Determining “safe” levels of exposure: The validity of the use of 10X safety factors

The current guideline for risk assessment of chemicals having a toxic end point routinely uses the reference dose (RfD) approach based on uncertainty factors of 10. With this method the quality of individual risk assessment varies among chemicals, often resulting in either an over‐ or under‐estimation of adverse health risk. The purpose of this investigation is to evaluate whether the magnitude of the 10X uncertainty factors have scientific merit against data from published experimental studies. A compilation and comparison of ratios between LOAEL/NOAEL (Lowest Observed Adverse Effect Level/No Observed Adverse Effect Level), subchronic/chronic, and animal/human values were made. The results of the present investigation revealed that the use of default factors could be over‐conservative or unprotective. More reasonable estimates of the risk to human health would result in a reduction of unnecessary, and expensive over‐regulation. In addition to the LOAEL to NOAEL, and subchronic to chronic ratios, the adequacy of uncertainty factors for animal to human extrapolations were examined. Although a 10‐fold uncertainty factor (UF) is most commonly used in the risk assessment process, an examination of the literature for the compounds presented here suggests that the use of different values is scientifically justifiable.     

Robust benchmark dose estimation using an infinite family of response functions

Researchers usually estimate benchmark dose (BMD) for dichotomous experimental data using a binomial model with a single response function. Several forms of response function have been proposed to fit dose–response models to estimate the BMD and the corresponding benchmark dose lower bound (BMDL). However, if the assumed response function is not correct, then the estimated BMD and BMDL from the fitted model may not be accurate. To account for model uncertainty, model averaging (MA) methods are proposed to estimate BMD averaging over a model space containing a finite number of standard models. Usual model averaging focuses on a pre-specified list of parametric models leading to pitfalls when none of the models in the list is the correct model. Here, an alternative which augments an initial list of parametric models with an infinite number of additional models having varying response functions has been proposed to estimate BMD for dichotomous response data. In addition, different methods for estimating BMDL based on the family of response functions are derived. The proposed approach is compared with MA in a simulation study and applied to a real dataset. Simulation studies are also conducted to compare the four methods of estimating BMDL.     

Benchmark dose profiles for joint‐action continuous data in quantitative risk assessment

Benchmark analysis is a widely used tool in biomedical and environmental risk assessment. Therein, estimation of minimum exposure levels, called benchmark doses (BMDs), that induce a prespecified benchmark response (BMR) is well understood for the case of an adverse response to a single stimulus. For cases where two agents are studied in tandem, however, the benchmark approach is far less developed. This paper demonstrates how the benchmark modeling paradigm can be expanded from the single‐agent setting to joint‐action, two‐agent studies. Focus is on continuous response outcomes. Extending the single‐exposure setting, representations of risk are based on a joint‐action dose–response model involving both agents. Based on such a model, the concept of a benchmark profile—a two‐dimensional analog of the single‐dose BMD at which both agents achieve the specified BMR—is defined for use in quantitative risk characterization and assessment.     

Benchmark Dose Profiles for Joint‐Action Quantal Data in Quantitative Risk Assessment

Summary Benchmark analysis is a widely used tool in public health risk analysis. Therein, estimation of minimum exposure levels, called Benchmark Doses (BMDs), that induce a prespecified Benchmark Response (BMR) is well understood for the case of an adverse response to a single stimulus. For cases where two agents are studied in tandem, however, the benchmark approach is far less developed. This article demonstrates how the benchmark modeling paradigm can be expanded from the single‐dose setting to joint‐action, two‐agent studies. Focus is on response outcomes expressed as proportions. Extending the single‐exposure setting, representations of risk are based on a joint‐action dose–response model involving both agents. Based on such a model, the concept of a benchmark profile (BMP) – a two‐dimensional analog of the single‐dose BMD at which both agents achieve the specified BMR – is defined for use in quantitative risk characterization and assessment. The resulting, joint, low‐dose guidelines can improve public health planning and risk regulation when dealing with low‐level exposures to combinations of hazardous agents.     

Improving Risk Assessment: Priorities for Epidemiologic Research

The Epidemiology Work Group at the Workshop on Future Research for Improving Risk Assessment Methods, Of Mice, Men, and Models, held August 16 to 18, 2000, at Snowmass Village, Aspen, Colorado, concluded that in order to improve the utility of epidemiologic studies for risk assessment, methodologic research is needed in the following areas: (1) aspects of epidemiologic study designs that affect doseresponse estimation; (2) alternative methods for estimating dose in human studies; and (3) refined methods for dose-response modeling for epidemiologic data. Needed research in aspects of epidemiologic study design includes recognition and control of study biases, identification of susceptible subpopulations, choice of exposure metrics, and choice of epidemiologic risk parameters. Much of this research can be done with existing data. Research needed to improve determinants of dose in human studies includes additional individual-level data (e.g., diet, co-morbidity), development of more extensive human data for physiologically based pharmacokinetic (PBPK) dose modeling, tissue registries to increase the availability of tissue for studies of exposure/dose and susceptibility biomarkers, and biomarker data to assess exposures in humans and animals. Research needed on dose-response modeling of human studies includes more widespread application of flexible statistical methods (e.g., general additive models), development of methods to compensate for epidemiologic bias in dose-response models, improved biological models using human data, and evaluation of the benchmark dose using human data. There was consensus among the Work Group that, whereas most prior risk assessments have focused on cancer, there is a growing need for applications to other health outcomes. Developmental and reproductive effects, injuries, respiratory disease, and cardiovascular disease were identified as especially high priorities for research. It was also a consensus view that epidemiologists, industrial hygienists, and other scientists focusing on human data need to play a stronger role throughout the risk assessment process. Finally, the group agreed that there was a need to improve risk communication, particularly on uncertainty inherent in risk assessments that use epidemiologic data.     

Uncertainty Evaluation of a Groundwater Conceptual Model by Using a Multimodel Averaging Method

ABSTRACT

A groundwater field is a complex and open system. Groundwater simulation and prediction often deviated from true values, which is attributed to the uncertainty of groundwater modeling. The conceptual model (model struture) is one of the main sources of groundwater modeling uncertianty. In this study, the mean Euclidean distance (MED) between model simulations and observations is proposed to assess the integrated likelihood value of a conceptual model in Bayesian model averaging (BMA). Moreover, this proposed BMA method is compared with the traditional generalized likelihood uncertainty estimation (GLUE) BMA method by a synthetical groundwater model, and the characteristics of these two BMA methods are summarized.     

Importance of dose‐response model form in probabilistic risk assessment: A case study of health effects from methylmercury in fish

We compared the effect of uncertainty in dose‐response model form on health risk estimates to the effect of uncertainty and variability in exposure. We used three different dose‐response models to characterize neurological effects in children exposed in utero to methylmercury, and applied these models to calculate risks to a native population exposed to potentially contaminated fish from a reservoir in British Columbia. Uncertainty in model form was explicitly incorporated into the risk estimates. The selection of dose‐response model strongly influenced both mean risk estimates and distributions of risk, and had a much greater impact than altering exposure distributions. We conclude that incorporating uncertainty in dose‐response model form is at least as important as accounting for variability and uncertainty in exposure parameters in probabilistic risk assessment.     

Probabilistic Analysis of Human Health Risks Associated with Background Concentrations of Inorganic Arsenic: Use of a Margin of Exposure Approach

Substantial evidence exists from epidemiological and mechanistic studies supporting a sublinear or threshold dose–response relationship for the carcinogenicity of ingested arsenic; nonetheless, current regulatory agency evaluations have quantified arsenic risks using default, generic risk assessment procedures that assume a linear, no-threshold dose–response relationship. The resulting slope factors predict risks from U.S. background arsenic exposures that exceed certain regulatory levels of concern, an outcome that presents challenges for risk communication and risk management decisions. To better reflect the available scientific evidence, this article presents the results of a Margin of Exposure (MOE) analysis to characterize risks associated with typical and high-end background exposures of the U.S. population to arsenic from food, water, and soil. MOE values were calculated by comparing a no-observable-adverse-effect-level (NOAEL) derived from the epidemiological literature with exposure estimates generated using a probabilistic (Monte Carlo) model. The plausibility and conservative nature of the exposure and risk estimates evaluated in this analysis are supported by sensitivity and uncertainty analyses and by comparing predicted urinary arsenic concentrations with empirical data. Using the more scientifically supported MOE approach, the analysis presented in this article indicates that typical and high-end background exposures to inorganic arsenic in U.S. populations do not present elevated risks of carcinogenicity.     

Confidence bands for low-dose risk estimation with quantal response data

We study the use of simultaneous confidence bands for low-dose risk estimation with quantal response data, and derive methods for estimating simultaneous upper confidence limits on predicted extra risk under a multistage model. By inverting the upper bands on extra risk, we obtain simultaneous lower bounds on the benchmark dose (BMD). Monte Carlo evaluations explore characteristics of the simultaneous limits under this setting, and a suite of actual data sets are used to compare existing methods for placing lower limits on the BMD.     

Harmonization: Developing Consistent Guidelines for Applying Mode of Action and Dosimetry Information to Cancer and Noncancer Risk Assessment

The 1983 book, Risk Assessment in the Federal Government: Managing the Process, recommended developing consistent inference guidelines for cancer risk assessment. Over the last 15 years, extensive guidance have been provided for hazard assessment for cancer and other endpoints. However, as noted in several recent reports, much less progress has occurred in developing consistent guidelines for quantitative dose response assessment methodologies. This paper proposes an approach for dose response assessment guided by consideration of mode of action (pharmacodynamics) and tissue dosimetry (pharmacokinetics). As articulated here, this systematic process involves eight steps in which available information is integrated, leading first to quantitative analyses of dose response behaviors in the test species followed by quantitative analyses of relevant human exposures. The process should be equally appropriate for both cancer and noncancer endpoints. The eight steps describe the necessary procedures for incorporating mechanistic data and provide multiple options based upon the mode of action by which the chemical causes the toxicity. Given the range of issues involved in developing such a procedure, we have simply sketched the process, focusing on major approaches for using toxicological data and on major options; many details remain to be filled in. However, consistent with the revised carcinogen risk assessment guidance (USEPA, 1996c), we propose a process that would ultimately utilize biologically based or chemical specific pharmacokinetic and pharmacodynamic models as the backbone of these analyses. In the nearer term, these approaches will be combined with analysis of data using more empirical models including options intended for use in the absence of detailed information. A major emphasis in developing any harmonized process is distinguishing policy decisions from those decisions that are affected by the quality and quantity of toxicological data. Identification of data limitations also identifies areas where further study should reduce uncertainty in the final risk evaluations. A flexible dose response assessment procedure is needed to insure that sound toxicological study results are appropriately used to influence risk management decision-making and to encourage the conduct of toxicological studies oriented toward application for dose response assessments.     

Automated estimation of ankle muscle EMG envelopes and resulting plantar-dorsi flexion torque from 64 garment-embedded electrodes uniformly distributed around the human leg

The design of personalized movement training and rehabilitation pipelines relies on the ability of assessing the activation of individual muscles concurrently with the resulting joint torques exerted during functional movements. Despite advances in motion capturing, force sensing and bio-electrical recording technologies, the estimation of muscle activation and resulting force still relies on lengthy experimental and computational procedures that are not clinically viable. This work proposes a wearable technology for the rapid, yet quantitative, assessment of musculoskeletal function. It comprises of (1) a soft leg garment sensorized with 64 uniformly distributed electromyography (EMG) electrodes, (2) an algorithm that automatically groups electrodes into seven muscle-specific clusters, and (3) a EMG-driven musculoskeletal model that estimates the resulting force and torque produced about the ankle joint sagittal plane. Our results show the ability of the proposed technology to automatically select a sub-set of muscle-specific electrodes that enabled accurate estimation of muscle excitations and resulting joint torques across a large range of biomechanically diverse movements, underlying different excitation patterns, in a group of eight healthy individuals. This may substantially decrease time needed for localization of muscle sites and electrode placement procedures, thereby facilitating applicability of EMG-driven modelling pipelines in standard clinical protocols.     

Interspecies extrapolation based on mechanistic determinants of chemical disposition

Uncertainty factors are applied in methods developed by the Environmental Protection Agency (EPA) to derive dose‐response estimates. The uncertainty factors are applied to account for uncertainties in defined extrapolations from the laboratory animal experimental data conditions to a dose‐response estimate appropriate for the assumed human scenario. The conceptual difference between these uncertainty factors and safety factors is best illustrated by how uncertainty factors can be modified as scientific data inform our understanding of the key factors that influence chemical disposition and toxicity. Mechanistic data help describe the major factors influencing chemical disposition and toxicant‐target tissue interactions, and should increase the accuracy of exposure‐dose‐response assessment. Mechanistic data on the determinants of inhaled chemical disposition were used to construct default dosimetry adjustments applied by the EPA in its inhalation Reference Concentration (RfC) methods. Because these adjustments account for interspecies dosimetric differences to some degree, the uncertainty factor for interspecies extrapolation was modified. A framework is presented that allows for incorporation of mechanistic data in order to ensure that required extrapolations are commensurate with the state‐of‐the‐science. Future applications of mechanistic data to modify additional uncertainty factors are outlined.     

Variance in Bacillus anthracis virulence assessed through Bayesian hierarchical dose-response modelling

Aims: To develop a predictive dose–response model for describing the survival of animals exposed to Bacillus anthracis to support risk management options. Methods and Results: Dose–response curves were generated from a large dose–mortality data set (>11 000 data points) consisting of guinea pigs exposed via the inhalation route to 76 different product preparations of B. anthracis. Because of the predictive nature of the Bayesian hierarchical approach (BHA), this method was used. The utility of this method in planning for a variety of scenarios from best case to worst case was demonstrated. Conclusions: A wide range of expected virulence was observed across products. Median estimates of virulence match well with previously published statistical estimates, but upper bound values of virulence are much greater than previous statistical estimates. Significance and Impact of the Study: This study is the first meta‐analysis in open literature to estimate the dose–response relationship for B. anthracis from a very large data set, generally a rare occurrence for highly infectious pathogens. The results are also the first to suggest the extent of variability, which is contributed by product preparation and/or dissemination methods, information needed for health‐based risk management decisions in response to a deliberate release. A set of possible benchmark values produced through this analysis can be tied to the risk tolerance of the decision‐maker or available intelligence. Further, the substantial size of the data set led to the ability to assess the appropriateness of the assumed distributional form of the prior, a common limitation in Bayesian analysis.     

Simultaneous versus sequential optimal experiment design for the identification of multi-parameter microbial growth kinetics as a function of temperature

Optimal experiment design for parameter estimation (OED/PE) has become a popular tool for efficient and accurate estimation of kinetic model parameters. When the kinetic model under study encloses multiple parameters, different optimization strategies can be constructed. The most straightforward approach is to estimate all parameters simultaneously from one optimal experiment (single OED/PE strategy). However, due to the complexity of the optimization problem or the stringent limitations on the system's dynamics, the experimental information can be limited and parameter estimation convergence problems can arise. As an alternative, we propose to reduce the optimization problem to a series of two-parameter estimation problems, i.e., an optimal experiment is designed for a combination of two parameters while presuming the other parameters known. Two different approaches can be followed: (i) all two-parameter optimal experiments are designed based on identical initial parameter estimates and parameters are estimated simultaneously from all resulting experimental data (global OED/PE strategy), and (ii) optimal experiments are calculated and implemented sequentially whereby the parameter values are updated intermediately (sequential OED/PE strategy).This work exploits OED/PE for the identification of the Cardinal Temperature Model with Inflection (CTMI) (Rosso et al., 1993). This kinetic model describes the effect of temperature on the microbial growth rate and encloses four parameters. The three OED/PE strategies are considered and the impact of the OED/PE design strategy on the accuracy of the CTMI parameter estimation is evaluated. Based on a simulation study, it is observed that the parameter values derived from the sequential approach deviate more from the true parameters than the single and global strategy estimates. The single and global OED/PE strategies are further compared based on experimental data obtained from design implementation in a bioreactor. Comparable estimates are obtained, but global OED/PE estimates are, in general, more accurate and reliable.     

Incorporating extra information in experimental design for bioassay

In a bioassay, under certain experimental circumstances, information on concentration (dose rate) and time to response for some subjects can be combined in a single analysis. An underlying logistic random variable is assumed and the resulting mixed- (continuous-quantal) response model is analyzed by likelihood methods. The estimation procedure for the mean and the variance is described, and expressions for asymptotic variances are obtained. A comparison of results from the mixed model and from the standard quantal-response model shows that there is a substantial reduction in the variance of the estimators for the mixed model. On the basis of the table of asymptotic variances, some design implications are discussed. An example from insect pheromone research is used to illustrate the main ideas.     

Studies on the use of uncertainty factors in deriving RfDs

Risk assessment of exposure to chemicals having a toxic end point routinely uses the reference dose (RfD) approach based on uncertainty factors of 10. RfD model can be used with widely different databases. However, the quality of individual risk assessment is unequal among chemicals, often resulting in either an over‐ or underestimation of adverse health risk. The purpose of this investigation was to evaluate whether the magnitude of the 10X uncertainty factors has scientific merit against data from recent human and animal experimental studies. Although we assessed the use of uncertainty factors for representative chemicals from various classes of compounds, such as volatile organics, alcohols, gasoline components, and pesticides, we are presenting our findings for 24 chemicals.

A compilation and comparison of ratios between LOAEL/NOAEL (Lowest Observed Adverse Effect Level/No Observed Adverse Effect Level), and subchronic/chronic values were made. Although a 10X uncertainty factor is most commonly used in the risk assessment processes, an examination of the datasets which have been used to calculate RfDs suggests different values which are scientifically justifiable.     

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.     

Human exposure to endocrine disrupters: carcinogenic risk assessment

Human exposure to endocrine disrupters (EDs) is widespread and is considered to pose a growing threat to human health. Recent advances in molecular and genetic research and better understanding of mechanisms of blastic cell transformation have led to efforts to improve cancer risk assessment for populations exposed to this family of xenobiotics. In risk assessment, low dose extrapolation of cancer incidence data from both experimental animals and epidemiology studies has been largely based on models assuming linear correlation at low doses, despite existence of evidence showing otherwise. Another weakness of ED risk assessment is poor exposure data in ecological studies. Those are frequently rough estimates derived from contaminated items of local food basket surveys. Polyhalogenated hydrocarbons are treated as examples. There is growing sense of urgency to develop a biologically based dose response model of cancer risk, integrating emerging data from molecular biology and epidemiology to provide more realistic data for risk assessors, public, public health managers and environmental issues administrators.