Screening of natural product libraries in MCF7 cell line reveals the pro-apoptotic properties of ß tetralone.

Despite the exponential increase in research toward better treatment options for breast cancer patients, developing an effective drug with fewer side effects continues to remain a challenge. Natural compounds have emerged as a viable option and several drugs have been derived or inspired from them. In this study, we screened a library of natural compounds with diverse chemical structures against selected kinase proteins using in silico methods such as molecular docking and dynamics simulation. The best results were obtained between ß tetralone and MDM2 E3 ubiquitin ligase protein. In vitro experiments such as cytotoxicity, scratch assays and flow cytometry analysis using an MCF7 cell line were performed to determine the anti-cancer potential of the compound. As the treatment resulted in cell death and apoptosis, ß tetralone was screened in silico against anti-apoptotic targets where the best results were obtained between Bcl-w and ß tetralone. This comprehensive study suggests that the anti-cancer activity of ß tetralone is probably through the dual targeting of MDM2 E3 ubiquitin kinase and Bcl-w anti-apoptotic protein.Communicated by Ramaswamy H. Sarma.

Comprehensive molecular docking and dynamic simulations for drug repurposing of clinical drugs against multiple cancer kinase targets.

Drug repurposing is a method to identify novel therapeutic agents from the existing drugs and clinical compounds. In the present comprehensive work, molecular docking, virtual screening and dynamics simulations were carried out for ten cancer types viz breast, colon, central nervous system, leukaemia, melanoma, ovarian, prostate, renal and lung (non-small and small cell) against validated eighteen kinase targets. The study aims to understand the action of chemotherapy drugs mechanism through binding interactions against selected targets via comparative docking simulations with the state-art molecular modelling suits such as MOE, Cresset-Flare, AutoDock Vina, GOLD and GLIDE. Chemotherapeutic drugs (n = 112) were shortlisted from standard drug databases with appropriate chemoinformatic filters. Based on docking studies it was revealed that leucovorin, nilotinib, ellence, thalomid and carfilzomib drugs possessed potential against other cancer targets. A library was built to enumerate novel molecules based on the scaffold and functional groups extracted from known drugs and clinical compounds. Twenty novel molecules were prioritised further based on drug-like attributes. These were cross docked against 1MQ4 Aurora-A Protein Kinase for prostate cancer and 4UYA Mitogen-activated protein kinase for renal cancer. All docking programs yielded similar results but interestingly AutoDock Vina yielded the lowest RMSD with the native ligand. To further validate the final docking results at atomistic level, molecular dynamics simulations were performed to ascertain the stability of the protein-ligand complex. The study enables repurposing of drugs and lead identification by employing a host of structure and ligand based virtual screening tools and techniques.Communicated by Ramaswamy H. Sarma.

A Review on Computational Analysis of Big Data in Breast Cancer for Predicting Potential Biomarkers.

Breast cancer is the most predominantly occurring cancer in the world. Several genes and proteins have been recently studied to predict biomarkers that enable early disease identification and monitor its recurrence. In the era of high-throughput technology, studies show several applications of big data for identifying potential biomarkers. The review aims to provide a comprehensive overview of big data analysis in breast cancer towards the prediction of biomarkers with emphasis on computational methods like text mining, network analysis, next-generation sequencing technology (NGS), machine learning (ML), deep learning (DL), and precision medicine. Integrating data from various computational approaches enables the stratification of cancer patients and the identification of molecular signatures in cancer and their subtypes. The computational methods and statistical analysis help expedite cancer prognosis and develop precision cancer medicine (PCM). As a part of case study in the present work, we constructed a large gene-drug interaction network to predict new biomarkers genes. The gene-drug network helped us to identify eight genes that could serve as novel potential biomarkers.