Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow (<i>Pimephales promelas</i>)

Russom, Christine L.; Bradbury, Steven P.; Broderius, Steven J.; Hammermeister, Dean E.; Drummond, Robert A.

doi:10.1002/etc.5620160514

Cited by 440 publications

(410 citation statements)

References 65 publications

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“…Assessing effects. Several rule-based approaches are available to identify degradates that have other nonspecific modes of action, such as reactive organic substances, polar narcotics, and respiratory uncouplers (45)(46)(47). Recently, these different approaches have been brought together with other approaches in a simple scheme for assessing the ecotoxicity of pesticides (33).…”

Section: Degradation Route Studies Are Complex and Costly And It Ismentioning

confidence: 99%

“…As with hydrophobicity, these physicochemical properties can also be estimated using quantitative structure-property relationships (QSPRs). A range of established QSPRs (such as those included in the EPI Suite) are available for estimating volatilization, water solubility, and sorption from chemical structure (44,45,51).…”

Section: Degradation Route Studies Are Complex and Costly And It Ismentioning

confidence: 99%

See 1 more Smart Citation

Peer Reviewed: When Synthetic Chemicals Degrade in the Environment

Boxall

Sinclair²,

Fenner³

et al. 2004

Environ. Sci. Technol.

327

185

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Section: Degradation Route Studies Are Complex and Costly And It Ismentioning

confidence: 99%

Section: Degradation Route Studies Are Complex and Costly And It Ismentioning

confidence: 99%

Peer Reviewed: When Synthetic Chemicals Degrade in the Environment

Boxall

Sinclair²,

Fenner³

et al. 2004

Environ. Sci. Technol.

327

185

View full text Add to dashboard Cite

“…A total of 568 organic chemicals representing several chemical classes and mechanisms of toxic action were considered in this study. Their acute toxicity to the fish Pimephales promelas (fathead minnow) was published elsewhere 21 and is provided electronically as Supporting Information.…”

Section: Methodsmentioning

confidence: 99%

Description of the Electronic Structure of Organic Chemicals Using Semiempirical and ab initio Methods for Development of Toxicological QSARs.

et al. 2005

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The quality of quantitative structure-activity relationship (QSAR) models depends on the quality of their constitutive elements including the biological activity, statistical procedure applied, and the physicochemical and structural descriptors. The aim of this study was to assess the comparative use of ab initio and semiempirical quantum chemical calculations for the development of toxicological QSARs applied to a large and chemically diverse data set. A heterogeneous collection of 568 organic compounds with 96 h acute toxicity measured to the fish fathead minnow (Pimephales promelas) was utilized. A total of 162 descriptors were calculated using the semiempirical AM1 Hamiltonian, and 121 descriptors were compiled using an ab initio (B3LYP/6-31G**) method. The QSARs were derived using multiple linear regression (MLR) and partial least squares (PLS) analyses. Statistically similar models were obtained using AM1 and B3LYP calculated descriptors supported by the use of the logarithm of the octanol-water partition coefficient (log K ow ). The main difference between the models derived by both MLR and PLS with the two sets of quantum chemical descriptors was concentrated on the type of descriptors selected. It was concluded that for large-scale predictions, irrespective of the mechanism of toxic action, the use of precise but time-consuming ab initio methods does not offer considerable advantage compared to the semiempirical calculations and could be avoided.

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“…Although similar studies have been reported [3,4,10,14], they did not assess the relevancy of molecular descriptors in terms of toxicity prediction. We have applied the classification algorithms to predict the toxicity of compounds for the estrogen receptor (DSSTox NCTRER DataBase), mutagenicity using the carcinogenic potency database (DSSTox CPDBAS DataBase), the Fathead Minow Acute Toxicity (DSSTox EPAFHM DataBase) and the Disinfection By-products Carcinogenicity Estimates (DSSTox DBPCAN DataBase).…”

Section: Introductionmentioning

confidence: 93%

“…EPAFHM: The Fathead Minnow Acute Toxicity database was generated by the U.S. EPA Mid-Continental Ecology Division (MED) for the purpose of developing an expert system to predict acute toxicity from chemical structure based on mode of action considerations [14]. The database contains 614 organic chemicals, for the prediction of acute toxicity endpoint of environmental and industrial chemicals.…”

Section: Data Setsmentioning

confidence: 99%

Comparative Study of Classification Algorithms Using Molecular Descriptors in Toxicological DataBases

Pereira

Costa

Camacho

et al. 2009

Advances in Bioinformatics and Computational Biology

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Abstract. The rational development of new drugs is a complex and expensive process, comprising several steps. Typically, it starts by screening databases of small organic molecules for chemical structures with potential of binding to a target receptor and prioritizing the most promising ones. Only a few of these will be selected for biological evaluation and further refinement through chemical synthesis. Despite the accumulated knowledge by pharmaceutical companies that continually improve the process of finding new drugs, a myriad of factors affect the activity of putative candidate molecules in vivo and the propensity for causing adverse and toxic effects is recognized as the major hurdle behind the current "target-rich, lead-poor" scenario. In this study we evaluate the use of several Machine Learning algorithms to find useful rules to the elucidation and prediction of toxicity using 1D and 2D molecular descriptors. The results indicate that: i) Machine Learning algorithms can effectively use 1D molecular descriptors to construct accurate and simple models; ii) extending the set of descriptors to include 2D descriptors improve the accuracy of the models.

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Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow (Pimephales promelas)

Cited by 440 publications

References 65 publications

Peer Reviewed: When Synthetic Chemicals Degrade in the Environment

Peer Reviewed: When Synthetic Chemicals Degrade in the Environment

Description of the Electronic Structure of Organic Chemicals Using Semiempirical and ab initio Methods for Development of Toxicological QSARs.

Comparative Study of Classification Algorithms Using Molecular Descriptors in Toxicological DataBases

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