ReviewCurrent pharmaceutical design2022
Development of Peptide-based Metallo-β-lactamase Inhibitors as a New Strategy to Combat Antimicrobial Resistance: A Mini-review.
Review in Current pharmaceutical design, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Global dissemination of antimicrobial resistance (AMR) not only poses a significant threat to human health, food security, and social development but also results in millions of deaths each year. In Gram-negative bacteria, the primary mechanism of resistance to β-lactam antibiotics is the production of β-lactamases, one of which is carbapenem-hydrolyzing β-lactamases known as carbapenemases. As a general scheme, these enzymes are divided into Ambler class A, B, C, and D based on their protein sequence homology. Class B β-lactamases are also known as metallo-β-lactamases (MBLs). The incidence of recovery of bacteria expressing metallo-β- lactamases (MBLs) has increased dramatically in recent years, almost reaching a pandemic proportion. MBLs can be further divided into three subclasses (B1, B2, and B3) based on the homology of protein sequences as well as the differences in zinc coordination. The development of inhibitors is one effective strategy to suppress the activities of MBLs and restore the activity of β-lactam antibiotics. Although thousands of MBL inhibitors have been reported, none have been approved for clinical use. This review describes the clinical application potential of peptide-based drugs that exhibit inhibitory activity against MBLs identified in past decades. In this report, peptide-based inhibitors of MBLs are divided into several groups based on the mode of action, highlighting compounds of promising properties that are suitable for further advancement. We discuss how traditional computational tools, such as in silico screening and molecular docking, along with new methods, such as deep learning and machine learning, enable a more accurate and efficient design of peptide-based inhibitors of MBLs.
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Registered trials
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