Cargando…
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...
Autores principales: | , , , |
---|---|
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 |
_version_ | 1784658598911016960 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8878172 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT shaomengting computationaldrugrepurposingbasedonarecommendationsystemanddrugdrugfunctionalpathwaysimilarity AT jiangleiming computationaldrugrepurposingbasedonarecommendationsystemanddrugdrugfunctionalpathwaysimilarity AT mengzhigang computationaldrugrepurposingbasedonarecommendationsystemanddrugdrugfunctionalpathwaysimilarity AT xujianzhen computationaldrugrepurposingbasedonarecommendationsystemanddrugdrugfunctionalpathwaysimilarity |