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Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning
KTH.
KTH, Centres, Science for Life Laboratory, SciLifeLab.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
KTH, Centres, Science for Life Laboratory, SciLifeLab.
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2019 (English)In: EBioMedicine, E-ISSN 2352-3964, Vol. 42, p. 386-396Article in journal (Refereed) Published
Abstract [sv]

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: 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: 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: 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.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 42, p. 386-396
National Category
Medical and Health Sciences Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:kth:diva-248689DOI: 10.1016/j.ebiom.2019.03.009ISI: 000466175100052PubMedID: 30905848Scopus ID: 2-s2.0-85063114920OAI: oai:DiVA.org:kth-248689DiVA, id: diva2:1303482
Note

QC 20190424

Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-05-21Bibliographically approved

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Zhang, ChengKim, WoongheeBenfeitas, RuiUhlén, MathiasMardinoglu, Adil

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