Publications by authors named "Esra Nalbat"

3 Publications

  • Page 1 of 1

Synthesis and biological evaluation of novel isoxazole-piperazine hybrids as potential anti-cancer agents with inhibitory effect on liver cancer stem cells.

Eur J Med Chem 2021 Oct 24;221:113489. Epub 2021 Apr 24.

Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Gazi University, Ankara, Turkey. Electronic address:

In our effort for the development of novel anticancer therapeutics, a series of isoxazole-piperazine analogues were prepared, and primarily screened for their antiproliferative potential against hepatocellular carcinoma (HCC; Huh7/Mahlavu) and breast (MCF-7) cancer cells. All compounds demonstrated potent to moderate cytotoxicity on all cell lines with IC values in the range of 0.09-11.7 μM. Further biological studies with 6a and 13d in HCC cells have shown that both compounds induced G1 or G2/M arrests resulting in apoptotic cell death. Subsequent analysis of proteins involved in cell cycle progression as well as proliferation of HCC cells revealed that 6a and 13d may affect cellular survival pathways differently depending on the mutation profiles of cells (p53 and PTEN), epidermal/mesenchymal characteristics, and activation of cell mechanisms through p53 dependent/independent pathways. Lastly, we have demonstrated the potential anti-stemness properties of these compounds in which the proportion of liver CSCs in Huh7 cells (CD133+/EpCAM+) were significantly reduced by 6a and 13d. Furthermore, both compounds caused a significant reduction in expression of stemness markers, NANOG or OCT4 proteins, in Mahlavu and Huh7 cells, as well as resulted in a decreased sphere formation capacity in Huh7 cells. Together, these novel isoxazole-piperazine derivatives may possess potential as leads for development of effective anti-cancer drugs against HCC cells with stem cell-like properties.
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http://dx.doi.org/10.1016/j.ejmech.2021.113489DOI Listing
October 2021

DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations.

Chem Sci 2020 Mar 8;11(9):2531-2557. Epub 2020 Jan 8.

Department of Computer Engineering , Hacettepe University , Ankara , 06800 , Turkey . Email: ; Tel: +903122977193/117.

The identification of physical interactions between drug candidate compounds and target biomolecules is an important process in drug discovery. Since conventional screening procedures are expensive and time consuming, computational approaches are employed to provide aid by automatically predicting novel drug-target interactions (DTIs). In this study, we propose a large-scale DTI prediction system, DEEPScreen, for early stage drug discovery, using deep convolutional neural networks. One of the main advantages of DEEPScreen is employing readily available 2-D structural representations of compounds at the input level instead of conventional descriptors that display limited performance. DEEPScreen learns complex features inherently from the 2-D representations, thus producing highly accurate predictions. The DEEPScreen system was trained for 704 target proteins (using curated bioactivity data) and finalized with rigorous hyper-parameter optimization tests. We compared the performance of DEEPScreen against the state-of-the-art on multiple benchmark datasets to indicate the effectiveness of the proposed approach and verified selected novel predictions through molecular docking analysis and literature-based validation. Finally, JAK proteins that were predicted by DEEPScreen as new targets of a well-known drug cladribine were experimentally demonstrated on cancer cells through STAT3 phosphorylation, which is the downstream effector protein. The DEEPScreen system can be exploited in the fields of drug discovery and repurposing for screening of the chemogenomic space, to provide novel DTIs which can be experimentally pursued. The source code, trained "ready-to-use" prediction models, all datasets and the results of this study are available at ; https://github.com/cansyl/DEEPscreen.
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http://dx.doi.org/10.1039/c9sc03414eDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643205PMC
March 2020

Treatment of breast cancer with autophagy inhibitory microRNAs carried by AGO2-conjugated nanoparticles.

J Nanobiotechnology 2020 Apr 28;18(1):65. Epub 2020 Apr 28.

Sabanci University Nanotechnology Research and Application Center (SUNUM), Department of Biotechnology, Tuzla, 34956, Istanbul, Turkey.

Nanoparticle based gene delivery systems holds great promise. Superparamagnetic iron oxide nanoparticles (SPIONs) are being heavily investigated due to good biocompatibility and added diagnostic potential, rendering such nanoparticles theranostic. Yet, commonly used cationic coatings for efficient delivery of such anionic cargos, results in significant toxicity limiting translation of the technology to the clinic. Here, we describe a highly biocompatible, small and non-cationic SPION-based theranostic nanoparticles as novel gene therapy agents. We propose for the first-time, the usage of the microRNA machinery RISC complex component Argonaute 2 (AGO2) protein as a microRNA stabilizing agent and a delivery vehicle. In this study, AGO2 protein-conjugated, anti-HER2 antibody-linked and fluorophore-tagged SPION nanoparticles were developed (SP-AH nanoparticles) and used as a carrier for an autophagy inhibitory microRNA, MIR376B. These functionalized nanoparticles selectively delivered an effective amount of the microRNA into HER2-positive breast cancer cell lines in vitro and in a xenograft nude mice model of breast cancer in vivo, and successfully blocked autophagy. Furthermore, combination of the chemotherapy agent cisplatin with MIR376B-loaded SP-AH nanoparticles increased the efficacy of the anti-cancer treatment both in vitro in cells and in vivo in the nude mice. Therefore, we propose that AGO2 protein conjugated SPIONs are a new class of theranostic nanoparticles and can be efficiently used as innovative, non-cationic, non-toxic gene therapy tools for targeted therapy of cancer.
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http://dx.doi.org/10.1186/s12951-020-00615-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7189576PMC
April 2020