Proton therapy treatments are currently planned and delivered using the assumption that the proton relative biological effectiveness (RBE) relative to photons is 1.1. This assumption ignores strong experimental evidence that suggests the RBE varies along the treatment field, i.e. with linear energy transfer (LET) and with tissue type. A recent review study collected over 70 experimental reports on proton RBE, providing a comprehensive dataset for predicting RBE for cell survival. Using this dataset we developed a model to predict proton RBE based on dose, dose average LET (LET _d ) and the ratio of the linear-quadratic model parameters for the reference radiation ( α / β ) _x , as the tissue specific parameter. The proposed RBE model is based on the linear quadratic model and was derived from a nonlinear regression fit to 287 experimental data points. The proposed model predicts that the RBE increases with increasing LET _d and decreases with increasing ( α / β ) _x . This agrees with previous theoretical predictions on the relationship between RBE, LET _d and ( α / β ) _x . The model additionally predicts a decrease in RBE with increasing dose and shows a relationship between both α and β with LET _d . Our proposed phenomenological RBE model is derived using the most comprehensive collection of proton RBE experimental data to date. Previously published phenomenological models, based on a limited data set, may have to be revised.

Traditionally, dose in proton radiotherapy is prescribed as Gy(RBE) by scaling up the physical dose by 10%. The relative biological effectiveness (RBE) of protons is considered to vary with dose-averaged linear energy transfer (LET(d)), dose (d) and (α/β)(x). The increase of RBE with depth causes a shift of the falloff of the beam, i.e. a change of the beam range. The magnitude of this shift will depend on dose and (α/β)(x). The aim of this project was to quantify the dependence of the range shift on these parameters. Three double-scattered beams of different ranges incident on a computational phantom consisting of different regions of interest (ROIs) were used. Each ROI was assigned with (α/β)(x) values between 0.5 and 20 Gy. The distribution of LET(d) within each ROI was obtained from a Monte Carlo simulation. The LET(d) distribution depends on the beam energy and thus its nominal range. The RBE values within the ROIs were calculated for doses between 1 and 15 Gy using an in-house developed biophysical model. Dose-volume histograms of the RBE-weighted doses were extracted for each ROI for a 'fixed RBE' (RBE = 1.1) and a 'variable RBE' (RBE = f (d, α/β, LET(d))), and the percentage difference in range was obtained from the difference of the percentage volumes at the distal 80% of the dose. Range differences in normal tissue ((α/β)(x) = 3 Gy) of the order of 3-2 mm were obtained, respectively, for a shallow (physical range 4.8 cm) and a deep (physical range 12.8 cm) beam, when a dose of 1 Gy normalized to the mid-SOBP was delivered. As the dose increased to 15 Gy, the variable RBE decreases below 1.1 which induces ranges of about 1 mm shorter than those obtained with an RBE of 1.1. The shift in the range of an SOBP when comparing biological dose distributions obtained with a fixed or a variable RBE was quantified as a function of dose, (α/β)(x) and physical range (as a surrogate of the initial beam energy). The shift increases with the physical range but decreases with increasing dose or (α/β)(x). The results of our study allow a quantitative consideration of RBE-caused range uncertainties as a function of treatment site and dose in treatment planning.

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.

Gold nanoparticles (GNPs) have shown potential to be used as a radiosensitizer for radiation therapy. Despite extensive research activity to study GNP radiosensitization using photon beams, only a few studies have been carried out using proton beams. In this work Monte Carlo simulations were used to assess the dose enhancement of GNPs for proton therapy. The enhancement effect was compared between a clinical proton spectrum, a clinical 6 MV photon spectrum, and a kilovoltage photon source similar to those used in many radiobiology lab settings. We showed that the mechanism by which GNPs can lead to dose enhancements in radiation therapy differs when comparing photon and proton radiation. The GNP dose enhancement using protons can be up to 14 and is independent of proton energy, while the dose enhancement is highly dependent on the photon energy used. For the same amount of energy absorbed in the GNP, interactions with protons, kVp photons and MV photons produce similar doses within several nanometers of the GNP surface, and differences are below 15% for the first 10 nm. However, secondary electrons produced by kilovoltage photons have the longest range in water as compared to protons and MV photons, e.g. they cause a dose enhancement 20 times higher than the one caused by protons 10 μm away from the GNP surface. We conclude that GNPs have the potential to enhance radiation therapy depending on the type of radiation source. Proton therapy can be enhanced significantly only if the GNPs are in close proximity to the biological target.