Influence of Hydroxyl Functional Group on the Structure and Stability of Xanthone: A Computational Approach

Synthesis of 1-hydroxy-3-O-substituted xanthone derivatives and their structure-activity relationship on acetylcholinesterase inhibitory effect

This study focused on the synthesis of 1,3-dihydroxyxanthone (1) and its new derivatives with alkyl (2a-2f), alkenyl (2 g-2k), alkynyl (2 l-2n), and alkylated phenyl (2o-2r) groups at C3 position. The structures of these compounds were confirmed by MS, NMR, and FTIR spectroscopic data. All the substituted xanthones (2a-2r) showed significantly stronger acetylcholinesterase (AChE) inhibitory activities than 1. Compounds 2g and 2j exhibited the strongest activities with the IC50 values of 20.8 and 21.5 μM and their enzyme kinetic analyses indicated a mixed-mode inhibition. Molecular docking study revealed that 2g binds favourably to the active site of AChE via π-π stacking and hydrogen bonding from the xanthone ring, in addition to π-alkyl interaction from the substituent group. These xanthone derivatives are potential lead compounds to be further developed into Alzheimer's disease drugs.

Synthesis and in vitro assay of hydroxyxanthones as antioxidant and anticancer agents

In the present work, three hydroxyxanthones were synthesized in 11.15-33.42% yield from 2,6-dihydroxybenzoic acid as the starting material. The chemical structures of prepared hydroxyxanthones have been elucidated by using spectroscopic techniques. Afterward, the hydroxyxanthones were evaluated as antioxidant agents through radical scavenging assay; and anticancer agents through in vitro assays against WiDr, MCF-7, and HeLa cancer cell lines. Hydroxyxanthone 3b was categorized as a strong antioxidant agent (IC50 = 349 ± 68 µM), while the other compounds were categorized as moderate antioxidant agents (IC50 > 500 µM). On the other hand, hydroxyxanthone 3a exhibited the highest anticancer activity (IC50 = 184 ± 15 µM) and the highest selectivity (SI = 18.42) against MCF-7 cancer cells. From the molecular docking study, it was found that hydroxyxanthone 3a interacted with the active sites of Topoisomerase II protein through Hydrogen bonding with DG13 and π-π stacking interactions with DA12 and DC8. These findings revealed that hydroxyxanthones are potential candidates to be developed as antioxidant and anticancer agents in the future.

From Natural Products to New Synthetic Small Molecules: A Journey through the World of Xanthones

This work reviews the contributions of the corresponding author (M.M.M.P.) and her research group to Medicinal Chemistry concerning the isolation from plant and marine sources of xanthone derivatives as well as their synthesis, biological/pharmacological activities, formulation and analytical applications. Although her group activity has been spread over several chemical families with relevance in Medicinal Chemistry, the main focus of the investigation and research has been in the xanthone family. Xanthone derivatives have a variety of activities with great potential for therapeutic applications due to their versatile framework. The group has contributed with several libraries of xanthones derivatives, with a variety of activities such as antitumor, anticoagulant, antiplatelet, anti-inflammatory, antimalarial, antimicrobial, hepatoprotective, antioxidant, and multidrug resistance reversal effects. Besides therapeutic applications, our group has also developed xanthone derivatives with analytical applications as chiral selectors for liquid chromatography and for maritime application as antifouling agents for marine paints. Chemically, it has been challenging to afford green chemistry methods and achieve enantiomeric purity of chiral derivatives. In this review, the structures of the most significant compounds will be presented.

Spectral deep learning for prediction and prospective validation of functional groups

State-of-the-art identification of the functional groups present in an unknown chemical entity requires the expertise of a skilled spectroscopist to analyse and interpret Fourier transform infra-red (FTIR), mass spectroscopy (MS) and/or nuclear magnetic resonance (NMR) data. This process can be time-consuming and error-prone, especially for complex chemical entities that are poorly characterised in the literature, or inefficient to use with synthetic robots producing molecules at an accelerated rate. Herein, we introduce a fast, multi-label deep neural network for accurately identifying all the functional groups of unknown compounds using a combination of FTIR and MS spectra. We do not use any database, pre-established rules, procedures, or peak-matching methods. Our trained neural network reveals patterns typically used by human chemists to identify standard groups. Finally, we experimentally validated our neural network, trained on single compounds, to predict functional groups in compound mixtures. Our methodology showcases practical utility for future use in autonomous analytical detection.