2. Academic Validation
  3. Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase

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

  • J Chem Inf Model. 2024 May 10. doi: 10.1021/acs.jcim.3c02015.
Zishuo Cheng 1,  Mahesh Aitha 2,  Caitlyn A Thomas 1,  Aidan Sturgill 1,  Mitch Fairweather 1,  Amy Hu 1,  Christopher R Bethel 3,  Dann D Rivera 4,  Patricia Dranchak 2,  Pei W Thomas 4,  Han Li 1,  Qi Feng 1,  Kaicheng Tao 1,  Minshuai Song 1,  Na Sun 1,  Shuo Wang 1,  Surendra Bikram Silwal 1,  Richard C Page 1,  Walt Fast 4,  Robert A Bonomo 3 5 6 7,  Maria Weese 1,  Waldyn Martinez 1,  James Inglese 2 8,  Michael W Crowder 1
Affiliations

Affiliations

  • 1 Department of Chemistry and Biochemistry, Miami University, Oxford ,Ohio 45056, United States.
  • 2 Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville ,Maryland 20850, United States.
  • 3 Research Service, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland ,Ohio 44106, United States.
  • 4 Division of Chemical Biology and Medicinal Chemistry, College of Pharmacy, University of Texas, Austin ,Texas 78712, United States.
  • 5 Departments of Medicine, Biochemistry, Molecular Biology and Microbiology, Pharmacology, and Proteomics and Bioinformatics, Case Western Reserve University School of Medicine, Cleveland ,Ohio 44106, United States.
  • 6 Clinician Scientist Investigator, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland ,Ohio 44106, United States.
  • 7 CWRU-Cleveland VAMC Center for Antimicrobial Resistance and Epidemiology (Case VA CARES) ,Cleveland ,Ohio 44106, United States.
  • 8 Metabolic Medicine Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda ,Maryland 20817, United States.
PMID: 38727192 DOI: 10.1021/acs.jcim.3c02015
Abstract

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 learning-based 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]-4H-pyrido[1,2-a]pyrimidin-4-one.

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