Drug target identification using side-effect similarity

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Drug target identification using side-effect similarity. / Campillos, Monica; Kuhn, Michael; Gavin, Anne-Claude; Jensen, Lars Juhl; Bork, Peer.

In: Science, Vol. 321, No. 5886, 2008, p. 263-6.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Campillos, M, Kuhn, M, Gavin, A-C, Jensen, LJ & Bork, P 2008, 'Drug target identification using side-effect similarity', Science, vol. 321, no. 5886, pp. 263-6. https://doi.org/10.1126/science.1158140

APA

Campillos, M., Kuhn, M., Gavin, A-C., Jensen, L. J., & Bork, P. (2008). Drug target identification using side-effect similarity. Science, 321(5886), 263-6. https://doi.org/10.1126/science.1158140

Vancouver

Campillos M, Kuhn M, Gavin A-C, Jensen LJ, Bork P. Drug target identification using side-effect similarity. Science. 2008;321(5886):263-6. https://doi.org/10.1126/science.1158140

Author

Campillos, Monica ; Kuhn, Michael ; Gavin, Anne-Claude ; Jensen, Lars Juhl ; Bork, Peer. / Drug target identification using side-effect similarity. In: Science. 2008 ; Vol. 321, No. 5886. pp. 263-6.

Bibtex

@article{c7f900c07f9a11df928f000ea68e967b,
title = "Drug target identification using side-effect similarity",
abstract = "Targets for drugs have so far been predicted on the basis of molecular or cellular features, for example, by exploiting similarity in chemical structure or in activity across cell lines. We used phenotypic side-effect similarities to infer whether two drugs share a target. Applied to 746 marketed drugs, a network of 1018 side effect-driven drug-drug relations became apparent, 261 of which are formed by chemically dissimilar drugs from different therapeutic indications. We experimentally tested 20 of these unexpected drug-drug relations and validated 13 implied drug-target relations by in vitro binding assays, of which 11 reveal inhibition constants equal to less than 10 micromolar. Nine of these were tested and confirmed in cell assays, documenting the feasibility of using phenotypic information to infer molecular interactions and hinting at new uses of marketed drugs.",
author = "Monica Campillos and Michael Kuhn and Anne-Claude Gavin and Jensen, {Lars Juhl} and Peer Bork",
note = "Keywords: Adverse Drug Reaction Reporting Systems; Algorithms; Chemistry, Pharmaceutical; Databases, Factual; Drug Evaluation, Preclinical; Drug Labeling; Drug Therapy; Humans; Pharmaceutical Preparations; Probability; Proteins",
year = "2008",
doi = "10.1126/science.1158140",
language = "English",
volume = "321",
pages = "263--6",
journal = "Science",
issn = "0036-8075",
publisher = "American Association for the Advancement of Science",
number = "5886",

}

RIS

TY - JOUR

T1 - Drug target identification using side-effect similarity

AU - Campillos, Monica

AU - Kuhn, Michael

AU - Gavin, Anne-Claude

AU - Jensen, Lars Juhl

AU - Bork, Peer

N1 - Keywords: Adverse Drug Reaction Reporting Systems; Algorithms; Chemistry, Pharmaceutical; Databases, Factual; Drug Evaluation, Preclinical; Drug Labeling; Drug Therapy; Humans; Pharmaceutical Preparations; Probability; Proteins

PY - 2008

Y1 - 2008

N2 - Targets for drugs have so far been predicted on the basis of molecular or cellular features, for example, by exploiting similarity in chemical structure or in activity across cell lines. We used phenotypic side-effect similarities to infer whether two drugs share a target. Applied to 746 marketed drugs, a network of 1018 side effect-driven drug-drug relations became apparent, 261 of which are formed by chemically dissimilar drugs from different therapeutic indications. We experimentally tested 20 of these unexpected drug-drug relations and validated 13 implied drug-target relations by in vitro binding assays, of which 11 reveal inhibition constants equal to less than 10 micromolar. Nine of these were tested and confirmed in cell assays, documenting the feasibility of using phenotypic information to infer molecular interactions and hinting at new uses of marketed drugs.

AB - Targets for drugs have so far been predicted on the basis of molecular or cellular features, for example, by exploiting similarity in chemical structure or in activity across cell lines. We used phenotypic side-effect similarities to infer whether two drugs share a target. Applied to 746 marketed drugs, a network of 1018 side effect-driven drug-drug relations became apparent, 261 of which are formed by chemically dissimilar drugs from different therapeutic indications. We experimentally tested 20 of these unexpected drug-drug relations and validated 13 implied drug-target relations by in vitro binding assays, of which 11 reveal inhibition constants equal to less than 10 micromolar. Nine of these were tested and confirmed in cell assays, documenting the feasibility of using phenotypic information to infer molecular interactions and hinting at new uses of marketed drugs.

U2 - 10.1126/science.1158140

DO - 10.1126/science.1158140

M3 - Journal article

C2 - 18621671

VL - 321

SP - 263

EP - 266

JO - Science

JF - Science

SN - 0036-8075

IS - 5886

ER -

ID: 20473602