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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]

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]

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]

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]

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]

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.