Novel methods for the estimation of acceptable daily intake

Probabilistic methods for non-cancer health effects

Noncancer risk assessment methods and harmonization with cancer assessment methods have advanced from the simple divide a No Observed Adverse Effect Level (NOAEL) by a default safety factor or a linear extrapolation to background of the early 1980's. This advance is due in part due to groups such as the American Industrial Health Council, the National Institute of Environmental Health Sciences, the Society for Risk Analysis, the Society of Toxicology, and the U.S. Environmental Protection Agency (Bogdanffy et al., 2001), the National Academy of Sciences (1983 and 2009), the International Programme on Chemical Safety (2005, 2009), and to many independent researchers outside of and within a workshop series sponsored by the Alliance for Risk Assessment (ARA, 2023) prompted by the NAS (2009). Several of the case studies from this workshop series, and earlier work such as Bogdanffy et al. (2001), demonstrate that the dose response assessment of non-cancer toxicity and the harmonization of cancer and non-cancer methods are more than just a simple reflection of treating all non-cancer toxicity as if it has a threshold, or all cancer toxicity as if it did not. Moreover, one recommendation of NAS (2009) was to develop a problem formulation with risk managers prior to conducting any risk assessment. If the development of this problem formulation only necessitates the determination of a safe, or virtually safe dose, then the estimation of a Reference Dose (RfD) or virtually safe dose (VSD) or similar constructs should be encouraged. Not all of our environmental problems need a precise quantitative solution.

A common approach for ranking of microbiological and chemical hazards in foods based on risk assessment - useful but is it possible?

This article compares and contrasts microbial and chemical risk assessment methodologies in order to evaluate the potential for a common framework for ranking of risk of chemical and microbiological hazards, and developments needed for such a framework. An overview of microbial (MRA) and chemical (CRA) risk assessment is presented and important differences are highlighted. Two microbiological and two chemical hazard-food combinations were ranked based on both a margin of exposure and a risk assessment approach. The comparisons illustrated that it is possible to rank chemical and microbiological hazard-food combinations with traditional approaches from each domain and indicated that the rank order but not the absolute measures is similar using either approach. Including severity in the assessment using DALY reduced differences between hazards and affected the outcome more than which approach was used. Ranking frameworks should include assessment of uncertainty as an integral part of the ranking, and be based on assessment of risk, not safety, and expressed in a common health metric such as disease burden. Necessary simplifications to address data gaps can involve the use of default scenarios. Challenges include comparisons of case-based vs. non-case-based health-endpoints, e.g. biomarker concentration, and integration of the severity of health effects into ranking.

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

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

Benchmark dose (BMD) modeling: current practice, issues, and challenges

Benchmark dose (BMD) modeling is now the state of the science for determining the point of departure for risk assessment. Key advantages include the fact that the modeling takes account of all of the data for a particular effect from a particular experiment, increased consistency, and better accounting for statistical uncertainties. Despite these strong advantages, disagreements remain as to several specific aspects of the modeling, including differences in the recommendations of the US Environmental Protection Agency (US EPA) and the European Food Safety Authority (EFSA). Differences exist in the choice of the benchmark response (BMR) for continuous data, the use of unrestricted models, and the mathematical models used; these can lead to differences in the final BMDL. It is important to take confidence in the model into account in choosing the BMDL, rather than simply choosing the lowest value. The field is moving in the direction of model averaging, which will avoid many of the challenges of choosing a single best model when the underlying biology does not suggest one, but additional research would be useful into methods of incorporating biological considerations into the weights used in the averaging. Additional research is also needed regarding the interplay between the BMR and the UF to ensure appropriate use for studies supporting a lower BMR than default values, such as for epidemiology data. Addressing these issues will aid in harmonizing methods and moving the field of risk assessment forward.

Comparative risk assessment of tobacco smoke constituents using the margin of exposure approach: the neglected contribution of nicotine

Nicotine was not included in previous efforts to identify the most important toxicants of tobacco smoke. A health risk assessment of nicotine for smokers of cigarettes was conducted using the margin of exposure (MOE) approach and results were compared to literature MOEs of various other tobacco toxicants. The MOE is defined as ratio between toxicological threshold (benchmark dose) and estimated human intake. Dose-response modelling of human and animal data was used to derive the benchmark dose. The MOE was calculated using probabilistic Monte Carlo simulations for daily cigarette smokers. Benchmark dose values ranged from 0.004 mg/kg bodyweight for symptoms of intoxication in children to 3 mg/kg bodyweight for mortality in animals; MOEs ranged from below 1 up to 7.6 indicating a considerable consumer risk. The dimension of the MOEs is similar to those of other tobacco toxicants with high concerns relating to adverse health effects such as acrolein or formaldehyde. Owing to the lack of toxicological data in particular relating to cancer, long term animal testing studies for nicotine are urgently necessary. There is immediate need of action concerning the risk of nicotine also with regard to electronic cigarettes and smokeless tobacco.

A novel application of the Margin of Exposure approach: segregation of tobacco smoke toxicants

This paper presents a rationale for utilising a Margin of Exposure (MOE) approach to the segregation of tobacco smoke toxicants for risk assessment and management purposes. Future regulatory frameworks and product modifications aimed at tobacco harm reduction could utilise data that segregate toxicants using associations with specific diseases caused by cigarette smoking together with an indication of their relative contribution to that disease. Compounds with MOEs >10,000 accompanied by appropriate narrative are considered "low priority for risk management actions". This paper applies the MOE model to representative examples of tobacco smoke toxicants associated with respiratory tract carcinogenesis and other respiratory diseases. A multiplicity of published dose response data on individual toxicants has been used to determine the range of possible MOE values, thus demonstrating the consistency of the relationships. Acetaldehyde, acrolein, acrylonitrile, cadmium, ethylene oxide, formaldehyde and isoprene all segregate with MOEs <10,000 and should be considered as high priority for exposure reduction research whereas benzo(a)pyrene and vinyl chloride segregate with an MOE >10,000 and therefore may be considered as a low priority. 1,3-Butadiene, m-/p-cresols, NNK and NNN are assumed to segregate with high priority although additional data would be required to complete a full MOE assessment.

Copper and human health: biochemistry, genetics, and strategies for modeling dose-response relationships

Copper (Cu) and its alloys are used extensively in domestic and industrial applications. Cu is also an essential element in mammalian nutrition. Since both copper deficiency and copper excess produce adverse health effects, the dose-response curve is U-shaped, although the precise form has not yet been well characterized. Many animal and human studies were conducted on copper to provide a rich database from which data suitable for modeling the dose-response relationship for copper may be extracted. Possible dose-response modeling strategies are considered in this review, including those based on the benchmark dose and categorical regression. The usefulness of biologically based dose-response modeling techniques in understanding copper toxicity was difficult to assess at this time since the mechanisms underlying copper-induced toxicity have yet to be fully elucidated. A dose-response modeling strategy for copper toxicity was proposed associated with both deficiency and excess. This modeling strategy was applied to multiple studies of copper-induced toxicity, standardized with respect to severity of adverse health outcomes and selected on the basis of criteria reflecting the quality and relevance of individual studies. The use of a comprehensive database on copper-induced toxicity is essential for dose-response modeling since there is insufficient information in any single study to adequately characterize copper dose-response relationships. The dose-response modeling strategy envisioned here is designed to determine whether the existing toxicity data for copper excess or deficiency may be effectively utilized in defining the limits of the homeostatic range in humans and other species. By considering alternative techniques for determining a point of departure and low-dose extrapolation (including categorical regression, the benchmark dose, and identification of observed no-effect levels) this strategy will identify which techniques are most suitable for this purpose. This analysis also serves to identify areas in which additional data are needed to better define the characteristics of dose-response relationships for copper-induced toxicity in relation to excess or deficiency.

Scientific criteria used for the development of occupational exposure limits for metals and other mining-related chemicals

The scientific approaches employed by selected internationally recognized organizations in developing occupational exposure limits (OELs) for metals and other mining-related chemicals were surveyed, and differences and commonalities were identified. The analysis identified an overriding need to increase transparency in current OEL documentation. OEL documentation should adhere to good risk characterization principles and should identify (1) the methodology used and scientific judgments made; (2) the data used as the basis for the OEL calculation; and (3) the uncertainties and overall confidence in the OEL derivation. At least within a single organization, a consistent approach should be used to derive OELs. Opportunities for harmonization of scientific criteria were noted, including (1) consideration of severity in identification of the point of departure; (2) definition of the minimum data set; (3) approaches for interspecies extrapolation; (4) identification of default uncertainty factors for developing OELs; and (5) approaches for consideration of speciation and essentiality of metals. Potential research approaches to provide the fundamental data needed to address each individual scientific criterion are described. Increased harmonization of scientific criteria will ultimately lead to OEL derivation approaches rooted in the best science and will facilitate greater pooling of resources among organizations that establish OELs and improved protection of worker health.

Mathematical modelling and quantitative methods

The present review reports on the mathematical methods and statistical techniques presently available for hazard characterisation. The state of the art of mathematical modelling and quantitative methods used currently for regulatory decision-making in Europe and additional potential methods for risk assessment of chemicals in food and diet are described. Existing practices of JECFA, FDA, EPA, etc., are examined for their similarities and differences. A framework is established for the development of new and improved quantitative methodologies. Areas for refinement, improvement and increase of efficiency of each method are identified in a gap analysis. Based on this critical evaluation, needs for future research are defined. It is concluded from our work that mathematical modelling of the dose-response relationship would improve the risk assessment process. An adequate characterisation of the dose-response relationship by mathematical modelling clearly requires the use of a sufficient number of dose groups to achieve a range of different response levels. This need not necessarily lead to an increase in the total number of animals in the study if an appropriate design is used. Chemical-specific data relating to the mode or mechanism of action and/or the toxicokinetics of the chemical should be used for dose-response characterisation whenever possible. It is concluded that a single method of hazard characterisation would not be suitable for all kinds of risk assessments, and that a range of different approaches is necessary so that the method used is the most appropriate for the data available and for the risk characterisation issue. Future refinements to dose-response characterisation should incorporate more clearly the extent of uncertainty and variability in the resulting output.

Health risk above the reference dose for multiple chemicals

Recent work indicates that the regression of toxicity data viewed as categories of pathological staging is useful for exploring the likely health risk at doses above a Reference Dose (RfD), which is an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. Toxic effects, which may include both quantal and continuous data, are classified into ordered categories of total toxic severity (e.g., none, mild, adverse, severe). These severity categories are regressed on explanatory variables, such as dose or exposure duration, to estimate the probability of observing an adverse or severe effect. In this paper, categorical regression has been expanded to compare the likely risks across multiple chemicals when exposures are above their RfDs. Existing health risk data for diazinon, disulfoton, S-ethyl dipropylthiocarbamate, fenamiphos, and lindane were analyzed. As expected, the estimated risks of adverse effects above the RfD varied among the chemicals. For example, at 10-fold above the RfD these risks were modeled to be 0.002, 0.0001, 0.0007, 0.002, and 0.02, respectively. The results and impacts of this analysis indicate that categorical regression is a useful screening tool to analyze risks above the RfD for specific chemicals and suggest its application in evaluating comparative risks where multiple chemical exposures exist.

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.

Categorical regression of toxicity data: a case study using aldicarb

Categorical regression is a mathematical tool that can be adapted to estimate potential health risk from chemical exposures. By regressing ordered categories of toxic severity or pathological staging on exposure dose, this method can estimate the likelihood of observing any of the categories of severity at any dose level. Depending on the nature of the available data, these estimates can take the form of incidence rates for any of the categories in an exposed population or the probability of a new study conducted at a specified dose level being classified as one of the categories. Categorical regression is illustrated using toxicity data on aldicarb. For aldicarb, the data fall into three different groups: human clinical studies, dietary exposures in experimental animals, and accidental human exposure by contaminated crops. The U.S. EPA has assessed this literature and developed a reference dose (RfD) of 0.001 mg/kg-day. The results of applying categorical regression to data from human clinical studies suggests a maximum likelihood risk estimate of adverse effects of 0.008% at a 10-fold higher dose than the RfD when blood cholinesterase inhibition is not considered as an adverse effect. When blood cholinesterase inhibition of 20% or more is considered as an adverse effect, a maximum likelihood risk estimate of adverse effects is 0.1% at a dose 10-fold higher than the RfD.

Risk assessment of essential trace elements: new approaches to setting recommended dietary allowances and safety limits

By definition, every essential trace element must have a range of intakes safe from toxicity but adequate enough to meet nutrition requirements. That range is part of the total dose-response curve and its lower and upper limits are delineated on the basis of nutrition and toxicology data, respectively. Close coordination of activities to set these limits is necessary to avoid recommendations that are either impractical (narrow zones of safe and adequate intakes) or contradictory (overlapping limits, i.e., no zones of safe and adequate intakes).

A multidisciplinary approach to toxicological screening: IV. Comparison of results

Toxicity data collected under standardized test conditions may be of the utmost importance in health risk assessment, in which human exposure limits are often derived from laboratory experiments. A standardized approach to data collection is also important for evaluating the sensitivity and specificity of test methods used to determine toxic potential. Several experiments were undertaken to determine the effects of chemical exposures using a multidisciplinary screening battery, which included tests for systemic, neurological and developmental toxicity. The effects of 1- and 14-d exposures to 10 chemicals on systemic and neurological indices of toxicity were determined in female F344 rats using standardized test batteries. Parallel experiments determined chemical effects on prenatal and postnatal development following exposure of the dams for 14 d. The chemicals included four pesticides (carbaryl, triadimefon, chlordane, and heptachlor), four solvents (trichloroethylene, tetrachloroethylene, carbon tetrachloride, and dichloromethane), and two industrial compounds (phenol and diethylhexyl phthalate). The results showed that the chemicals produced markedly different qualitative patterns of effect on systemic, neurological, and developmental indices of toxicity. Differences in the pattern of systemic and neurological effects were also obtained that depended on dosing duration. Quantitative analyses indicated that the highest ineffective dose as well as the lowest effective dose could vary by as much as two orders of magnitude across the different indices of toxicity. These results clearly show that a test battery focused on a single endpoint of toxicity cannot be used to accurately predict either qualitatively or quantitatively a chemical's systemic, neurological, and developmental toxicity profile.

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

Bioassay data from Teratology, Vol. 1 (1968) through Vol. 40 (1990), were utilized which were sufficient to establish no observed adverse effect levels (NOAEL's) for 120 experiments on 93 developmental toxicants in animals. The observed incidence (risk) at the NOAEL was calculated as the proportion of affected fetuses minus the proportion affected in the control animals. This calculation did not require any dose-response modeling. Data were primarily from experiments on rats and mice with a few studies on rabbits and hamsters. There did not appear to be differences in risks at the NOAEL among these four species. For each experiment, the risk at the NOAEL was tabulated for all of the adverse effects which shared the same NOAEL. Since the observed risk at the NOAEL for either dead/resorbed or abnormal fetuses exceeded 1% in about one-fourth of the cases, this suggests that a benchmark dose with a risk on this order would eliminate the higher risks and serve as a basis for establishing reference doses. If the lower confidence limit on a benchmark dose is used in place of the NOAEL, better experimental designs with more animals would result in tighter confidence limits, giving larger (less stringent) reference doses than poorer experiments.

Dose-effect approaches to risk assessment

Risk assessment is the attempt to characterize the chance of obtaining an adverse effect after exposure to an agent. Traditionally, high levels of an agent have been used to estimate the likelihood a lower dose might have an effect either by using low-dose extrapolation models or by attempting to establish a dose with no observable effects (NOEL). Low-dose extrapolation models yield estimates for small effects, but these estimates may vary by orders of magnitude depending upon the function chosen to represent the data. NOEL's are imprecise because a true no-effect level is indeterminant and the inability to determine an observable effect depends primarily on background variability. Newer methods use data from portions of the dose-effect function where error is smaller to estimate risks. Risk estimates using two of these approaches are compared for two different sample sizes. Each method produced the same estimate with the larger samples at low risk, but with increasing levels of risk and smaller samples the estimates obtained using these methods diverged.

Risk assessment initiatives for noncancer endpoints: implications for risk characterization of chemical mixtures

Current methods employed in risk assessment for noncarcinogens are associated with the estimation of reference doses (RfDs). These strategies reflect (appropriately) a protective philosophy in both theory and practice. The approaches are limited, however, in terms of the ability to project the likelihood of specific hazard above the reference dose and to integrate the health hazards of exposure to chemical mixtures (including both cancer and noncancer endpoints). Ongoing efforts that address guidelines for risk assessment of non-carcinogens, both singly and as components of mixtures, are presented. Included is a description of the range of potential biological response categories and associated parallel issues of adversity and severity. For example, the progression of histopathological change, organ system dysfunction and organismal disability is examined as it may affect risk characterization of mixtures. Mechanistic principles are suggested as an appropriate focus to systematically evaluate this progression. Once established, these principles may provide a reasonable framework in which to more accurately characterize risks associated with chemical mixtures.