Literature DB >> 18484504

Toxmatch-a new software tool to aid in the development and evaluation of chemically similar groups.

G Patlewicz1, N Jeliazkova, A Gallegos Saliner, A P Worth.   

Abstract

Chemical similarity is a widely used concept in toxicology, and is based on the hypothesis that similar compounds should have similar biological activities. This forms the underlying basis for performing read-across, forming chemical groups and developing (Quantitative) Structure-Activity Relationships ((Q)SARs). Chemical similarity is often perceived as structural similarity but in fact there are a number of other approaches that can be used to assess similarity. A systematic similarity analysis usually comprises two main steps. Firstly the chemical structures to be compared need to be characterised in terms of relevant descriptors which encode their physicochemical, topological, geometrical and/or surface properties. A second step involves a quantitative comparison of those descriptors using similarity (or dissimilarity) indices. This work outlines the use of chemical similarity principles in the formation of endpoint specific chemical groupings. Examples are provided to illustrate the development and evaluation of chemical groupings using a new software application called Toxmatch that was recently commissioned by the European Chemicals Bureau (ECB), of the European Commission's Joint Research Centre. Insights from using this software are highlighted with specific focus on the prospective application of chemical groupings under the new chemicals legislation, REACH.

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Year:  2008        PMID: 18484504     DOI: 10.1080/10629360802083848

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


  12 in total

1.  QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes.

Authors:  Nikita Basant; Shikha Gupta
Journal:  Environ Sci Pollut Res Int       Date:  2017-04-24       Impact factor: 4.223

2.  Navigating through the minefield of read-across tools: A review of in silico tools for grouping.

Authors:  Patlewicz Grace; Helman George; Pradeep Prachi; Shah Imran
Journal:  Comput Toxicol       Date:  2017-08

3.  In silico prediction of the developmental toxicity of diverse organic chemicals in rodents for regulatory purposes.

Authors:  Nikita Basant; Shikha Gupta; Kunwar P Singh
Journal:  Toxicol Res (Camb)       Date:  2016-02-29       Impact factor: 3.524

4.  Exploring current read-across applications and needs among selected U.S. Federal Agencies.

Authors:  Grace Patlewicz; Lucina E Lizarraga; Diego Rua; David G Allen; Amber B Daniel; Suzanne C Fitzpatrick; Natàlia Garcia-Reyero; John Gordon; Pertti Hakkinen; Angela S Howard; Agnes Karmaus; Joanna Matheson; Moiz Mumtaz; Andrea-Nicole Richarz; Patricia Ruiz; Louis Scarano; Takashi Yamada; Nicole Kleinstreuer
Journal:  Regul Toxicol Pharmacol       Date:  2019-05-10       Impact factor: 3.271

5.  Modeling the reactivities of hydroxyl radical and ozone towards atmospheric organic chemicals using quantitative structure-reactivity relationship approaches.

Authors:  Shikha Gupta; Nikita Basant; Dinesh Mohan; Kunwar P Singh
Journal:  Environ Sci Pollut Res Int       Date:  2016-04-04       Impact factor: 4.223

6.  A systems biology approach to predictive developmental neurotoxicity of a larvicide used in the prevention of Zika virus transmission.

Authors:  Karine Audouze; Olivier Taboureau; Philippe Grandjean
Journal:  Toxicol Appl Pharmacol       Date:  2018-02-21       Impact factor: 4.219

7.  Determination of structural factors affecting binding to mu, kappa and delta opioid receptors.

Authors:  Svetoslav Slavov; William Mattes; Richard D Beger
Journal:  Arch Toxicol       Date:  2020-02-27       Impact factor: 5.153

8.  QSAR modeling for predicting reproductive toxicity of chemicals in rats for regulatory purposes.

Authors:  Nikita Basant; Shikha Gupta; Kunwar P Singh
Journal:  Toxicol Res (Camb)       Date:  2016-04-26       Impact factor: 3.524

9.  Using Pareto points for model identification in predictive toxicology.

Authors:  Anna Palczewska; Daniel Neagu; Mick Ridley
Journal:  J Cheminform       Date:  2013-03-22       Impact factor: 5.514

10.  Alignment-independent technique for 3D QSAR analysis.

Authors:  Jon G Wilkes; Iva B Stoyanova-Slavova; Dan A Buzatu
Journal:  J Comput Aided Mol Des       Date:  2016-03-30       Impact factor: 3.686

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