Partial inclusion of bis(1,10-phenanthroline)silver(I) salicylate in β-cyclodextrin: Spectroscopic characterization, in vitro and in silico antimicrobial evaluation.
Edwin BriÑez-OrtegaVera L DE AlmeidaJulio Cesar Dias LopesAna E BurgosPublished in: Anais da Academia Brasileira de Ciencias (2020)
Silver complexes containing 1,10-phenanthroline as a coordinated ligand have been of great interest due to their antibacterial and antifungal pharmacological properties. In this paper, we describe the synthesis of a new partial inclusion complex of bis(1,10-phenanthroline)silver(I) salicylate in β-cyclodextrin (β-CD) which was synthesized with a good yield. The compounds were characterized by FTIR, 1H, 13C NMR including 1H-1H COSY, TGA/DSC, elemental analysis (CHN), and X-ray powder diffraction. The results suggest the presence of non-covalent interactions such as hydrogen bonds, van der Waals forces, and hydrophobic interactions in the formation of the partial inclusion compound between β-CD and bis(1,10-phenanthroline)silver(I) salicylate [Ag(phen)2]salH. Additionally, an in silico prediction of 1,10-phenanthroline biological activities was carried out and the acquired data suggests several potential targets associated with the antimicrobial activity of this compound and its silver complex. Most predicted targets are related to antimicrobial virulence and resistance that are a serious threat to global public health. The inclusion compound showed a higher inhibiting growth of Candida albicans than the free complex [Ag(phen)2]salH indicating that the formation of the inclusion complex with β-CD increases the bioavailability of the antimicrobial active species [Ag(phen)2]+ of the new silver(I) compound.
Keyphrases
- gold nanoparticles
- silver nanoparticles
- candida albicans
- ionic liquid
- staphylococcus aureus
- public health
- biofilm formation
- molecular docking
- quantum dots
- high resolution
- escherichia coli
- pseudomonas aeruginosa
- highly efficient
- magnetic resonance imaging
- computed tomography
- electronic health record
- artificial intelligence
- data analysis
- genetic diversity