MBLinhibitors.com, a Website Resource Offering Information and Expertise for the Continued Development of Metallo-β-Lactamase Inhibitors

Cheng, Zishuo; Thomas, Charles Swain; Joyner, Adam R.; Kimble, Robert L.; Sturgill, Aidan M.; Tran, Nhu-Y; Vulcan, Maya; Klinsky, Spencer A.; Orea, Diego J.; Platt, Cody R.; Cao, Fanpu; Li, Bo; Yang, Qilin; Yurkiewicz, Cole J.; Fast, Walter; Crowder, Michael W.

doi:10.3390/biom10030459

Cited by 9 publications

(5 citation statements)

References 37 publications

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“…in-silico identification of NDM-1 inhibitors), it is necessary to generate the largest database of compounds based on all published literature outputs. Some databases of NDM-1 inhibitors have been already established through publications and/or websites [ 23 , 40 ]. Unfortunately, all of the existing databases were largely incomplete and in some cases they incorporated structural or biological mistakes.…”

Section: Architecture/implementation/workflowmentioning

confidence: 99%

Discovering NDM-1 inhibitors using molecular substructure embeddings representations

Papastergiou

Azé

Bringay

et al. 2023

Journal of Integrative Bioinformatics

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NDM-1 (New-Delhi-Metallo-β-lactamase-1) is an enzyme developed by bacteria that is implicated in bacteria resistance to almost all known antibiotics. In this study, we deliver a new, curated NDM-1 bioactivities database, along with a set of unifying rules for managing different activity properties and inconsistencies. We define the activity classification problem in terms of Multiple Instance Learning, employing embeddings corresponding to molecular substructures and present an ensemble ranking and classification framework, relaying on a k-fold Cross Validation method employing a per fold hyper-parameter optimization procedure, showing promising generalization ability. The MIL paradigm displayed an improvement up to 45.7 %, in terms of Balanced Accuracy, in comparison to the classical Machine Learning paradigm. Moreover, we investigate different compact molecular representations, based on atomic or bi-atomic substructures. Finally, we scanned the Drugbank for strongly active compounds and we present the top-15 ranked compounds.

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Section: Architecture/implementation/workflowmentioning

confidence: 99%

Discovering NDM-1 inhibitors using molecular substructure embeddings representations

Papastergiou

Azé

Bringay

et al. 2023

Journal of Integrative Bioinformatics

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Section: Carbapenemases: the Nightmare Of Anti‐infective Therapies Based On β‐Lactam Antibioticsmentioning

confidence: 99%

“…Thus, there are marked differences between these two types of enzymes, both in the mechanism of action and the structural topology, which hinders the development of effective inhibitors based on previous scaffolds. [60,[64][65][66] Nowadays, there is a great deal of concern about the impact of the infections caused by carbapenem-resistant A. baumannii, P. aeruginosa, and Enterobacteriaceae that are frequently found in hospitalized patients fitted with invasive devices or exposed to extended antibiotic regimens. [67][68][69][70] Of particular concern is the global incidence of class A carbapenemases, such as KPC, SME, IMI, NMC-A, and GES-2, carbapenem-hydrolyzing class D -lactamases, such as OXA-23, OXA-24/40, and OXA-48, as well as metallo--lactamases such as IMP, VIM, and NDM (Figure 4).…”

Section: Carbapenemases: the Nightmare Of Anti-infective Therapies Based On -Lactam Antibioticsmentioning

confidence: 99%

Bicyclic Boronate β‐Lactamase Inhibitors: The Present Hope against Deadly Bacterial Pathogens

Lence

González‐Bello

2021

Advanced Therapeutics

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The use of β‐lactamase inhibitors in combination with β‐lactam antibiotics is an emerging area in drug discovery. This strategy allows the restoration of the therapeutic efficacy of these antibiotics in clinical use against multiresistant bacteria. These pathogens are drug resistant because they express β‐lactamase enzymes, which prevent the antibiotic therapeutic action by catalyzing the hydrolysis of the β‐lactam ring. These enzymes are quite diverse in both their structural architecture and hydrolytic capability, as well as in the mechanism of action. The ever‐increasing emergence of pathogens that are capable of coproducing different types of β‐lactamases has triggered the search for ultrabroad‐spectrum inhibitors capable of deactivating both serine‐ and metallo‐β‐lactamases. A recent breakthrough in this long‐pursued and unmet need is the discovery of bicyclic boronate inhibitors, specifically taniborbactam, VNRX‐7145, and QPX7728, which are currently under clinical development in combination with cefepime, ceftibuten, and QPX2014, respectively. The present article highlights the therapeutic potential of these inhibitors and their spectrum of efficacy is compared with those of other β‐lactam/β‐lactamase inhibitor combinations recently approved by the food and drug administration. The molecular basis of the ultrabroad‐spectrum of activity of boron‐based inhibitors is also discussed, on the basis of the available crystal structures and the results of computational studies.

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“…■ METHODS AND MATERIALS Data Collection. The initial chemical compound data set was composed of previously published chemical compounds obtained from MBLInhibitors.com 18 and compounds obtained from a high-throughput screening (HTS) experiment 7 (Figure 1a). The combined data set was filtered to remove any compounds deemed unusable due to lack of relevant data.…”

Section: ■ Introductionmentioning

confidence: 99%

Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase

Cheng,

Aitha,

Thomas

et al. 2024

J. Chem. Inf. Model.

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The worldwide spread of the metallo-β-lactamases (MBL), especially New Delhi metallo-β-lactamase-1 (NDM-1), is threatening the efficacy of β-lactams, which are the most potent and prescribed class of antibiotics in the clinic. Currently, FDA-approved MBL inhibitors are lacking in the clinic even though many strategies have been used in inhibitor development, including quantitative high-throughput screening (qHTS), fragment-based drug discovery (FBDD), and molecular docking. Herein, a machine learningbased prediction tool is described, which was generated using results from HTS of a large chemical library and previously published inhibition data. The prediction tool was then used for virtual screening of the NIH Genesis library, which was subsequently screened using qHTS. A novel MBL inhibitor was identified and shown to lower minimum inhibitory concentrations (MICs) of Meropenem for a panel of E. coli and K. pneumoniae clinical isolates expressing NDM-1. The mechanism of inhibition of this novel scaffold was probed utilizing equilibrium dialyses with metal analyses, native state electrospray ionization mass spectrometry, UV−vis spectrophotometry, and molecular docking. The uncovered inhibitor, compound 72922413, was shown to be 9-hydroxy-3-[(5-hydroxy-1-oxa-9-azaspiro[5.5]undec-9-yl)carbonyl]-4Hpyrido[1,2-a]pyrimidin-4-one.

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MBLinhibitors.com, a Website Resource Offering Information and Expertise for the Continued Development of Metallo-β-Lactamase Inhibitors

Cited by 9 publications

References 37 publications

Discovering NDM-1 inhibitors using molecular substructure embeddings representations

Discovering NDM-1 inhibitors using molecular substructure embeddings representations

Bicyclic Boronate β‐Lactamase Inhibitors: The Present Hope against Deadly Bacterial Pathogens

Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase

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