Articles producció científica> Enginyeria Química

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

  • Datos identificativos

    Identificador: imarina:9298203
    Handle: http://hdl.handle.net/20.500.11797/imarina9298203
  • Autores:

    Guimera, Roger
    Sales-Pardo, Marta
  • Otros:

    Autor según el artículo: Guimera, Roger; Sales-Pardo, Marta;
    Departamento: Enginyeria Química
    Autor/es de la URV: Guimera Manrique, Roger / Sales Pardo, Marta
    Palabras clave: Targets Systems Resistance Pharmacology Metabolic networks Complex networks Combinations Blockmodels
    Resumen: 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.
    Áreas temáticas: Saúde coletiva Psicología Molecular biology Modeling and simulation Medicina ii Medicina i Mathematics, interdisciplinary applications Mathematical & computational biology Matemática / probabilidade e estatística Interdisciplinar Genetics Ensino Engenharias iv Engenharias iii Ecology, evolution, behavior and systematics Ecology Computational theory and mathematics Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência da computação Cellular and molecular neuroscience Biotecnología Biodiversidade Biochemical research methods Astronomia / física
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: roger.guimera@urv.cat marta.sales@urv.cat
    Identificador del autor: 0000-0002-3597-4310 0000-0002-8140-6525
    Fecha de alta del registro: 2023-05-20
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003374
    Referencia al articulo segun fuente origial: Plos Computational Biology. 9 (12):
    Referencia de l'ítem segons les normes APA: Guimera, Roger; Sales-Pardo, Marta; (2013). A Network Inference Method for Large-Scale Unsupervised Identification of Novel Drug-Drug Interactions. Plos Computational Biology, 9(12), -. DOI: 10.1371/journal.pcbi.1003374
    URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.1371/journal.pcbi.1003374
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2013
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Biochemical Research Methods,Cellular and Molecular Neuroscience,Computational Theory and Mathematics,Ecology,Ecology, Evolution, Behavior and Systematics,Genetics,Mathematical & Computational Biology,Mathematics, Interdisciplinary Applications,Modeling and Simulation,Molecular Biology
    Targets
    Systems
    Resistance
    Pharmacology
    Metabolic networks
    Complex networks
    Combinations
    Blockmodels
    Saúde coletiva
    Psicología
    Molecular biology
    Modeling and simulation
    Medicina ii
    Medicina i
    Mathematics, interdisciplinary applications
    Mathematical & computational biology
    Matemática / probabilidade e estatística
    Interdisciplinar
    Genetics
    Ensino
    Engenharias iv
    Engenharias iii
    Ecology, evolution, behavior and systematics
    Ecology
    Computational theory and mathematics
    Ciências biológicas ii
    Ciências biológicas i
    Ciências agrárias i
    Ciência da computação
    Cellular and molecular neuroscience
    Biotecnología
    Biodiversidade
    Biochemical research methods
    Astronomia / física
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