Transcriptomics in predictive toxicology

被引:0
|
作者
Storck, T [1 ]
von Brevern, MC [1 ]
Behrens, CK [1 ]
Scheel, J [1 ]
Bach, A [1 ]
机构
[1] Axaron Biosci AG, D-69120 Heidelberg, Germany
关键词
hepatocytes; hierarchical cluster analysis; in vitro-in vivo correlation; microarray; mode of action; predictive toxicology; rat liver; toxicogenomics; transcription profiling;
D O I
暂无
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Once again, genomics is about to change drug development. Following its major impact on target discovery and assay development, which increased the number of compounds at early stages of the process, genomics is now zeroing in on the prediction of potential toxicological problems of compounds. Toxicogenomics is the analysis of toxicological processes at the transcriptome level of a target organ or cell. By simultaneously monitoring the effect of a compound on the transcription levels of hundreds to thousands of genes, toxicogenomics can provide an enormous amount of data. This data bears information on the way in which compounds act at the molecular level, reaching far beyond the mere conclusion of whether or not a particular toxicological outcome is elicited. By compiling transcription profiles for well-known toxicants, we are beginning to learn how to analyze this novel type of data in the context of mechanistic and predictive toxicology.
引用
收藏
页码:90 / 97
页数:8
相关论文
共 50 条
  • [31] Predictive toxicology using QSAR: A perspective
    Kar, Supratik
    Roy, Kunal
    JOURNAL OF THE INDIAN CHEMICAL SOCIETY, 2010, 87 (12) : 1455 - 1515
  • [32] Alternative animal models in predictive toxicology
    Khabib, Muhammad Nur Hamizan
    Sivasanku, Yogeethaa
    Lee, Hong Boon
    Kumar, Suresh
    Kue, Chin Siang
    TOXICOLOGY, 2022, 465
  • [33] Collaborative development of predictive toxicology applications
    Barry Hardy
    Nicki Douglas
    Christoph Helma
    Micha Rautenberg
    Nina Jeliazkova
    Vedrin Jeliazkov
    Ivelina Nikolova
    Romualdo Benigni
    Olga Tcheremenskaia
    Stefan Kramer
    Tobias Girschick
    Fabian Buchwald
    Joerg Wicker
    Andreas Karwath
    Martin Gütlein
    Andreas Maunz
    Haralambos Sarimveis
    Georgia Melagraki
    Antreas Afantitis
    Pantelis Sopasakis
    David Gallagher
    Vladimir Poroikov
    Dmitry Filimonov
    Alexey Zakharov
    Alexey Lagunin
    Tatyana Gloriozova
    Sergey Novikov
    Natalia Skvortsova
    Dmitry Druzhilovsky
    Sunil Chawla
    Indira Ghosh
    Surajit Ray
    Hitesh Patel
    Sylvia Escher
    Journal of Cheminformatics, 2
  • [34] Applying toxicogenomics in mechanistic and predictive toxicology
    Cunningham, ML
    Lehman-McKeeman, L
    TOXICOLOGICAL SCIENCES, 2005, 83 (02) : 205 - 206
  • [35] Factors influencing predictive models for toxicology
    Benfenati, E
    Piclin, N
    Roncaglioni, A
    Varì, MR
    SAR AND QSAR IN ENVIRONMENTAL RESEARCH, 2001, 12 (06) : 593 - 603
  • [36] Elementary predictive toxicology for advanced applications
    Kreatsoulas, Constantine
    Durham, Stephen K.
    Custer, Laura L.
    Pearl, Greg M.
    OPTIMIZING THE DRUG-LIKE PROPERTIES OF LEADS IN DRUG DISCOVERY, 2006, 4 : 301 - +
  • [37] Data governance in predictive toxicology: A review
    Xin Fu
    Anna Wojak
    Daniel Neagu
    Mick Ridley
    Kim Travis
    Journal of Cheminformatics, 3
  • [38] Editorial: Methods in predictive toxicology 2023
    Jain, Sankalp
    Manganelli, Serena
    Gryshkova, Vitalina
    Rodrigues, Maria Armanda
    Magarkar, Aniket
    FRONTIERS IN PHARMACOLOGY, 2025, 16
  • [39] Predictive toxicology of chemicals and database mining
    WANG Jiansuo
    Chinese Science Bulletin, 2000, (12) : 1093 - 1097
  • [40] Toxicogenomics in predictive toxicology in drug development
    Suter, L
    Babiss, LE
    Wheeldon, EB
    CHEMISTRY & BIOLOGY, 2004, 11 (02): : 161 - 171