Bayesian Modeling and Intrabacterial Drug Metabolism Applied to Drug-Resistant Staphylococcus aureus

Jimmy 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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)2508-2521
Number of pages14
JournalACS Infectious Diseases
Issue number8
StatePublished - Aug 13 2021
Externally publishedYes


  • AzoR
  • Bayesian modeling
  • Staphylococcus aureus
  • YceI
  • intrabacterial drug metabolism
  • quinoline

ASJC Scopus subject areas

  • Infectious Diseases


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