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Computational Drug Repurposing Based on a Recommendation System and Drug–Drug Functional Pathway Similarity

Drug repurposing identifies new clinical indications for existing drugs. It can be used to overcome common problems associated with cancers, such as heterogeneity and resistance to established therapies, by rapidly adapting known drugs for new treatment. In this study, we utilized a recommendation s...

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Detalles Bibliográficos
Autores principales: Shao, Mengting, Jiang, Leiming, Meng, Zhigang, Xu, Jianzhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8878172/
https://www.ncbi.nlm.nih.gov/pubmed/35209193
http://dx.doi.org/10.3390/molecules27041404
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author Shao, Mengting
Jiang, Leiming
Meng, Zhigang
Xu, Jianzhen
author_facet Shao, Mengting
Jiang, Leiming
Meng, Zhigang
Xu, Jianzhen
author_sort Shao, Mengting
collection PubMed
description Drug repurposing identifies new clinical indications for existing drugs. It can be used to overcome common problems associated with cancers, such as heterogeneity and resistance to established therapies, by rapidly adapting known drugs for new treatment. In this study, we utilized a recommendation system learning model to prioritize candidate cancer drugs. We designed a drug–drug pathway functional similarity by integrating multiple genetic and epigenetic alterations such as gene expression, copy number variation (CNV), and DNA methylation. When compared with other similarities, such as SMILES chemical structures and drug targets based on the protein–protein interaction network, our approach provided better interpretable models capturing drug response mechanisms. Furthermore, our approach can achieve comparable accuracy when evaluated with other learning models based on large public datasets (CCLE and GDSC). A case study about the Erlotinib and OSI-906 (Linsitinib) indicated that they have a synergistic effect to reduce the growth rate of tumors, which is an alternative targeted therapy option for patients. Taken together, our computational method characterized drug response from the viewpoint of a multi-omics pathway and systematically predicted candidate cancer drugs with similar therapeutic effects.
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spelling pubmed-88781722022-02-26 Computational Drug Repurposing Based on a Recommendation System and Drug–Drug Functional Pathway Similarity Shao, Mengting Jiang, Leiming Meng, Zhigang Xu, Jianzhen Molecules Article Drug repurposing identifies new clinical indications for existing drugs. It can be used to overcome common problems associated with cancers, such as heterogeneity and resistance to established therapies, by rapidly adapting known drugs for new treatment. In this study, we utilized a recommendation system learning model to prioritize candidate cancer drugs. We designed a drug–drug pathway functional similarity by integrating multiple genetic and epigenetic alterations such as gene expression, copy number variation (CNV), and DNA methylation. When compared with other similarities, such as SMILES chemical structures and drug targets based on the protein–protein interaction network, our approach provided better interpretable models capturing drug response mechanisms. Furthermore, our approach can achieve comparable accuracy when evaluated with other learning models based on large public datasets (CCLE and GDSC). A case study about the Erlotinib and OSI-906 (Linsitinib) indicated that they have a synergistic effect to reduce the growth rate of tumors, which is an alternative targeted therapy option for patients. Taken together, our computational method characterized drug response from the viewpoint of a multi-omics pathway and systematically predicted candidate cancer drugs with similar therapeutic effects. MDPI 2022-02-18 /pmc/articles/PMC8878172/ /pubmed/35209193 http://dx.doi.org/10.3390/molecules27041404 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shao, Mengting
Jiang, Leiming
Meng, Zhigang
Xu, Jianzhen
Computational Drug Repurposing Based on a Recommendation System and Drug–Drug Functional Pathway Similarity
title Computational Drug Repurposing Based on a Recommendation System and Drug–Drug Functional Pathway Similarity
title_full Computational Drug Repurposing Based on a Recommendation System and Drug–Drug Functional Pathway Similarity
title_fullStr Computational Drug Repurposing Based on a Recommendation System and Drug–Drug Functional Pathway Similarity
title_full_unstemmed Computational Drug Repurposing Based on a Recommendation System and Drug–Drug Functional Pathway Similarity
title_short Computational Drug Repurposing Based on a Recommendation System and Drug–Drug Functional Pathway Similarity
title_sort computational drug repurposing based on a recommendation system and drug–drug functional pathway similarity
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8878172/
https://www.ncbi.nlm.nih.gov/pubmed/35209193
http://dx.doi.org/10.3390/molecules27041404
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