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Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow ( Pimephales promelas )
Author(s) -
Russom Christine L.,
Bradbury Steven P.,
Broderius Steven J.,
Hammermeister Dean E.,
Drummond Robert A.
Publication year - 1997
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1002/etc.5620160514
Subject(s) - toxicodynamics , pimephales promelas , minnow , aquatic toxicology , acute toxicity , toxicity , quantitative structure–activity relationship , toxicology , chemical compound , toxicokinetics , chemistry , environmental chemistry , pharmacology , biology , bioinformatics , fish <actinopterygii> , organic chemistry , fishery
In the field of aquatic toxicology, quantitative structure‐activity relationships (QSARs) have developed as scientifically credible models for predicting the toxicity of chemicals when little or no empirical data are available. In recent years, there has been an evolution of QSAR development and application from that of a chemical‐class perspective to one that is more consistent with assumptions regarding modes of toxic action. The objective of this research was to develop procedures that relate modes of acute toxic action in the fathead minnow ( Pimephales promelas ) to chemical structures and properties. An empirically derived database for diverse chemical structures of acute toxicity and corresponding modes of toxic action was developed through joint toxic action studies, the establishment of toxicodynamic profiles, and behavioral and dose‐response interpretation of 96‐h LC50 tests. Using the results from these efforts, as well as principles in the toxicological literature, approximately 600 chemicals were classified as narcotics (three distinct groups), oxidative phosphorylation uncouplers, respiratory inhibitors, electrophiles/proelectrophiles, acetylcholinesterase inhibitors, or central nervous system seizure agents. Using this data set, a computer‐based expert system has been established whereby chemical structures are associated with likely modes of toxic action and, when available, corresponding QSARs.