Literature DB >> 10890502

Prediction of toxicity from chemical structure.

M D Barratt1.   

Abstract

The basis for the prediction of toxicity from chemical structure is that the properties of a chemical are implicit in its molecular structure. Biological activity can be expressed as a function of partition and reactivity, that is, for a chemical to be able to express its toxicity, it must be transported from its site of administration to its site of action and then it must bind to or react with its receptor or target. This process may also involve metabolic transformation of the chemical. The application of these principles to the prediction of the toxicity of new or untested chemicals has been achieved in a number of different ways covering a wide range of complexity, from computer systems containing databases of hundreds of chemicals, to simple "reading across" between chemicals with similar chemical/toxicological functionality. The common feature of the approaches described in this article is that their starting point is a mechanistic hypothesis linking chemical structure and/or functionality with the toxicological endpoint of interest. The prediction of toxicity from chemical structure can make a valuable contribution to the reduction of animal usage in the screening out of potentially toxic chemicals at an early stage and in providing data for making positive classifications of toxicity.

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Year:  2000        PMID: 10890502     DOI: 10.1023/a:1007676602908

Source DB:  PubMed          Journal:  Cell Biol Toxicol        ISSN: 0742-2091            Impact factor:   6.691


  7 in total

1.  In vitro permeability of poorly aqueous soluble compounds using different solubilizers in the PAMPA assay with liquid chromatography/mass spectrometry detection.

Authors:  Hanlan Liu; Chantel Sabus; Guy T Carter; Chao Du; Alex Avdeef; Mark Tischler
Journal:  Pharm Res       Date:  2003-11       Impact factor: 4.200

Review 2.  Novel paradigms for drug discovery: computational multitarget screening.

Authors:  Ekachai Jenwitheesuk; Jeremy A Horst; Kasey L Rivas; Wesley C Van Voorhis; Ram Samudrala
Journal:  Trends Pharmacol Sci       Date:  2008-01-10       Impact factor: 14.819

Review 3.  Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation.

Authors:  Kirk E Hevener
Journal:  Methods Mol Biol       Date:  2018

4.  Prediction of the effect of formulation on the toxicity of chemicals.

Authors:  Pritesh Mistry; Daniel Neagu; Antonio Sanchez-Ruiz; Paul R Trundle; Jonathan D Vessey; John Paul Gosling
Journal:  Toxicol Res (Camb)       Date:  2016-10-31       Impact factor: 3.524

5.  Anti-HIV Drugs Cause Mitochondrial Dysfunction in Monocyte-Derived Macrophages.

Authors:  Jennillee Wallace; Hemil Gonzalez; Reshma Rajan; Srinivas D Narasipura; Amber K Virdi; Arnold Z Olali; Ankur Naqib; Zarema Arbieva; Mark Maienschein-Cline; Lena Al-Harthi
Journal:  Antimicrob Agents Chemother       Date:  2022-03-16       Impact factor: 5.938

6.  Köln-Timişoara Molecular activity combined models toward interspecies toxicity assessment.

Authors:  Sergiu A Chicu; Mihai V Putz
Journal:  Int J Mol Sci       Date:  2009-11-20       Impact factor: 6.208

Review 7.  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

  7 in total

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