Quantitative modeling of responses to chronic ionizing radiation exposure using targeted and non-targeted effects

Quantum Biology and the Potential Role of Entanglement and Tunneling in Non-Targeted Effects of Ionizing Radiation: A Review and Proposed Model

It is well established that cells, tissues, and organisms exposed to low doses of ionizing radiation can induce effects in non-irradiated neighbors (non-targeted effects or NTE), but the mechanisms remain unclear. This is especially true of the initial steps leading to the release of signaling molecules contained in exosomes. Voltage-gated ion channels, photon emissions, and calcium fluxes are all involved but the precise sequence of events is not yet known. We identified what may be a quantum entanglement type of effect and this prompted us to consider whether aspects of quantum biology such as tunneling and entanglement may underlie the initial events leading to NTE. We review the field where it may be relevant to ionizing radiation processes. These include NTE, low-dose hyper-radiosensitivity, hormesis, and the adaptive response. Finally, we present a possible quantum biological-based model for NTE.

Of Men and Mice: Using Terrestrial Radiation Epidemiology Methods to Inform Analysis of Animal Models for Space Radiation Risk Assessment

Prediction of cancer risk from space radiation exposure is critical to ensure spaceflight crewmembers are adequately informed of the risks they face when accepting assignments to ambitious long-duration exploratory missions. Although epidemiological studies have assessed the effects of exposure to terrestrial radiation, no robust epidemiological studies of humans exposed to space radiation exist to support estimates of the risk from space radiation exposure. Mouse data derived from recent irradiation experiments provides valuable information to successfully develop mouse-based excess risks models for assessing relative biological effectiveness for heavy ions that can provide information to scale unique space radiation exposures so that excess risks estimated for terrestrial radiation can be adjusted for space radiation risk assessment. Bayesian analyses were used to simulate linear slopes for excess risk models with several different effect modifiers for attained age and sex. Relative biological effectiveness values for all-solid cancer mortality were calculated from the ratio of the heavy-ion linear slope to the gamma linear slope using the full posterior distribution and resulted in values that were substantially lower than what is currently applied in risk assessment. These analyses provide an opportunity to improve characterization of parameters used in the current NASA Space Cancer Risk (NSCR) model and generate new hypotheses for future animal experiments using out-bred mouse populations.

Quantitative modeling of multigenerational effects of chronic ionizing radiation using targeted and nontargeted effects

Stress response signals can propagate between cells damaged by targeted effects (TE) of ionizing radiation (e.g. energy depositions and ionizations in the nucleus) and undamaged "bystander" cells, sometimes over long distances. Their consequences, called non-targeted effects (NTE), can substantially contribute to radiation-induced damage (e.g. cell death, genomic instability, carcinogenesis), particularly at low doses/dose rates (e.g. space exploration, some occupational and accidental exposures). In addition to controlled laboratory experiments, analysis of observational data on wild animal and plant populations from areas contaminated by radionuclides can enhance our understanding of radiation responses because such data span wide ranges of dose rates applied over many generations. Here we used a mechanistically-motivated mathematical model of TE and NTE to analyze published embryonic mortality data for plants (Arabidopsis thaliana) and rodents (Clethrionomys glareolus) from the Chernobyl nuclear power plant accident region. Although these species differed strongly in intrinsic radiosensitivities and post-accident radiation exposure magnitudes, model-based analysis suggested that NTE rather than TE dominated the responses of both organisms to protracted low-dose-rate irradiation. TE were predicted to become dominant only above the highest dose rates in the data. These results support the concept of NTE involvement in radiation-induced health risks from chronic radiation exposures.

REVIEW OF QUANTITATIVE MECHANISTIC MODELS OF RADIATION-INDUCED NON-TARGETED EFFECTS (NTE)

Quantitative mechanistic modeling of the biological effects of ionizing radiation has a long rich history. Initially, it was dominated by target theory, which quantifies damage caused by traversal of cellular targets like DNA by ionizing tracks. The discovery that mutagenesis, death and/or altered behavior sometimes occur in cells that were not themselves traversed by any radiation tracks but merely interacted with traversed cells was initially seen as surprising. As more evidence of such 'non-targeted' or 'bystander' effects accumulated, the importance of their contribution to radiation-induced damage became more recognized. Understanding and modeling these processes is important for quantifying and predicting radiation-induced health risks. Here we review the variety of mechanistic mathematical models of nontargeted effects that emerged over the past 2-3 decades. This review is not intended to be exhaustive, but focuses on the main assumptions and approaches shared or distinct between models, and on identifying areas for future research.

Enhancing low-dose risk assessment using mechanistic mathematical models of radiation effects

Mechanistic mathematical modeling of ionizing radiation (IR) effects has a long history spanning several decades. Models that mathematically represent current knowledge and hypotheses about how radiation damages cells and organs, leading to deleterious outcomes such as carcinogenesis, are particularly useful for estimating radiation risks at doses that are relevant for radiation protection, but are too low to provide a strong 'signal-to-noise ratio' in epidemiological or experimental studies with realistic sample sizes. Here, I discuss examples of models in several relevant areas, including radionuclide biokinetics, non-targeted IR effects, DNA double-strand break (DSB) rejoining and radiation carcinogenesis. I do not provide a detailed review of the vast modeling literature in these fields, but focus on concepts that we have implemented, such as using continuous probability distributions of exponential rates to model radionuclide biokinetics and DSB rejoining, and combining short and long time scales in carcinogenesis models. Improvements in models, including the ability to generate new hypotheses based on model predictions, may come from the introduction of additional novel concepts and from integrating multiple data types.

Synergy theory for murine Harderian gland tumours after irradiation by mixtures of high-energy ionized atomic nuclei

Experimental studies reporting murine Harderian gland (HG) tumourigenesis have been a NASA concern for many years. Studies used particle accelerators to produce beams that, on beam entry, consist of a single isotope also present in the galactic cosmic ray (GCR) spectrum. In this paper synergy theory is described, potentially applicable to corresponding mixed-field experiments, in progress, planned, or hypothetical. The "obvious" simple effect additivity (SEA) approach of comparing an observed mixture dose-effect relationship (DER) to the sum of the components' DERs is known from other fields of biology to be unreliable when the components' DERs are highly curvilinear. Such curvilinearity may be present at low fluxes such as those used in the one-ion HG experiments due to non-targeted ('bystander') effects, in which case a replacement for SEA synergy theory is needed. This paper comprises in silico modeling of published experimental data using a recently introduced, arguably optimal, replacement for SEA: incremental effect additivity (IEA). Customized open-source software is used. IEA is based on computer numerical integration of non-linear ordinary differential equations. To illustrate IEA synergy theory, possible rapidly-sequential-beam mixture experiments are discussed, including tight 95% confidence intervals calculated by Monte-Carlo sampling from variance-covariance matrices. The importance of having matched one-ion and mixed-beam experiments is emphasized. Arguments are presented against NASA over-emphasizing accelerator experiments with mixed beams whose dosing protocols are standardized rather than being adjustable to take biological variability into account. It is currently unknown whether mixed GCR beams sometimes have statistically significant synergy for the carcinogenesis endpoint. Synergy would increase risks for prolonged astronaut voyages in interplanetary space.