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Mapping Drug Physico-Chemical Features to Pathway Activity Reveals Molecular Networks Linked to Toxicity Outcome

Overview of attention for article published in PLOS ONE, August 2010
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Title
Mapping Drug Physico-Chemical Features to Pathway Activity Reveals Molecular Networks Linked to Toxicity Outcome
Published in
PLOS ONE, August 2010
DOI 10.1371/journal.pone.0012385
Pubmed ID
Authors

Philipp Antczak, Fernando Ortega, J. Kevin Chipman, Francesco Falciani

Abstract

The identification of predictive biomarkers is at the core of modern toxicology. So far, a number of approaches have been proposed. These rely on statistical inference of toxicity response from either compound features (i.e., QSAR), in vitro cell based assays or molecular profiling of target tissues (i.e., expression profiling). Although these approaches have already shown the potential of predictive toxicology, we still do not have a systematic approach to model the interaction between chemical features, molecular networks and toxicity outcome. Here, we describe a computational strategy designed to address this important need. Its application to a model of renal tubular degeneration has revealed a link between physico-chemical features and signalling components controlling cell communication pathways, which in turn are differentially modulated in response to toxic chemicals. Overall, our findings are consistent with the existence of a general toxicity mechanism operating in synergy with more specific single-target based mode of actions (MOAs) and provide a general framework for the development of an integrative approach to predictive toxicology.

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The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 5%
Sweden 1 2%
Belgium 1 2%
Denmark 1 2%
Spain 1 2%
Unknown 38 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 39%
Student > Ph. D. Student 10 23%
Professor > Associate Professor 4 9%
Other 3 7%
Student > Master 3 7%
Other 5 11%
Unknown 2 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 43%
Biochemistry, Genetics and Molecular Biology 5 11%
Environmental Science 4 9%
Medicine and Dentistry 4 9%
Computer Science 2 5%
Other 7 16%
Unknown 3 7%