| 1. |
- Turanli, Beste Calimlioglu, et al.
(författare)
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Systems biology based drug repositioning for development of cancer therapy
- 2021
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Ingår i: Seminars in Cancer Biology. - : Elsevier BV. - 1096-3650 .- 1044-579X. ; 68, s. 47-58
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Forskningsöversikt (refereegranskat)abstract
- Drug repositioning is a powerful method that can assists the conventional drug discovery process by using existing drugs for treatment of a disease rather than its original indication. The first examples of repurposed drugs were discovered serendipitously, however data accumulated by high-throughput screenings and advancements in computational biology methods have paved the way for rational drug repositioning methods. As chemotherapeutic agents have notorious side effects that significantly reduce quality of life, drug repositioning promises repurposed noncancer drugs with little or tolerable adverse effects for cancer patients. Here, we review current drug-related data types and databases including some examples of web-based drug repositioning tools. Next, we describe systems biology approaches to be used in drug repositioning for effective cancer therapy. Finally, we highlight examples of mostly repurposed drugs for cancer treatment and provide an overview of future expectations in the field for development of effective treatment strategies.
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| 2. |
- Turanli, Beste, et al.
(författare)
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Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning
- 2019
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Ingår i: EBioMedicine. - : Elsevier BV. - 2352-3964. ; 42, s. 386-396
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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