Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning.
Cell Line, Tumor
Cell Survival
/ genetics
Drug Discovery
/ methods
Drug Repositioning
Gene Expression Profiling
Genes, Reporter
Genome, Human
Genomics
/ methods
Humans
Male
Metabolic Networks and Pathways
Metabolomics
/ methods
Piperidines
/ pharmacology
Prostatic Neoplasms
/ drug therapy
Proteome
Proteomics
/ methods
Approved drugs
Drug repositioning
Drug repurposing
Genome-scale metabolic models
Prostate cancer
Journal
EBioMedicine
ISSN: 2352-3964
Titre abrégé: EBioMedicine
Pays: Netherlands
ID NLM: 101647039
Informations de publication
Date de publication:
Apr 2019
Apr 2019
Historique:
received:
17
10
2018
revised:
28
02
2019
accepted:
04
03
2019
pubmed:
25
3
2019
medline:
21
8
2019
entrez:
26
3
2019
Statut:
ppublish
Résumé
Genome-scale metabolic models (GEMs) offer insights into cancer metabolism and have been used to identify potential biomarkers and drug targets. Drug repositioning is a time- and cost-effective method of drug discovery that can be applied together with GEMs for effective cancer treatment. In this study, we reconstruct a prostate cancer (PRAD)-specific GEM for exploring prostate cancer metabolism and also repurposing new therapeutic agents that can be used in development of effective cancer treatment. We integrate global gene expression profiling of cell lines with >1000 different drugs through the use of prostate cancer GEM and predict possible drug-gene interactions. We identify the key reactions with altered fluxes based on the gene expression changes and predict the potential drug effect in prostate cancer treatment. We find that sulfamethoxypyridazine, azlocillin, hydroflumethiazide, and ifenprodil can be repurposed for the treatment of prostate cancer based on an in silico cell viability assay. Finally, we validate the effect of ifenprodil using an in vitro cell assay and show its inhibitory effect on a prostate cancer cell line. Our approach demonstate how GEMs can be used to predict therapeutic agents for cancer treatment based on drug repositioning. Besides, it paved a way and shed a light on the applicability of computational models to real-world biomedical or pharmaceutical problems.
Sections du résumé
BACKGROUND
BACKGROUND
Genome-scale metabolic models (GEMs) offer insights into cancer metabolism and have been used to identify potential biomarkers and drug targets. Drug repositioning is a time- and cost-effective method of drug discovery that can be applied together with GEMs for effective cancer treatment.
METHODS
METHODS
In this study, we reconstruct a prostate cancer (PRAD)-specific GEM for exploring prostate cancer metabolism and also repurposing new therapeutic agents that can be used in development of effective cancer treatment. We integrate global gene expression profiling of cell lines with >1000 different drugs through the use of prostate cancer GEM and predict possible drug-gene interactions.
FINDINGS
RESULTS
We identify the key reactions with altered fluxes based on the gene expression changes and predict the potential drug effect in prostate cancer treatment. We find that sulfamethoxypyridazine, azlocillin, hydroflumethiazide, and ifenprodil can be repurposed for the treatment of prostate cancer based on an in silico cell viability assay. Finally, we validate the effect of ifenprodil using an in vitro cell assay and show its inhibitory effect on a prostate cancer cell line.
INTERPRETATION
CONCLUSIONS
Our approach demonstate how GEMs can be used to predict therapeutic agents for cancer treatment based on drug repositioning. Besides, it paved a way and shed a light on the applicability of computational models to real-world biomedical or pharmaceutical problems.
Identifiants
pubmed: 30905848
pii: S2352-3964(19)30149-5
doi: 10.1016/j.ebiom.2019.03.009
pmc: PMC6491384
pii:
doi:
Substances chimiques
Piperidines
0
Proteome
0
ifenprodil
R8OE3P6O5S
Types de publication
Journal Article
Langues
eng
Pagination
386-396Informations de copyright
Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.
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