A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism
Identifieur interne : 000C19 ( Main/Exploration ); précédent : 000C18; suivant : 000C20A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism
Auteurs : James Corey Adams [États-Unis] ; Michael J. Keiser [États-Unis] ; Li Basuino [États-Unis] ; Henry F. Chambers [États-Unis] ; Deok-Sun Lee [États-Unis, Corée du Sud] ; Olaf G. Wiest [États-Unis] ; Patricia C. Babbitt [États-Unis]Source :
- PLoS Computational Biology [ 1553-734X ] ; 2009.
Abstract
Small molecule drugs target many core metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism. These maps predict the “effect space” comprising likely target enzymes for each of the 246 MDDR drug classes in humans. The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism.
Url:
DOI: 10.1371/journal.pcbi.1000474
PubMed: 19701464
PubMed Central: 2727484
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>Small molecule drugs target many core metabolic enzymes in humans and pathogens,
often mimicking endogenous ligands. The effects may be therapeutic or toxic, but
are frequently unexpected. A large-scale mapping of the intersection between
drugs and metabolism is needed to better guide drug discovery. To map the
intersection between drugs and metabolism, we have grouped drugs and metabolites
by their associated targets and enzymes using ligand-based set signatures
created to quantify their degree of similarity in chemical space. The results
reveal the chemical space that has been explored for metabolic targets, where
successful drugs have been found, and what novel territory remains. To aid other
researchers in their drug discovery efforts, we have created an online resource
of interactive maps linking drugs to metabolism. These maps predict the
“effect space” comprising likely target enzymes for each of
the 246 MDDR drug classes in humans. The online resource also provides
species-specific interactive drug-metabolism maps for each of the 385 model
organisms and pathogens in the BioCyc database collection. Chemical similarity
links between drugs and metabolites predict potential toxicity, suggest routes
of metabolism, and reveal drug polypharmacology. The metabolic maps enable
interactive navigation of the vast biological data on potential metabolic drug
targets and the drug chemistry currently available to prosecute those targets.
Thus, this work provides a large-scale approach to ligand-based prediction of
drug action in small molecule metabolism.</p>
</div>
</front>
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