John D. Walker

Quantitative structure–activity relationships (QSARs) in toxicology: a historical perspective

Abstract 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.

Guidelines for developing and using quantitative structure-activity relationships

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.

Overview of data and conceptual approaches for derivation of quantitative structure-activity relationships for ecotoxicological effects of organic chemicals

The use of quantitative structure-activity relationships (QSARs) in assessing potential toxic effects of organic chemicals on aquatic organisms continues to evolve as computational efficiency and toxicological understanding advance. With the ever-increasing production of new chemicals, and the need to optimize resources to assess thousands of existing chemicals in commerce, regulatory agencies have turned to QSARs as essential tools to help prioritize tiered risk assessments when empirical data are not available to evaluate toxicological effects. Progress in designing scientifically credible QSARs is intimately associated with the development of empirically derived databases of well-defined and quantified toxicity endpoints, which are based on a strategic evaluation of diverse sets of chemical structures, modes of toxic action, and species. This review provides a brief overview of four databases created for the purpose of developing QSARs for estimating toxicity of chemicals to aquatic organisms. The evolution of QSARs based initially on general chemical classification schemes, to models founded on modes of toxic action that range from nonspecific partitioning into hydrophobic cellular membranes to receptor-mediated mechanisms is summarized. Finally, an overview of expert systems that integrate chemical-specific mode of action classification and associated QSAR selection for estimating potential toxicological effects of organic chemicals is presented.

Predicting bioconcentration factors of highly hydrophobic chemicals. Effects of molecular size

The bioconcentration factor (BCF) is a parameter that describes the ability of chemicals to concentrate in aquatic organisms. Traditionally, it is modeled by the log–log quantitative structure -activity relationship (QSAR) between the BCF and the octanol- water partition coefficient (Kow). A significant scatter in the parabolic log(BCF)/log(Kow) curve has been observed for narcotics with log(Kow) greater than 5.5. This study shows that the scatter in the log(BCF)/log(Kow) relationship for highly hydrophobic chemicals can be explained by the molecular size. The significance of the maximal cross-sectional diameter on bioconcentration was compared with the traditionally accepted effective diameter. A threshold value of about 1.5 nm for this parameter has been found to discriminate chemicals with log(BCF) > 3.3 from those with log(BCF) < 3.3. This critical value for the maximum diameter is comparable with the architecture of the cell membrane. This threshold is half thickness of leaflet constituting the lipid bilayer. The existence of a size threshold governing bioconcentration is an indication of a possible switch in the uptake mechanism from passive diffusion to facilitated diffusion or active transport. The value of the transition point can be used as an additional parameter to hydrophobicity for predicting BCF variation. The effect of molecular size on bioconcentration has been studied by accounting for conformational flexibility of molecules.

Predicting the biodegradation products of perfluorinated chemicals using CATABOL

Perfluorinated chemicals (PFCs) form a special category of organofluorine compounds with particularly useful and unique properties. Their large use over the past decades increased the interest in the study of their environmental fate. Fluorocarbons may have direct or indirect environmental impact through the products of their decomposition in the environment. It is a common knowledge that biodegradation is restricted within non-perfluorinated part of molecules; however, a number of studies showed that defluorination can readily occur during biotransformation. To evaluate the fate of PFCs in the environment a set of principal transformations was developed and implemented in the simulator of microbial degradation using the catabolite software engine (CATABOL). The simulator was used to generate metabolic pathways for 171 perfluorinated substances on Canadas domestic substances list. It was found that although the extent of biodegradation of parent compounds could reach 60%, persistent metabolites could be formed in significant quantities. During the microbial degradation a trend was observed where PFCs are transformed to more bioaccumulative and more toxic products. Perfluorooctanoic acid and perfluorooctanesulfonate were predicted to be the persistent biodegradation products of 17 and 27% of the perfluorinated sulphonic acid and carboxylic acid containing compounds, respectively.

Global Government applications of analogues, SAR s and QSAR s to predict aquatic toxicity, chemical or physical properties, environmental fate parameters and health effects of organic chemicals

Faced with the need to predict physical and chemical properties, environmental fate, ecological effects and health effects of organic chemicals in the absence of experimental data, several Government organizations have been applying analogues, Structure Activity Relationships (SARs) and Quantitative Structure Activity Relationships (QSARs) to develop those predictions. To establish some benchmarks for monitoring future increases in applications of analogues, SARs and QSARs by global Government organizations, this paper describes the current applications of analogues, SARs and QSARs by Australian, Canadian, Danish, European, German, Japanese, Netherlands, and United States Government organizations to predict physical and chemical properties, environmental fate, ecological effects and health effects of organic chemicals.

Quantitative cationic‐activity relationships for predicting toxicity of metals

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.

Non-linear modeling of bioconcentration using partition coefficients for narcotic chemicals

Bioconcentration factors (BCFs) have traditionally been used to describe the tendency of chemicals to concentrate in aquatic organisms. A reexamination of the log-log QSAR between the BCF and K OW for non-congener narcotic chemicals is presented on the basis of recommended data for fish. The model is extended to give a simple correlation between BCF and the toxicity of highly, moderately and weakly hydrophilic chemicals. For the first time, in this study an equation for calculating BCF was applied in a QSAR model for predicting the acute toxicity of chemicals to aquatic organisms.

Interspecies quantitative structure‐activity relationship model for aldehydes: Aquatic toxicity

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 (D(O-atom)). The fish model [log 1/LC50 = -2.503(+/-1.950) + 0.480(+/-0.052) log Kow + 18.983(+/-6.573) D(O-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) D(O-atom), n = 81, r2 = 0.651, s2 = 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) = bE(Organism) + 0.505(+/-0.033) log Kow + 14.315(+/-3.731) D(O-atom), n = 143, r2 = 0.698, s2 = 0.187, S2(Fish) = 0.244, S2(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.

Quantitative structure‐activity relationship models for prediction of estrogen receptor binding affinity of structurally diverse chemicals

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.