nano-XD

Cybernano has collaborated with the CRAN UMR UL-CNRS 7039 and the Centre Hospitalier Régional de Metz-Thionville in the development of a simulation platform for computer-aided design of nanoparticles activated by X-Ray in Radiotherapy or Imaging. 

The increase of computational environments dedicated to the simulation of nanoparticles (NP)-X-Rays interactions has opened new perspectives in computer-aided-design of nanostructured materials for biomedical applications. Several published studies have shown a crucial need of standardization of these numerical simulations. That is why, a robustness multivariate analysis was performed in this paper. A gold nanoparticle (GNP) of 100 nm diameter was selected as a standard nanosystem activated by a X-ray source placed just below the NP. Two response variables were examined: the dose enhancement in seven different spatial regions of interest around the NP and the duration of the experiments. Nine factors were pre-identified as potentially critical. A Plackett-Burman design of numerical experiments was applied to estimate and test the effects of each simulation factors on the examined responses. Four factors-the working volume, the spatial resolution, the spatial cutoff, and the computational mode (parallelization)-do not significantly affect the dose deposition results and none except the last one may reduce the computational duration. The energy cutoff may cause significant variations of the dose enhancement in some specific regions of interest: the higher the cutoff, the closer the secondary particles will stop from the GNP. By contrast, the Auger effect as well as the choice of the physical medium and the fluence level clearly appear as critical simulation parameters. Consequently, these four factors may be compulsory examined before comparing and interpreting any simulation results coming from different simulation sessions.

References: 

[1] P. Retif, T. Bastogne, and M. Barberi-Heyob, “Robustness analysis of a Geant4-Gate simulator for nano- radiosensitizers characterization,” IEEE Transactions on NanoBioscience, vol. 15, no. 3, pp. 209–217, 2016.
[2] P. Retif, A. Reinhard, P. Héna, V. Jouan-Hureaux, A. Chateau, L. Sancey, M. Barberi-Heyob, S. Pinel, and T. Bastogne, “Monte Carlo simulations guided by imaging to predict the in vitro ranking of ra- diosensitizing nanoparticles,” International Journal of Nanomedicine, 2016.