An in-silico investigation and network pharmacology based approach to explore the anti-breast-cancer potential of Tecteria coadunata (Wall.) C. Chr.

Abstract

Uncontrolled cell proliferation is a common definition of cancer. After lung carcinoma, breast neoplasm is the second-most prevalent kind of cancer. The majority of breast cancer cells and healthy breast cells both have receptors for circulating oestrogen and progesterone. In order to promote the development and division of cancer cells, oestrogen and progesterone bind to the receptors and may collaborate with growth factors (such as oncogenes and mutant tumour suppressor genes). As per the literature, Tecteria coadunata (Wall.) C. Chr. has anticancer, antioxidant and anti-inflammatory potential. After the hydroalcoholic extraction of this rhizome, total of 200 phytochemicals were retrieved from HR-LCMS analysis. In this current study, Network pharmacology was carried out to explore the rationale of Tecteria coadunata (Wall.) C. Chr. by using different database using Cytoscape software. The network depicted the interaction of Bioactives with their targets and their association with several disease, especially breast cancer. Tecteria coadunata (Wall.) C. Chr. has offered new relationship with variety of genes and its applications in different types of breast cancers. Further Gene Ontology was carried out and it showed key targets were TP53, BRCA2, PGR and CHEK 2. Further Signalling pathways were also enriched. Flex-X software was used for molecular docking studies, and it verified that Dopaxanthin, Dantrolene and Orotidin shows the highest binding affinities with key targets. Additionally, Pharmacokinetic analysis revealed that all top three lead compounds which follows the Lipinski Rule (Rule of three) without interrupting the conditions of bioavailability with minimal toxicity.

Communicated by Ramaswamy H. Sarma

Keywords:

• Breast cancer
• tecteria coadunata (wall.) C. Chr
• HR-LCMS
• network pharmacology
• molecular docking
• ADMET profiling

Author contributions

AB and SN designed the study. SR contributed to the data collection, data analysis, and manuscript writing. SN and PMD contributed to data analysis, and data interpretation. AT and AP contributed to rectify the report.

Disclosure statement

No potential conflict of interest was reported by the authors.

Acknowledgement

The authors are appreciative to the School of Health Sciences and Technology, Dr Vishwanath Karad, MIT World Peace University, Pune, for providing research lab facilities and Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, for granting access to the Bioinformatics Research Laboratory and Optimized Supercomputer facilities.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.