Study of the Influence of NanOx Parameters

Monini, C.; Cunha, Maria João; Testa, É.; Beuve, M.

doi:10.3390/cancers10040087

Cited by 14 publications

(21 citation statements)

References 29 publications

(27 reference statements)

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“…The results were compared with experimental data and showed good agreement. This study was extended by Monini et al [151], who made a theoretical study using different sets of parameters.…”

Section: Water Radiolysis: Indirect Damage Studiesmentioning

confidence: 87%

Ionizing Radiation and Complex DNA Damage: Quantifying the Radiobiological Damage Using Monte Carlo Simulations

Chatzipapas

Papadimitroulas²,

Emfietzoglou

et al. 2020

Cancers

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Ionizing radiation is a common tool in medical procedures. Monte Carlo (MC) techniques are widely used when dosimetry is the matter of investigation. The scientific community has invested, over the last 20 years, a lot of effort into improving the knowledge of radiation biology. The present article aims to summarize the understanding of the field of DNA damage response (DDR) to ionizing radiation by providing an overview on MC simulation studies that try to explain several aspects of radiation biology. The need for accurate techniques for the quantification of DNA damage is crucial, as it becomes a clinical need to evaluate the outcome of various applications including both low- and high-energy radiation medical procedures. Understanding DNA repair processes would improve radiation therapy procedures. Monte Carlo simulations are a promising tool in radiobiology studies, as there are clear prospects for more advanced tools that could be used in multidisciplinary studies, in the fields of physics, medicine, biology and chemistry. Still, lot of effort is needed to evolve MC simulation tools and apply them in multiscale studies starting from small DNA segments and reaching a population of cells.

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“…The results were compared with experimental data and showed good agreement. This study was extended by Monini et al [151], who made a theoretical study using different sets of parameters.…”

Section: Water Radiolysis: Indirect Damage Studiesmentioning

confidence: 87%

Ionizing Radiation and Complex DNA Damage: Quantifying the Radiobiological Damage Using Monte Carlo Simulations

Chatzipapas

Papadimitroulas²,

Emfietzoglou

et al. 2020

Cancers

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“…1 have been obtained. The set of parameters of each model was determined to optimize the agreement with the experimental data, as reported in [8,13,16]. The experimental values gathered from the PIDE database [17] are compared with the predictions provided by the four versions of the LEM (when available), the MKM and NanOx.…”

Section: Discussionmentioning

confidence: 99%

“…The values of α predicted by NanOx, on the contrary, were computed by the authors especially for this work. Precisely, theoretical cell survival curves were first calculated as explained in [11] , [13] , and then a linear fit at low doses allowed extraction of the slope α.…”

Section: Methodsmentioning

confidence: 99%

“…1 have been obtained. The set of parameters of each model was determined to optimize the agreement with the experimental data, as reported in [8] , [13] , [16] . Cell line Model parameters LEM (I/II/III) LEM IV MKM NANOX HSG α x = 0.313 Gy −1 β x = 0.062 Gy −2 D t = 30/6/19 Gy α x = 0.316 Gy −1 β x = 0.062 Gy −2 D t = 7.5 Gy α 0 = 0.313 Gy −1 β = 0.062 Gy −2 R d = 0.02 µm z 0 = 15654 Gy σ = 549 Gy h = 179439 R N = 5 µm R N = 5 µm R n = 4.6 µm β G = 0.096 Gy −2 R SV = 7 µm V79 α x = 0.184 Gy −1 β x = 0.020 Gy −2 D t = 70/15/60 Gy α x = 0.129 Gy −1 β x = 0.049 Gy −2 D t = 3 Gy α 0 = 0.184 Gy −1 β = 0.020 Gy −2 R d = 0.1 µm z 0 = 22789 Gy σ = 8117 Gy h = 225841 R N = 4.2 µm R N = 5 µm R n = 4.2 µm β G = 0.041 Gy −2 R SV = 4.9 µm CHO-K1 α x = 0.228 Gy −1 β x = 0.020 Gy −2 D t = 40/9.5/55 Gy α 0 = 0.228 Gy −1 β = 0.020 Gy −2 R d = 0.12 µm z 0 = 14507 Gy σ = 2781 Gy h = 104810 R N = 4.7 µm R ...…”

Section: Methodsmentioning

confidence: 99%

“…More recently, NanOx [11] , [12] , [13] was developed by the authors to address the challenge of implementing the stochasticity of the energy depositions at nanometric and microscopic scales when predicting radiation-induced effects. This is fulfilled in the modeling of the number of radiation impacts associated to a given dose, of the dose-deposition pattern along the track and of the inter-track processes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of biophysical models with experimental data for three cell lines in response to irradiation with monoenergetic ions

Monini

Alphonse

Rodriguez-Lafrasse

et al. 2019

Physics and Imaging in Radiation Oncology

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The relative biological effectiveness (RBE) in particle therapy is currently estimated using biophysical models. We compared experimental measurements to the α curves as function of linear energy transfer computed by the Local Effect Model (LEM I-IV), the Microdosimetric Kinetic Model (MKM) and the NanOx model for HSG, V79 and CHO-K1 cells in response to monoenergetic irradiations. Although the LEM IV and the MKM predictions accurately reproduced the trend observed in the data, NanOx yielded a better agreement than the other models for more irradiation configurations. Its estimator was indeed the lowest for three over seven considered cases.

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Influence of gold nanoparticles embedded in water on nanodosimetry for keV photon irradiation

2021

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For the past two decades, high-Z nanoparticles have been of high interest to improve the therapeutic outcomes of radiation therapy, especially for low-energy x-rays. Monte Carlo (MC) simulations have been used to evaluate the boost of dose deposition induced by Auger electrons near the nanoparticle surface, by calculating average energy deposition at the nanoscale. In this study, we propose to go beyond average quantities and quantify the stochastic nature of energy deposition at such a scale. We present results of probability density of the specific energy (restricted to ionization, excitation and electron attachment events) in cylindrical nanotargets of height and radius set at 10 nm. This quantity was evaluated for nanotargets located within 200 nm around 5-50 nm gold nanoparticles (GNPs), for 20-90 keV photon irradiation. Methods: This nanodosimetry study was based on the MC simulation MDM that allows tracking of electrons down to thermalization energy. We introduced a new quantity, namely the probability enhancement ratio (PER), by estimating the probability of imparting to nanotargets a restricted specific energy larger than a threshold z 0 (1, 10, and 20 kGy), normalized to the probability for pure water. The PER was calculated as a function of the distance between the nanotarget and the GNP surface. The threshold values were chosen in light of the biophysical model NanOx that predicts cell survival by calculating local lethal events based on the restricted specific energy and an effective local lethal function. z 0 then represents a threshold above which the nanotarget damages induce efficiently cell death. Results: Our calculations showed that the PER varied a lot with the GNP radius, the photon energy, z 0 and the distance of the GNP to the nanotarget. The highest PER was 95 when the nanotarget was located at 5 nm from the GNP surface, for a photon energy of 20 keV, a threshold of 20 kGy, and a GNP radius of 50 nm. This enhancement dramatically decreased with increasing GNP-nanotarget distances as it went below 1.5 for distances larger than 200 nm. Conclusions: The PER seems better adapted than the mean dose deposition to describe the formation of biological damages. The significant increase of the PER within 200 nm around the GNP suggests that severe damages could occur for biological nanotargets located near the GNP. These calculations will be used as an input of the biophysical model NanOx to convert PER into estimation of radiation-induced cell death enhanced by GNPs.

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Study of the Influence of NanOx Parameters

Cited by 14 publications

References 29 publications

Ionizing Radiation and Complex DNA Damage: Quantifying the Radiobiological Damage Using Monte Carlo Simulations

Ionizing Radiation and Complex DNA Damage: Quantifying the Radiobiological Damage Using Monte Carlo Simulations

Comparison of biophysical models with experimental data for three cell lines in response to irradiation with monoenergetic ions

Influence of gold nanoparticles embedded in water on nanodosimetry for keV photon irradiation

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