Literature DB >> 16005536

Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action.

Brigitte Ganter1, Stuart Tugendreich, Cecelia I Pearson, Eser Ayanoglu, Susanne Baumhueter, Keith A Bostian, Lindsay Brady, Leslie J Browne, John T Calvin, Gwo-Jen Day, Naiomi Breckenridge, Shane Dunlea, Barrett P Eynon, L Mike Furness, Joe Ferng, Mark R Fielden, Susan Y Fujimoto, Li Gong, Christopher Hu, Radha Idury, Michael S B Judo, Kyle L Kolaja, May D Lee, Christopher McSorley, James M Minor, Ramesh V Nair, Georges Natsoulis, Peter Nguyen, Simone M Nicholson, Hang Pham, Alan H Roter, Dongxu Sun, Siqi Tan, Silke Thode, Alexander M Tolley, Antoaneta Vladimirova, Jian Yang, Zhiming Zhou, Kurt Jarnagin.   

Abstract

Successful drug discovery requires accurate decision making in order to advance the best candidates from initial lead identification to final approval. Chemogenomics, the use of genomic tools in pharmacology and toxicology, offers a promising enhancement to traditional methods of target identification/validation, lead identification, efficacy evaluation, and toxicity assessment. To realize the value of chemogenomics information, a contextual database is needed to relate the physiological outcomes induced by diverse compounds to the gene expression patterns measured in the same animals. Massively parallel gene expression characterization coupled with traditional assessments of drug candidates provides additional, important mechanistic information, and therefore a means to increase the accuracy of critical decisions. A large-scale chemogenomics database developed from in vivo treated rats provides the context and supporting data to enhance and accelerate accurate interpretation of mechanisms of toxicity and pharmacology of chemicals and drugs. To date, approximately 600 different compounds, including more than 400 FDA approved drugs, 60 drugs approved in Europe and Japan, 25 withdrawn drugs, and 100 toxicants, have been profiled in up to 7 different tissues of rats (representing over 3200 different drug-dose-time-tissue combinations). Accomplishing this task required evaluating and improving a number of in vivo and microarray protocols, including over 80 rigorous quality control steps. The utility of pairing clinical pathology assessments with gene expression data is illustrated using three anti-neoplastic drugs: carmustine, methotrexate, and thioguanine, which had similar effects on the blood compartment, but diverse effects on hepatotoxicity. We will demonstrate that gene expression events monitored in the liver can be used to predict pathological events occurring in that tissue as well as in hematopoietic tissues.

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Year:  2005        PMID: 16005536     DOI: 10.1016/j.jbiotec.2005.03.022

Source DB:  PubMed          Journal:  J Biotechnol        ISSN: 0168-1656            Impact factor:   3.307


  74 in total

Review 1.  The evolution of bioinformatics in toxicology: advancing toxicogenomics.

Authors:  Cynthia A Afshari; Hisham K Hamadeh; Pierre R Bushel
Journal:  Toxicol Sci       Date:  2010-12-22       Impact factor: 4.849

Review 2.  Accelerating Adverse Outcome Pathway Development Using Publicly Available Data Sources.

Authors:  Noffisat O Oki; Mark D Nelms; Shannon M Bell; Holly M Mortensen; Stephen W Edwards
Journal:  Curr Environ Health Rep       Date:  2016-03

3.  The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance.

Authors:  Charles Wang; Binsheng Gong; Pierre R Bushel; Jean Thierry-Mieg; Danielle Thierry-Mieg; Joshua Xu; Hong Fang; Huixiao Hong; Jie Shen; Zhenqiang Su; Joe Meehan; Xiaojin Li; Lu Yang; Haiqing Li; Paweł P Łabaj; David P Kreil; Dalila Megherbi; Stan Gaj; Florian Caiment; Joost van Delft; Jos Kleinjans; Andreas Scherer; Viswanath Devanarayan; Jian Wang; Yong Yang; Hui-Rong Qian; Lee J Lancashire; Marina Bessarabova; Yuri Nikolsky; Cesare Furlanello; Marco Chierici; Davide Albanese; Giuseppe Jurman; Samantha Riccadonna; Michele Filosi; Roberto Visintainer; Ke K Zhang; Jianying Li; Jui-Hua Hsieh; Daniel L Svoboda; James C Fuscoe; Youping Deng; Leming Shi; Richard S Paules; Scott S Auerbach; Weida Tong
Journal:  Nat Biotechnol       Date:  2014-08-24       Impact factor: 54.908

4.  Classification of a large microarray data set: algorithm comparison and analysis of drug signatures.

Authors:  Georges Natsoulis; Laurent El Ghaoui; Gert R G Lanckriet; Alexander M Tolley; Fabrice Leroy; Shane Dunlea; Barrett P Eynon; Cecelia I Pearson; Stuart Tugendreich; Kurt Jarnagin
Journal:  Genome Res       Date:  2005-05       Impact factor: 9.043

5.  Gene expression profiling and its practice in drug development.

Authors:  Murty V Chengalvala; Vargheese M Chennathukuzhi; Daniel S Johnston; Panayiotis E Stevis; Gregory S Kopf
Journal:  Curr Genomics       Date:  2007-06       Impact factor: 2.236

Review 6.  Use of transcriptomics in understanding mechanisms of drug-induced toxicity.

Authors:  Yuxia Cui; Richard S Paules
Journal:  Pharmacogenomics       Date:  2010-04       Impact factor: 2.533

7.  ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways.

Authors:  Ying Shen; Mumtahena Rahman; Stephen R Piccolo; Daniel Gusenleitner; Nader N El-Chaar; Luis Cheng; Stefano Monti; Andrea H Bild; W Evan Johnson
Journal:  Bioinformatics       Date:  2015-01-22       Impact factor: 6.937

8.  Toxicogenomic module associations with pathogenesis: a network-based approach to understanding drug toxicity.

Authors:  J J Sutherland; Y W Webster; J A Willy; G H Searfoss; K M Goldstein; A R Irizarry; D G Hall; J L Stevens
Journal:  Pharmacogenomics J       Date:  2017-04-25       Impact factor: 3.550

9.  Elucidating Compound Mechanism of Action by Network Perturbation Analysis.

Authors:  Jung Hoon Woo; Yishai Shimoni; Wan Seok Yang; Prem Subramaniam; Archana Iyer; Paola Nicoletti; María Rodríguez Martínez; Gonzalo López; Michela Mattioli; Ronald Realubit; Charles Karan; Brent R Stockwell; Mukesh Bansal; Andrea Califano
Journal:  Cell       Date:  2015-07-16       Impact factor: 41.582

10.  Human disease-drug network based on genomic expression profiles.

Authors:  Guanghui Hu; Pankaj Agarwal
Journal:  PLoS One       Date:  2009-08-06       Impact factor: 3.240

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