Harnessing Transition Metal Scaffolds for Targeted Antibacterial Therapy

Copper selenide nanosheets with photothermal therapy-related properties and multienzyme activity for highly effective eradication of drug resistance

Bacterial infections are among the most significant causes of death in humans. Chronic misuse or uncontrolled use of antibiotics promotes the emergence of multidrug-resistant superbugs that threaten public health through the food chain and cause environmental pollution. Based on the above considerations, copper selenide nanosheets (CuSe NSs) with photothermal therapy (PTT)- and photodynamic therapy (PDT)-related properties have been fabricated. These CuSe NSs possess enhanced PDT-related properties and can convert O2 into highly toxic reactive oxygen species (ROS), which can cause significant oxidative stress and damage to bacteria. In addition, CuSe NSs can efficiently consume glutathione (GSH) at bacterial infection sites, thus further enhancing their sterilization efficacy. In vitro antibacterial experiments with near-infrared (NIR) irradiation have shown that CuSe NSs have excellent photothermal bactericidal properties. These experiments also showed that CuSe NSs exerted excellent bactericidal effects on wounds infected with methicillin-resistant Staphylococcus aureus (MRSA) and significantly promoted the healing of infected wounds. Because of their superior biological safety, CuSe NSs are novel copper-based antimicrobial agents that are expected to enter clinical trials, serving as a modern approach to the major problem of treating bacterially infected wounds.

Pyridine-2,6-Dicarboxamide Proligands and their Cu(II)/Zn(II) Complexes Targeting Staphylococcus Aureus for the Attenuation of In Vivo Dental Biofilm

In the pursuit to combat stubborn bacterial infections, particularly those stemming from gram-positive bacteria, this study is an attempt to craft a precision-driven platform characterized by unparalleled selectivity, specificity, and synergistic antimicrobial mechanisms. Leveraging remarkable potential of metalloantibiotics in antimicrobial applications, herein, this work rationally designs, synthesizes, and characterizes a new library of Pyridine-2,6-dicarboxamide ligands and their corresponding transition metal Cu(II)/Zn(II) complexes. The lead compound L11 demonstrates robust antibacterial properties against Staphylococcus aureus (Minimum Inhibitory Concentration (MIC) = 2-16 µg mL-1), methicillin and vancomycin-resistant S. aureus (MIC = 2-4 µg mL-1) and exhibit superior antibacterial activity when compared to FDA-approved vancomycin, the drug of last resort. Additionally, the compound exhibits notable antimicrobial efficacy against resistant enterococcus strains (MIC = 2-8 µg mL-1). To unravel mechanistic profile, advanced imaging techniques including SEM and AFM are harnessed, collectively suggesting a mechanistic pathway involving cell wall disruption. Live/dead fluorescence studies further confirm efficacy of L11 and its complexes against S. aureus membranes. This translational exploration extends to a rat model, indicating promising in vivo therapeutic potential. Thus, this comprehensive research initiative has capabilities to transcends the confines of this laboratory, heralding a pivotal step toward combatting antibiotic-resistant pathogens and advancing the frontiers of metalloantibiotics-based therapy with a profound clinical implication.

Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes

Rising antimicrobial resistance (AMR) and lack of innovation in the antibiotic pipeline necessitate novel approaches to discovering new drugs. Metal complexes have proven to be promising antimicrobial compounds, but the number of studied compounds is still low compared to the millions of organic molecules investigated so far. Lately, machine learning (ML) has emerged as a valuable tool for guiding the design of small organic molecules, potentially even in low-data scenarios. For the first time, we extend the application of ML to the discovery of metal-based medicines. Utilising 288 modularly synthesized ruthenium arene Schiff-base complexes and their antibacterial properties, a series of ML models were trained. The models perform well and are used to predict the activity of 54 new compounds. These displayed a 5.7x higher hit-rate (53.7 %) against methicillin-resistant Staphylococcus aureus (MRSA) compared to the original library (9.4 %), demonstrating that ML can be applied to improve the success-rates in the search of new metalloantibiotics. This work paves the way for more ambitious applications of ML in the field of metal-based drug discovery.