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Developing and validating quantitative cationic-activity relationships or (Q)CARs to predict the toxicity metals is challenging because of issues associated with metal speciation, complexation and interactions within biological systems and the media used to study these interactions. However, a number of simplifying assumptions can be used to develop and validate (Q)CARs to predict the toxicity of metals: The ionic form is the most active form of a metal; the bioactivity of a dissolved metal is correlated with its free ion concentration or activity; most metals exist in biological systems as cations, and differences in metal toxicity result from differences in metal ion binding to biological molecules (ligand-binding). In summary, it appears that certain useful correlations can be made between several physical and chemical properties of ions (mostly cations) and toxicity of metals. This review provides a historical perspective of studies that have reported correlations between physical and chemical properties of cations and toxicity to mammalian and nonmammalian species using in vitro and in vivo assays. To prepare this review, approximately 100 contributions dating from 1839 to 2003 were evaluated and the relationships between about 20 physical and chemical properties of cations and their potential to produce toxic effects were examined. [ABSTRACT FROM AUTHOR]

Numerous quantitative structure-activity relationships (QSARs) have been developed to predict properties, fate, and effects of mostly discrete organic chemicals. As the demand for different types of regulatory testing increases and the cost of experimental testing escalates, there is a need to evaluate the use of QSARs and provide some guidance to avoid their misuse, especially as QSARs are being considered for regulatory purposes. This paper provides some guidelines that will promote the proper development and use of QSARs. While this paper uses examples of QSARs to predict toxicity, the proposed guidelines are applicable to QSARs used to predict physical or chemical properties, environmental fate, ecological effects and health effects. [ABSTRACT FROM AUTHOR]

Quantitative structure-activity relationships (QSARs) for predicting phase I and phase II metabolism and for modeling cytochrome P450 enzyme activities are described and reviewed. Papers dealing with three-dimensional techniques such as comparative molecular field analysis and pharmacophore modeling are included. This review focuses on those cytochrome P450 isoenzymes that are expressed in human hepatocytes and that are commonly responsible for the majority of drug and xenobiotic metabolism. Substrate-type selectivity information for those isoenzymes is included. The importance of lipophilicity correlations in xenobiotic metabolism predictions are outlined. A brief inclusion of available material on the prediction phase II conjugation biotransformations such as glucuronidation, sulfation, glycination, and glutathionation are included. Historical information is briefly discussed, but more detailed reviews are provided for papers published since 1997. [ABSTRACT FROM AUTHOR]

Quantitative structure-activity relationships (QSARs) for predicting percutaneous absorption rates were reviewed. Overall progress has been hampered by the sparseness of good quality experimental data. A number of researchers have used the same data set to develop QSARs for predicting percutaneous absorption rates, a fact that makes it difficult, at this time, to recommend one or two QSARs for predicting percutaneous absorption rates. Identification of chemicals within domains of large chemical universes that should be tested to improve QSARs and the subsequent development of experimental percutaneous absorption rates for those chemicals will facilitate the development of more robust QSARs for predicting percutaneous absorption rates. [ABSTRACT FROM AUTHOR]

The use of quantitative structure-activity relationships (QSARs) for deriving the predicted no-effect concentration of discrete organic chemicals for the purposes of conducting a regulatory risk assessment in Europe and the United States is described. In the United States, under the Toxic Substances Control Act (TSCA), the TSCA Interagency Testing Committee and the U.S. Environmental Protection Agency (U.S. EPA) use SARs to estimate the hazards of existing and new chemicals. Within the Existing Substances Regulation in Europe, QSARs may be used for data evaluation, test strategy indications, and the identification and filling of data gaps. To illustrate where and when QSARs may be useful and when their use is more problematic, an example, methyl tertiary-butyl ether (MTBE), is given and the predicted and experimental data are compared. Improvements needed for new QSARs and tools for developing and using QSARs are discussed. [ABSTRACT FROM AUTHOR]

This article is a review of the use, by regulatory agencies and authorities, of quantitative structure-activity relationships (QSARs) to predict ecologic effects and environmental fate of chemicals. For many years, the U.S. Environmental Protection Agency has been the most prominent regulatory agency using QSARs to predict the ecologic effects and environmental fate of chemicals. However, as increasing numbers of standard QSAR methods are developed and validated to predict ecologic effects and environmental fate of chemicals, it is anticipated that more regulatory agencies and authorities will find them to be acceptable alternatives to chemical testing. [ABSTRACT FROM AUTHOR]

As the regulatory incentives to provide data on commercial chemicals increases, there will be greater opportunities to apply Quantitative Structure Activity Relationships (QSARs) and Structure Activity Relationships (SARs) to promote more efficient use of chemical testing resources. This paper summarizes the current applications of QSARs and SARs by the US Government organizations represented on the Toxic Substances Control Act Interagency Testing Committee (ITC) to predict aquatic toxicity, chemical or physical properties, environmental fate parameters and health effects of organic chemicals. The US Government organizations represented on the ITC that currently apply QSARs include the Agency for Toxic Substances and Disease Registry, the US Environmental Protection Agency (US EPA), and the Food and Drug Administration (FDA). The US Government organizations represented on the ITC that currently apply SARs include the US EPA, the FDA, the National Cancer Institute, the National Toxicology Program, and the National Institute for Occupational Safety and Health. [Copyright &y& Elsevier]

The article focuses on the effect of consent, based on adaptive preferences, on the justification of government policy. In recent years feminist writers have called attention to the phenomenon of preference adaptivity, and they have raised a version of the problem of adaptive preferences as an objection to liberal political theory. The core of the objection is the claim that what liberals take to be women's free and voluntary consent to various social and political arrangements is spurious, because the beliefs and desires that give rise to the consent are simply oppression internalized. Liberals, it is said, conceal behind a mask of free consent institutions, such as pornography, prostitution, and heterosexuality, that are important constituents of women's oppression. The phenomenon of preference-adaptivity raises important questions that must be answered by any political theory, including liberalism, and this means that no political theory should simply take citizens' immanent preferences as given and ignore the causal history of the preferences that underlie choice.

Examines the concept of arrogance. Comparison with the concepts of vanity and self-confidence; Analyses of arrogance in celebrities and other popular characters; Interpersonal relationships of arrogant people.

Introduces articles about the use of quantitative structure-activity relationships for predicting physical properties and environmental fate and ecological effects, risks and other biological activities.

Abstract: Increasing concern is being shown by the scientific community, government regulators, and the public about endocrine-disrupting chemicals that are adversely affecting human and wildlife health through a variety of mechanisms. There is a great need for an effective means of rapidly assessing endocrine-disrupting activity, especially estrogen-simulating activity, because of the large number of such chemicals in the environment. In this study, quantitative structure activity relationship (QSAR) models were developed to quickly and effectively identify possible estrogen-like chemicals based on 232 structurally-diverse chemicals (training set) by using several nonlinear classification methodologies (least-square support vector machine (LS-SVM), counter-propagation artificial neural network (CP-ANN), and k nearest neighbour (kNN)) based on molecular structural descriptors. The models were externally validated by 87 chemicals (prediction set) not included in the training set. All three methods can give satisfactory prediction results both for training and prediction sets, and the most accurate model was obtained by the LS-SVM approach through the comparison of performance. In addition, our model was also applied to about 58,000 discrete organic chemicals; about 76% were predicted not to bind to Estrogen Receptor. The obtained results indicate that the proposed QSAR models are robust, widely applicable and could provide a feasible and practical tool for the rapid screening of potential estrogens. [Copyright &y& Elsevier]

Quantitative structure-activity relationships (QSARs) for predicting mutagenicity and carcinogenicity were reviewed. The QSARs for predicting mutagenicity and carcinogenicity have been mostly limited to specific classes of chemicals (e.g., aromatic amines and heteroaromatic nitro chemicals). The motivation to develop QSARs for predicting mutagenicity and carcinogenicity to screen inventories of chemicals has produced four major commercially available computerized systems that are able to predict these endpoints: Deductive estimation of risk from existing knowledge (DEREK) toxicity prediction by komputer assisted technology (TOPKAT), computer automated structure evaluation (CASE), and multiple computer automated structure evaluation (Multicase). A brief overview of these and some other expert systems for predicting mutagenicity and carcinogenicity is provided. The other expert systems for predicting mutagenicity and carcinogenicity include automatic data analysis using pattern recognition techniques (ADAPT), QSAR Expert System (QSAR-ES), OncoLogic computer optimized molecular parametric analysis of chemical toxicity system (COMPACT), and common reactivity pattern (COREPA). [ABSTRACT FROM AUTHOR]

Recent quantitative structure-activity relationships (QSARs) for the prediction of skin and eye irritation were reviewed. The QSARs in these areas are hindered by the lack of quality in vivo data and by a lack of understanding of the mechanisms of action. Creation of appropriate data sets for experimentation would facilitate the development of robust QSARs for predicting skin and eye irritation. [ABSTRACT FROM AUTHOR]

Quantitative structure-activity relationships (QSARs) for predicting skin and respiratory sensitization are reviewed. Overall, progress has been hampered by the sparseness of good quality experimental data, a fact that makes it difficult, at this time, to recommend one or two QSARs for predicting skin and respiratory sensitization. Creation of appropriate data sets for uninvestigated classes of chemicals by experimentation should facilitate the development of more robust QSARs for predicting skin and respiratory sensitization. Such QSARs will be valuable in the evaluation of identifiable toxic hazards where dose responses are relevant, as is the case for skin and respiratory sensitization. [ABSTRACT FROM AUTHOR]

This article describes the use of quantitative structure-activity relationships (QSARs) to predict toxicity endpoints for ecologically relevant and human-surrogate species. The interrelationships between the endpoints, and the possibilities of exploring the commonalities of chemical action from one species to another as well as from one endpoint to another, are evaluated. A number of toxic endpoints are discussed including mutagenicity and carcinogenicity; developmental toxicity (teratogenicity); acute toxicity; skin sensitization; skin, eye, and sensory irritation; and the modeling of membrane permeability. A number of electrophilic molecular substructures have been identified that are common to a number of toxicities. It is postulated that if such a substructure is observed in a molecule, it may exhibit a range of toxicities. Further, there appear to be relationships between the toxicity to ecologically relevant and human-surrogate species, which may allow for appreciation and possible extrapolation in both directions. Overall, however, QSARs are limited by the paucity of available toxicological data and information. [ABSTRACT FROM AUTHOR]

Ecological risk assessments for chemical stressors are used to establish linkages between likely exposure concentrations and adverse effects to ecological receptors. At times, it is useful to conduct screening risk assessments to assist in prioritizing or ranking chemicals on the basis of potential hazard and exposure assessment parameters. Ranking of large chemical inventories can provide evidence for focusing research and/or cleanup efforts on specific chemicals of concern. Because of financial and time constraints, data gaps exist, and the risk assessor is left with decisions on which models to use to estimate the parameter of concern. In this review, several methods are presented for using quantitative structure-activity relationships (QSARs) in conducting hazard screening or screening-level risk assessments. The ranking methods described include those related to current regulatory issues associated with chemical inventories from Canada, Europe, and the United States and an example of a screening-level risk assessment conducted on chemicals associated with a watershed in the midwest region of the United States. [ABSTRACT FROM AUTHOR]

This article is a review of the use of quantitative (and qualitative) structure-activity relationships (QSARs and SARs) by regulatory agencies and authorities to predict acute toxicity, mutagenicity, carcinogenicity, and other health effects. A number of SAR and QSAR applications, by regulatory agencies and authorities, are reviewed. These include the use of simple QSAR analyses, as well as the use of multivariate QSARs, and a number of different expert system approaches. [ABSTRACT FROM AUTHOR]

The history of the use of quantitative structure–activity relationships (QSARs) in toxicology, both for environmental, and human health effects is described. A particular emphasis is made on the science in response to the United States Toxic Substance Control Act of 1976. Specifically, the basic concepts and objectives of QSARs for toxicity are reviewed. QSARs for environmental and human health effects are discussed separately. Environmental, and more specifically, ecotoxicity, QSARs have focused historically on modeling congeneric series and non-specific effects in aquatic organisms through the use of the logarithm of the 1-octanol/water partition coefficient to describe hydrophobicity, and hence uptake. Compounds that do not fit these QSARs (namely the outliers) have been explained by differences in mechanism of acute toxicity, especially as a result of electro(nucleo)philic interactions. In light of this, mechanisms of acute toxicity are discussed. QSAR approaches to receptor-mediated effects, such as those exhibited by environmental estrogens, and competitive binding to the estrogen receptor, are different from those typically applied to model acute toxic endpoints. Several of these approaches, including three-dimensional QSAR techniques, are reviewed. Human health effects include both local and systemic effects. Local effects (e.g. corrosivity and skin sensitization) are often modeled by multivariate QSAR methods such as linear regression and discriminant analysis. The prediction of systemic effects such as mutagenesis and carcinogenesis requires consideration of the endpoint and a more mechanistic basis for modeling. Approaches to predict these endpoints include the use of expert systems. [Copyright &y& Elsevier]

Background: During the spring of 2009, a pandemic influenza A (H1N1) virus emerged and spread globally. We describe the clinical characteristics of patients who were hospitalized with 2009 H1N1 influenza in the United States from April 2009 to mid-June 2009. Methods: Using medical charts, we collected data on 272 patients who were hospitalized for at least 24 hours for influenza-like illness and who tested positive for the 2009 H1N1 virus with the use of a real-time reverse-transcriptase–polymerase-chain-reaction assay. Results: Of the 272 patients we studied, 25% were admitted to an intensive care unit and 7% died. Forty-five percent of the patients were children under the age of 18 years, and 5% were 65 years of age or older. Seventy-three percent of the patients had at least one underlying medical condition; these conditions included asthma; diabetes; heart, lung, and neurologic diseases; and pregnancy. Of the 249 patients who underwent chest radiography on admission, 100 (40%) had findings consistent with pneumonia. Of the 268 patients for whom data were available regarding the use of antiviral drugs, such therapy was initiated in 200 patients (75%) at a median of 3 days after the onset of illness. Data suggest that the use of antiviral drugs was beneficial in hospitalized patients, especially when such therapy was initiated early. Conclusions: During the evaluation period, 2009 H1N1 influenza caused severe illness requiring hospitalization, including pneumonia and death. Nearly three quarters of the patients had one or more underlying medical conditions. Few severe illnesses were reported among persons 65 years of age or older. Patients seemed to benefit from antiviral therapy. N Engl J Med 2009;361:1935-44. [ABSTRACT FROM AUTHOR]

Presents the results of a study on the enduring effects of abuse and related adverse experiences in childhood. Explanation on the organization and functional capacity of the human brain; Information on the studies in clinical populations of abuse survivors with posttraumatic stress disorder; Methods of the study.

The present study proposes a generic interspecies quantitative structure--activity relationship (QSAR) model that can be used to predict the acute toxicity of aldehydes to most species of aquatic organisms. The model is based on the flow-through fathead minnow (Pimephales promelas) 50% lethal concentration (LC50) data combined with other selected fish acute toxicity data and on the static ciliate (Tetrahymena pyriformis) 50% inhibitory growth concentration (IGC50) data. The toxicity of Schiff-base acting aldehydes was defined using hydrophobicity, as the calculated log 1-octanol/water partition coefficient (log Kow), and reactivity, as the donor delocalizability for the aldehyde O-site (DO-atom). The fish model [log 1/LC50 = -2.503(±1.950) + 0.480(±0.052) log Kow + 18.983(±6.573) DO-atom, n = 62, r² = 0.619, s² = 0.241, F = 48.0, Q² = 0.587] compares favorably with the ciliate model [log 1/IGC50 = -0.985(±1.309) + 0.530(±0.044) log Kow + 11.369(±4.350) DO-atom, n = 81, r² = 0.651, s = 0.147, F = 72.9, Q² = 0.626]. The fish and ciliate surfaces appear to be parallel, because they deviate significantly only by their intercepts. These observations lead to the development of a global QSAR for aldehyde aquatic toxicity [log E-1 = bOrganismE + 0.505(±0.033) log Kow + 14.315(±3.731) DO-atom, n = 143, r² = 0.698, s² = 0.187, s²Fish = 0.244, s⊃Ciliate = 0.149, F = 98, Q² = 0.681]. The general character of the model was validated using acute toxicity data for other aquatic species. The aldehydes global interspecies QSAR model could be used to predict the acute aquatic toxicity of untested aldehydes and to extrapolate the toxicity of aldehydes to other aquatic species. [ABSTRACT FROM AUTHOR]

The demonstrated ability of a variety of structurally diverse chemicals to bind to the estrogen receptor has raised the concern that chemicals in the environment may be causing adverse effects through interference with nuclear receptor pathways. Many structure-activity relationship models have been developed to predict chemical binding to the estrogen receptor as an indication of potential estrogenicity. Models based on either two-dimensional or three-dimensional molecular descriptions that have been used to predict potential for binding to the estrogen receptor are the subject of the current review. The utility of such approaches to predict binding potential of diverse chemical structures in large chemical inventories, with potential application in a tiered risk assessment scheme, is discussed. [ABSTRACT FROM AUTHOR]

A number of environmental chemicals, by mimicking natural hormones, can disrupt endocrine function in experimental animals, wildlife, and humans. These chemicals, called "endocrine-disrupting chemicals" (EDCs), are such a scientific and public concern that screening and testing 58,000 chemicals for EDC activities is now statutorily mandated. Computational chemistry tools are important to biologists because they identify chemicals most important for in vitro and in vivo studies. Here we used a computational approach with integration of two rejection filters, a tree-based model, and three structural alerts to predict and prioritize estrogen receptor (ER) ligands. The models were developed using data for 232 structurally diverse chemicals (training set) with a 10[sup6] range of relative binding affinities (RBAs); we then validated the models by predicting ER RBAs for 463 chemicals that had ER activity data (testing set). The integrated model gave a lower false negative rate than any single component for both training and testing sets. When the integrated model was applied to approximately 58,000 potential EDCs, 80% (~46,000 chemicals) were predicted to have negligible potential (log RBA < -4.5, with log RBA = 2.0 for estradiol) to bind ER. The ability to process large numbers of chemicals to predict inactivity for ER binding and to categorically prioritize the remainder provides one biologic measure to prioritize chemicals for entry into more expensive assays (most chemicals have no biologic data of any kind). The general approach for predicting ER binding reported here may be applied to other receptors and/or reversible binding mechanisms involved in endocrine disruption. Key words: endocrine-disrupting chemicals, estrogen receptor binding, relative binding affinties, risk assessment, structural alerts, tree-based models. Environ Health Perspect 110:29-36 (2002). [Online 10 December 2001] http://ehpnet1.niehs.nih.gov/docs/2002/110p29-36hong/abstract. html [ABSTRACT FROM AUTHOR]