Differential Sensitivity of Prefrontal Cortex and Hippocampus to Alcohol-Induced Toxicity

The prefrontal cortex (PFC) is a brain region responsible for executive functions including working memory, impulse control and decision making. The loss of these functions may ultimately lead to addiction. Using histological analysis combined with stereological technique, we demonstrated that the PFC is more vulnerable to chronic alcohol-induced oxidative stress and neuronal cell death than the hippocampus. This increased vulnerability is evidenced by elevated oxidative stress-induced DNA damage and enhanced expression of apoptotic markers in PFC neurons. We also found that one-carbon metabolism (OCM) impairment plays a significant role in alcohol toxicity to the PFC seen from the difference in the effects of acute and chronic alcohol exposure on DNA repair and from exaggeration of the damaging effects upon additional OCM impairment in mice deficient in a key OCM enzyme, methylenetetrahydrofolate reductase (MTHFR). Given that damage to the PFC leads to loss of executive function and addiction, our study may shed light on the mechanism of alcohol addiction.     

Subdivision of Uncertainty Factors to Allow for Toxicokinetics and Toxicodynamics

Tenfold uncertainty factors have been used in risk assessment for about 40 years to allow for species differences and inter-individual variability. Each factor has to allow for toxicokinetic and toxicodynamic differences. Subdividing the 10-fold factors into kinetic and dynamic defaults, which when multiplied give a product of 10, offers a number of advantages. A major advantage is that chemical-specific data can be introduced to replace one or more of the default subfactors, hence contributing to a chemical-related overall factor. Subdivision of the 10-fold factors also facilitates analysis of the appropriateness of the overall 10-fold defaults, and the development of a more refined approach to the use of uncertainty factors.     

Assessing the Default Assumption That Children Are Always at Risk

This paper establishes that non-elderly healthy adult animal models commonly display greater susceptibility than young animals to both acute and a wide range of chronic toxic effects from agents representing numerous chemical classes, including pharmaceutical agents and agents typically regulated as environmental/occupational pollutants. While the general belief that the young are likely to be at enhanced risk compared to adults remains the predominant perspective, the not-infrequent occurrence of greater susceptibility in adults raises questions about the utility of a generic default uncertainty factor (UF) for the young and suggests consideration of categorical UFs.     

Recommended Practice Regarding Selection of Sensitivity Analysis Methods Applied to Microbial Food Safety Process Risk Models

A guideline is presented for selection of sensitivity analysis methods applied to microbial food safety process risk (MFSPR) models. The guideline provides useful boundaries and principles for selecting sensitivity analysis methods for MSFPR models. Although the guideline is predicated on a specific branch of risk assessment models related to food-borne diseases, the principles and recommendations provided are typically generally applicable to other types of risk models. Applicable situations include: prioritizing potential critical control points; identifying key sources of variability and uncertainty; and refinement, verification, and validation of a model. Based on the objective of the analysis, characteristics of the model under study, amount of detail expected from sensitivity analysis, and characteristics of the sensitivity analysis method, recommendations for selection of sensitivity analysis methods are provided. A decision framework for method selection is introduced. The decision framework can substantially facilitate the process of selecting a sensitivity analysis method.     

Noncancer Risk Assessment: A Probabilistic Alternative to Current Practice

Based on imperfect data and theory, agencies such as the United States Environmental Protection Agency (USEPA) currently derive “reference doses” (RfDs) to guide risk managers charged with ensuring that human exposures to chemicals are below population thresholds. The RfD for a chemical is typically reported as a single number, even though it is widely acknowledged that there are significant uncertainties inherent in the derivation of this number.

In this article, the authors propose a probabilistic alternative to the EPA's method that expresses the human population threshold as a probability distribution of values (rather than a single RfD value), taking into account the major sources of scientific uncertainty in such estimates. The approach is illustrated using much of the same data that USEPA uses to justify their current RfD procedure.

Like the EPA's approach, our approach recognizes the four key extrapolations that are necessary to define the human population threshold based on animal data: animal to human, human heterogeneity, LOAEL to NOAEL, and subchronic to chronic. Rather than using available data to define point estimates of “uncertainty factors” for these extrapolations, the proposed approach uses available data to define a probability distribution of adjustment factors. These initial characterizations of uncertainty can then be refined when more robust or specific data become available for a particular chemical or class of chemicals.

Quantitative characterization of uncertainty in noncancer risk assessment will be useful to risk managers who face complex trade-offs between control costs and protection of public health. The new approach can help decision-makers understand how much extra control cost must be expended to achieve a specified increase in confidence that the human population threshold is not being exceeded.     

Relevance of the 10X Uncertainty Factor to the Risk Assessment of Drugs Used by Children and Geriatrics

Conventional risk assessment practices utilize a tenfold uncertainty factor (UF) to extrapolate from the general human population to sensitive subgroups, such as children and geriatrics. This study evaluated whether the tenfold UF can be reduced when pharmacokinetic and pharmacodynamic data for pharmaceuticals used by children and geriatrics are incorporated into the risk assessment for human sensitivity. Composite factors (kinetics X dynamics) were calculated from data-derived values for bumetanide, furosemide, metoprolol, atenolol, naproxen, and ibuprofen. For the compounds examined, all of the composite factors were lower than 10. Furthermore, 8 of the 12 composite factors were less than 5.5. Incorporation of human kinetic and dynamic data into risk assessment can aid in reducing the uncertainties associated with sensitive subgroups and further study is encouraged.     

Accounting for Missing Data in Noncancer Risk Assessment

Noncancer risk assessments are generally forced to rely on animal bioassay data to estimate a Tolerable Daily Intake or Reference Dose, as a proxy for the threshold of human response. In cases where animal bioassays are missing from a complete data base, the critical NOAEL (no-observed-adverse-effect level) needs to be adjusted to account for the impact of the missing bioassay(s). This paper presents two approaches for making such adjustments. One is based on regression analysis and seeks to provide a point estimate of the adjustment needed. The other relies on non-parametric analysis and is intended to provide a distributional estimate of the needed adjustment. The adjustment needed is dependent on the definition of a complete data base, the number of bioassays missing, the specific bioassays which are missing, and the method used for interspecies scaling. The results from either approach can be used in conjunction with current practices for computing the TDI or RfD, or as an element of distributional approaches for estimating the human population threshold.     

Application of Uncertainty Factors in the Priority Substances Program and International Harmonization

The Canadian Environmental Protection Act (CEPA) authorizes the Ministers of the Environment and of Health in Canada to investigate a wide variety of substances that may contaminate the environment and cause adverse effects on the environment and/or on human health. Under the Act, assessments have been completed for 44 environmental contaminants on the first Priority Substances List (PSL) and are relatively advanced for 25 compounds on the second PSL. The principles developed for the application of uncertainty factors in assessment of risks to human health for Priority Substances under CEPA are outlined, with emphasis on those aspects which are somewhat unique and/or evolving. The interface of developments in the Priority Substances program with an initiative of the International Programme on Chemical Safety in this area to effect greater harmonization of approaches is also described.     

A Methodology for Establishing Cleanup Objectives in the Unsaturated Soil Zone Using Sensitivity and Uncertainty Analysis for Chemical Fate and Transport

To support the Corrective-Measures and Cleanup-Alternatives Studies (CMS) prepared by Science Applications International Corporation (SAIC) at the Portsmouth Gaseous Diffusion Plant (PORTS) in Portsmouth, Ohio, a soil-leaching numerical analysis was conducted to help establish cleanup objectives for deep-soil contamination. For approximately 60 pollutants that exist at the PORTS site, the study defined those deep-soil concentrations that would most likely not cause groundwater contamination in excess of U.S. Environmental Protection Agency (USEPA) guidelines. These values were then used as the technical basis for defining soil-cleanup goals. Numerical modeling of environmental systems provides project managers with unique information that is not available from other sources. With its ability to quantify the important aspects of problem physics, modeling allows one to rapidly accumulate the physical insight needed to solve a problem in a systematic and focused manner. This increased understanding acquired early in the planning stages of a project permits managers to make decisions that are typically more thorough, cost effective, and defensible. This article describes one such numerical study conducted jointly by the Oak Ridge National Laboratory (ORNL) and SAIC for the PORTS.     

Case Studies of Categorical Data-Derived Adjustment Factors

Investigations were performed on representative compounds from five different therapeutic classes to evaluate the use of categorical data-derived adjustment factors to account for interindividual variability. The five classes included antidepressants, angiotensin converting enzyme (ACE) inhibitors, nonsteroidal anti-inflammatory drugs (NSAIDS), cholesterol lowering agents, and antibiotics. Each of the case studies summarized the mode of action of the class responsible for both the therapeutic and adverse effects and the key pharmacodynamic (PD) and pharmacokinetic (PK) parameters that determine the likelihood of these responses for individual compounds in the class. For each class, an attempt was made to identify the key factors that determine interindividual variability and whether there was a common basis to establish a categorical default adjustment factor that could be applied across the class (or at least across specific subclasses within the class). Linking the PK and PD parameters to the critical endpoint used to establish a safe level of exposure was an important underlying theme throughout the investigations. Despite the wealth of PK and PD information in the published literature on the surrogate compounds representing these classes, it was difficult to derive a categorical adjustment factor that could be applied broadly within each class. The amount of information available may have hindered rather than helped the evaluations. Derivation of categorical defaults for different classes of “common” chemicals may be more straightforward if sufficient data are available. In a few cases (e.g., tricyclic antibiotics, ACE inhibitors and selected antiinflammatory agents) categorical defaults could be proposed, although it is unclear whether the reduction in uncertainty resulting from their application would be offset by the additional uncertainties that may have resulted from their application. Residual uncertainties may remain depending on the level of confidence in the underlying assumptions used to support the categorical defaults. Regardless of the conclusions on the utility of categorical defaults, these investigations provided further support for the use of data-derived adjustment factors on a compound-specific basis.     

Ethanol and Excitotoxicity in Cultured Cortical Neurons: Differential Sensitivity of N-Methyl-D-Aspartate and Sodium Nitroprusside Toxicity

Neural injury due to ischemia and related insults is thought to involve the action of excitatory amino acids at N-methyl-D-aspartate receptors, which results in the influx of extracellular Ca2+ and the generation of nitric oxide. Because ethanol inhibits physiologic responses to excitatory amino acids, we examined its effect on toxicity induced by N-methyl-D-aspartate and by the nitric oxide donor sodium nitroprusside in neuron-enriched cultures prepared from rat cerebral cortex. Both N-methyl-D-aspartate and sodium nitroprusside were cytotoxic, as measured by the release of lactate dehydrogenase and by microfluorescent determination of cell viability. Ethanol (3-1,000 mM) protected cultures from N-methyl-D-aspartate but not sodium nitroprusside toxicity, and the ability of a series of n-alkanols to reproduce the effect of ethanol was related to carbon-chain length. Neuroprotection by ethanol was accompanied by a decrease in the N-methyl-D-aspartate-evoked elevation of free intracellular Ca2+ and did not appear to involve gamma-aminobutyric acid- or cyclic GMP-mediated mechanisms. These findings suggest that ethanol inhibits excitotoxicity at an early step in the N-methyl-D-aspartate signaling pathway, probably by reducing Ca2+ influx, and not by interfering with the action of nitric oxide.     

Fundamental Differences in Visual Perceptual Learning between Children and Adults

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Exposure of aboriginals in British Columbia to methylmercury in freshwater fish: A comparison to reference doses and estimated thresholds

We have analyzed the methylmercury exposures of native women consuming fish from a reservoir and two lakes in British Columbia. Probability density functions representing methylmercury dose were generated using reasonable distributions for exposure parameters. Sensitivity analyses were performed to assess the impact of alternative parameter values on the exposure estimates. The effect of ignoring variability and uncertainty in exposure was also assessed. Calculated mean daily doses of methylmercury for the target populations were compared to the estimated average dose for the general population. We also determined the percentages of the native women populations with exposures exceeding current guidance values as well as published thresholds for neurological effects. The analysis demonstrates the importance of better characterizing the low dose effects of methylmercury, as the predicted doses fall in the range of recommended maximum daily doses but well below the higher estimates of the effect threshold.     

Quantitative Exposure-Response Assessment Approaches to Evaluate Acute Inhalation Toxicity of Phosgene

Phosgene has been a long-term subject of toxicological research due to its widespread use, high toxicity, and status as a model of chemically induced lung injury. To take advantage of the abundant data set for the acute inhalation toxicity of phosgene, methods for exposure-response analysis that use more data than the traditional no-observed-adverse-effect level approach were used to perform an exposure-response assessment for phosgene. Categorical regression is particularly useful for acute exposures due to the ability to combine studies of various exposure durations, and thus provide estimates of effect severity for a range of both exposure concentrations and durations. Results from the categorical regression approach were compared to those from parametric curve fitting models (i.e., benchmark concentration models) that make use of information from an entire dose-response, but only for one exposure duration. While categorical regression analysis provided results that were comparable to benchmark concentration results, categorical regression provides an improvement over that technique by accounting for the effects of both exposure concentration and duration on response. The other major advantage afforded by categorical regression is the ability to combine studies, allowing the quantitative use of a larger data set, which increases confidence in the final result.     

Pathway-Related Factors: The Potential for Human Data to Improve the Scientific Basis of Risk Assessment

The default uncertainty factors used for risk assessment are applied either to allow for different aspects of extrapolation of the dose-response curve or to allow for database deficiencies. Replacement of toxicokinetic or toxicodynamics defaults by chemical-specific data allows the calculation of a chemical-specific “data-derived factor”, which is the product of chemical-specific values and default uncertainty factors. Such chemical-specific composite values will improve the scientific basis of the risk assessment of that chemical, but the necessary chemical-specific data are rarely available. Categorical defaults related to pathways of elimination and mechanisms of toxicity could be used when the overall fate or mechanism is known, but there are no chemical-specific data sufficient to allow replacement of the default, and the development of an overall data-derived factor. The development of pathway-related categorical defaults is being undertaken using data on selected probe substrates for which adequate data are available. The concept and difficulties of this approach are illustrated using data for CYP1A2.     

Assessing the Relative Sensitivity of Aquatic Organisms to Divalent Metals and Their Representation in Toxicity Datasets Compared to Natural Aquatic Communities

We compared the composition and richness of acute and chronic toxicity datasets for Cd, Cu, Ni, Pb, and Zn to several natural aquatic communities. The richness of acute datasets was reasonably representative, with the largest toxicity datasets containing a higher number of genera than some natural aquatic communities. Acute datasets also had a reasonably diverse composition compared to natural aquatic communities, although insects were under-represented and cladocerans over-represented. Given this robustness, we suggest manipulation of large acute datasets (Cd, Cu, Zn) to account for site-specific differences in aquatic community composition can be accomplished with confidence and that this will not result in under-protection of sensitive taxa. In contrast, the chronic datasets were not representative of natural aquatic communities in terms of composition or richness. Chronic dataset richness is an order of magnitude less than natural aquatic communities. Chronic datasets have minimal representation of insects, whereas cladocera and salmonids are grossly over-represented in some cases. Further, no real patterns in the relative sensitivity of genera groups can be discerned with such limited data. As a result, we conclude there is considerable uncertainty regarding how biases in genera representation may lead to under- or over-protection of aquatic communities on a chronic basis. Given this, manipulation of chronic datasets to better reflect site-specific aquatic communities is not recommended without additional chronic testing using a wider diversity of aquatic genera.     

Evaluating the 10X Uncertainty Factor for Children and Elderly with Data-Derived Values for Neuromuscular Blocking Agents

Conventional risk assessment process utilizes a 10-fold uncertainty factor (UF) to extrapolate from the general human population to sensitive subgroups, such as children and elderly. The purpose of this investigation was to evaluate whether the magnitude of the 10X-UF can be reduced when pharmacokinetic and pharmacody-namic data are incorporated to characterize human sensitivity. An extensive literature search was conducted on seven neuromuscular blocking agents (mivacurium, atracurium, rocuronium, vecuronium, doxacurium, pancuronium, pipecuronium). Composite factors (kinetics × dynamics) were calculated using the highest data-derived kinetic and dynamic values. For the drugs examined, all of the composite factors for the sensitivity of children were lower than 5. In the elderly, all of the composite factors were lower than 10, and five of seven composite factors were less than 5. From this study, it was concluded that relevant compound-specific kinetic and dynamic data can reduce the uncertainties associated with sensitive subgroups.