IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1003374.html
   My bibliography  Save this article

A Network Inference Method for Large-Scale Unsupervised Identification of Novel Drug-Drug Interactions

Author

Listed:
  • Roger Guimerà
  • Marta Sales-Pardo

Abstract

Characterizing interactions between drugs is important to avoid potentially harmful combinations, to reduce off-target effects of treatments and to fight antibiotic resistant pathogens, among others. Here we present a network inference algorithm to predict uncharacterized drug-drug interactions. Our algorithm takes, as its only input, sets of previously reported interactions, and does not require any pharmacological or biochemical information about the drugs, their targets or their mechanisms of action. Because the models we use are abstract, our approach can deal with adverse interactions, synergistic/antagonistic/suppressing interactions, or any other type of drug interaction. We show that our method is able to accurately predict interactions, both in exhaustive pairwise interaction data between small sets of drugs, and in large-scale databases. We also demonstrate that our algorithm can be used efficiently to discover interactions of new drugs as part of the drug discovery process.Author Summary: Over one in four adults older than 57 in the US take five or more prescriptions at the same time; as many as 4% are at risk of a major adverse drug-drug interaction. Potentially beneficial effects of drug combinations, on the other hand, are also important. For example, combinations of drugs with synergistic effects increase the efficacy of treatments and reduce side effects; and suppressing interactions between drugs, in which one drug inhibits the action of the other, have been found to be effective in the fight against antibiotic-resistant pathogens. With thousands of drugs in the market, and hundreds or thousands being tested and developed, it is clear that we cannot rely only on experimental assays, or even mechanistic pharmacological models, to uncover new interactions. Here we present an algorithm that is able to predict such interactions. Our algorithm is parameter-free, unsupervised, and takes, as its only input, sets of previously reported interactions. We show that our method is able to accurately predict interactions, even in large-scale databases containing thousands of drugs, and that it can be used efficiently to discover interactions of new drugs as part of the drug discovery process.

Suggested Citation

  • Roger Guimerà & Marta Sales-Pardo, 2013. "A Network Inference Method for Large-Scale Unsupervised Identification of Novel Drug-Drug Interactions," PLOS Computational Biology, Public Library of Science, vol. 9(12), pages 1-9, December.
  • Handle: RePEc:plo:pcbi00:1003374
    DOI: 10.1371/journal.pcbi.1003374
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003374
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003374&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1003374?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Remy Chait & Allison Craney & Roy Kishony, 2007. "Antibiotic interactions that select against resistance," Nature, Nature, vol. 446(7136), pages 668-671, April.
    2. Roger Guimerà & Alejandro Llorente & Esteban Moro & Marta Sales-Pardo, 2012. "Predicting Human Preferences Using the Block Structure of Complex Social Networks," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-7, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yanjie Xu & Tao Ren & Shixiang Sun, 2021. "Identifying Influential Edges by Node Influence Distribution and Dissimilarity Strategy," Mathematics, MDPI, vol. 9(20), pages 1-13, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Jie & Ye, Zifeng & Chen, Kun & Zhang, Panpan, 2024. "Variational Bayesian inference for bipartite mixed-membership stochastic block model with applications to collaborative filtering," Computational Statistics & Data Analysis, Elsevier, vol. 189(C).
    2. Joseph Peter Torella & Remy Chait & Roy Kishony, 2010. "Optimal Drug Synergy in Antimicrobial Treatments," PLOS Computational Biology, Public Library of Science, vol. 6(6), pages 1-9, June.
    3. Philip Gerlee & Linnéa Schmidt & Naser Monsefi & Teresia Kling & Rebecka Jörnsten & Sven Nelander, 2013. "Searching for Synergies: Matrix Algebraic Approaches for Efficient Pair Screening," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-10, July.
    4. Thorben Funke & Till Becker, 2019. "Stochastic block models: A comparison of variants and inference methods," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-40, April.
    5. Marianne Bauer & Isabella R Graf & Vudtiwat Ngampruetikorn & Greg J Stephens & Erwin Frey, 2017. "Exploiting ecology in drug pulse sequences in favour of population reduction," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-17, September.
    6. Elsa Hansen & Jason Karslake & Robert J Woods & Andrew F Read & Kevin B Wood, 2020. "Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations," PLOS Biology, Public Library of Science, vol. 18(5), pages 1-20, May.
    7. Greenspoon, Philip B. & Mideo, Nicole, 2017. "Evolutionary rescue of a parasite population by mutation rate evolution," Theoretical Population Biology, Elsevier, vol. 117(C), pages 64-75.
    8. Chih-Wei Chen & Nadja Leimer & Egor A. Syroegin & Clémence Dunand & Zackery P. Bulman & Kim Lewis & Yury S. Polikanov & Maxim S. Svetlov, 2023. "Structural insights into the mechanism of overcoming Erm-mediated resistance by macrolides acting together with hygromycin-A," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    9. Daniel Nichol & Peter Jeavons & Alexander G Fletcher & Robert A Bonomo & Philip K Maini & Jerome L Paul & Robert A Gatenby & Alexander RA Anderson & Jacob G Scott, 2015. "Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-19, September.
    10. Jeff Maltas & Kevin B Wood, 2019. "Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance," PLOS Biology, Public Library of Science, vol. 17(10), pages 1-34, October.
    11. Eva Stadler & Mohamed Maiga & Lukas Friedrich & Vandana Thathy & Claudia Demarta-Gatsi & Antoine Dara & Fanta Sogore & Josefine Striepen & Claude Oeuvray & Abdoulaye A. Djimdé & Marcus C. S. Lee & Lau, 2023. "Propensity of selecting mutant parasites for the antimalarial drug cabamiquine," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    12. Gael Poux-Medard & Sergio Cobo-Lopez & Jordi Duch & Roger Guimera & Marta Sales-Pardo, 2021. "Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies," Papers 2103.06121, arXiv.org.
    13. Yang, Xu-Hua & Chen, Guang & Chen, Sheng-Yong, 2013. "The impact of connection density on scale-free distribution in random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2547-2554.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1003374. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.