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Integrated Analysis of Drug-Induced Gene Expression Profiles Predicts Novel hERG Inhibitors

Overview of attention for article published in PLOS ONE, July 2013
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Title
Integrated Analysis of Drug-Induced Gene Expression Profiles Predicts Novel hERG Inhibitors
Published in
PLOS ONE, July 2013
DOI 10.1371/journal.pone.0069513
Pubmed ID
Authors

Joseph J. Babcock, Fang Du, Kaiping Xu, Sarah J. Wheelan, Min Li

Abstract

Growing evidence suggests that drugs interact with diverse molecular targets mediating both therapeutic and toxic effects. Prediction of these complex interactions from chemical structures alone remains challenging, as compounds with different structures may possess similar toxicity profiles. In contrast, predictions based on systems-level measurements of drug effect may reveal pharmacologic similarities not evident from structure or known therapeutic indications. Here we utilized drug-induced transcriptional responses in the Connectivity Map (CMap) to discover such similarities among diverse antagonists of the human ether-à-go-go related (hERG) potassium channel, a common target of promiscuous inhibition by small molecules. Analysis of transcriptional profiles generated in three independent cell lines revealed clusters enriched for hERG inhibitors annotated using a database of experimental measurements (hERGcentral) and clinical indications. As a validation, we experimentally identified novel hERG inhibitors among the unannotated drugs in these enriched clusters, suggesting transcriptional responses may serve as predictive surrogates of cardiotoxicity complementing existing functional assays.

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Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 2%
Brazil 1 2%
Unknown 49 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 25%
Researcher 11 22%
Student > Bachelor 6 12%
Other 5 10%
Student > Master 3 6%
Other 5 10%
Unknown 8 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 24%
Medicine and Dentistry 6 12%
Computer Science 6 12%
Agricultural and Biological Sciences 6 12%
Chemistry 4 8%
Other 8 16%
Unknown 9 18%