Literature DB >> 8790641

Predicting modes of toxic action from chemical structure: an overview.

S P Bradbury1.   

Abstract

In the field of environmental toxicology, and especially aquatic toxicology, quantitative structure activity relationships (QSARs) have developed as scientifically-credible tools for predicting the toxicity of chemicals when little or no empirical data are available. A basic and fundamental understanding of toxicological principles has been considered crucial to the continued acceptance and application of these techniques as biologically relevant. As a consequence, 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 assessment of a compound's likely mode of toxic action is critical for a correct QSAR selection; incorrect mode of action-based QSAR selections can result in 10- to 1000-fold errors in toxicity predictions. The establishment of toxicologically-credible techniques to assess mode of toxic action from chemical structure requires toxicodynamic knowledge bases that are clearly defined with regard to exposure regimes and biological models/endpoints and based on compounds that adequately span the diversity of chemicals anticipated for future applications. With such knowledge bases classification systems, including rule-based experts systems, have been established for use in predictive aquatic toxicology applications.

Entities:  

Mesh:

Substances:

Year:  1994        PMID: 8790641     DOI: 10.1080/10629369408028842

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


  8 in total

Review 1.  Neural networks as robust tools in drug lead discovery and development.

Authors:  David A Winkler
Journal:  Mol Biotechnol       Date:  2004-06       Impact factor: 2.695

2.  Expanded Target-Chemical Analysis Reveals Extensive Mixed-Organic-Contaminant Exposure in U.S. Streams.

Authors:  Paul M Bradley; Celeste A Journey; Kristin M Romanok; Larry B Barber; Herbert T Buxton; William T Foreman; Edward T Furlong; Susan T Glassmeyer; Michelle L Hladik; Luke R Iwanowicz; Daniel K Jones; Dana W Kolpin; Kathryn M Kuivila; Keith A Loftin; Marc A Mills; Michael T Meyer; James L Orlando; Timothy J Reilly; Kelly L Smalling; Daniel L Villeneuve
Journal:  Environ Sci Technol       Date:  2017-04-12       Impact factor: 9.028

3.  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

4.  Modeling the Sensitivity of Aquatic Macroinvertebrates to Chemicals Using Traits.

Authors:  Sanne J P Van den Berg; Hans Baveco; Emma Butler; Frederik De Laender; Andreas Focks; Antonio Franco; Cecilie Rendal; Paul J Van den Brink
Journal:  Environ Sci Technol       Date:  2019-04-30       Impact factor: 9.028

5.  Multi-region assessment of pharmaceutical exposures and predicted effects in USA wadeable urban-gradient streams.

Authors:  Paul M Bradley; Celeste A Journey; Daniel T Button; Daren M Carlisle; Bradley J Huffman; Sharon L Qi; Kristin M Romanok; Peter C Van Metre
Journal:  PLoS One       Date:  2020-01-30       Impact factor: 3.240

6.  Assessment of prediction confidence and domain extrapolation of two structure-activity relationship models for predicting estrogen receptor binding activity.

Authors:  Weida Tong; Qian Xie; Huixiao Hong; Leming Shi; Hong Fang; Roger Perkins
Journal:  Environ Health Perspect       Date:  2004-08       Impact factor: 9.031

Review 7.  Molecular adaptation mechanisms employed by ethanologenic bacteria in response to lignocellulose-derived inhibitory compounds.

Authors:  Omodele Ibraheem; Bongani K Ndimba
Journal:  Int J Biol Sci       Date:  2013-06-28       Impact factor: 6.580

Review 8.  Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis.

Authors:  Yunyi Wu; Guanyu Wang
Journal:  Int J Mol Sci       Date:  2018-08-10       Impact factor: 5.923

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.