A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism
Identifieur interne : 000596 ( Pmc/Curation ); précédent : 000595; suivant : 000597A 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
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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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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS Comput Biol</journal-id>
<journal-id journal-id-type="publisher-id">plos</journal-id>
<journal-id journal-id-type="pmc">ploscomp</journal-id>
<journal-title-group><journal-title>PLoS Computational Biology</journal-title>
</journal-title-group>
<issn pub-type="ppub">1553-734X</issn>
<issn pub-type="epub">1553-7358</issn>
<publisher><publisher-name>Public Library of Science</publisher-name>
<publisher-loc>San Francisco, USA</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">19701464</article-id>
<article-id pub-id-type="pmc">2727484</article-id>
<article-id pub-id-type="publisher-id">09-PLCB-RA-0379R2</article-id>
<article-id pub-id-type="doi">10.1371/journal.pcbi.1000474</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
<subj-group subj-group-type="Discipline"><subject>Chemical Biology/Small Molecule Chemistry</subject>
<subject>Computational Biology/Metabolic Networks</subject>
<subject>Computational Biology/Metagenomics</subject>
<subject>Computational Biology/Systems Biology</subject>
<subject>Genetics and Genomics</subject>
<subject>Genetics and Genomics/Comparative Genomics</subject>
<subject>Infectious Diseases</subject>
<subject>Pharmacology</subject>
<subject>Pharmacology/Drug Development</subject>
</subj-group>
</article-categories>
<title-group><article-title>A Mapping of Drug Space from the Viewpoint of Small Molecule
Metabolism</article-title>
<alt-title alt-title-type="running-head">Drug Discovery in Small Molecule
Metabolism</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author" equal-contrib="yes"><name><surname>Adams</surname>
<given-names>James Corey</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" equal-contrib="yes"><name><surname>Keiser</surname>
<given-names>Michael J.</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Basuino</surname>
<given-names>Li</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Chambers</surname>
<given-names>Henry F.</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Lee</surname>
<given-names>Deok-Sun</given-names>
</name>
<xref ref-type="aff" rid="aff4"><sup>4</sup>
</xref>
<xref ref-type="aff" rid="aff5"><sup>5</sup>
</xref>
<xref ref-type="aff" rid="aff6"><sup>6</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Wiest</surname>
<given-names>Olaf G.</given-names>
</name>
<xref ref-type="aff" rid="aff7"><sup>7</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Babbitt</surname>
<given-names>Patricia C.</given-names>
</name>
<xref ref-type="aff" rid="aff8"><sup>8</sup>
</xref>
<xref ref-type="aff" rid="aff9"><sup>9</sup>
</xref>
<xref ref-type="aff" rid="aff10"><sup>10</sup>
</xref>
<xref ref-type="corresp" rid="cor1"><sup>*</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff1"><label>1</label>
<addr-line>Graduate Program in Pharmaceutical Sciences and Pharmacogenomics, University of California, San Francisco, California, United States of America</addr-line>
</aff>
<aff id="aff2"><label>2</label>
<addr-line>Graduate Program in Bioinformatics, University of California, San Francisco, California, United States of America</addr-line>
</aff>
<aff id="aff3"><label>3</label>
<addr-line>San Francisco General Hospital, University of California San Francisco, San Francisco, California, United States of America</addr-line>
</aff>
<aff id="aff4"><label>4</label>
<addr-line>Center for Complex Network Research and Departments of Physics, Biology, and Computer Science, Northeastern University, Boston, Massachusetts, United States of America</addr-line>
</aff>
<aff id="aff5"><label>5</label>
<addr-line>Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America</addr-line>
</aff>
<aff id="aff6"><label>6</label>
<addr-line>Department of Natural Medical Sciences, Inha University, Incheon, Korea</addr-line>
</aff>
<aff id="aff7"><label>7</label>
<addr-line>Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana, United States of America</addr-line>
</aff>
<aff id="aff8"><label>8</label>
<addr-line>Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, United States of America</addr-line>
</aff>
<aff id="aff9"><label>9</label>
<addr-line>Department of Pharmaceutical Chemistry, University of California, San Francisco, California, United States of America</addr-line>
</aff>
<aff id="aff10"><label>10</label>
<addr-line>California Institute for Quantitative Biosciences, University of California, San Francisco, California, United States of America</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Bourne</surname>
<given-names>Philip E.</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1">University of California San Diego, United States of America</aff>
<author-notes><corresp id="cor1">* E-mail: <email>Babbitt@cgl.ucsf.edu</email>
</corresp>
<fn fn-type="con"><p>Conceived and designed the experiments: JCA MJK OGW PCB. Performed the
experiments: JCA MJK LB. Analyzed the data: JCA OGW. Contributed
reagents/materials/analysis tools: MJK D-SL HFC. Wrote the paper: JCA
PCB.</p>
</fn>
</author-notes>
<pub-date pub-type="collection"><month>8</month>
<year>2009</year>
</pub-date>
<pmc-comment> Fake ppub added to accomodate plos workflow change from 03/2008 and 03/2009 </pmc-comment>
<pub-date pub-type="ppub"><month>8</month>
<year>2009</year>
</pub-date>
<pub-date pub-type="epub"><day>21</day>
<month>8</month>
<year>2009</year>
</pub-date>
<volume>5</volume>
<issue>8</issue>
<elocation-id>e1000474</elocation-id>
<history><date date-type="received"><day>9</day>
<month>4</month>
<year>2009</year>
</date>
<date date-type="accepted"><day>16</day>
<month>7</month>
<year>2009</year>
</date>
</history>
<permissions><copyright-statement>Adams et al. This is an open-access article distributed under the
terms of the Creative Commons Attribution License, which permits unrestricted use,
distribution, and reproduction in any medium, provided the original author and
source are credited.</copyright-statement>
</permissions>
<abstract><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>
</abstract>
<abstract abstract-type="summary"><title>Author Summary</title>
<p>All humans, plants, and animals use enzymes to metabolize food for energy, build
and maintain the body, and get rid of toxins. Drugs used to clear infections or
cure cancer often target enzymes in bacteria or cancer cells, but the drugs can
interfere with the proper function of human enzymes as well. Recent studies have
mapped drugs to enzymes and many other targets in humans and other organisms,
but have not focused on metabolism. In this study, we present a new method to
predict what enzymes drugs might affect based on the chemical similarity between
classes of drugs and the natural chemicals used by enzymes. We have applied the
method to 246 known drug classes and a collection of 385 organisms (including 65
National Institutes of Health Priority Pathogens) to create maps of potential
drug action in metabolism. We also show how the predicted connections can be
used to find new ways to kill pathogens and to avoid unintentionally interfering
with human enzymes.</p>
</abstract>
<counts><page-count count="12"></page-count>
</counts>
</article-meta>
</front>
</pmc>
</record>
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