Prediction of drug combinations by integrating molecular and pharmacological data.

Combinatorial therapy is a promising strategy for combating complex disorders due to improved efficacy and reduced side effects. However, screening new drug combinations exhaustively is impractical considering all possible combinations between drugs. Here, we present a novel computational approach t...

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Autores principales:	Xing-Ming Zhao, Murat Iskar, Georg Zeller, Michael Kuhn, Vera van Noort, Peer Bork
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/38000355d8c24fcfb4f04ce0230d5de4
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spelling	oai:doaj.org-article:38000355d8c24fcfb4f04ce0230d5de42021-11-18T05:51:41ZPrediction of drug combinations by integrating molecular and pharmacological data.1553-734X1553-735810.1371/journal.pcbi.1002323https://doaj.org/article/38000355d8c24fcfb4f04ce0230d5de42011-12-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22219721/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Combinatorial therapy is a promising strategy for combating complex disorders due to improved efficacy and reduced side effects. However, screening new drug combinations exhaustively is impractical considering all possible combinations between drugs. Here, we present a novel computational approach to predict drug combinations by integrating molecular and pharmacological data. Specifically, drugs are represented by a set of their properties, such as their targets or indications. By integrating several of these features, we show that feature patterns enriched in approved drug combinations are not only predictive for new drug combinations but also provide insights into mechanisms underlying combinatorial therapy. Further analysis confirmed that among our top ranked predictions of effective combinations, 69% are supported by literature, while the others represent novel potential drug combinations. We believe that our proposed approach can help to limit the search space of drug combinations and provide a new way to effectively utilize existing drugs for new purposes.Xing-Ming ZhaoMurat IskarMurat IskarGeorg ZellerMichael KuhnVera van NoortPeer BorkPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 7, Iss 12, p e1002323 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Xing-Ming Zhao Murat Iskar Murat Iskar Georg Zeller Michael Kuhn Vera van Noort Peer Bork Prediction of drug combinations by integrating molecular and pharmacological data.
description	Combinatorial therapy is a promising strategy for combating complex disorders due to improved efficacy and reduced side effects. However, screening new drug combinations exhaustively is impractical considering all possible combinations between drugs. Here, we present a novel computational approach to predict drug combinations by integrating molecular and pharmacological data. Specifically, drugs are represented by a set of their properties, such as their targets or indications. By integrating several of these features, we show that feature patterns enriched in approved drug combinations are not only predictive for new drug combinations but also provide insights into mechanisms underlying combinatorial therapy. Further analysis confirmed that among our top ranked predictions of effective combinations, 69% are supported by literature, while the others represent novel potential drug combinations. We believe that our proposed approach can help to limit the search space of drug combinations and provide a new way to effectively utilize existing drugs for new purposes.
format	article
author	Xing-Ming Zhao Murat Iskar Murat Iskar Georg Zeller Michael Kuhn Vera van Noort Peer Bork
author_facet	Xing-Ming Zhao Murat Iskar Murat Iskar Georg Zeller Michael Kuhn Vera van Noort Peer Bork
author_sort	Xing-Ming Zhao
title	Prediction of drug combinations by integrating molecular and pharmacological data.
title_short	Prediction of drug combinations by integrating molecular and pharmacological data.
title_full	Prediction of drug combinations by integrating molecular and pharmacological data.
title_fullStr	Prediction of drug combinations by integrating molecular and pharmacological data.
title_full_unstemmed	Prediction of drug combinations by integrating molecular and pharmacological data.
title_sort	prediction of drug combinations by integrating molecular and pharmacological data.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/38000355d8c24fcfb4f04ce0230d5de4
work_keys_str_mv	AT xingmingzhao predictionofdrugcombinationsbyintegratingmolecularandpharmacologicaldata AT muratiskar predictionofdrugcombinationsbyintegratingmolecularandpharmacologicaldata AT muratiskar predictionofdrugcombinationsbyintegratingmolecularandpharmacologicaldata AT georgzeller predictionofdrugcombinationsbyintegratingmolecularandpharmacologicaldata AT michaelkuhn predictionofdrugcombinationsbyintegratingmolecularandpharmacologicaldata AT veravannoort predictionofdrugcombinationsbyintegratingmolecularandpharmacologicaldata AT peerbork predictionofdrugcombinationsbyintegratingmolecularandpharmacologicaldata
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Prediction of drug combinations by integrating molecular and pharmacological data.

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