Literature DB >> 21311849

Formation of mechanistic categories and local models to facilitate the prediction of toxicity.

Mark T D Cronin1, Steven J Enoch, Mark Hewitt, Judith C Madden.   

Abstract

There is a range of in silico techniques that can be applied to predict the toxicity of chemicals. This paper discusses the use of methods to create "local" models, particularly based around category formation and read-across, to predict toxicity. Specifically, this is illustrated with regard to categories for predicting skin sensitisation and teratogenicity. These were formed using mechanistic and structural similarity techniques to group chemicals. Local QSAR models based on grouping chemicals have the advantage that they are transparent, simple and mechanistically derived. In addition, there are a number of freely available software tools to assist in their derivation. The disadvantages include that they are labour-intensive to develop and restricted to local areas of chemistry.

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Year:  2011        PMID: 21311849     DOI: 10.14573/altex.2011.1.045

Source DB:  PubMed          Journal:  ALTEX        ISSN: 1868-596X            Impact factor:   6.043


  3 in total

1.  In silico prediction of pesticide aquatic toxicity with chemical category approaches.

Authors:  Fuxing Li; Defang Fan; Hao Wang; Hongbin Yang; Weihua Li; Yun Tang; Guixia Liu
Journal:  Toxicol Res (Camb)       Date:  2017-07-31       Impact factor: 3.524

2.  Modeling the toxicity of chemical pesticides in multiple test species using local and global QSTR approaches.

Authors:  Nikita Basant; Shikha Gupta; Kunwar P Singh
Journal:  Toxicol Res (Camb)       Date:  2015-12-10       Impact factor: 3.524

3.  Assessing the exposure risk and impacts of pharmaceuticals in the environment on individuals and ecosystems.

Authors:  Kathryn E Arnold; Alistair B A Boxall; A Ross Brown; Richard J Cuthbert; Sally Gaw; Thomas H Hutchinson; Susan Jobling; Judith C Madden; Chris D Metcalfe; Vinny Naidoo; Richard F Shore; Judit E Smits; Mark A Taggart; Helen M Thompson
Journal:  Biol Lett       Date:  2013-06-26       Impact factor: 3.703

  3 in total

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