Application of Mono and Trinuclear Cyclometalated Iridium (III) Complexes in Differential Bacterial Imaging and Antimicrobial Photodynamic Therapy.
Bishnu DasPrakash BiswasAmirul Islam MallickParna GuptaPublished in: Chemistry (Weinheim an der Bergstrasse, Germany) (2024)
The application of transition metal complexes for antimicrobial photodynamic therapy (PDT) has emerged as an attractive alternative in mitigating a broad range of bacterial pathogens, including multidrug-resistant pathogens. In view of their photostability, long excited-state lifetimes, and tunable emission properties, transition metal complexes also contribute as bioimaging agents. In the present work, we designed mono and trinuclear cyclometalated iridium (III) complexes to explore their imaging application and antibacterial potential. For this, we used Methicillin-resistant S. aureus (MRSA), the most prevalent of community-associated (CA) multidrug-resistant (MDR) bacteria (CA MDR) and Lactococcus lactis (L. lactis) as Gram-positive while Campylobacter jejuni (C. jejuni) and E. coli as Gram-negative bacteria. In addition to differential bioimaging of these bacteria, we assessed the antibacterial effects of both mono and trinuclear Ir(III) complexes under exposure to 427 nm LED light. The data presented herein strongly suggest better efficacy of trinuclear Ir(III) complex over the mononuclear complex in imparting photoinduced cell death of MRSA. Based on the safety profile of these complexes, we propose that trinuclear cyclometalated iridium(III) complex holds great promise for selective recognition and targeting MDR bacteria with minimal off-target effect.
Keyphrases
- multidrug resistant
- photodynamic therapy
- gram negative
- staphylococcus aureus
- transition metal
- drug resistant
- acinetobacter baumannii
- fluorescence imaging
- cell death
- methicillin resistant staphylococcus aureus
- high resolution
- healthcare
- mental health
- antimicrobial resistance
- biofilm formation
- quantum dots
- escherichia coli
- big data
- cancer therapy
- deep learning
- machine learning
- electronic health record
- fluorescent probe
- mass spectrometry
- protein kinase