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

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.