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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.

Ionizing radiation is a peculiar perturbation when it comes to damage to biological systems: it proceeds through discrete energy depositions, over a short temporal scale and a spatial scale critical for subcellular targets as DNA, whose damage complexity determines the outcome of the exposure. This lies at the basis of the success of track structure (and nanodosimetry) and microdosimetry in radiation biology. However, such reductionist approaches cannot account for the complex network of interactions regulating the overall response of the system to radiation, particularly when effects are manifest at the supracellular level and involve long times. Systems radiation biology is increasingly gaining ground, but the gap between reductionist and holistic approaches is becoming larger. This paper presents considerations on what roles track structure and microdosimetry can have in the attempt to fill this gap, and on how they can be further exploited to interpret radiobiological data and inform systemic approaches. [ABSTRACT FROM AUTHOR]

The biophysical simulation tool PARTRAC contains modules for DNA damage response representing non-homologous end joining of DNA double-strand breaks (DSB) and the formation of chromosomal aberrations. Individual DNA ends from the induced DSB are followed regarding both their enzymatic processing and spatial mobility, as is needed for chromosome aberrations to arise via ligating broken ends from different chromosomes. In particular, by tracking the genomic locations of the ligated fragments and the positions of centromeres, the induction of dicentrics can be modeled. In recent experiments, the impact of spatial clustering of DNA damage on dicentric yields has been assessed in AL human-hamster hybrid cells: Defined numbers of 20 MeV protons (linear energy transfer, LET 2.6 keV/μm), 45 MeV Li ions (60 keV/μm) and 55 MeV C ions (310 keV/μm) focused to sub-μm spot sizes were applied with the ion microbeam SNAKE in diverse grid modes, keeping the absorbed dose constant. The impact of the μm-scaled spatial distribution of DSB (focusing effect) has thus been separated from nm-scaled DSB complexity (LET effect). The data provide a unique benchmark for the model calculations. Model and parameter refinements are described that enabled the simulations to largely reproduce both the LET-dependence and the focusing effect as well as the usual biphasic rejoining kinetics. The predictive power of the refined model has been benchmarked against dicentric yields for photon irradiation. [ABSTRACT FROM AUTHOR]

Comprehensive track structure-based simulations of DNA damage induced in human cells by photons (5 keV–1.3 MeV) and light ions (0.25–512 MeV/u) were performed with PARTRAC. DNA strand breaks, double-strand breaks and their clustering were scored. Effective LET values were established for photons that provide LET-dependent damage yields in agreement with the data for ions. The resulting database captures the variations of biological effectiveness with radiation quality. In particular, it can help compare the effectiveness of conventional radiotherapy using photon beams with techniques relying on proton or ion beams. [ABSTRACT FROM AUTHOR]

We present predictions of neutron relative biological effectiveness (RBE) for cell irradiations with neutron beams at PTB-Braunschweig. A neutron RBE model is adopted to evaluate initial DNA damage induction given the neutron-induced charged particle field. RBE values are predicted for cell exposures to quasi-monoenergetic beams (0.56 MeV, 1.2 MeV) and to a broad energy distribution neutron field with dose-averaged energy of 5.75 MeV. Results are compared to what obtained with our RBE predictions for neutrons at similar energies, when a 30-cm sphere is irradiated in an isotropic neutron field. RBE values for experimental conditions are higher for the lowest neutron energies, because, as expected, target geometry determines the weight of the low-effectiveness photon component of the neutron dose. These results highlight the importance of characterizing neutron fields in terms of physical interactions, to fully understand neutron-induced biological effects, contributing to risk estimation and to the improvement of radiation protection standards. [ABSTRACT FROM AUTHOR]