Incidence of developmental defects at the no observed adverse effect level (NOAEL)

A signal-to-noise crossover dose as the point of departure for health risk assessment

BACKGROUND

The U.S. National Toxicology Program (NTP) cancer bioassay database provides an opportunity to compare both existing and new approaches to determining points of departure (PoDs) for establishing reference doses (RfDs).

OBJECTIVES

The aims of this study were a) to investigate the risk associated with the traditional PoD used in human health risk assessment [the no observed adverse effect level (NOAEL)]; b) to present a new approach based on the signal-to-noise crossover dose (SNCD); and c) to compare the SNCD and SNCD-based RfD with PoDs and RfDs based on the NOAEL and benchmark dose (BMD) approaches.

METHODS

The complete NTP database was used as the basis for these analyses, which were performed using the Hill model. We determined NOAELs and estimated corresponding extra risks. Lower 95% confidence bounds on the BMD (BMDLs) corresponding to extra risks of 1%, 5%, and 10% (BMDL01, BMDL05, and BMDL10, respectively) were also estimated. We introduce the SNCD as a new PoD, defined as the dose where the additional risk is equal to the "background noise" (the difference between the upper and lower bounds of the two-sided 90% confidence interval on absolute risk) or a specified fraction thereof.

RESULTS

The median risk at the NOAEL was approximately 10%, and the default uncertainty factor (UF = 100) was considered most applicable to the BMDL10. Therefore, we chose a target risk of 1/1,000 (0.1/100) to derive an SNCD-based RfD by linear extrapolation. At the median, this approach provided the same RfD as the BMDL10 divided by the default UF.

CONCLUSIONS

Under a standard BMD approach, the BMDL10 is considered to be the most appropriate PoD. The SNCD approach, which is based on the lowest dose at which the signal can be reliably detected, warrants further development as a PoD for human health risk assessment.

Assessing and managing risks arising from exposure to endocrine-active chemicals

Managing risks to human health and the environment produced by endocrine-active chemicals (EAC) is dependent on sound principles of risk assessment and risk management, which need to be adapted to address the uncertainties in the state of the science of EAC. Quantifying EAC hazard identification, mechanisms of action, and dose-response curves is complicated by a range of chemical structure/toxicology classes, receptors and receptor subtypes, and nonlinear dose-response curves with low-dose effects. Advances in risk science including toxicogenomics and quantitative structure-activity relationships (QSAR) along with a return to the biological process of hormesis are proposed to complement existing risk assessment strategies, including that of the Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC 1998). EAC represents a policy issue that has captured the public's fears and concerns about environmental health. This overview describes the process of EAC risk assessment and risk management in the context of traditional risk management frameworks, with emphasis on the National Research Council Framework (1983), taking into consideration the strategies for EAC management in Canada, the United States, and the European Union.

Statistics for risk assessment of chemical carcinogens

Risk assessment is a scientific process of evaluation of potential health risks of chemical exposures to humans from available information. It involves analysis of the relationship between exposure and health related outcomes to derive an allowable exposure level. Because of lack of human exposure data, the major source of information for studying potential health effects of chemicals on humans is generally obtained from animal dose response experiments. Animal data are often evaluated in two aspects via statistical analysis: qualitative testing and quantitative estimation. The qualitative testing is to determine if the chemical causes an adverse health effect, i.e., if there is a statistically significant difference between treated and control animals. Quantitative estimation involves fitting a dose-response model to derive an allowable exposure level for humans. This paper reviews statistical principles and procedures for qualitative and quantitative approaches to human risk assessment.

Bootstrap estimation of benchmark doses and confidence limits with clustered quantal data

The benchmark dose (BMD) is an exposure level that would induce a small risk increase (BMR level) above the background. The BMD approach to deriving a reference dose for risk assessment of noncancer effects is advantageous in that the estimate of BMD is not restricted to experimental doses and utilizes most available dose-response information. To quantify statistical uncertainty of a BMD estimate, we often calculate and report its lower confidence limit (i.e., BMDL), and may even consider it as a more conservative alternative to BMD itself. Computation of BMDL may involve normal confidence limits to BMD in conjunction with the delta method. Therefore, factors, such as small sample size and nonlinearity in model parameters, can affect the performance of the delta method BMDL, and alternative methods are useful. In this article, we propose a bootstrap method to estimate BMDL utilizing a scheme that consists of a resampling of residuals after model fitting and a one-step formula for parameter estimation. We illustrate the method with clustered binary data from developmental toxicity experiments. Our analysis shows that with moderately elevated dose-response data, the distribution of BMD estimator tends to be left-skewed and bootstrap BMDL s are smaller than the delta method BMDL s on average, hence quantifying risk more conservatively. Statistically, the bootstrap BMDL quantifies the uncertainty of the true BMD more honestly than the delta method BMDL as its coverage probability is closer to the nominal level than that of delta method BMDL. We find that BMD and BMDL estimates are generally insensitive to model choices provided that the models fit the data comparably well near the region of BMD. Our analysis also suggests that, in the presence of a significant and moderately strong dose-response relationship, the developmental toxicity experiments under the standard protocol support dose-response assessment at 5% BMR for BMD and 95% confidence level for BMDL.

A benchmark dose analysis for sodium monofluoroacetate (1080) using dichotomous toxicity data

The use of a benchmark dose (BMD) as an alternative to a no-observed-adverse-effect-level (NOAEL) approach was investigated as a means to improve current risk assessment values of sodium monofluoroacetate (1080). The feasibility of implementing the two approaches was investigated for three critical toxicological end points, namely cardiomyopathy, testicular toxicity and teratogenic effects identified from the few available critical studies. The BMD provides better representation of the dose-response relationship, offering an advantage over the current NOAEL approach. The calculated BMDs and lower-bound confidence limits (BMDLs) for the three end points were estimated using the Weibull, probit and quantal linear models for each end point. All models passed the chi2 test statistics (p > or = 0.1) for all three toxicity endpoints tested. A benchmark response (BMR) of 10% (extra risk) was chosen and the Akaike's information criterion (AIC) was used in selecting the appropriate model. The BMDL estimates derived were found to be generally slightly higher but comparable to the NOAEL for those same endpoints. The BMD(10) and BMDL(10) for cardiomyopathy and testicular effects were 0.21 mgkg(-1) bw and 0.10 mgkg(-1) bw, respectively. These values are proposed for use in the eventual determination of the tolerable daily intake (TDI) for 1080.

The use of myocardial and testicular end points as a basis for estimating a proposed tolerable daily intake for sodium monofluoroacetate (1080)

This paper presents the development of a tolerable daily intake (TDI) for sodium monofluoroacetate (1080) using the quantal myocardial and testicular toxicity end points derived from the traditional NOAEL and newer benchmark dose (BMD) methods. 1080 is a highly toxic vertebrate pesticide that has been proven to be effective in controlling possums and other pests. By convention, the TDIs are derived using the traditional no-observed-adverse-effect-level (NOAEL) and applying appropriate default uncertainty factors (UF). In addition to the default UF, a statistically derived UF was also employed in deriving the TDI. The TDIs derived from the NOAEL and BMD approach, 0.075 and 0.10 mg/kg bw/day, respectively, were compared. The resulting TDI estimates using the BMDL, a statistical lower confidence bound on the BMD, were generally consistently slightly higher than those derived using the NOAEL approach. Based on the best fit of modelled dose-response data, a TDI of 0.03 micro g/kg bw/day is proposed for human health risk assessment of 1080.

Quantitative risk assessment for developmental neurotoxic effects

Developmental neurotoxicity concerns the adverse health effects of exogenous agents acting on neurodevelopment. Because human brain development is a delicate process involving many cellular events, the developing fetus is rather susceptible to compounds that can alter the structure and function of the brain. Today, there is clear evidence that early exposure to many neurotoxicants can severely damage the developing nervous system. Although in recent years, there has been much attention given to model development and risk assessment procedures for developmental toxicants, the area of developmental neurotoxicity has been largely ignored. Here, we consider the problem of risk estimation for developmental neurotoxicants from animal bioassay data. Since most responses from developmental neurotoxicity experiments are nonquantal in nature, an adverse health effect will be defined as a response that occurs with very small probability in unexposed animals. Using a two-stage hierarchical normal dose-response model, upper confidence limits on the excess risk due to a given level of added exposure are derived. Equivalently, the model is used to obtain lower confidence limits on dose for a small negligible level of risk. Our method is based on the asymptotic distribution of the likelihood ratio statistic (cf. Crump, 1995). An example is used to provide further illustration.

Development of acute exposure guideline levels for airborne exposures to hazardous substances

Hazardous substances can be released into the atmosphere due to industrial and transportation accidents, fires, tornadoes, earthquakes, and terrorists, thereby exposing workers and the nearby public to potential adverse health effects. Various enforceable guidelines have been set by regulatory agencies for worker and ambient air quality. However, these exposure levels generally are not applicable to rare lifetime acute exposures, which possibly could occur at high concentrations. Acute exposure guideline levels (AEGLs) provide estimates of concentrations for airborne exposures for an array of short durations that possibly could cause mild (AEGL-1), severe, irreversible, potentially disabling adverse health effects (AEGL-2), or life threatening effects (AEGL-3). These levels can be useful for emergency responders and planners in reducing or eliminating potential risks to the public. Procedures and methodologies for deriving AEGLs are reviewed in this paper that have been developed in the United States, with direct input from international representatives of OECD member-countries, by the National Advisory Committee for Acute Exposure Guidelines for Hazardous Substances and reviewed by the National Research Council. Techniques are discussed for the extrapolation of effects across different exposure durations. AEGLs provide a viable approach for assisting in the prevention, planning, and response to acute airborne exposures to toxic agents.

Oral-to-inhalation route extrapolation in occupational health risk assessment: a critical assessment

Due to a lack of route-specific toxicity data, the health risks resulting from occupational exposure are frequently assessed by route-to-route (RtR) extrapolation based on oral toxicity data. Insight into the conditions for and the uncertainties connected with the application of RtR extrapolation has not been clearly described in a systematic manner. In our opinion, for a reliable occupational health risk assessment, it is necessary to have insight into the accuracy of the routinely applied RtR extrapolation and, if possible, to give a (semi-)quantitative estimate of the possible error introduced. Therefore, experimentally established no-observed-adverse-effect-levels for inhalation studies were compared to no-adverse-effect-levels predicted from oral toxicity studies by RtR extrapolation. From our database analysis it can be concluded that the widely used RtR extrapolation methodology based on correction for differences in (estimates of) absorption is not generally reliable and certainly not valid for substances inducing local effects. More experimental data are required (from unpublished data or new experiments) to get insight into the reliability of RtR extrapolation and the possibility to derive an assessment factor to account for the uncertainties. Moreover, validated screening methods to predict/exclude the occurrence of local effects after repeated exposure are warranted. Especially, in cases where chemical exposure by inhalation or skin contact cannot be excluded route-specific toxicity studies should be considered to prevent from inadequate estimates of human health risks.

Optimal designs for estimating the effective dose in developmental toxicity experiments

Recent advances in risk assessment have led to the development of joint dose-response models to describe prenatal death and fetal malformation rates in developmental toxicity experiments. These models can be used to estimate the effective dose corresponding to a 5% excess risk for both these toxicological endpoints, as well as for overall toxicity. In this article, we develop optimal experimental designs for the estimation of the effective dose for developmental toxicity using joint Weibull dose-response models for prenatal death and fetal malformation. Based on an extended series of developmental studies, near-optimal designs for prenatal death, malformation, and overall toxicity were found to involve three dose groups: an unexposed control group, a high dose equal to the maximum tolerated dose, and a low dose above or comparable to the effective dose. The effect on the optimal designs of changing the number of implants and the degree of intra-litter correlation is also investigated. Although the optimal design has only three dose groups in most cases, practical considerations involving model lack of fit and estimation of the shape of the dose-response curve suggest that, in practice, suboptimal designs with more than three doses will often be preferred.

Assessing human health response in life cycle assessment using ED10s and DALYs: part 2--Noncancer effects

In Part 1 of this article we developed an approach for the calculation of cancer effect measures for life cycle assessment (LCA). In this article, we propose and evaluate the method for the screening of noncancer toxicological health effects. This approach draws on the noncancer health risk assessment concept of benchmark dose, while noting important differences with regulatory applications in the objectives of an LCA study. We adopt the centraltendency estimate of the toxicological effect dose inducing a 10% response over background, ED10, to provide a consistent point of departure for default linear low-dose response estimates (betaED10). This explicit estimation of low-dose risks, while necessary in LCA, is in marked contrast to many traditional procedures for noncancer assessments. For pragmatic reasons, mechanistic thresholds and nonlinear low-dose response curves were not implemented in the presented framework. In essence, for the comparative needs of LCA, we propose that one initially screens alternative activities or products on the degree to which the associated chemical emissions erode their margins of exposure, which may or may not be manifested as increases in disease incidence. We illustrate the method here by deriving the betaED10 slope factors from bioassay data for 12 chemicals and outline some of the possibilities for extrapolation from other more readily available measures, such as the no observable adverse effect levels (NOAEL), avoiding uncertainty factors that lead to inconsistent degrees of conservatism from chemical to chemical. These extrapolations facilitated the initial calculation of slope factors for an additional 403 compounds; ranging from 10(-6) to 10(3) (risk per mg/kg-day dose). The potential consequences of the effects are taken into account in a preliminary approach by combining the betaED10 with the severity measure disability adjusted life years (DALY), providing a screening-level estimate of the potential consequences associated with exposures, integrated over time and space, to a given mass of chemical released into the environment for use in LCA.

Characterization of the LOAEL-to-NOAEL uncertainty factor for mild adverse effects from acute inhalation exposures

This analysis was undertaken to reduce uncertainty in acute inhalation risk assessment for mild acute effects. Applying uncertainty factors (UFs) to the no-observed-adverse-effect level (NOAEL) is the primary approach used in threshold-based risk assessments. When a NOAEL is unavailable, a UF of 10 is often applied to a lowest-observed-adverse-effect level (LOAEL) to estimate the NOAEL. We evaluated the LOAEL-to-NOAEL relationship for mild acute inhalation toxicity for 215 data sets for 36 hazardous air pollutants. The LOAEL-to-NOAEL ratios were 2.0, 5.0, 6.3, and 10.0 for the 50th, 90th, 95th, and 99 th percentile, respectively. The 90% confidence interval for the 95th percentile was 5.0-7.5. Consequently, based on previous dose placement practice, the LOAEL-to-NOAEL UF of 6 would be protective for 95% of the responses, and a value of 10 would be protective of 99% of the responses. The ratio values were not associated with the size of the experimental group. There was little variability among species, particularly at the median. This analysis is reflective only of mild acute inhalation toxicity. For other exposure routes, exposure durations, or more severe toxicity, the distributions are likely to be different.

Critical effect sizes in toxicological risk assessment: a comprehensive and critical evaluation

A key issue in toxicological risk assessment is determining the effect level below which there is no reason for concern. In the Benchmark approach, this breaking point between adverse and non-adverse is called the critical effect size (CES). This study aimed to investigate the possibilities to determine CESs for toxicological effect parameters commonly used in human risk assessment and includes a literature review and an opinion analysis among European toxicologists. The results indicate that the current knowledge is insufficient to define CESs for all individual parameters. Furthermore, the use of a single universal CES seems no option. It is concluded that it is not yet possible to reach international consensus on CESs for most toxicological parameters. However, every parameter for which consensus on the CES is reached is a step forward, because this can facilitate discussions on the adversity and relevance of certain changes in that parameter, irrespective of the method applied in risk assessment.

Addressing human variability in risk assessment--the robustness of the intraspecies uncertainty factor

Addressing human variability and sensitive subpopulations is one of the challenges of risk assessment and is an important aspect of the Food Quality Protection Act, the law passed in 1996 that regulates food use pesticides in the United States. The intraspecies uncertainty factor is intended to address differences in susceptibility within the human population. This paper examines the history and scientific basis for the intraspecies uncertainty factor. Our best source of knowledge about human variability in the response to chemicals comes from clinical trials of pharmaceuticals. This large body of data allows both qualitative and quantitative characterization of variability in pharmacokinetic and pharmacodynamic parameters in the general population and in subgroups such as children. The preponderance of evidence in the areas of pharmacodynamics and pharmacokinetics supports the routine use of an intraspecies uncertainty factor in the range of 1-10 as being protective of greater than 99% of the human population. The intraspecies uncertainty factor is highly protective of various subpopulations, including infants and children.

Procedures for calculating benchmark doses for health risk assessment

Safety assessment for noncancer health effects generally has been based upon dividing a no observed adverse effect (NOAEL) by uncertainty (safety) factors to provide an acceptable daily intake (ADI) or reference dose (RfD). Since the NOAEL does not utilize all of the available dose-response data, allows higher ADI from poorer experiments, and may have an unknown, unacceptable level of risk, the benchmark dose (BD) with a specified, controlled low level of risk has become popular as an adjunct to the NOAEL or the low observed adverse effect level (LOAEL) in the safety assessment process. The purpose of this paper is to summarize statistical procedures available for calculating BDs and their confidence limits for noncancer endpoints. Procedures are presented and illustrated for quantal (binary), quasicontinuous (proportion), and continuous data. Quasicontinuous data arise in developmental studies where the measure of an effect for a fetus is quantal (normal or abnormal) but the experimental unit is the mother (litter) so that results can be expressed as the proportion of abnormal fetuses per litter. However, the correlation of effects among fetuses within a litter poses some additional statistical problems. Also, developmental studies usually include some continuous measures, such as fetal body weight or length. With continuous data there generally is not a clear demarcation between normal and adverse measurements. In such cases, extremely high and/or low measurements at some designated percentile(s) can be considered abnormal. Then the probability (risk) of abnormal individuals can be estimated as a function of dose. The procedure for estimating a BD with continuous data is illustrated using neurotoxicity data. When multiple measures of adverse effects are available, a BD can be estimated based on a selected endpoint or the appearance of any combination of endpoints. Multivariate procedures are illustrated using developmental and reproductive toxicity data.

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.

A probabilistic framework for the reference dose (probabilistic RfD)

Determining the probabilistic limits for the uncertainty factors used in the derivation of the Reference Dose (RfD) is an important step toward the goal of characterizing the risk of noncarcinogenic effects from exposure to environmental pollutants. If uncertainty factors are seen, individually, as "upper bounds" on the dose-scaling factor for sources of uncertainty, then determining comparable upper bounds for combinations of uncertainty factors can be accomplished by treating uncertainty factors as distributions, which can be combined by probabilistic techniques. This paper presents a conceptual approach to probabilistic uncertainty factors based on the definition and use of RfDs by the U.S. EPA. The approach does not attempt to distinguish one uncertainty factor from another based on empirical data or biological mechanisms but rather uses a simple displaced lognormal distribution as a generic representation of all uncertainty factors. Monte Carlo analyses show that the upper bounds for combinations of this distribution can vary by factors of two to four when compared to the fixed-value uncertainty factor approach. The probabilistic approach is demonstrated in the comparison of Hazard Quotients based on RfDs with differing number of uncertainty factors.

Comparison of noncancer risk assessment approaches for use in deriving drinking water criteria

The development and promulgation of drinking water regulations to protect exposed human populations from contaminants that may occur in public drinking water supplies has been a major regulatory concern and effort of the United States Environmental Protection Agency for decades. Risk assessment, as applied in the development of drinking water regulations, involves the quantification of the level below which adverse health effects are not expected to occur. Traditionally, the oral reference dose (RfD) has been the preferred approach for characterizing these noncancer health risks. The benchmark dose approach to derive RfDs has increasingly gained scientific and regulatory acceptance as a risk assessment methodology since its introduction in 1984. Similarly, the use of categorical regression techniques were introduced at about the same time. The objective of this paper is to present an evaluation of the strengths and weaknesses of each risk assessment method as related to the development of drinking water criteria for noncarcinogenic chemicals. The data base requirements, performance record, mathematical or statistical basis, and other parameters are described and compared.

Health risk assessment practices in the U.S. Food and Drug Administration

The U.S. Food and Drug Administration (FDA) regulates a wide variety of consumer products. Safety issues involve chemical and microbial contaminants in food, biologies, and medical devices; side effects from prescription and nonprescription drugs; residues of animal drugs in food; and radiation from electronic devices. Because of this wide diversity, the legal standards, rules, and policies governing the regulation of these products differ considerably. Hence, risk assessment and risk management practices within the FDA are of necessity quite diverse. This paper presents a summary of risk assessment practices at each of the product centers of the FDA (Center for Food Safety and Applied Nutrition, Center for Drug Evaluation and Research, Center for Biologics Evaluation and Research, Center for Devices and Radiological Health, and Center for Veterinary Medicine) and of the development of risk assessment procedures at the National Center for Toxicological Research.

A semiparametric approach to risk assessment for quantitative outcomes

Characterizing the dose-effect relationship and estimating acceptable exposure levels are the primary goals of quantitative risk assessment. A semiparametric approach is proposed for risk assessment with continuously measured or quantitative outcomes which has advantages over existing methods by requiring fewer assumptions. The approach is based on pairwise ranking between the response values in the control group and those in the exposed groups. The work generalizes the rank-based Wilcoxon-Mann-Whitney test, which for the two-group comparison is effectively a test of whether a response from the control group is different from (larger than) a response in an exposed group. We develop a regression framework that naturally extends this metric to model the dose effect in terms of a risk function. Parameters of the regression model can be estimated with standard software. However, inference requires an additional step to estimate the variance structure of the estimated parameters. An effective dose (ED) and associated lower confidence limit (LED) are easily calculated. The method is supported by a simulation study and is illustrated with a study on the effects of aconiazide. The method offers flexible modeling of the dose effect, and since it is rank-based, it is more resistant to outliers, nonconstant variance, and other departures from normality than previously described approaches.

Risk estimation--an overview: disease risk estimation based upon animal bioassays

Much more could be written about the issues addressed here, as well as about issues that are not even mentioned. The goal was to present a brief overview of some of the techniques and issues in quantitative health risk assessment based upon animal data. Hopefully, this overview will provoke some attention to specific in risk assessment that require more research. Perhaps the bibliographic references given will lead to other papers.

Modeling for risk assessment of neurotoxic effects

The regulation of noncancer toxicants, including neurotoxicants, has usually been based upon a reference dose (allowable daily intake). A reference dose is obtained by dividing a no-observed-effect level by uncertainty (safety) factors to account for intraspecies and interspecies sensitivities to a chemical. It is assumed that the risk at the reference dose is negligible, but no attempt generally is made to estimate the risk at the reference dose. A procedure is outlined that provides estimates of risk as a function of dose. The first step is to establish a mathematical relationship between a biological effect and the dose of a chemical. Knowledge of biological mechanisms and/or pharmacokinetics can assist in the choice of plausible mathematical models. The mathematical model provides estimates of average responses as a function of dose. Secondly, estimates of risk require selection of a distribution of individual responses about the average response given by the mathematical model. In the case of a normal or lognormal distribution, only an estimate of the standard deviation is needed. The third step is to define an adverse level for a response so that the probability (risk) of exceeding that level can be estimated as a function of dose. Because a firm response level often cannot be established at which adverse biological effects occur, it may be necessary to at least establish an abnormal response level that only a small proportion of individuals would exceed in an unexposed group. That is, if a normal range of responses can be established, then the probability (risk) of abnormal responses can be estimated.(ABSTRACT TRUNCATED AT 250 WORDS)