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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...

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Detalles Bibliográficos
Autores principales: Han, Lichy, Sayyid, Zahra N., Altman, Russ B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
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.
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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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