Evidence map›Paper›PMID 19701464›Full record

ArticlePLoS computational biology2009

A mapping of drug space from the viewpoint of small molecule metabolism.

James Corey Adams, Michael J Keiser, Li Basuino, Henry F Chambers, Deok-Sun Lee, Olaf G Wiest, Patricia C Babbitt

Abstract read
In one paragraph

Article in PLoS computational biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. A Computational Approach Using Bioinformatics to Screening Drug Targets forEvidence-based complementary and alternative medicine : eCAM · 2018
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. Polypharmacology: drug discovery for the future.Expert review of clinical pharmacology · 2013
    Review
  11. Article
  12. Article
  13. Toward mechanistic classification of enzyme functions.Current opinion in chemical biology · 2011
    Review
  14. Article
  15. A 'rule of 0.5' for the metabolite-likeness of approved pharmaceutical drugs.Metabolomics : Official journal of the Metabolomic Society
    Article
  16. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

James Corey AdamsGraduate Program in Pharmaceutical Sciences and Pharmacogenomics, University of California, San Francisco, California, USA.
Michael J Keiser
Li Basuino
Henry F Chambers
Deok-Sun Lee
Olaf G Wiest
Patricia C Babbitt

Funding

XRAY CRYSTALLOGRAPHIC STRUCTURE ANALYSIS: PROTEIN SORTINGP41RR001081 · NCRR · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FERRIN, THOMAS E · 1985 to 2011
$17.6M
BMI Bioinformatics Training GrantsT32GM067547 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HERNANDEZ, RYAN D. · 2003 to 2022
$6.6M
Laying the Foundation of Genomic EnzymologyR01GM060595 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BABBITT, PATRICIA CLEMENT · 2000 to 2018
$5.4M
Integrated Antimicrobial Drug Discovery Scheme for Multidrug Resistant BacteriaU01AI070499 · NIAID · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI OLTVAI, ZOLTAN N. · 2006 to 2010
$3.0M
NCRR NIH HHS P41 RR001081NCRR NIH HHS P41 RR-01081NIAID NIH HHS U01 AI070499NIAID NIH HHS U01-AI070499NIGMS NIH HHS GM60595NIGMS NIH HHS GM67547NIGMS NIH HHS R01 GM060595NIGMS NIH HHS T32 GM067547PHS HHS GA1070499-01
6 · The paper itself

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.

Indexed as

AnimalsComputational BiologyDatabases, FactualDrug Delivery SystemsDrug DiscoveryEnzymesHumansMetabolic Networks and PathwaysMethicillin-Resistant Staphylococcus aureusPharmaceutical PreparationsEnzymesPharmaceutical Preparations

Identifiers

PMID19701464
PMCPMC2727484

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.