Dose Rate Effects from the 1950s through to the Era of FLASH.

Numerous dose rate effects have been described over the past 6-7 decades in the radiation biology and radiation oncology literature depending on the dose rate range being discussed. This review focuses on the impact and understanding of altering dose rates in the context of radiation therapy, but does not discuss dose rate effects as relevant to radiation protection. The review starts with a short historic review of early studies on dose rate effects, considers mechanisms thought to underlie dose rate dependencies, then discusses some current issues in clinical findings with altered dose rates, the importance of dose rate in brachytherapy, and the current timely topic of the use of very high dose rates, so-called FLASH radiotherapy. The discussion includes dose rate effects in vitro in cultured cells, in in vivo experimental systems and in the clinic, including both tumors and normal tissues. Gaps in understanding dose rate effects are identified, as are opportunities for improving clinical use of dose rate modulation.

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.

Absence of Tissue-Sparing Effects in Partial Proton FLASH Irradiation in Murine Intestine.

Ultra-high dose rate irradiation has been reported to protect normal tissues more than conventional dose rate irradiation. This tissue sparing has been termed the FLASH effect. We investigated the FLASH effect of proton irradiation on the intestine as well as the hypothesis that lymphocyte depletion is a cause of the FLASH effect. A 16 × 12 mm2 elliptical field with a dose rate of ~120 Gy/s was provided by a 228 MeV proton pencil beam. Partial abdominal irradiation was delivered to C57BL/6j and immunodeficient Rag1-/-/C57 mice. Proliferating crypt cells were counted at 2 days post exposure, and the thickness of the muscularis externa was measured at 280 days following irradiation. FLASH irradiation did not reduce the morbidity or mortality of conventional irradiation in either strain of mice; in fact, a tendency for worse survival in FLASH-irradiated mice was observed. There were no significant differences in lymphocyte numbers between FLASH and conventional-dose-rate mice. A similar number of proliferating crypt cells and a similar thickness of the muscularis externa following FLASH and conventional dose rate irradiation were observed. Partial abdominal FLASH proton irradiation at 120 Gy/s did not spare normal intestinal tissue, and no difference in lymphocyte depletion was observed. This study suggests that the effect of FLASH irradiation may depend on multiple factors, and in some cases dose rates of over 100 Gy/s do not induce a FLASH effect and can even result in worse outcomes.

Proton FLASH effects on mouse skin at different oxygen tensions.

Objective. Irradiation at FLASH dose rates (>40 Gy s-1) has received great attention due to its reported normal tissue sparing effect. The FLASH effect was originally observed in electron irradiations but has since been shown to also occur with both photon and proton beams. Several mechanisms have been proposed to explain the tissue sparing at high dose rates, including effects involving oxygen, such as depletion of oxygen within the irradiated cells. In this study, we investigated the protective role of FLASH proton irradiation on the skin when varying the oxygen concentration.Approach. Our double scattering proton system provided a 1.2 × 1.6 cm2elliptical field at a dose rate of ∼130 Gy s-1. The conventional dose rate was ∼0.4 Gy s-1. The legs of the FVB/N mice were marked with two tattooed dots and fixed in a holder for exposure. To alter the skin oxygen concentration, the mice were breathing pure oxygen or had their legs tied to restrict blood flow. The distance between the two dots was measured to analyze skin contraction over time.Main results. FLASH irradiation mitigated skin contraction by 15% compared to conventional dose rate irradiation. The epidermis thickness and collagen deposition at 75 d following 25 to 30 Gy exposure suggested a long-term protective function in the skin from FLASH irradiation. Providing the mice with oxygen or reducing the skin oxygen concentration removed the dose-rate-dependent difference in response.Significance. FLASH proton irradiation decreased skin contraction, epidermis thickness and collagen deposition compared to standard dose rate irradiations. The observed oxygen-dependence of the FLASH effect is consistent with, but not conclusive of, fast oxygen depletion during the exposure.

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.

A systematic review of clinical studies on variable proton Relative Biological Effectiveness (RBE).

Recently, a number of clinical studies have explored links between possible Relative Biological Effectiveness (RBE) elevations and patient toxicities and/or image changes following proton therapy. Our objective was to perform a systematic review of such studies. We applied a "Problem [RBE], Intervention [Protons], Population [Patients], Outcome [Side effect]" search strategy to the PubMed database. From our search, we retrieved studies which: (a) performed novel voxel-wise analyses of patient effects versus physical dose and LET (n = 13), and (b) compared image changes between proton and photon cohorts with regard to proton RBE (n = 9). For each retrieved study, we extracted data regarding: primary tumour type; size of patient cohort; type of image change studied; image-registration method (deformable or rigid); LET calculation method, and statistical methodology. We compared and contrasted their methods in order to discuss the weight of clinical evidence for variable proton RBE. We concluded that clinical evidence for variable proton RBE remains statistically weak at present. Our principal recommendation is that proton centres and clinical trial teams collaborate to standardize follow-up protocols and statistical analysis methods, so that larger patient cohorts can ultimately be considered for RBE analyses.

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

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

The impact of variable relative biological effectiveness in proton therapy for left-sided breast cancer when estimating normal tissue complications in the heart and lung.

The aim of this study was to evaluate the clinical impact of relative biological effectiveness (RBE) variations in proton beam scanning treatment (PBS) for left-sided breast cancer versus the assumption of a fixed RBE of 1.1, particularly in the context of comparisons with photon-based three-dimensional conformal radiotherapy (3DCRT) and volumetric modulated arc therapy (VMAT). Ten patients receiving radiation treatment to the whole breast/chest wall and regional lymph nodes were selected for each modality. For PBS, the dose distributions were re-calculated with both a fixed RBE and a variable RBE using an empirical RBE model. Dosimetric indices based on dose-volume histogram analysis were calculated for the entire heart wall, left anterior descending artery (LAD) and left lung. Furthermore, normal tissue toxicity probabilities for different endpoints were evaluated. The results show that applying a variable RBE significantly increases the RBE-weighted dose and consequently the calculated dosimetric indices increases for all organs compared to a fixed RBE. The mean dose to the heart and the maximum dose to the LAD and the left lung are significantly lower for PBS assuming a fixed RBE compared to 3DCRT. However, no statistically significant difference is seen when a variable RBE is applied. For a fixed RBE, lung toxicities are significantly lower compared to 3DCRT but when applying a variable RBE, no statistically significant differences are noted. A disadvantage is seen for VMAT over both PBS and 3DCRT. One-to-one plan comparison on 8 patients between PBS and 3DCRT shows similar results. We conclude that dosimetric analysis for all organs and toxicity estimation for the left lung might be underestimated when applying a fixed RBE for protons. Potential RBE variations should therefore be considered as uncertainty bands in outcome analysis.

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.

A tumor-immune interaction model for hepatocellular carcinoma based on measured lymphocyte counts in patients undergoing radiotherapy.

PURPOSE: The impact of radiation therapy on the immune system has recently gained attention particularly when delivered in combination with immunotherapy. However, it is unclear how different treatment fractionation regimens influence the interaction between the immune system and radiation. The goal of this work was to develop a mathematical model that quantifies both the immune stimulating as well as the immunosuppressive effects of radiotherapy and simulates the effects of different fractionation regimens based on patient data. METHODS AND MATERIALS: The framework describes the temporal evolution of tumor cells, lymphocytes, and inactivated dying tumor cells releasing antigens during radiation therapy, specifically modeling how recruited lymphocytes inhibit tumor progression. The parameters of the model were partly taken from the literature and in part extracted from blood samples (circulating lymphocytes: CLs) collected from hepatocellular carcinoma patients undergoing radiotherapy and their outcomes. The dose volume histograms to circulating lymphocytes were calculated with a probability-based model. RESULTS: Based on the fitted parameters, the model enabled a study into the depletion and recovery of CLs in patients as a function of fractionation regimen. Our results quantify the ability of short fractionation regimens to lead to shorter periods of lymphocyte depletion and predict faster recovery after the end of treatment. The model shows that treatment breaks between fractions can prolong the period of lymphocyte depletion and should be avoided. CONCLUSIONS: This study introduces a mathematical model for tumor-immune interactions using clinically extracted radiotherapy patient data, which can be applied to design trials aimed at minimizing lymphocyte depleting effects in radiation therapy.

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.

The TOPAS tool for particle simulation, a Monte Carlo simulation tool for physics, biology and clinical research.

PURPOSE: This paper covers recent developments and applications of the TOPAS TOol for PArticle Simulation and presents the approaches used to disseminate TOPAS. MATERIALS AND METHODS: Fundamental understanding of radiotherapy and imaging is greatly facilitated through accurate and detailed simulation of the passage of ionizing radiation through apparatus and into a patient using Monte Carlo (MC). TOPAS brings Geant4, a reliable, experimentally validated MC tool mainly developed for high energy physics, within easy reach of medical physicists, radiobiologists and clinicians. Requiring no programming knowledge, TOPAS provides all of the flexibility of Geant4. RESULTS: After 5 years of development followed by its initial release, TOPAS was subsequently expanded from its focus on proton therapy physics to incorporate radiobiology modeling. Next, in 2018, the developers expanded their user support and code maintenance as well as the scope of TOPAS towards supporting X-ray and electron therapy and medical imaging. Improvements have been achieved in user enhancement through software engineering and a graphical user interface, calculational efficiency, validation through experimental benchmarks and QA measurements, and either newly available or recently published applications. A large and rapidly increasing user base demonstrates success in our approach to dissemination of this uniquely accessible and flexible MC research tool. CONCLUSIONS: The TOPAS developers continue to make strides in addressing the needs of the medical community in applications of ionizing radiation to medicine, creating the only fully integrated platform for four-dimensional simulation of all forms of radiotherapy and imaging with ionizing radiation, with a design that promotes inter-institutional collaboration.

Perspectives on the model-based approach to proton therapy trials: A retrospective study of a lung cancer randomized trial.

PURPOSE: The goal of this study was to assess whether a model-based approach applied retrospectively to a completed randomized controlled trial (RCT) would have significantly altered the selection of patients of the original trial, using the same selection criteria and endpoint for testing the potential clinical benefit of protons compared to photons. METHODS AND MATERIALS: A model-based approach, based on three widely used normal tissue complication probability (NTCP) models for radiation pneumonitis (RP), was applied retrospectively to a completed non-small cell lung cancer RCT (NCT00915005). It was assumed that patients were selected by the model-based approach if their expected ΔNTCP value was above a threshold of 5%. The endpoint chosen matched that of the original trial, the first occurrence of severe (grade ≥3) RP. RESULTS: Our analysis demonstrates that NTCP differences between proton and photon therapy treatments may be too small to support a model-based trial approach for lung cancer using RP as the normal tissue endpoint. The analyzed lung trial showed that less than 19% (32/165) of patients enrolled in the completed trial would have been enrolled in a model-based trial, prescribing photon therapy to all other patients. The number of patients enrolled was also found to be dependent on the type of NTCP model used for evaluating RP, with the three models enrolling 3%, 13% or 19% of patients. This result does show limitations in NTCP models which would affect the success of a model-based trial approach. No conclusion regarding the development of RP in patients randomized by the model-based approach could statistically be made. CONCLUSIONS: Uncertainties in the outcome models to predict NTCP are the inherent drawback of a model-based approach to clinical trials. The impact of these uncertainties on enrollment in model-based trials depends on the predicted difference between the two treatment arms and on the set threshold for patient stratification. Our analysis demonstrates that NTCP differences between proton and photon therapy treatments may be too small to support a model-based trial approach for specific treatment sites, such as lung cancer, depending on the chosen normal tissue endpoint.

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.

Modelling variable proton relative biological effectiveness for treatment planning.

Dose in proton radiotherapy is generally prescribed by scaling the physical proton dose by a constant value of 1.1. Relative biological effectiveness (RBE) is defined as the ratio of doses required by two radiation modalities to cause the same level of biological effect. The adoption of an RBE of 1.1. assumes that the biological efficacy of protons is similar to photons, allowing decades of clinical dose prescriptions from photon treatments and protocols to be utilized in proton therapy. There is, however, emerging experimental evidence that indicates that proton RBE varies based on technical, tissue and patient factors. The notion that a single scaling factor may be used to equate the effects of photons and protons across all biological endpoints and doses is too simplistic and raises concern for treatment planning decisions. Here, we review the models that have been developed to better predict RBE variations in tissue based on experimental data as well as using a mechanistic approach.

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.

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.

IMPACT OF NANOPARTICLE CLUSTERING ON DOSE RADIO-ENHANCEMENT.

High atomic number nanoparticles (NPs) have been shown to enhance the effects of radiation in vitro and in vivo. However, NPs are often observed to cluster together, leading to inhomogeneous distribution within the tissue and within cells themselves. The effect of this clustering on the capability of NPs to enhance radiation dose has not yet been fully investigated. In this Monte Carlo simulation study, the dependence of radio-enhancement on a separation parameter characterising NP clustering was investigated. A target water cube of side length 100 µm was simulated containing gold NPs constituting ~1% by mass. The NPs were placed in a cubic grid pattern and the separation distance between nanoparticles was varied. For NPs of 100 nm radius widely separated 2 µm apart, 91% of the total energy deposit was found to occur in the surrounding water, compared to only 56% when the NPs were moved closer together to 0.2 µm. The remaining energy deposit was absorbed by the NPs themselves. A similar trend was observed for NPs of radius 50 nm. The clustering effect was found to persist to greater separations for the larger NPs. The proportion of energy deposit in the available water of the target impacts the potential for cellular damage. Energy deposited within nanoparticles is unlikely to cause biological damage, as ionisations in the surrounding water are required to create radical oxygen species which then progress to cause the biological response to radiation. Clustering of nanoparticles is therefore expected to decrease their effectiveness for enhancing radiotherapy.

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.