203 patients in the cohorts exhibited a total of 25 fractures. The LETd in fractured areas is increased (6.1 ± 2.0 keV/µm, mean ± standard deviation), suggesting possible end-of-range radiobiological effects with increased RBE. The D0.5 of the fractured ribs is 80.3 ± 9.4 Gy3(RBE) with a generic factor of 1.1 and is relatively low compared with historical photon results. On the other hand, the D0.5 of the fractured ribs is 100.0 ± 12.5 Gy3(RBE) using the LETd-based model with a dose-response curve that is more consistent with historical photon data. CONCLUSIONS: The increased rib fracture rate seen in our trial is probably associated with the increased LETd and RBE at the distal edge of proton beams. This phenomenon warrants further investigation and possible integration of LETd into treatment planning and optimization in proton therapy.

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

Monte Carlo (MC) track structure simulation tools are commonly used for predicting radiation induced DNA damage by modeling the physical and chemical reactions at the nanometer scale. However, the outcome of these MC simulations is particularly sensitive to the adopted parameters which vary significantly across studies. In this study, a previously developed full model of nuclear DNA was used to describe the DNA geometry. The TOPAS-nBio MC toolkit was used to investigate the impact of physics and chemistry models as well as three key parameters (the energy threshold for direct damage, the chemical stage time length, and the probability of damage between hydroxyl radical reactions with DNA) on the induction of DNA damage. Our results show that the difference in physics and chemistry models alone can cause differences up to 34% and 16% in the DNA double strand break (DSB) yield, respectively. Additionally, changing the direct damage threshold, chemical stage length, and hydroxyl damage probability can cause differences of up to 28%, 51%, and 71% in predicted DSB yields, respectively, for the configurations in this study.

The microdosimetric extension in TOPAS: development and comparison with published data.

Microdosimetric energy depositions have been suggested as a key variable for the modeling of the relative biological effectiveness (RBE) in proton and ion radiation therapy. However, microdosimetry has been underutilized in radiation therapy. Recent advances in detector technology allow the design of new mico- and nano-dosimeters. At the same time Monte Carlo (MC) simulations have become more widely used in radiation therapy. In order to address the growing interest in the field, a microdosimetric extension was developed in TOPAS. The extension provides users with the functionality to simulate microdosimetric spectra as well as the contribution of secondary particles to the spectra, calculate microdosimetric parameters, and determine RBE with a biological weighting function approach or with the microdosimetric kinetic (MK) model. Simulations were conducted with the extension and the results were compared with published experimental data and other simulation results for three types of microdosimeters, a spherical tissue equivalent proportional counter (TEPC), a cylindrical TEPC and a solid state microdosimeter. The corresponding microdosimetric spectra obtained with TOPAS from the plateau region to the distal tail of the Bragg curve generally show good agreement with the published data.

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.

Extension of TOPAS for the simulation of proton radiation effects considering molecular and cellular endpoints.

The aim of this work is to extend a widely used proton Monte Carlo tool, TOPAS, towards the modeling of relative biological effect (RBE) distributions in experimental arrangements as well as patients. TOPAS provides a software core which users configure by writing parameter files to, for instance, define application specific geometries and scoring conditions. Expert users may further extend TOPAS scoring capabilities by plugging in their own additional C++ code. This structure was utilized for the implementation of eight biophysical models suited to calculate proton RBE. As far as physics parameters are concerned, four of these models are based on the proton linear energy transfer, while the others are based on DNA double strand break induction and the frequency-mean specific energy, lineal energy, or delta electron generated track structure. The biological input parameters for all models are typically inferred from fits of the models to radiobiological experiments. The model structures have been implemented in a coherent way within the TOPAS architecture. Their performance was validated against measured experimental data on proton RBE in a spread-out Bragg peak using V79 Chinese Hamster cells. This work is an important step in bringing biologically optimized treatment planning for proton therapy closer to the clinical practice as it will allow researchers to refine and compare pre-defined as well as user-defined models.

Characterization of optical transport effects on EPID dosimetry using Geant4.

PURPOSE: Current amorphous silicon electronic portal imaging devices (a-Si EPIDs) that are frequently used in radiotherapy applications employ a metal plate/phosphor screen configuration to optimize x-ray detection efficiency. The phosphor acts to convert x rays into an optical signal that is detected by an underlying photodiode array. The dosimetric response of EPIDs has been well characterized, in part through the development of computational models. Such models, however, have generally made simplifying assumptions with regards to the transport of optical photons within these detectors. The goal of this work was to develop and experimentally validate a new Monte Carlo (MC) model of an a-Si EPID that simulates both x-ray and optical photon transport in a self-contained manner. Using this model the authors establish a definitive characterization of the effects of optical transport on the dosimetric response of a-Si EPIDs employing gadolinium oxysulfide phosphor screens. METHODS: The Geant4 MC toolkit was used to develop a model of an a-Si EPID that employs standard electromagnetic and optical physics classes. The sensitivity of EPID response to uncertainties in optical transport parameters was evaluated by investigating their effects on the EPID point spread function (PSF). An optical blur kernel was also calculated to isolate the component of the PSF resulting purely from optical transport. A 6 MV photon source model was developed and integrated into the MC model to investigate EPID dosimetric response. Field size output factors and relative dose profiles were calculated for a set of open fields by separately scoring energy deposited in the phosphor and optical absorption events in the photodiode. These were then compared to quantify effects resulting from optical photon transport. The EPID model was validated against experimental measurements taken using a research EPID. RESULTS: Optical photon scatter within the phosphor screen noticeably broadened the PSF. Variations in optical transport parameters reported in the literature caused fluctuations in the PSF full width at half maximum (FWHM) and full width at tenth maximum (FWTM) of less than 3% and 5%, respectively, confirming model robustness. Greater deviations (up to 9.5% and 36% for FWHM and FWTM, respectively) were observed when optical parameters were largely different from reference values. When scoring energy deposition in the phosphor, measured and calculated output factors agreed within statistical uncertainties and at least 94% of the MC simulated profile data points passed 3%/3 mm γ-index criterion for all field sizes considered. Despite statistical uncertainties in optical simulations arising from computational limitations, no differences were observed between optical and energy deposition profiles. CONCLUSIONS: Simulations demonstrated noticeable blurring of the EPID PSF when scoring optical absorption events in the photodiode relative to energy deposition in the phosphor. However, modeling the standard electromagnetic transport alone should suffice when using MC methods to predict EPID dose-response to static, open 6 MV fields with a standard a-Si photodiode array. Therefore, using energy deposition in the phosphor as a surrogate for EPID dose-response is a valid approach that should not require additional corrections for optical transport effects in current a-Si EPIDs employing phosphor screens.

Predicted ionisation in mitochondria and observed acute changes in the mitochondrial transcriptome after gamma irradiation: a Monte Carlo simulation and quantitative PCR study.

It is a widely accepted that the cell nucleus is the primary site of radiation damage while extra-nuclear radiation effects are not yet systematically included into models of radiation damage. We performed Monte Carlo simulations assuming a spherical cell (diameter 11.5 µm) modelled after JURKAT cells with the inclusion of realistic elemental composition data based on published literature. The cell model consists of cytoplasm (density 1g/cm(3)), nucleus (diameter 8.5 µm; 40% of cell volume) as well as cylindrical mitochondria (diameter 1 µm; volume 0.5 µm(3)) of three different densities (1, 2 and 10 g/cm(3)) and total mitochondrial volume relative to the cell volume (10, 20, 30%). Our simulation predicts that if mitochondria take up more than 20% of a cell's volume, ionisation events will be the preferentially located in mitochondria rather than in the cell nucleus. Using quantitative polymerase chain reaction, we substantiate in JURKAT cells that human mitochondria respond to gamma radiation with early (within 30 min) differential changes in the expression levels of 18 mitochondrially encoded genes, whereby the number of regulated genes varies in a dose-dependent but non-linear pattern (10 Gy: 1 gene; 50 Gy: 5 genes; 100 Gy: 12 genes). The simulation data as well as the experimental observations suggest that current models of acute radiation effects, which largely focus on nuclear effects, might benefit from more systematic considerations of the early mitochondrial responses and how these may subsequently determine cell response to ionising radiation.

A comparison of X-ray and proton beam low energy secondary electron track structures using the low energy models of Geant4.

PURPOSE: Lethal cell damage by ionising radiation is generally initiated by the formation of complex strand breaks, resulting from ionisation clusters in the DNA molecule. A better understanding of the effect of the distribution of ionisation clusters within the cell and particularly in regard to DNA segments could be beneficial to radiation therapy treatment planning. Low energy X-rays generate an abundance of low energy electrons similar to that associated with MeV protons. The study and comparison of the track structure of photon and proton beams could permit the substitution of photon microbeams for single cell ion irradiations at proton facilities used to predict the relative biological effectiveness (RBE) of charged particle fields. MATERIALS AND METHODS: The track structure of X-ray photons is compared with proton pencil beams in voxels of approximate DNA strand size (2 × 2 × 5 nm). The Very Low Energy extension models of the Monte Carlo simulation toolkit GEometry ANd Tracking 4 (Geant4) is used. Simulations were performed in a water phantom for an X-ray and proton beam of energies 100 keV and 20 MeV, respectively. RESULTS: The track structure of the photon and proton beams are evaluated using the ionisation cluster size distribution as well as the radial dose deposition of the beam. CONCLUSIONS: A comparative analysis of the ionisation cluster distribution and radial dose deposition obtained is presented, which suggest that low energy X-rays could produce similar ionisation cluster distributions to MeV protons on the DNA scale of size at depths greater than ∼10 µm and at distances greater than ∼1 µm from the beam centre. Here the ionisation cluster size for each beam is less than ∼100. The radial dose deposition is also approximately equal at large depths and at distances greater than 10 µm from the beam centre.