Drug repurposing by simulating flow through protein-protein interaction networks
As drug development is extremely expensive, the identification of novel indications for in-market drugs is financially attractive. Multiple algorithms are used to support such drug repurposing, but highly reliable methods combining simulation of intracellular networks and machine learning are curre...
Elmentve itt :
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| Dokumentumtípus: | Cikk |
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Szakszervezetek Elméleti Kutatóintézete
2018
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| Sorozat: | CLINICAL PHARMACOLOGY & THERAPEUTICS
103 No. 3 |
| doi: | 10.1002/cpt.769 |
| mtmt: | 3244345 |
| Online Access: | http://publicatio.bibl.u-szeged.hu/12948 |
| Tartalmi kivonat: | As drug development is extremely expensive, the identification of novel indications for in-market drugs is financially attractive. Multiple algorithms are used to support such drug repurposing, but highly reliable methods combining simulation of intracellular networks and machine learning are currently not available. We developed an algorithm that simulates drug effects on the flow of information through protein-protein interaction networks, and uses Support Vector Machine to identify potentially effective drugs in our model disease, psoriasis. Using this method, we screened about 1500 marketed and investigational substances, identified fifty-one drugs that were potentially effective and selected three of them for experimental confirmation. All drugs inhibited TNF-induced NFkappaB activity in vitro, suggesting they might be effective for treating psoriasis in humans. Additionally, these drugs significantly inhibited imiquimod-induced ear thickening and inflammation in the mouse model of the disease. All results suggest high prediction performance for the algorithm. This article is protected by copyright. All rights reserved. |
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| Terjedelem/Fizikai jellemzők: | 511-520 |
| ISSN: | 0009-9236 |