Neuer Hedgehog-Inhibitor bei AML zugelassen

2020 ◽  
Vol 23 (5) ◽  
pp. 85-85
Author(s):  
Martina-Jasmin Utzt
Keyword(s):  
Author(s):  
Hitarth Patel ◽  
Jigna Joshi ◽  
Apexa Raval ◽  
Franky Shah

Background: Conventional treatment resistance remains a significant problem in cancer care. Cancer stem cells might play a major role in treatment resistance, and as a result, basic stem cell pathways are instrumental in cancer. Sonic Hedgehog signaling has not been widely studied in oral cancer, and being one of the major cancer stem cell pathways, targeting it with natural compounds could open many opportunities in the treatment scenario. Objective: The objective of the study was to identify the role of various natural compounds as an anti-cancer agent for oral cancer by targeting the Hedgehog signaling pathway. Methods: The selection of natural compounds were identified through literature review and NPACT database. The protein (3M1N and 3MXW) and ligand molecules were retrieved through the PDB and PubChem database. To carry out docking experiments, the AutoDock 4.2 program was used to study the interaction between the identified protein and ligand. Results: Among the 13 identified natural compounds, the top three were selected based on their binding energy. The higher the binding energy on the negative side, the better the interaction formed between protein and ligand. The natural compound showing best results with 3M1N protein were Butein, Biochanin-A, and Curcumin, whereas, with 3MXW, Zerumbone, Curcumin, and Butein were identified. Conclusion: The identified natural compounds have shown better binding energy to bind the Hh ligands in the absence/presence of a known Sonic Hedgehog inhibitor. Based on the results, natural compounds can be utilized in the current treatment modality for oral cancer either as an individual anti-cancer agent or in combination with the known Sonic Hedgehog inhibitor to curb the increasing incidence rate. Yet, in-vitro evidence in lab setup is required.


2014 ◽  
Vol 32 (15_suppl) ◽  
pp. 7111-7111
Author(s):  
Koji Sasaki ◽  
Jason R. Gotlib ◽  
Ruben A. Mesa ◽  
Farhad Ravandi ◽  
Jorge E. Cortes ◽  
...  

2013 ◽  
pp. 1643 ◽  
Author(s):  
Jun Shi ◽  
feng chai ◽  
jiangang zhou ◽  
Cheng Chen ◽  
song xie ◽  
...  

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