β-Lactamase-activated antimicrobial dendron <i>via</i> the amine uncaging strategy

The development of new antimicrobial agents to combat multidrug-resistant (MDR) bacteria, especially those that produce β-lactamase enzymes, is a critical step in preventing a post-antibiotic era. Herein, we develop a new membrane-active antimicrobial prodrug (BLM-Dendron) based on the amine uncaging strategy (AUS) whereby the amine groups of a cationic amphipathic dendron are caged/masked initially but can be uncaged specifically in the presence of β-lactamase enzymes (<i>e.g.</i>, penicillinase) to enable precise antimicrobial activation. BLM-Dendron undergoes self-assembly in water to form spherical nanoparticles with average hydrodynamic diameter (<i>D</i> <sub>H-avg</sub>) of <i>ca.</i> 200 nm and is bacteriostatic against (MDR) <i>P. aeruginosa</i>, <i>E. coli</i> and <i>S. aureus</i> in the presence of penicillinase. In addition, the uncaged dendron also has bactericidal and antibiofilm activities against wild-type <i>P. aeruginosa</i>. For instance, once uncaged, the dendron has the capacity to eliminate ≥99.99999% of planktonic cells after 24 h of treatment. Mechanistic studies show that the activated dendron is indeed membrane-active and disrupts the inner and outer membranes of bacteria cells. Notably, the prodrug BLM-Dendron has excellent hemocompatibility (at least 3.6 times higher) and low cytotoxicity (at least twice better) compared to the original molecule with exposed cationic groups. This study importantly demonstrates the benefit of using AUS to bestow cationic amphipathic antimicrobial agents with higher biocompatibility and targeted activation capabilities, as these features are key for translation into clinical settings.

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Publication Details

Journal
Chemical Science
Published
2025-01-01
DOI
https://doi.org/10.1039/d5sc02412a
Citations
4
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
3.22

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article

β-Lactamase-activated antimicrobial dendron via the amine uncaging strategy

Zeyu Shao, Edgar H. H. Wong, Rhiannon P. Kuchel, Karen Hakobyan et al.
4 citations
Chemical Science
Machine Learning in Bioinformatics
3.22
article

β-Lactamase-activated antimicrobial dendron via the amine uncaging strategy

Zeyu Shao, Edgar H. H. Wong, Rhiannon P. Kuchel, Karen Hakobyan, Jiangtao Xu, Mark D. P. Willcox, Hao Luo, Shyam Kumar Mishra
article en
4 citations

Abstract

The development of new antimicrobial agents to combat multidrug-resistant (MDR) bacteria, especially those that produce β-lactamase enzymes, is a critical step in preventing a post-antibiotic era. Herein, we develop a new membrane-active antimicrobial prodrug (BLM-Dendron) based on the amine uncaging strategy (AUS) whereby the amine groups of a cationic amphipathic dendron are caged/masked initially but can be uncaged specifically in the presence of β-lactamase enzymes (<i>e.g.</i>, penicillinase) to enable precise antimicrobial activation. BLM-Dendron undergoes self-assembly in water to form spherical nanoparticles with average hydrodynamic diameter (<i>D</i> <sub>H-avg</sub>) of <i>ca.</i> 200 nm and is bacteriostatic against (MDR) <i>P. aeruginosa</i>, <i>E. coli</i> and <i>S. aureus</i> in the presence of penicillinase. In addition, the uncaged dendron also has bactericidal and antibiofilm activities against wild-type <i>P. aeruginosa</i>. For instance, once uncaged, the dendron has the capacity to eliminate ≥99.99999% of planktonic cells after 24 h of treatment. Mechanistic studies show that the activated dendron is indeed membrane-active and disrupts the inner and outer membranes of bacteria cells. Notably, the prodrug BLM-Dendron has excellent hemocompatibility (at least 3.6 times higher) and low cytotoxicity (at least twice better) compared to the original molecule with exposed cationic groups. This study importantly demonstrates the benefit of using AUS to bestow cationic amphipathic antimicrobial agents with higher biocompatibility and targeted activation capabilities, as these features are key for translation into clinical settings.

Chemical ScienceVol. 16(23)
UNSW Sydney (AU)
Analytical Center for the Government of the Russian Federation, University of New South Wales
Openalex Percentile: Top 8%
Machine Learning in Bioinformatics
3.22
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