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Schultz, T.W.; Sinks, G.D.; Bearden, A.P. QSAR in Aquatic Toxicology: A Mechanism of Action Approach Comparing Toxic Potency to Pimephales promelas, Tetrahymena pyriformis, and Vibrio fisheri. In Comparative QSAR; Devillers, J., Ed.; Taylor & Francis; 1998.

QsarDB Repository

Schultz, T.W.; Sinks, G.D.; Bearden, A.P. QSAR in Aquatic Toxicology: A Mechanism of Action Approach Comparing Toxic Potency to Pimephales promelas, Tetrahymena pyriformis, and Vibrio fisheri. In Comparative QSAR; Devillers, J., Ed.; Taylor & Francis; 1998.

QDB archive DOI: 10.15152/QDB.79   DOWNLOAD

QsarDB content

Property MOA: Mode of action

Compounds: 536 | Models: 0 | Predictions: 0

Property pIGC50: 40-h Tetrahymena toxicity as log(1/IGC50) [log(L/mmol)]

Compounds: 490 | Models: 11 | Predictions: 11

11: Nonpolar narcosis

Regression model (regression)

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Name Type n

R2

σ

Training training 148 0.944 0.242
17: Phenol polar narcosis

Regression model (regression)

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Name Type n

R2

σ

Training training 117 0.800 0.325
24: Aniline polar narcosis

Regression model (regression)

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Name Type n

R2

σ

Training training 75 0.723 0.343
31: Amine narcosis

Regression model (regression)

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Name Type n

R2

σ

Training training 29 0.893 0.273
38: Ester narcosis

Regression model (regression)

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Name Type n

R2

σ

Training training 25 0.916 0.286
44: Weak acid respiratory uncoupling

Regression model (regression)

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Name Type n

R2

σ

Training training 19 0.772 0.288
51: Aldehyde soft electrophilicity

Regression model (regression)

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Name Type n

R2

σ

Training training 14 0.963 0.157
60: Nitrobenzene soft electrophilicity

Regression model (regression)

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Name Type n

R2

σ

Training training 22 0.158 0.406
68: Soft electrophilicity

Regression model (regression)

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Name Type n

R2

σ

Training training 17 0.015 0.948
73: Pro-electrophiles

Regression model (regression)

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Name Type n

R2

σ

Training training 24 0.282 0.665
74: Selected pro-electrophiles

Regression model (regression)

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Name Type n

R2

σ

Training training 21 0.925 0.194

Citing

When using this data, please cite the original article and this QDB archive:

  • Schultz, T. W.; Sinks, G. D.; Bearden, A. P. QSAR in Aquatic Toxicology: A Mechanism of Action Approach Comparing Toxic Potency to Pimephales promelas, Tetrahymena pyriformis, and Vibrio fisheri. In Comparative QSAR; Devillers, J., Ed.; Taylor & Francis; 1998.

  • Ruusmann, V. QDB archive #79. QsarDB repository, 2012. http://dx.doi.org/10.15152/QDB.79

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Title: Schultz, T.W.; Sinks, G.D.; Bearden, A.P. QSAR in Aquatic Toxicology: A Mechanism of Action Approach Comparing Toxic Potency to Pimephales promelas, Tetrahymena pyriformis, and Vibrio fisheri. In Comparative QSAR; Devillers, J., Ed.; Taylor & Francis; 1998.
Abstract: Hydrophobic- and electrophilic-dependent structure-toxicity relationships were developed and compared for several mechanisms of toxic action. Potency data included values from the 96-h Pimephales promelas mortality assay, the 40-h Tetrahymena pyriformis population growth inhibition assay, and the 15-min Vibrio fischeri luminescence inhibition assay. In addition to expanded protocol comparisons for electrophilic toxicants, data for the 14-day Poecilia reticulata mortality assay were included in the analyses. Mechanisms of action that were evaluated included nonpolar narcosis, polar narcosis, amine narcosis, ester narcosis, and weak acid respiratory uncoupling. In addition, three groups of direct-acting electrophiles and selected proelectrophiles were evaluated. In general, there is reliable agreement between relative potency in the Pimephales promelas and Tetrahymena pyriformis endpoints. When differences occur, they usually reflect differences in protocol (I.e., flow-through versus static) or metabolic differences. Most discrepancies involved the Vibrio fischeri endpoint. The toxicity of neutral organic nonpolar narcotics, amine narcotics, and weak acid uncouplers is modeled by high-quality, hydrophobic-dependent QSARs in all three endpoints. For these three mechanisms, toxic potency is correlated highly between endpoints. The measured value for one endpoint can be used to predict the toxicity in one of the other two endpoints. The toxicity of polar narcotics is modeled by quality hydrophobic-dependent QSARs in the Pimephales promelas and Tetrahymena pyriformis assays, and toxic potency is correlated between the two endpoints. However, unexplained variability in the potency data for Vibrio fischeri in both phenols and anilines sharply limits comparisons and therefore the use of a measured value in Vibrio fischeri to estimate the toxicity in one of the other systems. Although fish exhibit in vivo hydrolysis of esters and ciliates and bacteria do not, all three endpoints manifest excel1ent hydrophobic-dependent QSARs. Moreover, for ester narcosis, potency is strongly correlated between endpoints. However, caution is suggested in extrapolating to and from fish data, especial1y for aromatic and diesters. The modeling of potency data for direct-acting electrophiles and proelectrophiles is strongly influenced by the specific chemicals in the data set and quantitated by nonhydrophobic parameters. The results with the aldehydes show that testing protocol influences modeling and the ability to extrapolate from one endpoint to another. Although' quality hydrophobic-dependent QSARs can be developed for static and renewal systems, such models are lacking for flow-through systems. A measured value for one static endpoint can be used to predict the toxicity in another static endpoint. However, extrapolations to flow-through systems are not advised. The nitrobenzene and proelectrophile data divulged another difficulty with modeling electrophilic chemicals: Even within restricted structural classes, toxicity may be seriously influenced by subtle changes in molecular· structure. The result is that potency may be dependent or independent of hydrophobicity.
URI: http://hdl.handle.net/10967/79
http://dx.doi.org/10.15152/QDB.79
Date: 2012-05-23


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