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Identification of novel inhibitors of Transforming, Growth factor, Receptor by QSAR, pharmacophore modeling, virtual screening and docking analysis
Transforming Growth Factor ï¢ Receptor (TGFï¢R) is a multifunctional cytokine family of protein which plays a vital role in many cellular processes. Number of studies revealed that the deregulation of TGFâR leads to tumor progression and it is considered to be a potential target for therapeutic intervention. Regulating the tumor promoting activity of TGFï¢R by suitable inhibitors provides better therapy for human cancer. However, very limited numbers of TGF ï¢ receptor inhibitors are now in the market and developing small molecule inhibitors of TGFï¢R will be extremely useful in cancer therapy. Hence, in this study, we employed systematic computational approaches of quantitative structure activity relationship (QSAR) analysis, virtual screening (VS) and docking analysis for the identification of potential inhibitors of TGFï¢R. A QSAR analysis in which, series of Indol – 6- carbaxamide analogs of experimentally proven Transforming Growth Factor Receptor (TGFï¢R) inhibitors were considered with an aim of developing the relationship between biological activity (PIC50) and their physico chemical properties. The variables were selected by principle component analysis (PCA). Then, the QSAR model was built for training set compounds by using Build QSAR software and a three variable QSAR model consisting of Bcute13, L2U and Morsee4 was developed with statistical value of R2 Training= 0.8837, Q2 = 0.823, Std.Dev = 0.291, F = 48.109, P = <0.0001. The developed model was then validated using test set compounds which results significant statistics of R2 = 0.9732, std. deviation = 0.13. We then identified the best pharmacophore for this dataset analog. The third part of our work consists of Then a pharmacophore based virtual screening was done against Zinc database which resulted in 32 novel ligands. By docking analysis using Autodock Vina five novel compounds were identified as TGFï¢R inhibitors. Results from this study may help in rational drug design and synthesis of new selective TGFï¢R inhibitors with better affinity and activity
TGFï¢R inhibitors; Indol – 6- carbaxamide analogs; QSAR model; Pharmacophore; virtual screening; docking.
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