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Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease
For many prevalent complex diseases, treatment regimens are frequently ineffective. For example, despite multiple available immunomodulators and immunosuppressants, inflammatory bowel disease (IBD) remains difficult to treat. Heterogeneity in the disease across patients makes it challenging to selec...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7891788/ https://www.ncbi.nlm.nih.gov/pubmed/33544718 http://dx.doi.org/10.1371/journal.pcbi.1008631 |
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author | Han, Lichy Sayyid, Zahra N. Altman, Russ B. |
author_facet | Han, Lichy Sayyid, Zahra N. Altman, Russ B. |
author_sort | Han, Lichy |
collection | PubMed |
description | For many prevalent complex diseases, treatment regimens are frequently ineffective. For example, despite multiple available immunomodulators and immunosuppressants, inflammatory bowel disease (IBD) remains difficult to treat. Heterogeneity in the disease across patients makes it challenging to select the optimal treatment regimens, and some patients do not respond to any of the existing treatment choices. Drug repurposing strategies for IBD have had limited clinical success and have not typically offered individualized patient-level treatment recommendations. In this work, we present NetPTP, a Network-based Personalized Treatment Prediction framework which models measured drug effects from gene expression data and applies them to patient samples to generate personalized ranked treatment lists. To accomplish this, we combine publicly available network, drug target, and drug effect data to generate treatment rankings using patient data. These ranked lists can then be used to prioritize existing treatments and discover new therapies for individual patients. We demonstrate how NetPTP captures and models drug effects, and we apply our framework to individual IBD samples to provide novel insights into IBD treatment. |
format | Online Article Text |
id | pubmed-7891788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-78917882021-03-01 Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease Han, Lichy Sayyid, Zahra N. Altman, Russ B. PLoS Comput Biol Research Article For many prevalent complex diseases, treatment regimens are frequently ineffective. For example, despite multiple available immunomodulators and immunosuppressants, inflammatory bowel disease (IBD) remains difficult to treat. Heterogeneity in the disease across patients makes it challenging to select the optimal treatment regimens, and some patients do not respond to any of the existing treatment choices. Drug repurposing strategies for IBD have had limited clinical success and have not typically offered individualized patient-level treatment recommendations. In this work, we present NetPTP, a Network-based Personalized Treatment Prediction framework which models measured drug effects from gene expression data and applies them to patient samples to generate personalized ranked treatment lists. To accomplish this, we combine publicly available network, drug target, and drug effect data to generate treatment rankings using patient data. These ranked lists can then be used to prioritize existing treatments and discover new therapies for individual patients. We demonstrate how NetPTP captures and models drug effects, and we apply our framework to individual IBD samples to provide novel insights into IBD treatment. Public Library of Science 2021-02-05 /pmc/articles/PMC7891788/ /pubmed/33544718 http://dx.doi.org/10.1371/journal.pcbi.1008631 Text en © 2021 Han et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Han, Lichy Sayyid, Zahra N. Altman, Russ B. Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease |
title | Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease |
title_full | Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease |
title_fullStr | Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease |
title_full_unstemmed | Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease |
title_short | Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease |
title_sort | modeling drug response using network-based personalized treatment prediction (netptp) with applications to inflammatory bowel disease |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7891788/ https://www.ncbi.nlm.nih.gov/pubmed/33544718 http://dx.doi.org/10.1371/journal.pcbi.1008631 |
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