In silico predictions on the possible mechanism of action of selected bioactive compounds against breast cancer
- PMID: 33194532
- PMCID: PMC7652978
- DOI: 10.1007/s40203-020-00057-8
In silico predictions on the possible mechanism of action of selected bioactive compounds against breast cancer
Abstract
Abstract: Breast cancer is one of the leading causes of death among women. We employed in silico model to predict the mechanism of actions of selected novel compounds reported against breast cancer using ADMET profiling, drug likeness and molecular docking analyses. The selected compounds were andrographolide (AGP), dipalmitoylphosphatidic acid (DPA), 3-(4-Bromo phenylazo)-2,4-pentanedione (BPP), atorvastatin (ATS), benzylserine (BZS) and 3β,7β,25-trihydroxycucurbita-5,23(E)-dien-19-al (TCD). These compounds largely conform to ADMETlab and Lipinki's rule of drug likeness criteria in addition to their lesser hepatotoxic and mutagenic effects. Docking studies revealed a strong affinity of AGP versus NF-kB (- 6.8 kcal/mol), DPA versus Cutlike-homeobox (- 5.1 kcal/mol), BPP versus Hypoxia inducing factor 1 (- 7.7 kcal/mol), ATS versus Sterol Regulatory Element Binding Protein 2 (- 7.2 kcal/mol), BZS versus Ephrin type-A receptor 2 (- 4.4 kcal/mol) and TCD versus Ying Yang 1 (- 9.4 kcal/mol). Likewise, interaction between the said compounds and respective gene products were evidently observed with strong affinities; AGP versus COX-2 (- 9.6 kcal/mol), DPA versus Fibroblast growth factor receptor (- 5.9 kcal/mol), BPP versus Vascular endothelial growth factor (- 5.8 kcal/mol), ATS versus HMG-COA reductase (- 9.1 kcal/mol), BZS versus L-type amino acid transporter 1 (- 5.3 kcal/mol) and TCD versus Histone deacytylase (- 7.7 kcal/mol), respectively. The compounds might potentially target transcription through inhibition of promoter-transcription factor binding and/or inactivation of final gene product. Thus, findings from this study provide a possible mechanism of action of these xenobiotics to guide in vitro and in vivo studies in breast cancer.
Keywords: Breast cancer; Compounds; Docking; In silico; Mechanism; Predictions.
© Springer-Verlag GmbH Germany, part of Springer Nature 2020.
Conflict of interest statement
Conflict of interestAuthors declare that there is no conflict of interest with regards to the publication of this paper.
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