Damnacanthal from Morinda citrifolia L.: Experimental Characterization, Biological Evaluation, In Silico ADMET Assessment, and Future Perspectives in AI-Assisted Anticancer Drug Discovery
TL;DR
It is demonstrated that Damnacanthal is a potential natural anticancer candidate for further drug discovery and development and highlighted the recent trends in AI-driven drug discovery for anticancer drug development, including QSAR, ADMET prediction, molecular docking, molecular dynamics simulation, and generative AI for de novo design.
Abstract
Natural products are a prominent source of structurally diverse bioactive molecules for anticancer drug discovery. Among these, anthraquinones are a class of compounds with antioxidant, anti-inflammatory, antimicrobial, and anticancer activities. Damnacanthal is a naturally occurring anthraquinone from Morinda citrifolia L., which has been reported to possess diverse biological activities; however, its detailed experimental characterisation and in silico analysis are lacking. The current study aimed to isolate Damnacanthal from M. citrifolia and evaluate its antioxidant activity. In addition, the cytotoxic potential of the compound was assessed against Dalton's lymphoma ascites (DLA) cells. The drug-likeness and pharmacokinetic properties of the compound were predicted in silico. The compound isolated from M. citrifolia was identified as Damnacanthal through UV-Vis, FTIR, 1H, and 13C NMR, and LC-HRMS spectroscopy. Damnacanthal showed significant antioxidant activity through the FRAP assay. The compound exhibited cytotoxic potential against Dalton's lymphoma ascites cells in a concentration-dependent manner. Furthermore, the compound showed excellent physicochemical properties, oral drug likeness, and promising pharmacokinetic properties. The present study demonstrated that Damnacanthal is a potential natural anticancer candidate for further drug discovery and development. This study also highlighted the recent trends in AI-driven drug discovery for anticancer drug development, including QSAR, ADMET prediction, molecular docking, molecular dynamics simulation, XAI, and generative AI for de novo design. Although AI-driven approaches were not used in the present study, the results provide an AI-ready dataset to explore the anticancer potential of Damnacanthal and other anthraquinone derivatives for anticancer drug discovery and development. Keywords: Morinda citrifolia; Damnacanthal; Anticancer activity; In silico; AI-assisted drug discovery