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Published on: December 9, 2013
Computational investigation of natural phenolic-3,4,5-trimethoxybenzoates as potential anticancer agent targeting
R Ritmaleni1, K Kuswandi2, M Ikawati2
1Laboratory of Medicinal Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia; Curcumin Research Center, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia.
Abstract:
Breast cancer (BC) remains one of the leading cause of mortality among women worldwide, with no universally effective treatment available despite the development of various therapeutic approaches. This study sought to address this gap by synthesizing potential anticancer agents derived from natural phenolic compounds. These compounds were reacted with 3,4,5-trimethoxybenzoyl chloride to generate novel derivatives, termed natural phenolic-3,4,5-trimethoxybenzoates. To evaluate their therapeutic potential, molecular docking, molecular dynamics simulations, and MM/PBSA free energy binding calculations were performed. Among the synthesized derivatives, sesamol-3,4,5-trimethoxybenzoate, thymol-3,4,5-trimethoxybenzoate, carvacrol-3,4,5-trimethoxybenzoate, and umbelliferone-3,4,5-trimethoxybenzoate demonstrated the most promising binding affinities, with MM/PBSA free energy values of -151.377 kJ/mol, -137.344 kJ/mol, -136.645 kJ/mol, and -131.628 kJ/mol, respectively. These results indicate strong and specific interactions with cancer cell receptors, suggesting their potential as effective therapeutic agents. Furthermore, molecular dynamics analyses including RMSD, RMSF, SASA, Rg, and RDF confirmed the stability of these compounds, further enhancing their candidacy as viable drug leads. This study underscores the critical role of computational techniques in drug discovery, offering valuable insights into molecular interactions and stability prior to experimental validation. By identifying promising natural compound derivatives, specifically natural phenolic-3,4,5-trimethoxybenzoates, this research establishes a foundation for developing targeted and effective treatments for BC. Overall, these findings highlight the potential of computational approaches in oncology drug development and pave the way for future in vitro and in vivo studies to confirm therapeutic efficacy.
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