Using metal-ligand binding characteristics to predict metal toxicity: quantitative ion character-activity relationships (QICARs).
Ecological risk assessment can be enhanced with predictive models for metal toxicity. Modelings of published data were done under the simplifying assumption that intermetal trends in toxicity reflect relative metal-ligand complex stabilities. This idea has been invoked successfully since 1904 but has yet to be applied widely in quantitative ecotoxicology. Intermetal trends in toxicity were successfully modeled with ion characteristics reflecting metal binding to ligands for a wide range of effects. Most models were useful for predictive purposes based on an F-ratio criterion and cross-validation, but anomalous predictions did occur if speciation was ignored. In general, models for metals with the same valence (i.e., divalent metals) were better than those combining mono-, di-, and trivalent metals. The softness parameter (sigma p) and the absolute value of the log of the first hydrolysis constant ([symbol: see text] log KOH [symbol: see text]) were especially useful in model construction. Also, delta E0 contributed substantially to several of the two-variable models. In contrast, quantitative attempts to predict metal interactions in binary mixtures based on metal-ligand complex stabilities were not successful.
Newman
M C
MC
College of William and Mary, Virginia Institute of Marine Science, Gloucester Point 23062, USA. newman@vims.edu
McCloskey
J T
JT
Tatara
C P
CP
D000465:Algorithms; D000818:Animals; D001181:Arthropods; D015415:Biomarkers; D004798:Enzymes / Q000378:metabolism; D005399:Fishes; D008024:Ligands; D008670:Metals / Q000378:metabolism* / Q000633:toxicity*; D051379:Mice; D008954:Models, Biological; D011237:Predictive Value of Tests; D013329:Structure-Activity Relationship; D014871:Water Microbiology
D015415:Biomarkers; D004798:Enzymes; D008024:Ligands; D008670:Metals