Mathematical Model of ATM Activation and Chromatin Relaxation by Ionizing Radiation

Model-based assessment of combination therapies - ranking of radiosensitizing agents in oncology

BACKGROUND

To increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method for ranking radiosensitizers using preclinical data.

METHODS

We used data from three xenograft mice studies to calibrate a model that accounts for radiation treatment combined with radiosensitizers. A nonlinear mixed effects approach was utilized where between-subject variability and inter-study variability were considered. Using the calibrated model, we ranked three different Ataxia telangiectasia-mutated inhibitors in terms of anticancer activity. The ranking was based on the Tumor Static Exposure (TSE) concept and primarily illustrated through TSE-curves.

RESULTS

The model described data well and the predicted number of eradicated tumors was in good agreement with experimental data. The efficacy of the radiosensitizers was evaluated for the median individual and the 95% population percentile. Simulations predicted that a total dose of 220 Gy (5 radiation sessions a week for 6 weeks) was required for 95% of tumors to be eradicated when radiation was given alone. When radiation was combined with doses that achieved at least 8 [Formula: see text] of each radiosensitizer in mouse blood, it was predicted that the radiation dose could be decreased to 50, 65, and 100 Gy, respectively, while maintaining 95% eradication.

CONCLUSIONS

A simulation-based method for calculating TSE-curves was developed, which provides more accurate predictions of tumor eradication than earlier, analytically derived, TSE-curves. The tool we present can potentially be used for radiosensitizer selection before proceeding to subsequent phases of the drug discovery and development process.

Effectiveness of Flattening-Filter-Free versus Flattened Beams in V79 and Glioblastoma Patient-Derived Stem-like Cells

Literature data on the administration of conventional high-dose beams with (FF) or without flattening filters (FFF) show conflicting results on biological effects at the cellular level. To contribute to this field, we irradiated V79 Chinese hamster lung fibroblasts and two patient-derived glioblastoma stem-like cell lines (GSCs-named #1 and #83) using a clinical 10 MV accelerator with FF (at 4 Gy/min) and FFF (at two dose rates 4 and 24 Gy/min). Cell killing and DNA damage induction, determined using the γ-H2AX assay, and gene expression were studied. No significant differences in the early survival of V79 cells were observed as a function of dose rates and FF or FFF beams, while a trend of reduction in late survival was observed at the highest dose rate with the FFF beam. GSCs showed similar survival levels as a function of dose rates, both delivered in the FFF regimen. The amount of DNA damage measured for both dose rates after 2 h was much higher in line #1 than in line #83, with statistically significant differences between the two dose rates only in line #83. The gene expression analysis of the two GSC lines indicates gene signatures mimicking the prognosis of glioblastoma (GBM) patients derived from a public database. Overall, the results support the current use of FFF and highlight the possibility of identifying patients with candidate gene signatures that could benefit from irradiation with FFF beams at a high dose rate.

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

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.