Drug off-target effects predicted using structural analysis in the context of a metabolic network model.
Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of...
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2010-09-01
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doaj-411025646d134aacb96038b54f586b962020-11-25T01:32:34ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582010-09-0169e100093810.1371/journal.pcbi.1000938Drug off-target effects predicted using structural analysis in the context of a metabolic network model.Roger L ChangLi XieLei XiePhilip E BourneBernhard Ø PalssonRecent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine.http://europepmc.org/articles/PMC2950675?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Roger L Chang Li Xie Lei Xie Philip E Bourne Bernhard Ø Palsson |
spellingShingle |
Roger L Chang Li Xie Lei Xie Philip E Bourne Bernhard Ø Palsson Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLoS Computational Biology |
author_facet |
Roger L Chang Li Xie Lei Xie Philip E Bourne Bernhard Ø Palsson |
author_sort |
Roger L Chang |
title |
Drug off-target effects predicted using structural analysis in the context of a metabolic network model. |
title_short |
Drug off-target effects predicted using structural analysis in the context of a metabolic network model. |
title_full |
Drug off-target effects predicted using structural analysis in the context of a metabolic network model. |
title_fullStr |
Drug off-target effects predicted using structural analysis in the context of a metabolic network model. |
title_full_unstemmed |
Drug off-target effects predicted using structural analysis in the context of a metabolic network model. |
title_sort |
drug off-target effects predicted using structural analysis in the context of a metabolic network model. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS Computational Biology |
issn |
1553-734X 1553-7358 |
publishDate |
2010-09-01 |
description |
Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine. |
url |
http://europepmc.org/articles/PMC2950675?pdf=render |
work_keys_str_mv |
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