Low-dose Risk Research Articles

A Review of Recent Low-dose Research and Recommendations for Moving Forward.

The linear no-threshold (LNT) model has been the regulatory "law of the land" for decades. Despite the long-standing use of LNT, there is significant ongoing scientific disagreement on the applicability of LNT to low-dose radiation risk. A review of the low-dose risk literature of the last 10 y does not provide a clear answer, but rather the body of literature seems to be split between LNT, non-linear risk functions (e.g., supra- or sub-linear), and hormetic models. Furthermore, recent studies have started to explore whether radiation can play a role in the development of several non-cancer effects, such as heart disease, Parkinson's disease, and diabetes, the mechanisms of which are still being explored. Based on this review, there is insufficient evidence to replace LNT as the regulatory model despite the fact that it contributes to public radiophobia, unpreparedness in radiation emergency response, and extreme cleanup costs both following radiological or nuclear incidents and for routine decommissioning of nuclear power plants. Rather, additional research is needed to further understand the implications of low doses of radiation. The authors present an approach to meaningfully contribute to the science of low-dose research that incorporates machine learning and Edisonian approaches to data analysis.

A generalisation of the method of regression calibration and comparison with Bayesian and frequentist model averaging methods

For many cancer sites low-dose risks are not known and must be extrapolated from those observed in groups exposed at much higher levels of dose. Measurement error can substantially alter the dose–response shape and hence the extrapolated risk. Even in studies with direct measurement of low-dose exposures measurement error could be substantial in relation to the size of the dose estimates and thereby distort population risk estimates. Recently, there has been considerable attention paid to methods of dealing with shared errors, which are common in many datasets, and particularly important in occupational and environmental settings. In this paper we test Bayesian model averaging (BMA) and frequentist model averaging (FMA) methods, the first of these similar to the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, and both fairly recently proposed, against a very newly proposed modification of the regression calibration method, the extended regression calibration (ERC) method, which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. The quasi-2DMC with BMA method performs well when a linear model is assumed, but very poorly when a linear-quadratic model is assumed, with coverage probabilities both for the linear and quadratic dose coefficients that are under 5% when the magnitude of shared Berkson error is large (50%). For the linear model the bias is generally under 10%. However, using a linear-quadratic model it produces substantially biased (by a factor of 10) estimates of both the linear and quadratic coefficients, with the linear coefficient overestimated and the quadratic coefficient underestimated. FMA performs as well as quasi-2DMC with BMA when a linear model is assumed, and generally much better with a linear-quadratic model, although the coverage probability for the quadratic coefficient is uniformly too high. However both linear and quadratic coefficients have pronounced upward bias, particularly when Berkson error is large. By comparison ERC yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the quasi-2DMC with BMA or FMA methods, particularly for the linear-quadratic model. The bias of the predicted relative risk at a variety of doses is generally smallest for ERC, and largest for the quasi-2DMC with BMA and FMA methods (apart from unadjusted regression), with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between ERC and the other two methods. In general ERC performs best in the scenarios presented, and should be the method of choice in situations where there may be substantial shared error, or suspected curvature in the dose response.

Multiscale Monte Carlo simulations for dosimetry in x-ray breast imaging: Part II - Microscopic scales.

Although the benefits of breast screening and early diagnosis are known for reducing breast cancer mortality rates, the effects and risks of low radiation doses to the cells in the breast are still ongoing topics ofstudy. To study specific energy distributions ( ) in cytoplasm and nuclei of cells corresponding to glandular tissue for different x-ray breast imagingmodalities. A cubic lattice (500 μm length side) containing 4064 spherical cells was irradiated with photons loaded from phase space files with varying glandular voxel doses ( ). Specific energy distributions were scored for nucleus and cytoplasm compartments using the PENELOPE (v. 2018) + penEasy (v. 2020) Monte Carlo (MC) code. The phase space files, generated in part I of this work, were obtained from MC simulations in a voxelized anthropomorphic phantom corresponding to glandular voxels for different breast imaging modalities, including digital mammography (DM), digital breast tomosynthesis (DBT), contrast enhanced digital mammography (CEDM) and breast CT (BCT). In general, the average specific energy in nuclei is higher than the respective glandular dose scored in the same region, by up to 10%. The specific energy distributions for nucleus and cytoplasm are directly related to the magnitude of the glandular dose in the voxel ( ), with little dependence on the spatial location. For similar values, for nuclei is different between DM/DBT and CEDM/BCT, indicating that distinct x-ray spectra play significant roles in . In addition, this behavior is also present when the specific energy distribution ( ) is considered taking into account the GDD in thebreast. Microdosimetry studies are complementary to the traditional macroscopic breast dosimetry based on the mean glandular dose (MGD). For the same MGD, the specific energy distribution in glandular tissue varies between breast imaging modalities, indicating that this effect could be considered for studying the risks of exposing the breast to ionizingradiation.

Quantitative Integration of Mode of Action Information in Dose-Response Modeling and POD Estimation for Nonmutagenic Carcinogens: A Case Study of TCDD.

Traditional dose-response assessment applies different low-dose extrapolation methods for cancer and noncancer effects and assumes that all carcinogens are mutagenic unless strong evidence suggests otherwise. Additionally, primarily focusing on one critical effect, dose-response modeling utilizes limited mode of action (MOA) data to inform low-dose risk. We aimed to build a dose-response modeling framework that continuously extends the curve into the low-dose region via a quantitative integration of MOA information and to estimate MOA-based points of departure (PODs) for nonmutagenic carcinogens. 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) was used as an example to demonstrate the new dose-response modeling framework. There were three major steps included: a) identifying and extracting key quantifiable events (KQEs), b) calculating essential doses that sequentially activate KQEs using the benchmark dose (BMD) methodology, and c) characterizing pathway dose-response relationship for MOA-based POD estimation. We identified and extracted six KQEs and corresponding essential events composing the MOA of TCDD-induced liver tumors. With the essential doses estimated from the BMD method using various settings, three link functions were applied to model the pathway dose-response relationship. Given a toxicologically plausible definition of adversity, an MOA-based POD was derived from the pathway dose-response curve. The estimated MOA-based PODs were generally comparable with traditional PODs and can be further used to calculate reference doses (RfDs). The proposed framework quantitatively integrated mechanistic information in the modeling process and provided a promising strategy to harmonize cancer and noncancer dose-response assessment through pathway dose-response modeling. However, the framework can also be limited by data availability and the understanding of the underlying mechanism. https://doi.org/10.1289/EHP12677.

Is the linear no-threshold (LNT) model relevant for radiotherapy?

Initially considered as a kind of radiobiological law, the linear no-threshold (LNT) model, which by definition supports the absence of any threshold for cancer risk induction after irradiation, is nowadays more reasonably described as a pragmatic and prudent approach by the International Organizations. However, it remains today a dogma in radiological protection. Actually, this model had been essentially developed for the radiological protection of a general population against low, and sometimes very low, doses of irradiation. Radiation oncologists are dealing with a totally different situation since they deliver, on purpose, high doses of radiations in more or less limited volumes of the body of cancer patients, patients for whom no other alternatives do exist to get rid of their malignant tumors. Simultaneously, the radiation oncologists inevitably give low and even very low doses at distance from the so-called target volumes. In such a specific situation, what is the carcinogenicity of these low doses and the relevance of the LNT model in radiotherapy? Thus, this paper addresses three critical questions: 1) what is the risk acceptability of the radiation doses delivered by radiotherapy of malignant tumors? 2) what is the real carcinogenic risk of (very) low doses delivered at distance from the target volume? 3) are the clinical radiotherapy data, i.e., the number of second primary cancers, accumulated since more than a century, consistent with the LNT model? In conclusion, the LNT model appears to be poorly adapted to the high doses locally delivered to cancer patients and dramatically overestimates, in most cases, the risk of secondary radio-induced cancers. In fine, the real risk of the LNT model in radiotherapy would be to promote radiophobia in cancer patients and to see some of them turning away from a life-saving treatment.

Biological and cellular responses of humans to high-level natural radiation: A clarion call for a fresh perspective on the linear no-threshold paradigm

There remains considerable uncertainty in obtaining risk estimates of adverse health outcomes of chronic low-dose radiation. In the absence of reliable direct data, extrapolation through the linear no-threshold (LNT) hypothesis forms the cardinal tenet of all risk assessments for low doses (≤ 100 mGy) and for the radiation protection principle of As Low As Reasonably Achievable (ALARA). However, as recent evidences demonstrate, LNT assumptions do not appropriately reflect the biology of the cell at the low-dose end of the dose-response curve. In this regard, human populations living in high-level natural radiation areas (HLNRA) of the world can provide valuable insights into the biological and cellular effects of chronic radiation to facilitate improved precision of the dose-response relationship at low doses. Here, data obtained over decades of epidemiological and radiobiological studies on HLNRA populations is summarized. These studies do not show any evidence of unfavourable health effects or adverse cellular effects that can be correlated with high-level natural radiation. Contrary to the assumptions of LNT, no excess cancer risks or untoward pregnancy outcomes have been found to be associated with cumulative radiation dose or in-utero exposures. Molecular biology-driven studies demonstrate that chronic low-dose activates several cellular defence mechanisms that help cells to sense, recover, survive, and adapt to radiation stress. These mechanisms include stress-response signaling, DNA repair, immune alterations and most importantly, the radiation-induced adaptive response. The HLNRA data is consistent with the new evolving paradigms of low-dose radiobiology and can help develop the theoretical framework of an alternate dose-response model. A rational integration of radiobiology with epidemiology data is imperative to reduce uncertainties in predicting the potential health risks of chronic low doses of radiation.

PO-19 Initiation of low-dose aspirin and risk of colorectal neoplasms: a population-based matched cohort study in Denmark

PO-19 Initiation of low-dose aspirin and risk of colorectal neoplasms: a population-based matched cohort study in Denmark

A Review on Gastro-Retentive Floating Microspheres

The floating microsphere's purpose is to improve gastric retention time. Floating drug delivery systems are lower in bulk thickness than gastric juice and remain floating on gastric juice for a long period of time without impacting the gastric-emptying rate and increasing bioavailability. Gastro-retentive microspheres are particularly suitable for the continuous or late release of oral formulations with blending versatility to achieve various release patterns, low dose risk as a reproducible and short gastric retention time. The aim of this review is to address literature on the floating device, techniques, selection of suitable or inappropriate drug candidates for GRDDS, low density polymers used to swim over gastric fluid, processes, and floating microsphere assessment and application.
 Keywords: GRDDS, Floating system, Approaches, Polymer, Mechanism, Methods

Early 3+3 Trial Dose-Escalation Phase I Clinical Trial Design and Suitability for Immune Checkpoint Inhibitors.

Despite the expansion of immune checkpoint inhibitor (ICI) indications, the relationship between ICI dose and toxicity or response is not well established. To understand this correlation, we performed a meta-analysis of ICI trials that used dose escalation. We searched PubMed and abstracts presented at (inter)national meetings for trials using FDA-approved ICIs. The reported rates of grade 3-5 adverse events (G3-5 AE), immune-related adverse events (irAE), and response were correlated with doses within each ICI using marginal exact generalized linear models. A total of 74 trials (7,469 patients) published between January 2010 and January 2017 were included. For ipilimumab, the incidence of G3-5 AEs was 34% with a significant 27% reduced risk in lower doses (P = 0.002). However, no relationship was observed between dose and irAEs or response. For nivolumab, the incidence of G3-5 AEs was 20.1% which was lower in non-small cell lung cancer (NSCLC) compared with renal cell carcinoma (RCC) or melanoma (P ≤ 0.05) with no dose-toxicity relationship. In melanoma and NSCLC, a dose-response association was observed, which was not observed in RCC. For pembrolizumab, the incidence of G3-5 AEs was 13.3%, which was lower in melanoma compared with NSCLC (P = 0.03) with no dose-toxicity relationship. In melanoma, lower dose levels correlated with decreased odds of response (P = 0.01), a relationship that was not observed in NSCLC. Our analysis shows a lack of consistent dose-toxicity or dose-response correlation with ICIs. Therefore, dose escalation is not an appropriate design to conduct ICI studies. Here we present an innovative trial design for immune-modulating agents.

Оценка радиационных рисков неонкологических заболеваний среди российских участников ликвидации последствий аварии на Чернобыльской АЭС

In the absence of man-made sources of ionizing radiation health risks are related primarily to non-malignant diseases, particularly circulation diseases. However, risks of radiation-induced non-cancer diseases can make a notable contribution to the overall radiation-associated detri-mental effects to the human health. The impacts of harm caused by radiation induced non-cancer and cancer diseases are comparable. That is why the study of risks of non-cancer diseas-es for health of the Chernobyl cleanup workers is actual scientific problem, it is also essential for radiation protection. The aim of the study is to estimate radiation risks of incidence and mortality from non-cancer diseases identified in the Russian cohort of the Chernobyl liquidators during the follow-up period. All considered non-communicable diseases have proven diagnoses identified according to ICD-10 chapters and code sets. The data for the study are available in the system of the National Radiation Epidemiological Registry (NRER). For estimating risks of low radiation doses the linear non-threshold (LNT) model was used. For 9 of 19 disease diagnosis codes in-vestigated for statistical significance of associations between excess incidence of a disease and radiation dose received by liquidators can be considered as significant code sets. These are the following diseases: 1) Diseases of the blood and blood-forming organs and certain disorders in-volving the immune mechanism (D50-D89); 2) Diseases of the endocrine system, nutritional dis-orders and disorders of metabolism (E00-E90); 3) Mental and behavioural disorders (F00-F99); 4) Diseases of the nervous system (G00-G99); 5) Diseases of the circulatory system (I00-I99); 6) Diseases of the respiratory system (J00-J99); 7) Diseases of the digestive system (K00-K93); 8) Diseases of the musculoskeletal system and connective tissue (M00-M99); 9) Diseases of the genitourinary system (N00-N99). Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism (D50-D89); diseases of the circulatory system (I00-I99); blood diseases (D50-D89) are characterized by the highest radiation risk coefficients (ERR/Gy). The estimates are ERR/Gy=0.55 and ERR/Gy=0.57, respectively. To date, epidemiolog-ical studies provide sufficient evidence for the existence of statistically significant radiation risks of the circulatory system diseases (I00-I99). For estimating contribution of non-cancer diseases to the overall radiation detriment further research is required.

Model averaging methods for the evaluation of dose-response model uncertainty when assessing the suitability of studies for estimating risk

This paper describes the use of multiple models and model averaging for considering dose–response uncertainties when extrapolating low-dose risk from studies of populations with high levels of exposure. The model averaging approach we applied builds upon innovative methods developed by the U.S. Food and Drug Administration (FDA), principally through the relaxing of model constraints. The relaxing of model constraints allowed us to evaluate model uncertainty using a broader set of model forms and, within the context of model averaging, did not result in the extreme supralinearity that is the primary concern associated with the application of individual unconstrained models. A study of the relationship between inorganic arsenic exposure to a Taiwanese population and potential carcinogenic effects is used to illustrate the approach. We adjusted the reported number of cases from two published prospective cohort studies of bladder and lung cancer in a Taiwanese population to account for potential covariates and less-than-lifetime exposure (for estimating effects on lifetime cancer incidence), used bootstrap methods to estimate the uncertainty surrounding the µg/kg-day inorganic arsenic dose from drinking water and dietary intakes, and fit multiple models weighted by Bayesian Information Criterion to the adjusted incidence and dose data to generate dose-specific mean, 2.5th and 97.5th percentile risk estimates. Widely divergent results from adequate model fits for a broad set of constrained and unconstrained models applied individually and in a model averaging framework suggest that substantial model uncertainty exists in risk extrapolation from estimated doses in the Taiwanese studies to lower doses more relevant to countries like the U.S. that have proportionally lower arsenic intake levels.

Scientific Needs to Move Forward in Low-dose Biology and Risk.

Scientific Needs to Move Forward in Low-dose Biology and Risk.

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.

Propagule pressure and native community connectivity interact to influence invasion success in metacommunities

Mechanistic insights from invasion biology indicate that propagule pressure of exotic species and native community structure can independently influence establishment success. The role of native community connectivity via species dispersal and its potential interaction with propagule pressure on invasion success in metacommunities, however, remains unknown. Native community connectivity may increase biotic resistance to invasion by enhancing species richness and evenness, but the effects could depend upon the level of propagule pressure. In this study, a mesocosm experiment was used to evaluate the independent and combined effects of exotic propagule pressure and native community connectivity on invasion success. The effects of three levels of exotic Daphnia lumholtzi propagule pressure on establishment success, community structure and ecosystem attributes were evaluated in native zooplankton communities connected by species dispersal versus unconnected communities, and relative to a control without native species. Establishment of the exotic species exhibited a propagule dose‐dependent relationship with high levels of propagule pressure resulting in the greatest establishment success. Native community connectivity, however, effectively reduced establishment at the low level of propagule pressure and further augmented native species richness across propagule pressure treatments. Propagule pressure largely determined the negative impacts of the exotic species on native species richness, native biomass and edible producer biomass. The results highlight that native community connectivity can reduce invasion success at a low propagule dose and decrease extinction risk of native competitors, but high propagule pressure can overcome connectivity‐mediated biotic resistance to influence establishment and impact of the exotic species. Together, the results emphasize the importance of the interaction of propagule pressure and community connectivity as a regulator of invasion success, and argue for the maintenance of metacommunity connectivity to confer invasion resistance.

Cohort profile – MSK radiation workers: a feasibility study to establish a deceased worker sub-cohort as part of a multicenter medical radiation worker component in the million person study of low-dose radiation health effects

Background The National Council on Radiation Protection and Measurements (NCRP) is coordinating an expansive epidemiologic effort entitled the Million Person Study of Low-Dose Radiation Health Effects (MPS). Medical workers constitute the largest occupational radiation-exposed group whose doses are typically received gradually over time. Methods A unique opportunity exists to establish an Institutional Review Board/Privacy Board (IRB/PB) approved, retrospective feasibility sub-cohort of diseased Memorial Sloan Kettering Cancer Center (MSK) medical radiation workers to reconstruct occupational/work history, estimate organ-specific radiation absorbed doses, and review existing publicly available records for mortality from cancer (including leukemia) and other diseases. Special emphasis will be placed on dose reconstruction approaches as a means to provide valid organ dose estimates that are as accurate and precise as possible based on the available data, and to allow proper evaluation of accompanying uncertainties. Such a study that includes validated dose measurements and information on radiation exposure conditions would significantly reduce dose uncertainties and provided greatly improved information on chronic low-dose risks. Results The feasibility sub-cohort will include deceased radiation workers from MSK who worked during the nearly seventy-year timeframe from 1946 through 2010 and were provided individual personal radiation dosimetry monitors. A feasibility assessment focused on obtaining records for about 25–30,000 workers, with over 124,000 annual doses, including personnel/work histories, specific dosimetry data, and appropriate information for epidemiologic mortality tracing will be conducted. MSK radiation dosimetry measurements have followed stringent protocols complying with strict worker protection standards in order to provide accurate dose information for radiation workers that include detailed records of work practices (including specific task exposure conditions, radiation type, energy, geometry, personal protective equipment usage, badge position, and missed doses), as well as recorded measurements. These dose measurements have been ascertained through a variety of techniques that have evolved over the years, from film badges to thermoluminescent dosimetry technology to optically stimulated luminescent methodologies. It is expected that individual total doses for the sub-cohort will have a broad range from <10 mSv to > =1000 mSv. Conclusions MSK has pioneered the use of novel radiation diagnostic and therapeutic approaches over time (including initial work with x-rays, radium, and radon), with workplace safety in mind, resulting in a variety of radiation worker exposure scenarios. The results of this feasibility sub-cohort of deceased radiation workers, and associated lessons learned may potentially be applied to an expanded multicenter study of about 170,000 medical radiation worker component of the MPS.

Mapping the research trends on the biological effects of radiation less than 100 mSv: a bibliometric analysis for 30 years publication

Purpose: The purpose of this study is to evaluate the global scientific publication of biological research of low dose radiation for the past 30 years and provide the insights into the characteristics of research activities and major topics regarding biological effects of low dose radiation exposure.Materials and methods: We bibliometrically investigated the biological research publication of radiation exposure less than 100 mSv. References published from 1987 to 2016 were achieved from the Scopus database and filtered by several criteria such as publication types, research fields, and radiation dose range.Results: Total 753 references were assembled for the bibliometric analysis on the biological studies of radiation effect less than 100 mSv. It provided fundamental knowledge of research, including production tendency, contribution, impact journals, and major research themes. Based on the keyword analysis, we found that specific topics on the biological response to radiation exposure have been changed from the examination of low dose radiation-induced phenomena to the investigation of how to induce a physiological response. In addition, featured articles showed the various views on the biological effects of radiation less than 100 mSv in 30 years publication, depending on radiation doses and types.Conclusions: Continuous studies in large programs of low dose radiation led to the increment of research achievements in accordance with societal needs in radiation safety regulation for health protection. Our findings can surely help radiation researchers to gain insights and penetration in low dose risk research for radiation protection, and establish a further research direction.

Occupational radiation exposure and risk of cataract incidence in a cohort of US radiologic technologists.

It has long been known that relatively high-dose ionising radiation exposure (> 1Gy) can induce cataract, but there has been no evidence that this occurs at low doses (< 100mGy). To assess low-dose risk, participants from the US Radiologic Technologists Study, a large, prospective cohort, were followed from date of mailed questionnaire survey completed during 1994-1998 to the earliest of self-reported diagnosis of cataract/cataract surgery, cancer other than non-melanoma skin, or date of last survey (up to end 2014). Cox proportional hazards models with age as timescale were used, adjusted for a priori selected cataract risk factors (diabetes, body mass index, smoking history, race, sex, birth year, cumulative UVB radiant exposure). 12,336 out of 67,246 eligible technologists reported a history of diagnosis of cataract during 832,479 person years of follow-up, and 5509 from 67,709 eligible technologists reported undergoing cataract surgery with 888,420 person years of follow-up. The mean cumulative estimated 5-year lagged eye-lens absorbed dose from occupational radiation exposures was 55.7mGy (interquartile range 23.6-69.0mGy). Five-year lagged occupational radiation exposure was strongly associated with self-reported cataract, with an excess hazard ratio/mGy of 0.69 × 10-3 (95% CI 0.27× 10-3 to 1.16× 10-3, p < 0.001). Cataract risk remained statistically significant (p = 0.030) when analysis was restricted to < 100mGy cumulative occupational radiation exposure to the eye lens. A non-significantly increased excess hazard ratio/mGy of 0.34 × 10-3 (95% CI - 0.19 × 10-3 to 0.97 × 10-3, p = 0.221) was observed for cataract surgery. Our results suggest that there is excess risk for cataract associated with radiation exposure from low-dose and low dose-rate occupational exposures.

Dose-Related Severity Sequence, and Risk-Based Integration, of Chemically Induced Health Effects.

Risk assessment of chemical hazards is typically based on single critical health effects. This work aims to expand the current approach by characterizing the dose-related sequence of the development of multiple (lower- to higher-order) toxicological health effects caused by a chemical. To this end a “reference point profile” is defined as the relation between benchmark doses for considered health effects, and a standardized severity score determined for these effects. For a given dose of a chemical or mixture the probability for exceeding the reference point profile, thereby provoking lower- to higher-order effects, can be assessed. The overall impact at the same dose can also be derived by integrating contributions across all health effects following severity-weighting. In its generalized form the new impact metric relates to the probability of response for the most severe health effects. Reference points (points of departure) corresponding to defined levels of response can also be estimated. The proposed concept, which is evaluated for dioxin-like chemicals, provides an alternative for characterizing the low-dose region below the reference point for a severe effect like cancer. The shape and variability of the reference point profile add new dimensions to risk assessment, which for example extends the characterization of chemical potency, and the concept of acceptable effect sizes for individual health effects. Based on the present data the method shows high stability at low doses/responses, and is also robust to differences in severity categorization of effects. In conclusion, the novel method proposed enables risk-based integration of multiple dose-related health effects. It provides a first step towards a more comprehensive characterization of chemical toxicity, and suggests a potential for improved low-dose risk assessment.

Mo1980 - Low-Dose Acetylsalicylic Acid and Risk of Colorectal Cancer: Evidence from a Primary Care Study in Spain

Mo1980 - Low-Dose Acetylsalicylic Acid and Risk of Colorectal Cancer: Evidence from a Primary Care Study in Spain

Evidence for dose and dose rate effects in human and animal radiation studies.

For stochastic effects such as cancer, linear-quadratic models of dose are often used to extrapolate from the experience of the Japanese atomic bomb survivors to estimate risks from low doses and low dose rates. The low dose extrapolation factor (LDEF), which consists of the ratio of the low dose slope (as derived via fitting a linear-quadratic model) to the slope of the straight line fitted to a specific dose range, is used to derive the degree of overestimation (if LDEF > 1) or underestimation (if LDEF < 1) of low dose risk by linear extrapolation from effects at higher doses. Likewise, a dose rate extrapolation factor (DREF) can be defined, consisting of the ratio of the low dose slopes at high and low dose rates. This paper reviews a variety of human and animal data for cancer and non-cancer endpoints to assess evidence for curvature in the dose response (i.e. LDEF) and modifications of the dose response by dose rate (i.e. DREF). The JANUS mouse data imply that LDEF is approximately 0.2-0.8 and DREF is approximately 1.2-2.3 for many tumours following gamma exposure, with corresponding figures of approximately 0.1-0.9 and 0.0-0.2 following neutron exposure. This paper also cursorily reviews human data which allow direct estimates of low dose and low dose rate risk.