Low-dose Linearity Research Articles

Historical perspective on the role of cell proliferation in carcinogenesis for DNA-reactive and non-DNA-reactive carcinogens: Arsenic as an example

Our understanding of the etiology of cancer has developed significantly over the past fifty years, beginning with a single-hit linear no-threshold (LNT) conceptual model based on early studies conducted in Drosophila. Over the past several decades, multiple lines of evidence have accumulated to support a contemporary model of chemical carcinogenesis: a multi-hit model involving a prolonged stress environment that over time may drive the mutation of multiple cells into an injured state that ultimately could lead to uncontrolled proliferation via clonal expansion of mutation-carrying daughter cells. Arsenic carcinogenicity offers a useful case study for further exploration of advanced conceptual models for chemical carcinogenesis. A threshold for arsenic carcinogenicity is supported by its mode of action, characterized by repeating cycles of cytotoxicity and cellular regeneration. Furthermore, preliminary meta-analyses of epidemiology dose-response data for inorganic arsenic (iAs) and bladder cancer, correlated to dose-response data measured in vitro, support a threshold of effect in humans on the order of 50−100 μg/L in drinking water. In light of recent developments in our understanding of cancer etiology, we urge strong consideration of the existing mode-of-action evidence supporting a threshold of effect for arsenic carcinogenicity, as well as consideration of the potential methodological pitfalls in evaluating epidemiology dose-response data that could potentially bias in the direction of low-dose linearity.

Mechanistic modeling of salmonellosis: Update and future directions

ABSTRACTMicrobial risk assessors often make simplifying assumptions that lead to the selection of simple concave functions with low-dose linearity, consistent with no-threshold and single-hit hypotheses, as default dose–response model forms. However, evidence is accumulating as the “microbiome revolution” progresses that challenge these assumptions that influence the estimates of the nature and magnitude of uncertainties associated with microbial risks. Scientific advances in the knowledge of the human “superorganism” (hybrid consortium of human plus microbial communities that cooperatively regulates health and disease) enable the design of definitive studies to estimate the pathogen doses overcome by the innate defenses, including the protective microbiota. The systematic investigation of the events of non-typhoid salmonellosis in humans undertaken nearly 2 decades ago was updated to incorporate recent scientific advances in the understanding of impact of the healthy superorganism that strengthens and extends the biological motivations for sublinear or convex dose–response curves in microbial risk assessment. The knowledge of colonization resistance (innate protection of the human superorganism from low doses of ingested pathogens) and microbiota-mediated clearance is advancing mechanistically for many pathosystems. However, until more detailed mechanistic data become available for salmonellosis, the consideration of a variety of empirical model forms is essential for depicting the uncertainty of the “true” dose–response model.

Dose-response relationships for environmentally mediated infectious disease transmission models.

Environmentally mediated infectious disease transmission models provide a mechanistic approach to examining environmental interventions for outbreaks, such as water treatment or surface decontamination. The shift from the classical SIR framework to one incorporating the environment requires codifying the relationship between exposure to environmental pathogens and infection, i.e. the dose–response relationship. Much of the work characterizing the functional forms of dose–response relationships has used statistical fit to experimental data. However, there has been little research examining the consequences of the choice of functional form in the context of transmission dynamics. To this end, we identify four properties of dose–response functions that should be considered when selecting a functional form: low-dose linearity, scalability, concavity, and whether it is a single-hit model. We find that i) middle- and high-dose data do not constrain the low-dose response, and different dose–response forms that are equally plausible given the data can lead to significant differences in simulated outbreak dynamics; ii) the choice of how to aggregate continuous exposure into discrete doses can impact the modeled force of infection; iii) low-dose linear, concave functions allow the basic reproduction number to control global dynamics; and iv) identifiability analysis offers a way to manage multiple sources of uncertainty and leverage environmental monitoring to make inference about infectivity. By applying an environmentally mediated infectious disease model to the 1993 Milwaukee Cryptosporidium outbreak, we demonstrate that environmental monitoring allows for inference regarding the infectivity of the pathogen and thus improves our ability to identify outbreak characteristics such as pathogen strain.

Bogen's Critique of Linear-No-Threshold Default Assumptions.

In an article recently published in this journal, Bogen(1) concluded that an NRC committee's recommendations that default linear, nonthreshold (LNT) assumptions be applied to dose- response assessment for noncarcinogens and nonlinear mode of action carcinogens are not justified. Bogen criticized two arguments used by the committee for LNT: when any new dose adds to a background dose that explains background levels of risk (additivity to background or AB), or when there is substantial interindividual heterogeneity in susceptibility (SIH) in the exposed human population. Bogen showed by examples that SIH can be false. Herein is outlined a general proof that confirms Bogen's claim. However, it is also noted that SIH leads to a nonthreshold population distribution even if individual distributions all have thresholds, and that small changes to SIH assumptions can result in LNT. Bogen criticizes AB because it only applies when there is additivity to background, but offers no help in deciding when or how often AB holds. Bogen does not contradict the fact that AB can lead to LNT but notes that, even if low-dose linearity results, the response at higher doses may not be useful in predicting the amount of low-dose linearity. Although this is theoretically true, it seems reasonable to assume that generally there is some quantitative relationship between the low-dose slope and the slope suggested at higher doses. Several incorrect or misleading statements by Bogen are noted.

The Influence of Human Interindividual Variability on the Low-Dose Region of Dose-Response Curve Induced by 2,3,7,8-Tetrachlorodibenzo-p-Dioxin in Primary B Cells.

The influence of interindividual variability is not typically assessed in traditional toxicological studies. Given that chemical exposures occur in heterogeneous populations, this knowledge gap has the potential to cause undue harm within the realms of public health and industrial and municipal finances. A recent report from the National Research Council (NRC) suggests that when accounting for interindividual variation in responses, traditionally assumed nonlinear dose-response relationships (DRRs) for noncancer-causing endpoints would better be explained with a linear relationship within the low-dose region. To address this knowledge gap and directly test the NRC's assumption, this study focused on assessing the DRR between 2,3,7,8-tetracholorodibenzo-p-dioxin (TCDD) exposure and immune suppression in a cohort of unique human donors. Human B cells were isolated from 51 individual donors and treated with logarithmically increasing concentrations of TCDD (0-30 nM TCDD). Two endpoints sensitive to TCDD were assessed: (1) number of IgM-secreting B cells and (2) quantity of IgM secreted. The results show that TCDD significantly suppressed both the number of IgM-secreting B cells and the quantity of IgM secreted (P < .05). Statistical model comparisons indicate that the low-dose region of the two DRRs is best explained with a nonlinear relationship. Rather than assuming low-dose linearity for all noncancer-causing DRRs, our study suggests the need to consider the specific mode of action of toxicants and pharmaceuticals during risk-management decision making.

Toward Making Epidemiologic Data More Useful for Quantitative Risk Assessment

This paper discusses issues faced in using epidemiologic data to develop quantitative estimates of risk from specified patterns of exposure to a toxicant. We focus on use of data from cohort studies with binary endpoints (occurrence or non-occurrence of disease). Relative advantages of Cox regression and Poisson regression are presented. A general form of exposure metric is presented, and criteria for selecting an appropriate metric are discussed. Advantages and disadvantages of various dose-response models are discussed. It is argued that, unless low-dose linearity of the dose response can be ruled out on non-statistical grounds, then bounds for low-dose risk should incorporate low-dose linearity; a sequential procedure for computing such bounds is illustrated. Limitations in exposure data and their impact on risk assessments are discussed. Issues arising when using meta-analytic techniques to combine data from multiple epidemiologic studies are discussed. Limitations in risk assessments resulting from reliance upon published results alone are described. Methods for converting from measures of risk used in epidemiologic studies (e.g., relative risk) to measures appropriate for a risk assessment (e.g., additional lifetime probability of disease occurrence resulting from a specific exposure pattern) are described in detail. Several examples from the asbestos epidemiologic literature are presented to illustrate these issues.

Practical Risk Assessment and Management Issues Arising were we to Adopt Low-Dose Linearity for all Endpoints

The 2009 National Research Council report, Science and Decisions, proposes harmonizing dose-response approaches for cancer and non-cancer endpoints, and for non-cancer quantitative risk assessment, this would usually take the form of a low-dose linear no-threshold dose-response curve. The soundness of this recommendation has been questioned, but I focus on its consequences if adopted, many of them apparently unintended. If most endpoints for most agents are assumed to have non-zero low-dose risks, then the critical-effect concept, choosing the one endpoint on which to calculate acceptable doses, loses its basis. All regulatory decisions, since they entail substituting some exposures (and their attendant risks) for others, become risk-risk trade-off decisions, and equity questions are raised since risk transfer is inevitably involved. A valid basis for estimating low-dose linear components is not evident, and upper-bound approaches fail to be reliably public health-protective owing to the risk trade-off decisions that need to be faced.

Application of Key Events Analysis to Chemical Carcinogens and Noncarcinogens

The existence of thresholds for toxicants is a matter of debate in chemical risk assessment and regulation. Current risk assessment methods are based on the assumption that, in the absence of sufficient data, carcinogenesis does not have a threshold, while noncarcinogenic endpoints are assumed to be thresholded. Advances in our fundamental understanding of the events that underlie toxicity are providing opportunities to address these assumptions about thresholds. A key events dose-response analytic framework was used to evaluate three aspects of toxicity. The first section illustrates how a fundamental understanding of the mode of action for the hepatic toxicity and the hepatocarcinogenicity of chloroform in rodents can replace the assumption of low-dose linearity. The second section describes how advances in our understanding of the molecular aspects of carcinogenesis allow us to consider the critical steps in genotoxic carcinogenesis in a key events framework. The third section deals with the case of endocrine disrupters, where the most significant question regarding thresholds is the possible additivity to an endogenous background of hormonal activity. Each of the examples suggests that current assumptions about thresholds can be refined. Understanding inter-individual variability in the events involved in toxicological effects may enable a true population threshold(s) to be identified.

K‐Ras mutant fraction in A/J mouse lung increases as a function of benzo[a]pyrene dose

K-Ras mutant fraction (MF) was measured to examine the default assumption of low-dose linearity in the benzo[a]pyrene (B[a]P) mutational response. Groups of 10 male A/J mice (7- to 9-weeks old) received a single i.p. injection of 0, 0.05, 0.5, 5, or 50 mg/kg B[a]P and were sacrificed 28 days after treatment. K-Ras codon 12 TGT and GAT MFs in lung DNAs were measured using Allele-specific Competitive Blocker-PCR (ACB-PCR). The K-Ras codon 12 TGT geometric mean MF was 3.88 x 10(-4) in controls, indicating an average of 1 mutation in every approximately 1,288 lung cells. The K-Ras codon 12 TGT geometric mean MFs were as follows: 3.56 x 10(-4); 6.19 x 10(-4); 2.02 x 10(-3), and 3.50 x 10(-3) for the 0.05, 0.5, 5, and 50 mg/kg B[a]P treatment groups, respectively. The 5 and 50 mg/kg dose groups had TGT MFs significantly higher than did controls. Although 10(-5) is considered as the limit of accurate ACB-PCR quantitation, K-Ras codon 12 GAT geometric mean MFs were as follows: 8.38 x 10(-7), 1.47 x 10(-6), 2.19 x 10(-6), 5.71 x 10(-6), and 8.99 x 10(-6) for the 0, 0.05, 0.5, 5, and 50 mg/kg B[a]P treatment groups, respectively. The K-Ras TGT and GAT MFs increased in a B[a]P-dose-dependent manner, with response approximately linear over the 0.05 to 5 mg/kg dose range. K-Ras MF increased with B[a]P adduct burden measured for identical doses in a separate study. Thus, ACB-PCR may be useful in characterizing the shape of a dose-response curve at low doses and establishing relationships between DNA adducts and tumor-associated mutations.

Linear Low-Dose Extrapolation for Noncancer Responses Is Not Generally Appropriate

I disagree with the assertion of White et al. (2009) that linear low-dose extrapolation is generally appropriate as a default for noncancer, as well as cancer, end points. Such a stance would radically alter a basic tenet of toxicology and risk analysis, and any such course should be considered only after rigorous and broadly based examination of the scientific principles involved. White et al. (2009) offered only a cursory summary of arguments that were discussed in a 2007 workshop, but any fuller discussion that may have occurred in that workshop was not recounted. Continual revisiting of the scientific thinking under lying risk assessment policies is valuable, but a change of this nature—which would depart from decades of well-established practice—needs to be carefully and critically examined. I believe that most observers would find fault with all of the proffered lines of reasoning that White et al. (2009) cited in advocating that noncancer dose–response relationships should be treated as linear. Although harmonization of cancer and non cancer toxicity assessment holds some value (regarding commonality of pharmacokinetics and, potentially, elements of modes of action), there are still fundamental differences between carcinogenicity on the one hand, in which the probability of constellations of rare events (that get rarer with lower doses) drives the dose–response function, and most noncancer responses on the other, in which the dose–response function hinges on the degree of perturbation of physiologic and homeo static processes (which becomes less pronounced and less efficacious with lower doses) (Rhomberg 2004). The argument of White et al. (2009) that epidemiologic studies often show no thresholds, even for end points having thresholds in animal studies, is readily attributable to the small range of exposure levels and the approximate nature of exposure measurements in most human studies; these artifically flatten apparent dose–response curves and tend to make any dose-related effect (even those that are truly threshold in nature) look more or less linear as an artifact of the analysis. Heterogeneity in sensitivity and in modifying factors among people in the target population may tend to broaden the dose–response relationship, but it does not linearize it, as White et al. (2009) asserted; indeed, the logic they invoked (the combined effect of variation in many modifying factors) leads to the expectation of a cumulative log-normal dose–response function, which is always nonlinear, rather than a linear one. Similarly, when examined rigorously, the invocation by White et al. (2009) of the principle of additivity to background fails to support general linearity; unless it is framed in the discussion of a specific mode of action, the additivity-to-background argument amounts to begging the question—assuming the hypothetical existence of an underlying and rate-limiting no-threshold mechanistic effect to argue for linearity of the end result caused by that process. In fact, many biological processes—notably homeostasis and switch mechanisms—are inherently nonlinear, with threshold effects for their perturbation to a degree sufficient to have health consequences. A rigorous examination of how additivity to background affects dose response for different modes of action should be undertaken before its general applicability is assumed. The approach taken when extrapolating dose–response relationships to low doses has profound impact on risk-management decision making. If a change is proposed that is to be justified by invoking general principles, then the bearing of those principles needs to be rigorously articulated, well understood, and evaluated through broad discussion and debate. At present, this discussion has yet to occur.

Application of benzo(a)pyrene and coal tar tumor dose-response data to a modified benchmark dose method of guideline development.

Assessment of cancer risk from exposure to polycyclic aromatic hydrocarbons (PAHs) has been traditionally conducted by applying the conservative linearized multistage (LMS) model to animal tumor data for benzo(a)pyrene (BaP), considered the most potent carcinogen in PAH mixtures. Because it has been argued that LMS use of 95% lower confidence limits on dose is unnecessarily conservative, that assumptions of low-dose linearity to zero in the dose response imply clear mechanistic understanding, and that “acceptable” cancer risk rests on a policy decision, an alternative cancer risk assessment approach has been developed. Based in part on the emerging benchmark dose (BMD) method, the modified BMD method we used involves applying a suite of conventional mathematical models to tumor dose–response data. This permits derivation of the average dose corresponding to 5% extra tumor incidence (BMD0.05) to which a number of modifying factors are applied to achieve a guideline dose, that is, a daily dose considered safe for human lifetime exposure. Application of the modified BMD method to recent forestomach tumor data from BaP ingestion studies in mice suggests a guideline dose of 0.08 μg/kg/day. Based on this and an understanding of dietary BaP, and considering that BaP is a common contaminant in soil and therefore poses human health risk via soil ingestion, we propose a BaP soil guideline value of 5 ppm (milligrams per kilogram). Mouse tumor data from ingestion of coal tar mixtures containing PAHs and BaP show that lung and not forestomach tumors are most prevalent and that BaP content cannot explain the lung tumors. This calls into question the common use of toxicity equivalence factors based on BaP for assessing risk from complex PAH mixtures. Emerging data point to another PAH compound—7H-benzo(c)fluorene—as the possible lung tumorigen.

Practical Implications of Nonlinear Effects in Risk-Assessment Harmonization

Cancer and noncancer health effects have traditionally been handled differently in quantitative risk assessment. A threshold (i.e., safe exposure) has been assumed for noncancer health effects, and low-dose linearity without a threshold has been assumed for cancer. "Harmonization" attempts to reconcile these contrasting assumptions under one paradigm. Recent regulatory initiatives suggest that the U.S. Environmental Protection Agency may be leaning toward a harmonized, probabilistic/linear approach for noncancer health effects. Proponents of this approach cite variability in human susceptibility as an argument against thresholds (i.e., some individuals may be exquisitely sensitive at exposures well below threshold levels). They also cite the results of epidemiological models that suggest low-dose linearity for noncancer health effects. We will discuss the implications of these arguments and compare them to what is known about human biological variability in general. We will also touch on the regulatory implications of hormesis within this framework.

Risk-assessment implications of mechanistic model's prediction of low-dose nonlinearity of liver tumor risk for mice fed fumonisin b(1).

A two-stage, clonal-expansion model of liver tumor risk in mice was developed by Kodell et al. (Food Addit Contam 18:237-253, 2001) based on the hypothesis that fumonisin B(1), a naturally occurring mycotoxin in corn, is not genotoxic, but rather causes cancer through the disruption of sphingolipid metabolism. This disruption is assumed to cause an increase in apoptosis, in response to which cells proliferate to compensate for reduced tissue mass. The resulting differential increase in the number of pre-neoplastic cells at risk of mutation during cell division is assumed to lead to an increase in the incidence of tumors. Two-year liver tumor incidences predicted by the model using data on organ weight, cell proliferation, and sphingolipid metabolism provided a reasonable match to the actual 2-year observed incidences in a study conducted at the National Center for Toxicological Research. The predictions indicated no risk at low doses (even a possible hormetic effect) and high risk at high doses in females, as well as a complete absence of a dose response (or perhaps, a hormetic effect) in males. This paper provides a commentary on the risk-assessment implications of the modeling results, pointing out that the model's low-dose predictions provide scientific support and justification for the U.S. Food and Drug Administration's low-ppm guidance levels in corn products. These guidance levels are significantly higher than would be obtained using linear extrapolation, the method most often used for genotoxic carcinogens and other carcinogens for which low-dose linearity cannot be ruled out.

Perspectives in low dose risk estimation

Currently, debate ranges from whether very low doses have disproportionately large consequences, to whether there is a practical threshold. Whilst epidemiology for low doses can achieve somewhat higher power from conflation of results, the uncertainties are such that advances in radiobiology are essential. Conceptually, deviations from low-dose linearity can arise from changes in the cell, or in neighbouring cells, such that subsequent insults from irradiation or other carcinogens are more likely (sensitisation) or less likely (desensitisation) to result in cancer. Such feedback changes are common in living organisms (allergies and homeostatic stabilisation). Bystander effects indicate that neighbouring cells may be influenced. The effects of conditioning doses at high doses indicate that in some circumstances cells can retain a memory of earlier damage. What is measured in epidemiology is the net excess of cancers from irradiation above the much higher rates of cancer from environmental and spontaneous causes. Epidemiological dose–response curves are ‘dressed’. Memory and group influences provide mechanisms which enable the ‘naked’ dose–response curve for the cell alone to be clothed. Junk DNA and anonymous carcinogens are also discussed.

Dose–response relationships in chemical carcinogenesis: superposition of different mechanisms of action, resulting in linear–nonlinear curves, practical thresholds, J-shapes

The shape of a carcinogen dose–cancer incidence curve is discussed as the result of a superposition of dose–response relationships for various effects of the carcinogen on the process of carcinogenesis. Effects include direct DNA damage, e.g., by covalent binding, indirect DNA damage, e.g., by increased formation of reactive oxygen species or interaction with DNA replication or chromosome integrity. The `fixation' of a DNA adduct as a heritable mutation depends on its pro-mutagenic potency and on the rates of DNA repair and DNA replication. Endogenous and unavoidable DNA damage is responsible for a background rate of the process of mutagenesis and carcinogenesis and forms the basis of spontaneous cancer incidence. For DNA-reactive carcinogens, linearity of the dose response at the low-dose end is expected. With increasing dose, saturation of DNA repair can introduce a sublinearity (example: dimethylnitrosamine). Stimulation of cell division as a result of high-dose toxicity and regenerative proliferation also results in a sublinear deviation from low-dose linearity. If the DNA-damaging potency of the carcinogen is low in comparison with the high-dose effects, the linear part of the low dose–cancer incidence curve might be hidden within the background variability. Under such conditions, `practical thresholds' could be discussed (formaldehyde). If a carcinogen increases the rate of cell division or the level of oxidative stress at high dose but has an antimitogenic or antioxidative effect at low dose, a J-shaped (or: U-shaped) curve with a decrease of the spontaneous tumor incidence at low dose could result (caffeic acid; TCDD). This phenomenon has been observed even under conditions of a genotoxic contribution (ionizing radiation; diesel exhaust particles). For a mechanism-based assessment of a low-dose cancer risk, information on the various modes of action and modulations should be available over the full dose range, and models should be refined to incorporate the respective information.

Inorganic arsenic: a need and an opportunity to improve risk assessment.

This paper presents views on the current status of (inorganic) arsenic risk assessment in the United States and recommends research needed to set standards for drinking water. The opinions are those of the Arsenic Task Force of the Society for Environmental Geochemistry and Health, which has met periodically since 1991 to study issues related to arsenic risk assessment and has held workshops and international conferences on arsenic. The topic of this paper is made timely by current scientific interest in exposure to and adverse health effects of arsenic in the United States and passage of the Safe Drinking Water Act Amendment of 1996, which has provisions for a research program on arsenic and a schedule mandating the EPA to revise the maximum contaminant level of arsenic in drinking water by the year 2001. Our central premise and recommendations are straightforward: the risk of adverse health effects associated with arsenic in drinking water is unknown for low arsenic concentrations found in the United States, such as at the current interim maximum contaminant level of 50 microg/l and below. Arsenic-related research should be directed at answering that question. New epidemiological studies are needed to provide data for reliable dose-response assessments of arsenic and for skin cancer, bladder cancer, or other endpoints to be used by the EPA for regulation. Further toxicological research, along with the observational data from epidemiology, is needed to determine if the dose-response relationship at low levels is more consistent with the current assumption of low-dose linearity or the existence of a practical threshold. Other recommendations include adding foodborne arsenic to the calculation of total arsenic intake, calculation of total arsenic intake, and encouraging cooperative research within the United States and between the United States and affected countries.

Using PBPK modeling and comprehensive realism methodology for the quantitative cancer risk assessment of butadiene

The National Academy of Sciences and many others have noted the need for quantitative health risk assessment methodology that goes beyond a simple screening analysis based on upper bounds on risk. The Academy recommended adoption of methodologies which provide a higher-tier analysis based on realistic estimates of risk which reflect more of the available biological information. In recent years, scientists have challenged the assumption of low-dose linearity and other default assumptions in cancer risk assessment. These challenges have stimulated the continued evolution of quantitative risk assessment methodologies, because effective risk management requires accurate characterizations of uncertainty and greater utilization of cost-benefit analyses for decision making. “Comprehensive Realism” is an emerging quantitative weight-of-evidence based risk assessment methodology for both cancer and noncancer health effects which utilizes probability distributions and decision analysis techniques to reflect more of the available human and animal dose-response data. The current state of knowledge about the relative plausibility of alternative dose-response analyses is also addressed in this approach. The framework discussed here should lead to a higher-tier assessment of butadiene.

Comprehensive Realism's Weight-of-Evidence Based Distributional Dose-Response Characterization

Abstract Challenges to low-dose linearity and other default assumptions in cancer risk assessment and the limitations associated with NOAELs, LOAELs, and constant uncertainty factor values in the evaluation of noncancer health effects have stimulated the continued evolution of risk assessment methodologies. The increasing need for more realistic estimates of the dose-response relationship, better uncertainty characterization, and greater utilization of cost-benefit analyses have also contributed to this evolution. “Comprehensive Realism” is an emerging quantitative weight-of-evidence based risk assessment methodology for both cancer and noncancer health effects which utilizes probability distributions and decision analysis techniques to reflect more of the relevant human exposure data, more of the available and pertinent human and animal dose-response data, and the current state of knowledge about the relative plausibility of alternative dose-response analyses. A tree (like a decision tree and a probabili...

Challenges to Default Assumptions Stimulate Comprehensive Realism as a New Tier in Quantitative Cancer Risk Assessment

The current practice in carcinogen risk assessment of using a linearized multistage model and assuming low-dose linearity is based on several false premises. In many cases linearity at low doses would not be expected based on the interaction between the multiple components in the carcinogenic process. The two-stage growth models, involving multiple mutations and cell birth and death rates, provide one means of exploring these interactions. In addition, if carcinogenesis is considered to be the imbalance between invading substances and defense mechanisms, then the cancer probability depends on how much the substance increases or decreases the number of defenders or their efficiency as well as increasing or decreasing the number of invaders. Challenges to low-dose linearity and other default assumptions have stimulated the development of new risk assessment methodologies as have the need for more realistic estimates of risk, better uncertainty characterization, and greater utilization of cost-benefit analyses, and other tools for risk management decision making. "Comprehensive realism" is an emerging quantitative weight-of-evidence risk. assessment methodology which is designed to reflect all of the relevant and available information and the current state of knowledge about the health risks associated with a substance.

A model-free approach to low-dose extrapolation.

Estimates of risk associated with exposure to low levels of carcinogenic substances present in the environment are generally obtained by linear extrapolation from higher exposure levels at which risks can be estimated directly. In this paper, we examine the scientific basis for the assumption of low-dose linearity in carcinogenic risk assessment and the different statistical methods that have been proposed for linear extrapolation. A model-free approach to linear extrapolation is described and illustrated using epidemiological data on radiation carcinogenesis. The statistical properties of this method are empirically assessed using 572 selected sets of bioassay data.