Literature DB >> 22305969

Metabolomics: a tool for early detection of toxicological effects and an opportunity for biology based grouping of chemicals-from QSAR to QBAR.

B van Ravenzwaay1, M Herold, H Kamp, M D Kapp, E Fabian, R Looser, G Krennrich, W Mellert, A Prokoudine, V Strauss, T Walk, J Wiemer.   

Abstract

BASF has developed a Metabolomics database (MetaMap(®) Tox) containing approximately 500 data rich chemicals, agrochemicals and drugs. This metabolome-database has been built based upon 28-day studies in rats (adapted to OECD 407 guideline) with blood sampling and metabolic profiling after 7, 14 and 28 days of test substance treatment. Numerous metabolome patterns have been established for different toxicological targets (liver, kidney, thyroid, testes, blood, nervous system and endocrine system) which are specific for different toxicological modes of action. With these patterns early detection of toxicological effects and the underlying mechanism can now be obtained from routine studies. Early recognition of toxicological mode of action will help to develop new compounds with a more favourable toxicological profile and will also help to reduce the number of animal studies necessary to do so. Thus this technology contributes to animal welfare by means of reduction through refinement (2R), but also has potential as a replacement method by analyzing samples from in vitro studies. With respect to the REACH legislation for which a large number of animal studies will need to be performed, one of the most promising methods to reduce the number of animal experiments is grouping of chemicals and read-across to those which are data rich. So far mostly chemical similarity or QSAR models are driving the selection process of chemical grouping. However, "omics" technologies such as metabolomics may help to optimize the chemical grouping process by providing biologically based criteria for toxicological equivalence. "From QSAR to QBAR" (quantitative biological activity relationship).
Copyright © 2012 Elsevier B.V. All rights reserved.

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Year:  2012        PMID: 22305969     DOI: 10.1016/j.mrgentox.2012.01.006

Source DB:  PubMed          Journal:  Mutat Res        ISSN: 0027-5107            Impact factor:   2.433


  24 in total

1.  Predicting the future: opportunities and challenges for the chemical industry to apply 21st-century toxicity testing.

Authors:  Raja S Settivari; Nicholas Ball; Lynea Murphy; Reza Rasoulpour; Darrell R Boverhof; Edward W Carney
Journal:  J Am Assoc Lab Anim Sci       Date:  2015-03       Impact factor: 1.232

Review 2.  Framework for the quality assurance of 'omics technologies considering GLP requirements.

Authors:  Hans-Martin Kauffmann; Hennicke Kamp; Regine Fuchs; Brian N Chorley; Lize Deferme; Timothy Ebbels; Jörg Hackermüller; Stefania Perdichizzi; Alan Poole; Ursula G Sauer; Knut E Tollefsen; Tewes Tralau; Carole Yauk; Ben van Ravenzwaay
Journal:  Regul Toxicol Pharmacol       Date:  2017-10-05       Impact factor: 3.271

Review 3.  Future of environmental research in the age of epigenomics and exposomics.

Authors:  Nina Holland
Journal:  Rev Environ Health       Date:  2017-03-01       Impact factor: 3.458

4.  In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities.

Authors:  Arianna Bassan; Vinicius M Alves; Alexander Amberg; Lennart T Anger; Lisa Beilke; Andreas Bender; Autumn Bernal; Mark T D Cronin; Jui-Hua Hsieh; Candice Johnson; Raymond Kemper; Moiz Mumtaz; Louise Neilson; Manuela Pavan; Amy Pointon; Julia Pletz; Patricia Ruiz; Daniel P Russo; Yogesh Sabnis; Reena Sandhu; Markus Schaefer; Lidiya Stavitskaya; David T Szabo; Jean-Pierre Valentin; David Woolley; Craig Zwickl; Glenn J Myatt
Journal:  Comput Toxicol       Date:  2021-09-13

Review 5.  Review: toxicometabolomics.

Authors:  Mounir Bouhifd; Thomas Hartung; Helena T Hogberg; Andre Kleensang; Liang Zhao
Journal:  J Appl Toxicol       Date:  2013-05-30       Impact factor: 3.446

6.  Metabolomics in toxicology and preclinical research.

Authors:  Tzutzuy Ramirez; Mardas Daneshian; Hennicke Kamp; Frederic Y Bois; Malcolm R Clench; Muireann Coen; Beth Donley; Steven M Fischer; Drew R Ekman; Eric Fabian; Claude Guillou; Joachim Heuer; Helena T Hogberg; Harald Jungnickel; Hector C Keun; Gerhard Krennrich; Eckart Krupp; Andreas Luch; Fozia Noor; Erik Peter; Bjoern Riefke; Mark Seymour; Nigel Skinner; Lena Smirnova; Elwin Verheij; Silvia Wagner; Thomas Hartung; Bennard van Ravenzwaay; Marcel Leist
Journal:  ALTEX       Date:  2013       Impact factor: 6.043

7.  Prediction of clinically relevant safety signals of nephrotoxicity through plasma metabolite profiling.

Authors:  W B Mattes; H G Kamp; E Fabian; M Herold; G Krennrich; R Looser; W Mellert; A Prokoudine; V Strauss; B van Ravenzwaay; T Walk; H Naraoka; K Omura; I Schuppe-Koistinen; S Nadanaciva; E D Bush; N Moeller; P Ruiz-Noppinger; S P Piccoli
Journal:  Biomed Res Int       Date:  2013-05-21       Impact factor: 3.411

8.  Metabolic profiling provides a system understanding of hypothyroidism in rats and its application.

Authors:  Si Wu; Guangguo Tan; Xin Dong; Zhenyu Zhu; Wuhong Li; Ziyang Lou; Yifeng Chai
Journal:  PLoS One       Date:  2013-02-07       Impact factor: 3.240

Review 9.  Pesticides: formulants, distribution pathways and effects on human health - a review.

Authors:  Valeriya P Kalyabina; Elena N Esimbekova; Kseniya V Kopylova; Valentina A Kratasyuk
Journal:  Toxicol Rep       Date:  2021-06-06

10.  In-silico predictive mutagenicity model generation using supervised learning approaches.

Authors:  Abhik Seal; Anurag Passi; Uc Abdul Jaleel; David J Wild
Journal:  J Cheminform       Date:  2012-05-15       Impact factor: 5.514

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