Quantitative Cationic Activity Relationships for Predicting Toxicity of Metal Ions from Physicochemical Properties and Natural Occurrence Levels

Systematic analysis of freshwater metal toxicity with WHAM-FTOX

We used the WHAM chemical speciation model and the WHAM-F_TOX toxicity model to analyse the published results of laboratory toxicity experiments covering 52 different freshwater biological test species and 24 different metals, a total of 2037 determinations of EC₅₀ with accompanying data on solution composition. The key extracted parameter was α_M, the parameter in WHAM-F_TOX that characterises the toxic potency of a metal on the basis of its estimated metabolically active body burden. For 16 data sets applying to metal-test species pairs with appreciable variations in solution composition, values of EC₅₀ back-calculated from averaged values of α_M showed significantly (p < 0.001) less deviation from the measured EC₅₀ values than did the simple average EC₅₀, confirming that the modelling calculations could account for some of the dependence of toxicity on chemical speciation. Data for different exposure times permitted a simple parameterisation of temporal effects, enabling values of α_M,max (values at infinite exposure time) to be obtained, and the effects of different exposure times to be factored out for further analysis. Comparison of averaged values of α_M,max for different metals showed little difference among major taxa (invertebrates, plants, and vertebrates). For Cd, Cu, Ni and Zn (the four metals with most data) there were significant differences among α_M,max values for different species, but within-species variabilities were greater. Reasonably similar species sensitivity distributions of standardised α_M,max applied to Cd, Cu, Ni and Zn. The average values, over all species, of α_M,max increased in the order Al < lanthanides < Zn ∼ UO₂ < Ni ∼ Cu < Pb < Cd < Ag. Considering all the α_M,max values, there was a strong dependence (r² = 0.56, p < 0.001) on Pearson's hardness-softness categories, and a slightly stronger relationship (r² = 0.59) if ionic radius was included in the statistical model, indicating that softer, larger cations are the most effective toxicants.

Predicting criteria continuous concentrations of metals or metalloids for protecting marine life by use of quantitative ion characteristic-activity relationships-species sensitivity distributions (QICAR-SSD)

Marine pollution by metals has been a major challenge for ecological systems; however, water quality criteria (WQC) for metals in saltwater is still lacking. Especially from a regulatory perspective, chronic effects of metals on marine organisms should receive more attention. A quantitative ion characteristic-activity relationships-species sensitivity distributions (QICAR-SSD) model, based on chronic toxicities for eight marine organisms, was established to predict the criteria continuous concentrations (CCCs) of 21 metals. The results showed that the chronic toxicities of various metals had good relationships with their physicochemical properties. Predicted CCCs of six metals (Hg²⁺, Cu²⁺, Pb²⁺, Cd²⁺, Ni²⁺ and Zn²⁺) were in accordance with the values recommended by the U.S. EPA, with prediction errors being less than an order of magnitude. The QICAR-SSD approach provides an alternative tool to empirical methods and can be useful for deriving scientifically defensible WQC for metals for marine organisms and conducting ecological risk assessments.

The History and Development of Quantitative Structure-Activity Relationships (QSARs)

It is widely accepted that modern QSAR began in the early 1960s. However, as long ago as 1816 scientists were making predictions about physical and chemical properties. The first investigations into the correlation of biological activities with physicochemical properties such as molecular weight and aqueous solubility began in 1841, almost 60 years before the important work of Overton and Meyer linking aquatic toxicity to lipid-water partitioning. Throughout the 20th century QSAR progressed, though there were many lean years. In 1962 came the seminal work of Corwin Hansch and co-workers, which stimulated a huge interest in the prediction of biological activities. Initially that interest lay largely within medicinal chemistry and drug design, but in the 1970s and 1980s, with increasing ecotoxicological concerns, QSAR modelling of environmental toxicities began to grow, especially once regulatory authorities became involved. Since then QSAR has continued to expand, with over 1400 publications annually from 2011 onwards.

Cardiovascular outcomes and the physical and chemical properties of metal ions found in particulate matter air pollution: a QICAR study

BACKGROUND

This paper presents an application of quantitative ion character-activity relationships (QICAR) to estimate associations of human cardiovascular (CV) diseases (CVDs) with a set of metal ion properties commonly observed in ambient air pollutants. QICAR has previously been used to predict ecotoxicity of inorganic metal ions based on ion properties.

OBJECTIVES

The objective of this work was to examine potential associations of biological end points with a set of physical and chemical properties describing inorganic metal ions present in exposures using QICAR.

METHODS

Chemical and physical properties of 17 metal ions were obtained from peer-reviewed publications. Associations of cardiac arrhythmia, myocardial ischemia, myocardial infarction, stroke, and thrombosis with exposures to metal ions (measured as inference scores) were obtained from the Comparative Toxicogenomics Database (CTD). Robust regressions were applied to estimate the associations of CVDs with ion properties.

RESULTS

CVD was statistically significantly associated (Bonferroni-adjusted significance level of 0.003) with many ion properties reflecting ion size, solubility, oxidation potential, and abilities to form covalent and ionic bonds. The properties are relevant for reactive oxygen species (ROS) generation, which has been identified as a possible mechanism leading to CVDs.

CONCLUSION

QICAR has the potential to complement existing epidemiologic methods for estimating associations between CVDs and air pollutant exposures by providing clues about the underlying mechanisms that may explain these associations.