3D-QSAR, Docking, ADME/Tox studies on Flavone analogs reveal anticancer activity through Tankyrase inhibition

• Published in: Scientific reports 2019-04, Vol.9 (1), p.5414, Article 5414
• Main Authors: Alam, Sarfaraz, Khan, Feroz
• Format: Article
• Language: English
• Subjects: 119/118; 631/114/2248; 631/114/2397; 631/154/570; 631/92/436/1729; 631/92/630; Algorithms; Antitumor activity; Bioavailability; Biological activity; Cancer; Catalytic Domain; Computer applications; Cost-Benefit Analysis; Flavones; Flavones - chemistry; Flavones - metabolism; Flavones - pharmacology; Flavonoids; Flavonoids - chemistry; Flavonoids - metabolism; Flavonoids - pharmacology; High-throughput screening; High-Throughput Screening Assays - economics; High-Throughput Screening Assays - methods; Humanities and Social Sciences; Humans; Metabolites; Models, Theoretical; Molecular Docking Simulation; Molecular Structure; multidisciplinary; Neoplasms - drug therapy; Neoplasms - metabolism; Pharmacokinetics; Protein Binding; Protein Domains; Quantitative Structure-Activity Relationship; Risk assessment; Science; Science (multidisciplinary); Structure-activity relationships; Tankyrases - antagonists & inhibitors; Tankyrases - chemistry; Tankyrases - metabolism
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-019-41984-7

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 r 2 (0.89) and q 2 (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 IC 50 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 (IC 50 ) 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.