ArticleEBioMedicine2019
Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning.
Article in EBioMedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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41 citing papers in PubMed, 93 citations in OpenAlex.
- Transcriptome-driven constraint-based modelling reveals metabolic targets for ovarian cancer.Cancer & metabolism · 2026Article
- GAN-enhanced machine learning and metabolic modeling identify reprogramming in pancreatic cancer.PLoS computational biology · 2026Article
- Genome-Scale Metabolic Modeling Identifies Synergistic Metabolites that Enhance 5-Fluorouracil Efficacy in Colon Cancer.Iranian biomedical journal · 2025Article
- Identification and validation of prognostic genes associated with mitochondrial nuclear genes in gastric cancer.Clinical and experimental medicine · 2025Article
- Integrative genomic and bioinformatic prioritization of drug repurposing candidates for prostate cancer.BMC pharmacology & toxicology · 2025Article
- Investigating Lipid and Energy Dyshomeostasis Induced by Per- and Polyfluoroalkyl Substances (PFAS) Congeners in Mouse Model Using Systems Biology Approaches.Metabolites · 2025Article
- Targeting Prostate Cancer Metabolism Through Transcriptional and Epigenetic Modulation: A Multi-Target Approach to Therapeutic Innovation.International journal of molecular sciences · 2025Review
- Genome-scale models in human metabologenomics.Nature reviews. Genetics · 2025Review
- Genome-scale metabolic network of human carotid plaque reveals the pivotal role of glutamine/glutamate metabolism in macrophage modulating plaque inflammation and vulnerability.Cardiovascular diabetology · 2024Article
- Article
- Multifunctional nanoparticle-mediated combining therapy for human diseases.Signal transduction and targeted therapy · 2024Review
- PaSTe. Blockade of the Lipid Phenotype of Prostate Cancer as Metabolic Therapy: A Theoretical Proposal.Current medicinal chemistry · 2024Review
- Data-driven transcriptomics analysis identifies PCSK9 as a novel key regulator in liver aging.GeroScience · 2023Article
- COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery.Briefings in bioinformatics · 2023Article
- Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine.Metabolites · 2023Review
- Pharmacological Efficacy of Repurposing Drugs in the Treatment of Prostate Cancer.International journal of molecular sciences · 2023Review
- Identifying essential genes in genome-scale metabolic models of consensus molecular subtypes of colorectal cancer.PloS one · 2023Article
- Fuzzy optimization for identifying anti-cancer targets with few side effects in constraint-based models of head and neck cancer.Royal Society open science · 2022Article
- Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases.Cells · 2022Review
- Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling.iScience · 2022Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenome-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.
methodsIn 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.
findingsWe 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.
interpretationOur 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.
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