posted on 2021-08-03, 15:07authored byJimmy
S. Patel, Javiera Norambuena, Hassan Al-Tameemi, Yong-Mo Ahn, Alexander L. Perryman, Xin Wang, Samer S. Daher, James Occi, Riccardo Russo, Steven Park, Matthew Zimmerman, Hsin-Pin Ho, David S. Perlin, Véronique Dartois, Sean Ekins, Pradeep Kumar, Nancy Connell, Jeffrey M. Boyd, Joel S. Freundlich
We
present the application of Bayesian modeling to identify chemical
tools and/or drug discovery entities pertinent to drug-resistant Staphylococcus aureus infections. The quinoline JSF-3151
was predicted by modeling and then empirically demonstrated to be
active against in vitro cultured clinical methicillin-
and vancomycin-resistant strains while also exhibiting efficacy in
a mouse peritonitis model of methicillin-resistant S. aureus infection. We highlight the utility of an intrabacterial drug metabolism
(IBDM) approach to probe the mechanism by which JSF-3151 is transformed
within the bacteria. We also identify and then validate two mechanisms
of resistance in S. aureus: one mechanism involves
increased expression of a lipocalin protein, and the other arises
from the loss of function of an azoreductase. The computational and
experimental approaches, discovery of an antibacterial agent, and
elucidated resistance mechanisms collectively hold promise to advance
our understanding of therapeutic regimens for drug-resistant S. aureus.