Abstract
Flavones are known as an inhibitor of tankyrase, a potential drug target of cancer. We here expedited the use of different computational approaches and presented a fast, easy, cost-effective and high throughput screening method to identify flavones analogs as potential tankyrase inhibitors. For this, we developed a field point based (3D-QSAR) quantitative structure-activity relationship model. The developed model showed acceptable predictive and descriptive capability as represented by standard statistical parameters r2 (0.89) and q2 (0.67). This model may help to explain SAR data and illustrated the key descriptors which were firmly related with the anticancer activity. Using the QSAR model a dataset of 8000 flavonoids were evaluated to classify the bioactivity, which resulted in the identification of 1480 compounds with the IC50 value of less than 5 µM. Further, these compounds were scrutinized through molecular docking and ADMET risk assessment. Total of 25 compounds identified which further analyzed for drug-likeness, oral bioavailability, synthetic accessibility, lead-likeness, and alerts for PAINS & Brenk. Besides, metabolites of screened compounds were also analyzed for pharmacokinetics compliance. Finally, compounds F2, F3, F8, F11, F13, F20, F21 and F25 with predicted activity (IC50) of 1.59, 1, 0.62, 0.79, 3.98, 0.79, 0.63 and 0.64, respectively were find as top hit leads. This study is offering the first example of a computationally-driven tool for prioritization and discovery of novel flavone scaffold for tankyrase receptor affinity with high therapeutic windows.
Highlights
Www.nature.com/scientificreports the extent of the devastation complex of β-catenin, which led decrease levels of β-catenin and increased the levels of phosphorylated β–catenin triggering inhibition of the Wnt/β-catenin driven proliferation of cancer cells[10]
The modern drug discovery aspects were applied such as 3D-QSAR, molecular docking, ADMET, etc.[15,16]
Allowing for our interest in developing new flavone analogs that inhibit the tankyrase, a 3D-QSAR model for predicting the tankyrase receptor affinity has been built with the objective of providing a convenient tool for the identification, design, and optimization of new flavones ligands
Summary
Www.nature.com/scientificreports the extent of the devastation complex of β-catenin, which led decrease levels of β-catenin and increased the levels of phosphorylated β–catenin triggering inhibition of the Wnt/β-catenin driven proliferation of cancer cells[10]. In 2010, Yashiroda et al carried out a high-throughput screening of natural products library to restore growth inhibited by TANKs expression Through this study, they identified flavone as a tankyrase inhibitor[13]. Flavones have been shown to have antiproliferative properties in, prostate, lung, pancreas, colorectal, and ovarian cancer cells[14] This useful activity of flavone generates our interest in developing a tool for screening novel flavone derivatives/analogs that inhibit the tankyrase receptor. With the advances in computer science and release of several compounds databases, there is a particular interest to filter these databases for finding any compound which can bind the desired receptors This job may be conceded out by using the virtual screening methods, which helps the end user in filtering many compounds based on virtual model specifications. This led to identify eight active compounds, namely, F2, F3, F8, F11, F13, F20, F21 and F25 with predicted activity (IC50 value) of 1.59, 1, 0.62, 0.79, 3.98, 0.79, 0.63 & 0.64, respectively
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