A simulation study of gold nanoparticles localisation effects on radiation enhancement at the mitochondrion scale

Significance of the proton energy loss mechanism to gold nanoparticles in proton therapy: a Geant4 simulation

The biological effectiveness in proton therapy is only slightly greater than of the treatment by X-ray and hence, many researches have suggested the use of gold nanoparticles for increasing the ionization interactions to produce more secondary electrons and elevate the yield of DNA damage. But the ionization interactions also lead to protons energy loss inside the nanoparticles. The present study shows that, the protons slowed-down by High-Z nanoparticles are responsible for dose enhancement rather than the produced secondary electrons. To this purpose, using Geant4 Monte Carlo tool, one million nanoparticles distributed in a proton irradiated volume and variation of the proton's spectra and the dose related to this variation has been demonstrated. It was found that, the elevation in proton's LET values when passing through the gold nanoparticles will lead to a more significant dose enhancement than the increased dose due to the extra secondary electrons. Also, it was found that, the mechanism of protons slowing-down by gold nanoparticles has another useful aspect in proton therapy in which, the dose leakage to surrounding healthy tissues will be reduced which must be considered in future investigations more precisely.

Secondary Electrons in Gold Nanoparticle Clusters and Their Role in Therapeutic Ratio: The Outcome of a Monte Carlo Simulation Study

Gold nanoparticles (GNPs) are used in proton therapy radio-sensitizers to help increase the dose of radiation to targeted tumors by the emission of secondary electrons. Thus, this study aimed to investigate the link between secondary electron yields produced from a nanoshell of GNPs and dose absorption according to the distance from the center of the nanoparticles by using a Monte Carlo model. Microscopic evaluation was performed by modeling the interactions of secondary electrons in a phase-space file (PSF), where the number of emitted electrons was calculated within a spherical GNP of 15 nm along with the absorbed dose near it. Then, the Geant4-DNA physics list was used to facilitate the tracking of low-energy electrons down to an energy below 50 eV in water. The results show a remarkable change in the number of secondary electrons, which can be compared at concentrations less than and greater than 5 mg/mL, with increased secondary electron production exhibited around NPs within a distance of 10–100 nm from the surface of all nanospheres. It was found that there was a steep dose enhancement drop-off up to a factor of dose enhancement factor (DFE) ≤ 1 within a short distance of 100 nm from the surface of the GNPs, which revealed that the dose enhancement existed locally at nanometer distances from the GNPs. Overall, our results indicate that the physical interactions of protons with GNP clusters should not be considered as being directly responsible for the radio-sensitization effect, but should be regarded as playing a major role in NP properties and concentrations, which has a subsequent impact on local dose enhancement.

Nanoparticle radiosensitization: from extended local effect modeling to a survival modification framework of compound Poisson additive killing and its carbon dots validation

Objective. To construct an analytical model instead of local effect modeling for the prediction of the biological effectiveness of nanoparticle radiosensitization. Approach. An extended local effects model is first proposed with a more comprehensive description of the nanoparticles mediated local killing enhancements, but meanwhile puts forward challenging issues that remain difficult and need to be further studied. As a novel method instead of local effect modeling, a survival modification framework of compound Poisson additive killing is proposed, as the consequence of an independent additive killing by the assumed equivalent uniform doses of individual nanoparticles per cell under the LQ model. A compound Poisson killing (CPK) model based on the framework is thus derived, giving a general expression of nanoparticle mediated LQ parameter modification. For practical use, a simplified form of the model is also derived, as a concentration dependent correction only to the α parameter, with the relative correction (α″/α) dominated by the mean number, and affected by the agglomeration of nanoparticles per cell. For different agglomeration state, a monodispersion model of the dispersity factor η = 1, and an agglomeration model of 2/3 < η < 1, are provided for practical prediction of (α″/α) value respectively. Main results. Initial validation by the radiosensitization of HepG2 cells by carbon dots showed a high accuracy of the CPK model. In a safe range of concentration (0.003–0.03 μg μl−1) of the carbon dots, the prediction errors of the monodispersion and agglomeration models were both within 2%, relative to the clonogenic survival data of the sensitized HepG2 cells. Significance. The compound Poisson killing model provides a novel approach for analytical prediction of the biological effectiveness of nanoparticle radiosensitization, instead of local effect modeling.

An in-silico method to predict and quantify the effect of gold nanoparticles in X-ray imaging

PURPOSE

Over the last few years studies are conducted, highlighting the feasibility of Gold Nanoparticles (GNPs) to be used in clinical CT imaging and as an efficient contrast agent for cancer research. After ensuring that GNPs formulations are appropriate for in vivo or clinical use, the next step is to determine the parameters for an X-ray system's optimal contrast for applications and to extract quantitative information. There is currently a gap and need to exploit new X-ray imaging protocols and processing algorithms, through specific models avoiding trial-and-error procedures and provide an imaging prognosis tool. Such a model can be used to confirm the accumulation of GNPs in target organs before radiotherapy treatments with a system easily available in hospitals, as low energy X-rays.

METHODS

In this study a complete, easy-to-use, simulation platform is designed and built, where simple parameters, as the X-ray's specifications and experimentally defined biodistributions of specific GNPs are imported. The induced contrast and images can be exported, and accurate quantification can be performed. This platform is based on the GATE Monte Carlo simulation toolkit, based on the GEANT4 toolkit and the MOBY phantom, a realistic 4D digital mouse.

RESULTS

We have validated this simulation platform to predict the contrast induction and minimum detectable concentration of GNPs on any given X-ray system. The study was applied to preclinical studies but is also expandable to clinical studies.

CONCLUSIONS

According to our knowledge, no other such validated simulation model currently exists, and this model could help radiology imaging with GNPs to be truly deployed.