NanOx, a new model to predict cell survival in the context of particle therapy

Abstract

Particle therapy is increasingly attractive for the treatment of tumors and the number of facilities offering it is rising worldwide. Due to the well-known enhanced effectiveness of ions, it is of utmost importance to plan treatments with great care to ensure tumor killing and healthy tissues sparing. Hence, the accurate quantification of the relative biological effectiveness (RBE) of ions, used in the calculation of the biological dose, is critical. Nevertheless, the RBE is a complex function of many parameters and its determination requires modeling. The approaches currently used have allowed particle therapy to thrive, but still show some shortcomings. We present herein a short description of a new theoretical framework, NanOx, to calculate cell survival in the context of particle therapy. It gathers principles from existing approaches, while addressing some of their weaknesses. NanOx is a multiscale model that takes the stochastic nature of radiation at nanometric and micrometric scales fully into account, integrating also the chemical aspects of radiation-matter interaction. The latter are included in the model by means of a chemical specific energy, determined from the production of reactive chemical species induced by irradiation. Such a production represents the accumulation of oxidative stress and sublethal damage in the cell, potentially generating non-local lethal events in NanOx. The complementary local lethal events occur in a very localized region and can, alone, lead to cell death. Both these classes of events contribute to cell death. The comparison between experimental data and model predictions for the V79 cell line show a good agreement. In particular, the dependence of the typical shoulders of cell survival curves on linear energy transfer are well described, but also the effectiveness of different ions, including the overkill effect. These results required the adjustment of a number of parameters compatible with the application of the model in a clinical scenario thereby showing the potential of NanOx. Said parameters are discussed in detail in this paper.

‘Survival’: a simulation toolkit introducing a modular approach for radiobiological evaluations in ion beam therapy

“…This framework can be extended to host some of the models which are not implemented yet, such as the other versions of the MKM (see e.g. Inaniwa et al 2010(Inaniwa et al 2010 or the last version of the LEM, namely the LEM IV (Elsässer et al 2010), or even completely different models such as the repair-misrepair fixation (RMF) model by Carlson et al (Carlson et al 2008), the repairable-conditionally-repairable (RCR) model by Brahme (Brahme 2011) or the NanOx model (Cunha et al 2017). Moreover, it would also be interesting to include models which, instead of the RBE, aims at evaluating other factors that can modify the biological dose, such as the oxygen enhancement ratio (OER).…”

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

“…A theoretical framework was presented by Cuhna et al [150] that can be used to calculate cell survival after a particle therapy procedure, named NanOx. NanOx is a multiscale model that integrates physical and chemical aspects of the radiation-matter interaction.…”

Exploration and application of phenomenological RBE models for proton therapy

“…Phenomenological models try to describe the relationship of measurable empirical quantities (Belli, Campa, and Ermolli 1997, Tilly et al 2005 The last group of models are fundamentally different from the other two kind of models, as mechanistic models aim to model the biological effects on a microscopic scale within the cells, not only assuming and calculating relationship between experimental variables (Scholz et al 1997, Hawkins 1994, Carlson et al 2008, Cunha et al 2017, McMahon et al 2017. The microscopic dose distribution will give rise to lesions and local events within the nucleus, such as double strand breaks (DSB), which are estimated by the models.…”