Issues in Exposure and Dose Assessment for Epidemiology and Risk Assessment

Epidemiologic studies have been effective in identifying human environmental and occupational hazards. However, most epidemiologic data has been difficult to use in quantitative risk assessments because of the vague specification of exposure and dose. Toxicologic animal studies have used applied doses (quantities administered, or exposures with fixed duration) and well characterized end points to determine effects. However, direct use of animal data in human risk assessment has been limited by uncertainties in the extrapolation. The applied dose paradigm of toxicology is not suited for cross species extrapolation, nor for use in epidemiology as a dose metric because of the complexity of human exposures. Physiologically based pharmacokinetic (PBPK) modeling can estimate the time course of tissue concentrations in humans, given an exposure-time profile, and it has been used for extrapolating findings from animals to humans. It is proposed that human PBPK modeling can be used in appropriately designed epidemiologic studies to estimate tissue concentrations. Secondly, tissue time courses can be used to form dose metrics based on the type and time course of adverse effects. These dose metrics will strengthen the determination of epidemiologic dose-response relationships by reducing misclassification. Findings from this approach can be readily integrated into quantitative risk assessment.     

Improving the Development and Use of Biologically Based Dose Response Models (BBDR) in Risk Assessment

Biologically based dose-response (BBDR) models predict health outcomes (response) resulting from the presence of a toxicant at a biological target (dose). The benefits of BBDR models are many, and research programs are increasingly focusing on mechanistic research to support model development; however, progress has been slow. Impediments to progress include the complexity of dose response modeling, the need for a multidisciplinary team and consistent funding support, and difficulty in identifying and extracting the needed data. Of immediate concern is the lack of transparency of published models to the supporting data and literature, difficulty in accessing model code and simulation conditions sufficient to allow independent replication of results, and absence of well-defined quality criteria. Suggestions are presented to improve the development and use of BBDR models in risk assessment and to address the above limitations. Examples from BBDR models for methylmercury neurotoxicity and 5-fluorouracil embryotoxicity are presented to illustrate the suggestions including what kinds of databases are needed to support model development and transparency, quality assurance for modeling, and how the internet can advance database development and collaboration within the biological modeling community.     

Improving Risk Assessment: Research Opportunities in Dose Response Modeling to Improve Risk Assessment

Substantial improvements in dose response modeling for risk assessment may result from recent and continuing advances in biological research, biochemical techniques, biostatistical/mathematical methods and computational power. This report provides a ranked set of recommendations for proposed research to advance the state of the art in dose response modeling. The report is the result of a meeting of invited workgroup participants charged with identifying five areas of research in dose response modeling that could be incorporated in a national agenda to improve risk assessment methods. Leading topics of emphasis are interindividual variability, injury risk assessment modeling, and procedures to incorporate distributional methods and mechanistic considerations into now-standard methods of deriving a reference dose (RfD), reference concentration (RfC), minimum risk level (MRL) or similar dose-response parameter estimates.     

Quantitative risk assessment for multivariate continuous outcomes with application to neurotoxicology: the bivariate case

The neurotoxic effects of chemical agents are often investigated in controlled studies on rodents, with multiple binary and continuous endpoints routinely collected. One goal is to conduct quantitative risk assessment to determine safe dose levels. Such studies face two major challenges for continuous outcomes. First, characterizing risk and defining a benchmark dose are difficult. Usually associated with an adverse binary event, risk is clearly definable in quantal settings as presence or absence of an event; finding a similar probability scale for continuous outcomes is less clear. Often, an adverse event is defined for continuous outcomes as any value below a specified cutoff level in a distribution assumed normal or log normal. Second, while continuous outcomes are traditionally analyzed separately for such studies, recent literature advocates also using multiple outcomes to assess risk. We propose a method for modeling and quantitative risk assessment for bivariate continuous outcomes that address both difficulties by extending existing percentile regression methods. The model is likelihood based; it allows separate dose-response models for each outcome while accounting for the bivariate correlation and overall characterization of risk. The approach to estimation of a benchmark dose is analogous to that for quantal data without the need to specify arbitrary cutoff values. We illustrate our methods with data from a neurotoxicity study of triethyl tin exposure in rats.     

Statistical models for low dose exposure

Extrapolation of health risks from high to low doses has received a considerable amount of attention in carcinogenic risk assessment over decades. Fitting statistical dose-response models to experimental data collected at high doses and use of the fitted model for estimating effects at low doses lead to quite different risk predictions. Dissatisfaction with this procedure was formulated both by toxicologists who saw a deficit of biological knowledge in the models as well as by risk modelers who saw the need of mechanistically-based stochastic modeling. This contribution summarizes the present status of low dose modeling and the determination of the shape of dose-response curves. We will address the controversial issues of the appropriateness of threshold models, the estimation of no observed adverse effect levels (NOAEL), and their relevance for low dose modeling. We will distinguish between quantal dose-response models for tumor incidence and models of the more informative age/time dependent tumor incidence. The multistage model and the two-stage model of clonal expansion are considered as dose-response models accounting for biological mechanisms. Problems of the identifiability of mechanisms are addressed, the relation between administered dose and effective target dose is illustrated by examples, and the recently proposed Benchmark Dose concept for risk assessment is presented with its consequences for mechanistic modeling and statistical estimation.     

Methods for evaluating variability in human health dose–response characterization

Abstract

The Reference Dose (RfD) and Reference Concentration (RfC) are human health reference values (RfVs) representing exposure concentrations at or below which there is presumed to be little risk of adverse effects in the general human population. The 2009 National Research Council report Science and Decisions recommended redefining RfVs as “a risk-specific dose (for example, the dose associated with a 1 in 100,000 risk of a particular end point).” Distributions representing variability in human response to environmental contaminant exposures are critical for deriving risk-specific doses. Existing distributions estimating the extent of human toxicokinetic and toxicodynamic variability are based largely on controlled human exposure studies of pharmaceuticals. New data and methods have been developed that are designed to improve estimation of the quantitative variability in human response to environmental chemical exposures. Categories of research with potential to provide new data useful for developing updated human variability distributions include controlled human experiments, human epidemiology, animal models of genetic variability, in vitro estimates of toxicodynamic variability, and in vitro-based models of toxicokinetic variability. In vitro approaches, with further development including studies of different cell types and endpoints, and approaches to incorporate non-genetic sources of variability, appear to provide the greatest opportunity for substantial near-term advances.     

Detection and quantification of 14 <Emphasis Type="Italic">Campylobacter</Emphasis> species in pet dogs reveals an increase in species richness in feces of diarrheic animals

Background  

The genus Campylobacter includes many species, some of which are known human and animal pathogens. Even though studies have repeatedly identified domestic dogs as a risk factor for human campylobacteriosis, our understanding of Campylobacter ecology in this reservoir is limited. Work to date has focused primarily on a limited number of species using culture-based methods. To expand our understanding of Campylobacter ecology in dogs, a collection of fecal samples from 70 healthy and 65 diarrheic pet dogs were examined for the presence and levels of 14 Campylobacter species using quantitative PCR.     

Predicting cancer rates in astronauts from animal carcinogenesis studies and cellular markers

The radiation space environment includes particles such as protons and multiple species of heavy ions, with much of the exposure to these radiations occurring at extremely low average dose-rates. Limitations in databases needed to predict cancer hazards in human beings from such radiations are significant and currently do not provide confidence that such predictions are acceptably precise or accurate. In this article, we outline the need for animal carcinogenesis data based on a more sophisticated understanding of the dose-response relationship for induction of cancer and correlative cellular endpoints by representative space radiations. We stress the need for a model that can interrelate human and animal carcinogenesis data with cellular mechanisms. Using a broad model for dose-response patterns which we term the "subalpha-alpha-omega (SAO) model", we explore examples in the literature for radiation-induced cancer and for radiation-induced cellular events to illustrate the need for data that define the dose-response patterns more precisely over specific dose ranges, with special attention to low dose, low dose-rate exposure. We present data for multiple endpoints in cells, which vary in their radiosensitivity, that also support the proposed model. We have measured induction of complex chromosome aberrations in multiple cell types by two space radiations, Fe-ions and protons, and compared these to photons delivered at high dose-rate or low dose-rate. Our data demonstrate that at least three factors modulate the relative efficacy of Fe-ions compared to photons: (i) intrinsic radiosensitivity of irradiated cells; (ii) dose-rate; and (iii) another unspecified effect perhaps related to reparability of DNA lesions. These factors can produce respectively up to at least 7-, 6- and 3-fold variability. These data demonstrate the need to understand better the role of intrinsic radiosensitivity and dose-rate effects in mammalian cell response to ionizing radiation. Such understanding is critical in extrapolating databases between cellular response, animal carcinogenesis and human carcinogenesis, and we suggest that the SAO model is a useful tool for such extrapolation.     

Occupational exposures and reproductive health: 2003 Teratology Society Meeting Symposium summary

Assuring reproductive health in the workplace challenges researchers, occupational safety and health practitioners, and clinicians. Most chemicals in the workplace have not been evaluated for reproductive toxicity. Although occupational exposure limits are established to protect 'nearly all' workers, there is little research that characterizes reproductive hazards. For researchers, improvements in epidemiologic design and exposure assessment methods are needed to conduct adequate reproductive studies. Occupational safety and health programs' qualitative and quantitative evaluations of the workplace for reproductive hazards may differ from standardized approaches used for other occupational hazards in that estimates of exposure intensity must be considered in the context of the time-dependent windows of reproductive susceptibility. Clinicians and counselors should place the risk estimate into context by emphasizing the limitations of the available knowledge and the qualitative nature of the exposure estimates, as well as what is known about other non-occupational risk factors for adverse outcomes. This will allow informed decision-making about the need for added protections or alternative duty assignment when a hazard cannot be eliminated. These policies should preserve a worker's income, benefits, and seniority. Applying hazard control technologies and hazard communication training can minimize a worker's risk. Chemical reproductive hazard training is required for workers by the Occupational Safety and Health Administration's Hazard Communication Standard. The National Institute for Occupational Safety and Health (NIOSH) has formed a National Occupational Research Agenda Team to promote communication and partnering among reproductive toxicologists, clinicians and epidemiologists, to improve reproductive hazard exposure assessment and management, and to encourage needed research.     

Bioavailability of Soil-Borne Chemicals: Method Development and Validation

Chemicals present in contaminated soils generally exhibit altered bioavailability compared to other vehicles used in studies of chemical toxicity. Methods used to assess the bioavailability of soil-borne chemicals have generally been modified versions of methods that are widely used in biomedical research. Oral and dermal bioavailability of semivolatile organic chemicals and metals in soil has been assessed by a variety of in vivo and in vitro methods. Due to variations in metabolism and excretion of different chemicals, approaches to measuring bioavailability must be selected with an understanding of disposition of the chemical being studied. Standard methods need to be modified due to constraints associated with doses relevant to environmental concentrations, the need to reflect weathering behavior in soils over time, and the need to generate data applicable to human health risk assessments. Estimates of relative bioavailability for chemicals in soil can be used directly to modify exposure estimates. Application of bioavailability data in a site-specific risk assessment requires regulatory acceptance of the data. Acceptance of the data will generally be dependent on either the use of a validated test method or a careful scientific review of the test method employed. A process for validating newly developed alternative toxicity methods for routine use developed by the Interagency Coordinating Committee on the Validation of Alternative Methods provides relevant guidance for assessing in vitro methods, but method validation should not be the only litmus test for inclusion of bioavailability data in risk assessments.     

Methodologic Implications of the Precautionary Principle: Causal Criteria

Applying the Precautionary Principle to public health requires a re-evaluation of the methods of inference currently used to make claims about disease causation from epidemiologic and other forms of scientific evidence. In current thinking, a well-established, near-certain causal relationship implies highly consistent statistically significant results across many different studies, large relative risk estimates, extensive understanding of biological mechanisms and dose-response relationships, positive prevention trial results, a clear temporal relationship between cause and effect, and other conditions spelled out in terms of the widely-used causal criteria. The Precautionary Principle, however, states that preventive measures are to be taken when cause and effect relationships are not fully established scientifically. What evidentiary conditions, as reflected in the causal criteria, will be certain enough to warrant precautionary preventive action? This paper argues that minimum evidentiary requirements for causation need to be articulated if the Precautionary Principle is to be successfully incorporated into public health practice. Two precautionary changes to criteria-based methods of causal inference are examined: reducing the number of criteria and weakening the rules of inference accompanying the criteria. Such changes point in the direction of identifying minimum evidentiary conditions, but would be premature without better understanding how well current methods of causal inference work.     

Mechanistic Modeling of Salmonellosis

The serious limitation of the available human data contributes to the need for making simplifying assumptions for dose-response modeling which has led to frequent use of a single function, the beta-Poisson function, as a default dose-response model form. This function is a concave, low-dose linear function. Sub-linear or convex curves may be more appropriate for some host-pathogen interactions due to the series of highly regulated innate and acquired defense systems of the healthy human body that protect against most microbial challenges. A systematic investigation of the steps of non-typhoid salmonellosis in humans leads to biological motivations for sub-linear, or non-concave, dose-response curves in microbial risk assessment. Three phenomena were identified that might contribute to sub-linear, or non-concave, dose-response curves: (1) clumping of bacterial cells in microcolonies in a food matrix; (2) quorum sensing, or density-dependency in expression of virulence genes or other metabolic actions; and (3) need, at least in some circumstances, for multiple lesions for progression to symptomatic illness. This investigation suggests that microbial risk assessors should routinely employ a variety of model forms in addition to the commonly used beta-Poisson model to depict more fully the uncertainty of the true dose-response model.     

Likelihood models for clustered binary and continuous outcomes: application to developmental toxicology

In developmental toxicology, methods based on dose response modeling and quantitative risk assessment are being actively pursued. Among live fetuses, the presence of malformations and reduction in fetal weight are of primary interest, but ordinarily, the dose-response relationships are characterized in each of the outcomes separately while appropriately accounting for clustering within litters. Jointly modeling the outcomes, allowing different relationships with dose while incorporating the correlation between the fetuses and the outcomes, may be more appropriate. We propose a likelihood-based model that is an extension of a correlated probit model to incorporate continuous outcomes. Our model maintains a marginal dose-response interpretation for the individual outcomes while taking into account both the correlations between outcomes on an individual fetus and those due to clustering. The joint risk of malformation and low birth weight can then be estimated directly. This approach is particularly well suited to estimating safe dose levels as part of quantitative risk assessment.     

The darkness at the end of the tunnel: summary and evaluation of an international symposium on light,endocrine systems and cancer

Research on light at night and cancer is evolving at an accelerating pace, fueled largely by exciting results in rodent toxicology and basic human biology. Epidemiologic research is at a relatively early stage of development in which the exposure surrogates such as shift work and blindness predominate. Causal graphs for shift work, light at night and breast cancer illustrate some of the subtleties that can arise in the use of exposure surrogates of different kinds. Baseline data on circadian rhythms and melatonin cycles among human populations living at different latitudes are needed. Epidemiologic study of this topic is expected to mature soon as studies begin to incorporate quantitative and semiquantitative measurements and personal histories of exposure to light at night. The current emphasis on breast cancer should widen to include other cancers and intermediate outcomes. An advance in epidemiologic studies of blind persons would be to compare cancer rates between the "cortically blind" and the "retinally blind" within levels of visual impairment. Without a proposed intervention to reduce exposure to light at night, attributable fraction and attributable caseload estimates are meaningless. In the near future, both epidemiologic and laboratory research in this area are expected to grow appreciably in scope and scale.     

Proposed reference dose for toxaphene carcinogenicity based on constitutive androstane receptor-mediated mode of action

Toxaphene is a liver tumor promoter in B6C3F1 mice but not in F344 rats or hamsters. Recent studies demonstrate that key events leading to the mouse liver tumor response for toxaphene are mediated by activation of the constitutive androstane receptor (CAR). Benchmark dose modeling was conducted on available data for five endpoints in B6C3F1 mouse liver tissue or cultured liver cells (tumor response, cytotoxicity, proliferation, gap junction intercellular communication inhibition, and CAR-mediated CYP2B10 induction) and for CAR activation in human HepG2 cells, all reported in previous studies. The available evidence supports a nonlinear CAR-mediated mode of action (MOA) for toxaphene-induced mouse tumors including demonstration of a J-shaped dose-response pattern for human CAR activation, indicating that linear risk extrapolation at low doses is not supported for this MOA. Based on analysis of benchmark dose lower confidence limits at 10% response (BMDL10) and no observed effect levels (NOELs) for potential key events in the mouse liver tumor MOA for toxaphene, an RfD of 0.13 mg/kg-d is proposed based on a the BMDL10 for human CAR activation in human HepG2 cells. This value is below candidate RfD values based on BMDL10 estimates for both mouse liver tumors and mouse hepatocyte proliferation and therefore can be considered protective for human risk of liver tumor promotion and other CAR-mediated adverse health effects based on available data.     

Prospects for Biotechnology in Oil Palm (Elaeis guineensis) and Coconut (Cocos nucifera) Improvement

Abstract

Research into human metabolism is expanding rapidly due to the emergence of metabolism as a key factor in common diseases. Mathematical modeling of human cellular metabolism has traditionally been performed via kinetic approaches whose applicability for large-scale systems is limited by lack of kinetic constants data. An alternative computational approach bypassing this hurdle called constraint-based modeling (CBM) serves to analyze the function of large-scale metabolic networks by solely relying on simple physical-chemical constraints. However, while extensive research has been performed on constraint-based modeling of microbial metabolism, large-scale modeling of human metabolism is still in its infancy. Utilizing constraint-based modeling to model human cellular metabolism is significantly more complicated than modeling microbial metabolism as in multi-cellular organisms the metabolic behavior varies across cell-types and tissues. It is further complicated due to lack of data on cell type- and tissue-specific metabolite uptake from the surrounding microenvironments and tissue-specific metabolic objective functions. To overcome these problems, several studies suggested CBM methods that integrate metabolic networks with gene expression data that is easily measurable under various conditions. This specific objective functions are expected to improve the prediction accuracy of the presented methods. Such objective functions may be derived based on computational learning that would give optimal correspondence between predicted and measured metabolic phenotypes (Burgard, 2003).

The CBM methods presented here open the way for future computational investigations of metabolic disorders given the relevant expression data. A first attempt to visualize and interpret changes in gene expression data measured following gastric bypass surgery via a genome-scale metabolic network was done by Duarte et al (Duarte, 2007). Another potential application would be the prediction of diagnostic biomarkers for metabolic diseases that could be identified via biofluid metabolomics (Kell, 2007). Towards this goal, we have recently developed a CBM method for predicting metabolic biomarkers for in-born errors of metabolism by searching for changes in metabolite uptake and secretion rate due to genetic alterations (Shlomi, 2009). Incorporating cell type- and tissue-specific gene expression data within this framework can potentially improve the identification of diagnostic biomarkers. Overall, the methods presented here lay the foundation for studying normal and abnormal human cellular metabolism in tissue-specific manner based on commonly measured gene expression data.     

Dose-Response Envelope for Escherichia coli O157:H7

Escherichia coli O157:H7 is an emerging food and waterborne pathogen in the U.S. and internationally. The objective of this work was to develop a dose-response model for illness by this organism that bounds the uncertainty in the dose-response relationship. No human clinical trial data are available for E. coli O157:H7, but such data are available for two surrogate pathogens: enteropathogenic E. coli (EPEC) and Shigella dysenteriae. E. coli O157:H7 outbreak data provide an initial estimate of the most likely value of the dose-response relationship within the bounds of an envelope defined by beta-Poisson dose-response models fit to the EPEC and S. dysenteriae data. The most likely value of the median effective dose for E. coli O157:H7 is estimated to be approximately 190[emsp4 ]000 colony forming units (cfu). At a dose level of 100[emsp4 ]cfu, the median response predicted by the model is six percent.     

Univariate Dose-Response Models in Case-Control Studies

For modelling dose-response relationships in case-control studies the multiplicative logistic regression model, assuming the relative risk to be an exponential function of the dose, is widely known. If the relative risk is assumed to be a linear function of the dose, several authors (see e.g. BERRY (1980)) have proposed an additive (linear) model. This model has a better fit with the data if such a linear relation holds. Confidence limits for the relative risk derived from the information matrix, however, appear to be rather inaccurate. Therefore, use of the ‘standard’ logistic model in two different ways was studied: extension with a quadratic term or a logarithmic transformation of the dose. By applying the methods both to an empirical data set and in a simulation experiment, it is shown that appropriate transformation (often logarithmic) of the dosage and then applying the ‘standard’ logistic model is an useful approach if a linear dose-response relationship holds.     

Estimation of the benchmark dose by structural equation models

While epidemiological data typically contain a multivariate response and often also multiple exposure parameters, current methods for safe dose calculations, including the widely used benchmark approach, rely on standard regression techniques. In practice, dose-response modeling and calculation of the exposure limit are often based on the seemingly most sensitive outcome. However, this procedure ignores other available data, is inefficient, and fails to account for multiple testing. Instead, risk assessment could be based on structural equation models, which can accommodate both a multivariate exposure and a multivariate response function. Furthermore, such models will allow for measurement error in the observed variables, which is a requirement for unbiased estimation of the benchmark dose. This methodology is illustrated with the data on neurobehavioral effects in children prenatally exposed to methylmercury, where results based on standard regression models cause an underestimation of the true risk.     

Tea drinking and cancer risk: epidemiologic evidence

Tea and tea compounds have been shown to inhibit carcinogenic processes in experimental animals, raising the possibility that tea drinking may lower cancer risk in humans. However, epidemiologic studies have produced inconsistent evidence on the relation between tea drinking and cancer risk. Ecological data show considerable international variation in tea consumption but relatively small differences in cancer rates. Results from case-control and cohort studies also are inconclusive. Nevertheless, high consumption of tea has been linked to a reduced risk of digestive tract cancers in a number of epidemiologic studies. A lack of detailed information on duration and amount of tea drinking, a narrow range of tea intake in some study populations, inadequate control for confounding, and potential biases in recall and reporting of tea drinking patterns in case-control studies may have contributed to the diverse findings. Further research is needed before definitive conclusions on tea's impact upon cancer risk in humans can be reached.