Target inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways.

A recent trend in drug development is to identify drug combinations or multi-target agents that effectively modify multiple nodes of disease-associated networks. Such polypharmacological effects may reduce the risk of emerging drug resistance by means of attacking the disease networks through synerg...

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Autores principales:	Jing Tang, Leena Karhinen, Tao Xu, Agnieszka Szwajda, Bhagwan Yadav, Krister Wennerberg, Tero Aittokallio
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/464fb0e478384c6487cf3e21f71300fd
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spelling	oai:doaj.org-article:464fb0e478384c6487cf3e21f71300fd2021-11-18T05:53:35ZTarget inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways.1553-734X1553-735810.1371/journal.pcbi.1003226https://doaj.org/article/464fb0e478384c6487cf3e21f71300fd2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24068907/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358A recent trend in drug development is to identify drug combinations or multi-target agents that effectively modify multiple nodes of disease-associated networks. Such polypharmacological effects may reduce the risk of emerging drug resistance by means of attacking the disease networks through synergistic and synthetic lethal interactions. However, due to the exponentially increasing number of potential drug and target combinations, systematic approaches are needed for prioritizing the most potent multi-target alternatives on a global network level. We took a functional systems pharmacology approach toward the identification of selective target combinations for specific cancer cells by combining large-scale screening data on drug treatment efficacies and drug-target binding affinities. Our model-based prediction approach, named TIMMA, takes advantage of the polypharmacological effects of drugs and infers combinatorial drug efficacies through system-level target inhibition networks. Case studies in MCF-7 and MDA-MB-231 breast cancer and BxPC-3 pancreatic cancer cells demonstrated how the target inhibition modeling allows systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways in a given cancer type. The TIMMA prediction results were experimentally validated by means of systematic siRNA-mediated silencing of the selected targets and their pairwise combinations, showing increased ability to identify not only such druggable kinase targets that are essential for cancer survival either individually or in combination, but also synergistic interactions indicative of non-additive drug efficacies. These system-level analyses were enabled by a novel model construction method utilizing maximization and minimization rules, as well as a model selection algorithm based on sequential forward floating search. Compared with an existing computational solution, TIMMA showed both enhanced prediction accuracies in cross validation as well as significant reduction in computation times. Such cost-effective computational-experimental design strategies have the potential to greatly speed-up the drug testing efforts by prioritizing those interventions and interactions warranting further study in individual cancer cases.Jing TangLeena KarhinenTao XuAgnieszka SzwajdaBhagwan YadavKrister WennerbergTero AittokallioPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 9, p e1003226 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Jing Tang Leena Karhinen Tao Xu Agnieszka Szwajda Bhagwan Yadav Krister Wennerberg Tero Aittokallio Target inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways.
description	A recent trend in drug development is to identify drug combinations or multi-target agents that effectively modify multiple nodes of disease-associated networks. Such polypharmacological effects may reduce the risk of emerging drug resistance by means of attacking the disease networks through synergistic and synthetic lethal interactions. However, due to the exponentially increasing number of potential drug and target combinations, systematic approaches are needed for prioritizing the most potent multi-target alternatives on a global network level. We took a functional systems pharmacology approach toward the identification of selective target combinations for specific cancer cells by combining large-scale screening data on drug treatment efficacies and drug-target binding affinities. Our model-based prediction approach, named TIMMA, takes advantage of the polypharmacological effects of drugs and infers combinatorial drug efficacies through system-level target inhibition networks. Case studies in MCF-7 and MDA-MB-231 breast cancer and BxPC-3 pancreatic cancer cells demonstrated how the target inhibition modeling allows systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways in a given cancer type. The TIMMA prediction results were experimentally validated by means of systematic siRNA-mediated silencing of the selected targets and their pairwise combinations, showing increased ability to identify not only such druggable kinase targets that are essential for cancer survival either individually or in combination, but also synergistic interactions indicative of non-additive drug efficacies. These system-level analyses were enabled by a novel model construction method utilizing maximization and minimization rules, as well as a model selection algorithm based on sequential forward floating search. Compared with an existing computational solution, TIMMA showed both enhanced prediction accuracies in cross validation as well as significant reduction in computation times. Such cost-effective computational-experimental design strategies have the potential to greatly speed-up the drug testing efforts by prioritizing those interventions and interactions warranting further study in individual cancer cases.
format	article
author	Jing Tang Leena Karhinen Tao Xu Agnieszka Szwajda Bhagwan Yadav Krister Wennerberg Tero Aittokallio
author_facet	Jing Tang Leena Karhinen Tao Xu Agnieszka Szwajda Bhagwan Yadav Krister Wennerberg Tero Aittokallio
author_sort	Jing Tang
title	Target inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways.
title_short	Target inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways.
title_full	Target inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways.
title_fullStr	Target inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways.
title_full_unstemmed	Target inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways.
title_sort	target inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/464fb0e478384c6487cf3e21f71300fd
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Target inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways.

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