The ultrashort duration of laser-driven multi-MeV ion bursts offers the possibility of radiobiological studies at extremely high dose rates. Employing the TARANIS Terawatt laser at Queen’s University, the effect of proton irradiation at MeV-range energies on live cells has been investigated at dose rates exceeding 109 Gy/s as a single exposure. A clonogenic assay showed consistent lethal effects on V-79 live cells, which, even at these dose rates, appear to be in line with previously published results employing conventional sources. A Relative Biological Effectiveness (RBE) of 1.4±0.2 at 10% survival is estimated from a comparison with a 225kKVp X-ray source

Monte Carlo based simulation has proven useful in investigating the effect of proton-induced DNA damage and the processes through which this damage occurs. Clustering of ionizations within a small volume can be related to DNA damage through the principles of nanodosimetry. For simulation, it is standard to construct a small volume of water and determine spatial clusters. More recently, realistic DNA geometries have been used, tracking energy depositions within DNA backbone volumes. Traditionally a chromatin fiber is built within the simulation and identically replicated throughout a cell nucleus, representing the cell in interphase. However, the in vivo geometry of the chromatin fiber is still unknown within the literature, with many proposed models. In this work, the Geant4-DNA toolkit was used to build three chromatin models: the solenoid, zig-zag and cross-linked geometries. All fibers were built to the same chromatin density of 4.2 nucleosomes/11 nm. The fibers were then irradiated with protons (LET 5-80 keV/μm) or alpha particles (LET 63-226 keV/μm). Nanodosimetric parameters were scored for each fiber after each LET and used as a comparator among the models. Statistically significant differences were observed in the double-strand break backbone size distributions among the models, although nonsignificant differences were noted among the nanodosimetric parameters. From the data presented in this article, we conclude that selection of the solenoid, zig-zag or cross-linked chromatin model does not significantly affect the calculated nanodosimetric parameters. This allows for a simulation-based cell model to make use of any of these chromatin models for the scoring of direct ion-induced DNA damage.

Gold nanoparticles (GNPs) have been shown to sensitise cancer cells to X-ray radiation, particularly at kV energies where photoelectric interactions dominate and the high atomic number of gold makes a large difference to X-ray absorption. Protons have a high cross-section for gold at a large range of relevant clinical energies, and so potentially could be used with GNPs for increased therapeutic effect.Here, we investigate the contribution of secondary electron emission to cancer cell radiosensitisation and investigate how this parameter is affected by proton energy and a free radical scavenger. We simulate the emission from a realistic cell phantom containing GNPs after traversal by protons and X-rays with different energies. We find that with a range of proton energies (1 -250 MeV) there is a small increase in secondaries compared to a much larger increase with X-rays. Secondary electrons are known to produce toxic free radicals. Using a cancer cell line in vitro we find that a free radical scavenger has no protective effect on cells containing GNPs irradiated with 3 MeV protons, while it does protect against cells irradiated with X-rays.3

This work uses Monte Carlo simulations to investigate the dependence of residual and misrepaired double strand breaks (DSBs) at 24 hours on the initial damage pattern created during ion therapy. We present results from a nanometric DNA damage simulation coupled to a mechanistic model of Non-Homologous End Joining, capable of predicting the position, complexity, and repair of DSBs. The initial damage pattern is scored by calculating the average number of DSBs within 70 nm from every DSB. We show that this local DSB density, referred to as the cluster density, can linearly predict misrepair regardless of ion species. The models predict that the fraction of residual DSBs is constant, with 7.3% of DSBs left unrepaired following 24 hours of repair. Through simulation over a range of doses and linear energy transfer (LET) we derive simple correlations capable of predicting residual and misrepaired DSBs. These equations are applicable to ion therapy treatment planning where both dose and LET are scored. This is demonstrated by applying the correlations to an example of a clinical proton spread out Bragg peak. Here we see a considerable biological effect past the distal edge, dominated by residual DSBs.

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 under-standing of the underlying mechanisms of radiation-induced DNA damage and the resulting observable biological effects when radiation parameters and/or environmental conditions change.

Following radiation induced DNA damage, several repair pathways are activated to help preserve genome integrity. Double Strand Breaks (DSBs), which are highly toxic, have specified repair pathways to address them. The main repair pathways used to resolve DSBs are Non-Homologous End Joining (NHEJ) and Homologous Recombination (HR). Cell cycle phase determines the availability of HR, but the repair choice between pathways in the G2 phases where both HR and NHEJ can operate is not clearly understood. This study compares several in silico models of repair choice to experimental data published in the literature, each model representing a different possible scenario describing how repair choice takes place. Competitive only scenarios, where initial protein recruitment determines repair choice, are unable to fit the literature data. In contrast, the scenario which uses a more entwined relationship between NHEJ and HR, incorporating protein co-localisation and RNF138-dependent removal of the Ku/DNA-PK complex, is better able to predict levels of repair similar to the experimental data. Furthermore, this study concludes that co-localisation of the Mre11-Rad50-Nbs1 (MRN) complexes, with initial NHEJ proteins must be modeled to accurately depict repair choice.

The cell-to-cell variation of gold nanoparticle (GNP) uptake is important for therapeutic applications. We directly counted the GNPs in hundreds of individual cells, and showed that the large variation from cell-to-cell could be directly modelled by assuming log-normal distributions of both cell mass and GNP rate of uptake. This was true for GNPs non-specifically bound to fetal bovine serum or conjugated to a cell penetrating peptide. Within a population of cells, GNP content varied naturally by a factor greater than 10 between individual cells.

Gold nanoparticles have been proven as potential radiosensitizer when combined with protons. Initially the radiosensitization effect was attributed to the physical interactions of radiation with the gold and the production of secondary electrons that induce DNA damage. However, emerging data challenge this hypothesis, supporting the existence of alternative or supplementary radiosensitization mechanisms. In this work we incorporate a realistic cell model with detailed DNA geometry and a realistic gold nanoparticle biodistribution based on experimental data. The DNA single and double strand breaks, and damage complexity are counted under various scenarios of different gold nanoparticle size, biodistribution and concentration, and proton energy. The locality of the effect, i.e. the existence of higher damage at a location close to the gold distribution, is also addressed by investigating the DNA damage at a chromosomal territory. In all the cases we do not observe any significant increase in the single/double strand break yield or damage complexity in the presence of gold nanoparticles under proton irradiation; nor there is a locality to the effect. Our results show for the first time that the physical interactions of protons with the gold nanoparticles should not be considered directly responsible for the observed radiosensitization effect. The model used only accounts for DNA damage from direct interactions, whilst considering the indirect effect, and it is possible the radiosensitization effect to be due to other physical effects, although we consider that possibility unlikely. Our conclusion suggests that other mechanisms might have greater contribution to the radiosensitization effect and further investigation should be conducted.