Literature DB >> 8653384

Structure-toxicity relationships for phenols to Tetrahymena pyriformis.

M T Cronin1, T W Schultz.   

Abstract

Quantitative structure-activity relationships are developed for the toxicity of 166 varied phenol derivatives to the ciliate Tetrahymena pyriformis. A variety of physico-chemical descriptors were calculated but no significant relationship could be obtained for all 166 compounds. When certain chemical groups were omitted from the correlation however, notably the carboxyl-, amino-, nitro, nitroso and acetamide- substituted phenols, an excellent correlation was obtained between toxicity and two parameters. These two parameters (log P and energy of the lowest unoccupied molecular orbital) are explained mechanistically in that they model transport and electrophilicity. The resultant QSAR gave accurate prediction of the toxicity of alkyl, halogenated, alkoxy and aldehyde substituted phenols.

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Year:  1996        PMID: 8653384     DOI: 10.1016/0045-6535(96)00054-9

Source DB:  PubMed          Journal:  Chemosphere        ISSN: 0045-6535            Impact factor:   7.086


  10 in total

1.  Estimation of toxicity of monosubstituted phenols.

Authors:  I B Golovanov; S M Zhenodarova; G R Ivanitskii; E A Sedel'nikova
Journal:  Dokl Biochem Biophys       Date:  2002 Jan-Feb       Impact factor: 0.788

2.  Joint toxicity of aromatic compounds to algae and QSAR study.

Authors:  Guanghua Lu; Chao Wang; Zhuyun Tang; Xiaoling Guo
Journal:  Ecotoxicology       Date:  2007-06-28       Impact factor: 2.823

Review 3.  Modeling kinetics of subcellular disposition of chemicals.

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Journal:  Chem Rev       Date:  2009-05       Impact factor: 60.622

4.  QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action.

Authors:  A G Artemenko; E N Muratov; V E Kuz'min; N N Muratov; E V Varlamova; A V Kuz'mina; L G Gorb; A Golius; F C Hill; J Leszczynski; A Tropsha
Journal:  SAR QSAR Environ Res       Date:  2011-06-30       Impact factor: 3.000

5.  From data point timelines to a well curated data set, data mining of experimental data and chemical structure data from scientific articles, problems and possible solutions.

Authors:  Villu Ruusmann; Uko Maran
Journal:  J Comput Aided Mol Des       Date:  2013-07-25       Impact factor: 3.686

6.  Application of a genetic algorithm and an artificial neural network for global prediction of the toxicity of phenols to Tetrahymena pyriformis.

Authors:  Aziz Habibi-Yangjeh; Mohammad Danandeh-Jenagharad
Journal:  Monatsh Chem       Date:  2009-10-13       Impact factor: 1.451

7.  Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression.

Authors:  Qiang Su; Wencong Lu; Dongshu Du; Fuxue Chen; Bing Niu; Kuo-Chen Chou
Journal:  Oncotarget       Date:  2017-07-25

8.  Antimicrobial Activity of Naturally Occurring Phenols and Derivatives Against Biofilm and Planktonic Bacteria.

Authors:  Danica J Walsh; Tom Livinghouse; Darla M Goeres; Madelyn Mettler; Philip S Stewart
Journal:  Front Chem       Date:  2019-10-01       Impact factor: 5.221

Review 9.  In vitro or not in vitro: a short journey through a long history.

Authors:  Kristina Rehberger; Christian Kropf; Helmut Segner
Journal:  Environ Sci Eur       Date:  2018-06-26       Impact factor: 5.893

10.  Sulfenate Esters of Simple Phenols Exhibit Enhanced Activity against Biofilms.

Authors:  Danica J Walsh; Tom Livinghouse; Greg M Durling; Yenny Chase-Bayless; Adrienne D Arnold; Philip S Stewart
Journal:  ACS Omega       Date:  2020-03-13
  10 in total

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