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dc.contributor.author
Facchetti, Giuseppe
dc.contributor.author
Zampieri, Mattia
dc.contributor.author
Altafini, Claudio
dc.date.accessioned
2019-04-24T13:14:37Z
dc.date.available
2017-06-10T19:53:47Z
dc.date.available
2019-04-24T13:14:37Z
dc.date.issued
2012
dc.identifier.issn
1752-0509
dc.identifier.other
10.1186/1752-0509-6-115
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/70283
dc.identifier.doi
10.3929/ethz-b-000070283
dc.description.abstract
Background In the field of drug discovery, assessing the potential of multidrug therapies isa difficult task because of the combinatorial complexity (both theoretical andexperimental) and because of the requirements on the selectivity of the therapy.To cope with this problem, we have developed a novel method for the systematic insilico investigation of synergistic effects of currently available drugs ongenome-scale metabolic networks. Results The algorithm finds the optimal combination of drugs which guarantees theinhibition of an objective function, while minimizing the side effect on the othercellular processes. Two different applications are considered: finding drugsynergisms for human metabolic diseases (like diabetes, obesity and hypertension)and finding antitumoral drug combinations with minimal side effect on the normalhuman cell. The results we obtain are consistent with some of the availabletherapeutic indications and predict new multiple drug treatments. A clusteranalysis on all possible interactions among the currently available drugsindicates a limited variety on the metabolic targets for the approved drugs. Conclusion The in silico prediction of drug synergisms can represent an important tool forthe repurposing of drugs in a realistic perspective which considers also theselectivity of the therapy. Moreover, for a more profitable exploitation ofdrug-drug interactions, we have shown that also experimental drugs which have adifferent mechanism of action can be reconsider as potential ingredients of newmulticompound therapeutic indications. Needless to say the clues provided by acomputational study like ours need in any case to be thoroughly evaluatedexperimentally.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
BioMed Central
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/2.0/
dc.subject
Metabolic network
en_US
dc.subject
Drug synergism
en_US
dc.subject
Flux balance analysis
en_US
dc.subject
Metabolic diseases
en_US
dc.subject
Cancer
en_US
dc.title
Predicting and characterizing selective multiple drug treatments for metabolic diseases and cancer
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 2.0 Generic
dc.date.published
2012-08-29
ethz.journal.title
BMC Systems Biology
ethz.journal.volume
6
en_US
ethz.journal.abbreviated
BMC syst. biol.
ethz.pages.start
115
en_US
ethz.size
14 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
005468370
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2017-06-10T19:56:37Z
ethz.source
ECIT
ethz.identifier.importid
imp593650dd9dd3993107
ethz.ecitpid
pub:111299
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-18T08:43:50Z
ethz.rosetta.lastUpdated
2019-04-24T13:15:41Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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