Literature DB >> 19434832

Gaining insight into off-target mediated effects of drug candidates with a comprehensive systems chemical biology analysis.

Josef Scheiber1, Bin Chen, Mariusz Milik, Sai Chetan K Sukuru, Andreas Bender, Dmitri Mikhailov, Steven Whitebread, Jacques Hamon, Kamal Azzaoui, Laszlo Urban, Meir Glick, John W Davies, Jeremy L Jenkins.   

Abstract

We present a workflow that leverages data from chemogenomics based target predictions with Systems Biology databases to better understand off-target related toxicities. By analyzing a set of compounds that share a common toxic phenotype and by comparing the pathways they affect with pathways modulated by nontoxic compounds we are able to establish links between pathways and particular adverse effects. We further link these predictive results with literature data in order to explain why a certain pathway is predicted. Specifically, relevant pathways are elucidated for the side effects rhabdomyolysis and hypotension. Prospectively, our approach is valuable not only to better understand toxicities of novel compounds early on but also for drug repurposing exercises to find novel uses for known drugs.

Entities:  

Mesh:

Year:  2009        PMID: 19434832     DOI: 10.1021/ci800344p

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  34 in total

Review 1.  Designing antimicrobial peptides: form follows function.

Authors:  Christopher D Fjell; Jan A Hiss; Robert E W Hancock; Gisbert Schneider
Journal:  Nat Rev Drug Discov       Date:  2011-12-16       Impact factor: 84.694

2.  A lead discovery strategy driven by a comprehensive analysis of proteases in the peptide substrate space.

Authors:  Sai Chetan K Sukuru; Florian Nigsch; Jean Quancard; Martin Renatus; Rajiv Chopra; Natasja Brooijmans; Dmitri Mikhailov; Zhan Deng; Allen Cornett; Jeremy L Jenkins; Ulrich Hommel; John W Davies; Meir Glick
Journal:  Protein Sci       Date:  2010-11       Impact factor: 6.725

3.  Chemical structural novelty: on-targets and off-targets.

Authors:  Emmanuel R Yera; Ann E Cleves; Ajay N Jain
Journal:  J Med Chem       Date:  2011-09-14       Impact factor: 7.446

4.  Data-driven prediction of drug effects and interactions.

Authors:  Nicholas P Tatonetti; Patrick P Ye; Roxana Daneshjou; Russ B Altman
Journal:  Sci Transl Med       Date:  2012-03-14       Impact factor: 17.956

5.  Exploring the relationship between drug side-effects and therapeutic indications.

Authors:  Ping Zhang; Fei Wang; Jianying Hu; Robert Sorrentino
Journal:  AMIA Annu Symp Proc       Date:  2013-11-16

6.  Determining molecular predictors of adverse drug reactions with causality analysis based on structure learning.

Authors:  Mei Liu; Ruichu Cai; Yong Hu; Michael E Matheny; Jingchun Sun; Jun Hu; Hua Xu
Journal:  J Am Med Inform Assoc       Date:  2013-12-11       Impact factor: 4.497

7.  Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data.

Authors:  Bin Chen; Xiao Dong; Dazhi Jiao; Huijun Wang; Qian Zhu; Ying Ding; David J Wild
Journal:  BMC Bioinformatics       Date:  2010-05-17       Impact factor: 3.169

Review 8.  The chemical basis of pharmacology.

Authors:  Michael J Keiser; John J Irwin; Brian K Shoichet
Journal:  Biochemistry       Date:  2010-11-12       Impact factor: 3.162

9.  A structure-based approach for mapping adverse drug reactions to the perturbation of underlying biological pathways.

Authors:  Izhar Wallach; Navdeep Jaitly; Ryan Lilien
Journal:  PLoS One       Date:  2010-08-23       Impact factor: 3.240

Review 10.  Predicting drug side-effects by chemical systems biology.

Authors:  Nicholas P Tatonetti; Tianyun Liu; Russ B Altman
Journal:  Genome Biol       Date:  2009-09-02       Impact factor: 13.583

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