In Silico Anticancer Evaluation, Molecular Docking and Pharmacophore Modeling of Flavonoids against Various Cancer Targets

Abstract

Background: Designing and development of molecules for cancer treatment useful and with no side effects are a big challenge for the researchers in the field of drug discovery. The use of phytochemicals for chemoprevention is gaining more advantages, and intake of flavonoids has proved to reduce the occurrence of various cancers. Objectives: The present study was focused on selecting eight flavonoids and study them by in silico methods to analyse the interactions, affinity and pharmacophoric features that participate in the interactions between the flavonoid and the active sites of different cancer targets. Methods: The cancer targets were downloaded from the protein data bank, and flavonoids from PubChem and were docked by Glide XP molecular docking method to find the molecular interactions. The binding energy was calculated by Prime MM-GBSA application and ADMET analysis by Qikprop of Schrodinger. The anticancer potential of flavonoids screening was based on an online tool, Pass predictor. Phase module was used to find the common pharmacophore features that participate in essential interactions between the flavonoid and the active site. Results: In this study, myricetin has proved to be the best flavonoid for the treatment of breast and lung cancer with docking score of -11.50 kcal/mol and -10.56 kcal/mol respectively, whereas, quercetin has proved to be the best for prostate and colorectal cancer with docking score of -14.18 kcal/mol and -12.94 kcal/mol, respectively. The responsible forces for the interaction of these flavonoids are hydrogen bond, hydrophobic interactions, polar and pi-pi stackings. The PASS tool predicted the anticancer potential for the flavonoids, in particular, myricetin had responded highly active for most cancer cells. The hypothesis AADRR_1 has the highest survival score, which indicates the best alignment of the active ligands and represents the best pharmacophore model for anticancer activity. Conclusion: This work has screened eight flavonoids against various cancer targets and shown the binding interactions between them, stating that myricetin is the suitable lead candidate for breast and lung cancer; whereas, quercetin is the best lead for prostate and colorectal cancer. And these data are about the results obtained from PASS predictor. Moreover, the pharmacophore model has generated for the flavonoids, which correlate activities with the spatial arrangement of various chemical features. Therefore, this investigation strongly suggests that these flavonoids can be used as leads as anticancer agents.