Literature DB >> 11813810

Quantitative structure-toxicity relationships using TOPS-MODE. 2. Neurotoxicity of a non-congeneric series of solvents.

E Estrada1, E Molina, E Uriarte.   

Abstract

Neurotoxicities of a series of solvents in rats and mice have been modeled by means of the TOPS-MODE approach. Two quantitative structure-toxicity relationship (QSTR) models were obtained explaining more than 80% of the variance in the experimental values of neurotoxicity of 45 solvents. Only one compound was detected as statistical outlier for these models. In contrast, previous models explained less than 60% of the variance in this property for 44 solvents. Finally, the contributions to neurotoxicity in rats and mice for a series of structural fragments were found. Structural characteristics of chlorinated fragments responsible for their different neurotoxicities were analyzed. The differences in neurotoxic behavior of some fragments in rats and mice were also analyzed, which could give insights on the toxicological mechanism of action of solvents studied.

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Year:  2001        PMID: 11813810     DOI: 10.1080/10629360108035384

Source DB:  PubMed          Journal:  SAR QSAR Environ Res        ISSN: 1026-776X            Impact factor:   3.000


  7 in total

1.  Creating molecular diversity from antioxidants in Brazilian propolis. Combination of TOPS-MODE QSAR and virtual structure generation.

Authors:  Ernesto Estrada; Jose A Quincoces; Grace Patlewicz
Journal:  Mol Divers       Date:  2004       Impact factor: 2.943

2.  Design of novel antituberculosis compounds using graph-theoretical and substructural approaches.

Authors:  Alejandro Speck Planche; Marcus Tulius Scotti; América García López; Vicente de Paulo Emerenciano; Enrique Molina Pérez; Eugenio Uriarte
Journal:  Mol Divers       Date:  2009-04-02       Impact factor: 2.943

3.  Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates.

Authors:  Nerea Alonso; Olga Caamaño; Francisco J Romero-Duran; Feng Luan; M Natália D S Cordeiro; Matilde Yañez; Humberto González-Díaz; Xerardo García-Mera
Journal:  ACS Chem Neurosci       Date:  2013-07-29       Impact factor: 4.418

Review 4.  In Silico Models for Predicting Acute Systemic Toxicity.

Authors:  Ivanka Tsakovska; Antonia Diukendjieva; Andrew P Worth
Journal:  Methods Mol Biol       Date:  2022

5.  In silico prediction of chemical neurotoxicity using machine learning.

Authors:  Changsheng Jiang; Piaopiao Zhao; Weihua Li; Yun Tang; Guixia Liu
Journal:  Toxicol Res (Camb)       Date:  2020-04-29       Impact factor: 3.524

6.  International STakeholder NETwork (ISTNET): creating a developmental neurotoxicity (DNT) testing road map for regulatory purposes.

Authors:  Anna Bal-Price; Kevin M Crofton; Marcel Leist; Sandra Allen; Michael Arand; Timo Buetler; Nathalie Delrue; Rex E FitzGerald; Thomas Hartung; Tuula Heinonen; Helena Hogberg; Susanne Hougaard Bennekou; Walter Lichtensteiger; Daniela Oggier; Martin Paparella; Marta Axelstad; Aldert Piersma; Eva Rached; Benoît Schilter; Gabriele Schmuck; Luc Stoppini; Enrico Tongiorgi; Manuela Tiramani; Florianne Monnet-Tschudi; Martin F Wilks; Timo Ylikomi; Ellen Fritsche
Journal:  Arch Toxicol       Date:  2015-01-25       Impact factor: 5.153

7.  Scoring function for DNA-drug docking of anticancer and antiparasitic compounds based on spectral moments of 2D lattice graphs for molecular dynamics trajectories.

Authors:  Lázaro G Pérez-Montoto; Lourdes Santana; Humberto González-Díaz
Journal:  Eur J Med Chem       Date:  2009-06-17       Impact factor: 6.514

  7 in total

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