Bayesian Modeling and Intrabacterial Drug Metabolism Applied to Drug-Resistant Staphylococcus aureus
Year: 2021
Type: article
Abstract: 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 .
Source: ACS Infectious Diseases
Authors: 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
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: Mechanisms of cellular respiration-dependent cell lysis and its impact on biofilm formation and disassembly in Staphylococcus aureus., Program in Infection, Immunity and Inflammation, Center to develop innovative therapeutics to multidrug resistant high-threat bacterial agents, Center to develop therapeutic countermeasures to high-threat bacterial agents
OpenAlex ID: W3191239808