Literature DB >> 15123278

Toxicogenomics in predictive toxicology in drug development.

Laura Suter1, Lee E Babiss, Eric B Wheeldon.   

Abstract

The goal of toxicology is the assessment of possible risk to man. An emerging technology with the potential to have a major impact on risk assessment is toxicogenomics. In this review, we provide an overview of the many possibilities for toxicogenomics including technology platforms, data interpretation, and regulatory perspective and we give examples of toxicogenomics investigations. Toxicogenomics is a powerful tool for compound classification, for mechanistic studies, and for the detection of toxicity markers. Thus, toxicogenomics helps in the extrapolation of findings across species and increases predictability. Biomarkers are valuable in the evaluation of compounds at earlier development phases, improving clinical candidate selection. Caution regarding the interpretation of the results is still necessary. Nevertheless, toxicogenomics will accelerate preclinical safety assessments and improve the prediction of toxic liabilities, as well as of potential risk accumulation for drug-drug or drug-disease interactions.

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Year:  2004        PMID: 15123278     DOI: 10.1016/j.chembiol.2004.02.003

Source DB:  PubMed          Journal:  Chem Biol        ISSN: 1074-5521


  18 in total

1.  Clinical relevance of liquid chromatography tandem mass spectrometry as an analytical method in microdose clinical studies.

Authors:  Naoe Yamane; Zenzaburo Tozuka; Makiko Kusama; Kazuya Maeda; Toshihiko Ikeda; Yuichi Sugiyama
Journal:  Pharm Res       Date:  2011-04-07       Impact factor: 4.200

Review 2.  A review of toxicity and mechanisms of individual and mixtures of heavy metals in the environment.

Authors:  Xiangyang Wu; Samuel J Cobbina; Guanghua Mao; Hai Xu; Zhen Zhang; Liuqing Yang
Journal:  Environ Sci Pollut Res Int       Date:  2016-03-11       Impact factor: 4.223

3.  Mercury-induced hepatotoxicity in zebrafish: in vivo mechanistic insights from transcriptome analysis, phenotype anchoring and targeted gene expression validation.

Authors:  Choong Yong Ung; Siew Hong Lam; Mya Myintzu Hlaing; Cecilia Lanny Winata; Svetlana Korzh; Sinnakaruppan Mathavan; Zhiyuan Gong
Journal:  BMC Genomics       Date:  2010-03-30       Impact factor: 3.969

4.  Identification of structural alerts for liver and kidney toxicity using repeated dose toxicity data.

Authors:  Fabiola Pizzo; Domenico Gadaleta; Anna Lombardo; Orazio Nicolotti; Emilio Benfenati
Journal:  Chem Cent J       Date:  2015-11-05       Impact factor: 4.215

5.  Transcriptional response of zebrafish embryos exposed to neurotoxic compounds reveals a muscle activity dependent hspb11 expression.

Authors:  Nils Klüver; Lixin Yang; Wibke Busch; Katja Scheffler; Patrick Renner; Uwe Strähle; Stefan Scholz
Journal:  PLoS One       Date:  2011-12-19       Impact factor: 3.240

6.  The TAO-Gen algorithm for identifying gene interaction networks with application to SOS repair in E. coli.

Authors:  Takeharu Yamanaka; Hiroyoshi Toyoshiba; Hideko Sone; Frederick M Parham; Christopher J Portier
Journal:  Environ Health Perspect       Date:  2004-11       Impact factor: 9.031

7.  Cross-species toxicogenomic analyses and phenotypic anchoring in response to groundwater low-level pollution.

Authors:  Immacolata Porreca; Fulvio D'Angelo; Daniela Gentilcore; Emanuele Carchia; Angela Amoresano; Andrea Affuso; Michele Ceccarelli; Pasquale De Luca; Libera Esposito; Francesco M Guadagno; Massimo Mallardo; Antonio Nardone; Sergio Maccarone; Francesca Pane; Marzia Scarfò; Paolo Sordino; Mario De Felice; Concetta Ambrosino
Journal:  BMC Genomics       Date:  2014-12-05       Impact factor: 3.969

8.  ebTrack: an environmental bioinformatics system built upon ArrayTrack.

Authors:  Minjun Chen; Jackson Martin; Hong Fang; Sastry Isukapalli; Panos G Georgopoulos; William J Welsh; Weida Tong
Journal:  BMC Proc       Date:  2009-03-10

9.  Transcriptional profiling reveals barcode-like toxicogenomic responses in the zebrafish embryo.

Authors:  Lixin Yang; Jules R Kemadjou; Christian Zinsmeister; Matthias Bauer; Jessica Legradi; Ferenc Müller; Michael Pankratz; Jens Jäkel; Uwe Strähle
Journal:  Genome Biol       Date:  2007       Impact factor: 13.583

10.  Toxicity prediction from toxicogenomic data based on class association rule mining.

Authors:  Keisuke Nagata; Takashi Washio; Yoshinobu Kawahara; Akira Unami
Journal:  Toxicol Rep       Date:  2014-11-07
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