Cargando…
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 curren...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5836852/ https://www.ncbi.nlm.nih.gov/pubmed/28643328 http://dx.doi.org/10.1002/cpt.769 |
_version_ | 1783304018095767552 |
---|---|
author | Manczinger, M Bodnár, VÁ Papp, BT Bolla, SB Szabó, K Balázs, B Csányi, E Szél, E Erős, G Kemény, L |
author_facet | Manczinger, M Bodnár, VÁ Papp, BT Bolla, SB Szabó, K Balázs, B Csányi, E Szél, E Erős, G Kemény, L |
author_sort | Manczinger, M |
collection | PubMed |
description | 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 used support vector machine to identify potentially effective drugs in our model disease, psoriasis. Using this method, we screened about 1,500 marketed and investigational substances, identified 51 drugs that were potentially effective, and selected three of them for experimental confirmation. All drugs inhibited tumor necrosis factor alpha‐induced nuclear factor kappa B 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. |
format | Online Article Text |
id | pubmed-5836852 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-58368522018-03-07 Drug Repurposing by Simulating Flow Through Protein–Protein Interaction Networks Manczinger, M Bodnár, VÁ Papp, BT Bolla, SB Szabó, K Balázs, B Csányi, E Szél, E Erős, G Kemény, L Clin Pharmacol Ther Research 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 used support vector machine to identify potentially effective drugs in our model disease, psoriasis. Using this method, we screened about 1,500 marketed and investigational substances, identified 51 drugs that were potentially effective, and selected three of them for experimental confirmation. All drugs inhibited tumor necrosis factor alpha‐induced nuclear factor kappa B 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. John Wiley and Sons Inc. 2017-07-29 2018-03 /pmc/articles/PMC5836852/ /pubmed/28643328 http://dx.doi.org/10.1002/cpt.769 Text en © 2017 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs (http://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Manczinger, M Bodnár, VÁ Papp, BT Bolla, SB Szabó, K Balázs, B Csányi, E Szél, E Erős, G Kemény, L Drug Repurposing by Simulating Flow Through Protein–Protein Interaction Networks |
title | Drug Repurposing by Simulating Flow Through Protein–Protein Interaction Networks |
title_full | Drug Repurposing by Simulating Flow Through Protein–Protein Interaction Networks |
title_fullStr | Drug Repurposing by Simulating Flow Through Protein–Protein Interaction Networks |
title_full_unstemmed | Drug Repurposing by Simulating Flow Through Protein–Protein Interaction Networks |
title_short | Drug Repurposing by Simulating Flow Through Protein–Protein Interaction Networks |
title_sort | drug repurposing by simulating flow through protein–protein interaction networks |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5836852/ https://www.ncbi.nlm.nih.gov/pubmed/28643328 http://dx.doi.org/10.1002/cpt.769 |
work_keys_str_mv | AT manczingerm drugrepurposingbysimulatingflowthroughproteinproteininteractionnetworks AT bodnarva drugrepurposingbysimulatingflowthroughproteinproteininteractionnetworks AT pappbt drugrepurposingbysimulatingflowthroughproteinproteininteractionnetworks AT bollasb drugrepurposingbysimulatingflowthroughproteinproteininteractionnetworks AT szabok drugrepurposingbysimulatingflowthroughproteinproteininteractionnetworks AT balazsb drugrepurposingbysimulatingflowthroughproteinproteininteractionnetworks AT csanyie drugrepurposingbysimulatingflowthroughproteinproteininteractionnetworks AT szele drugrepurposingbysimulatingflowthroughproteinproteininteractionnetworks AT erosg drugrepurposingbysimulatingflowthroughproteinproteininteractionnetworks AT kemenyl drugrepurposingbysimulatingflowthroughproteinproteininteractionnetworks |