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

doi:10.1021/acsinfecdis.1c00265

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

Center for Discovery and Innovation

Hackensack Meridian School of Medicine

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	2508-2521
Number of pages	14
Journal	ACS infectious diseases
Volume	7
Issue number	8
DOIs	https://doi.org/10.1021/acsinfecdis.1c00265
State	Published - Aug 13 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Infectious Diseases

Keywords

AzoR
Bayesian modeling
Staphylococcus aureus
YceI
intrabacterial drug metabolism
quinoline

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10.1021/acsinfecdis.1c00265

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Patel, J. S., Norambuena, J., Al-Tameemi, H., Ahn, Y. M., Perryman, A. L., Wang, X., Daher, S. S., Occi, J., Russo, R., Park, S., Zimmerman, M., Ho, H. P., Perlin, D. S., Dartois, V., Ekins, S., Kumar, P., Connell, N., Boyd, J. M., & Freundlich, J. S. (2021). Bayesian Modeling and Intrabacterial Drug Metabolism Applied to Drug-Resistant Staphylococcus aureus. ACS infectious diseases, 7(8), 2508-2521. https://doi.org/10.1021/acsinfecdis.1c00265

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title = "Bayesian Modeling and Intrabacterial Drug Metabolism Applied to Drug-Resistant Staphylococcus aureus",

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.",

keywords = "AzoR, Bayesian modeling, Staphylococcus aureus, YceI, intrabacterial drug metabolism, quinoline",

author = "Patel, \{Jimmy S.\} and Javiera Norambuena and Hassan Al-Tameemi and Ahn, \{Yong Mo\} and Perryman, \{Alexander L.\} and Xin Wang and Daher, \{Samer S.\} and James Occi and Riccardo Russo and Steven Park and Matthew Zimmerman and Ho, \{Hsin Pin\} and Perlin, \{David S.\} and V{\'e}ronique Dartois and Sean Ekins and Pradeep Kumar and Nancy Connell and Boyd, \{Jeffrey M.\} and Freundlich, \{Joel S.\}",

note = "Publisher Copyright: {\textcopyright} 2021 American Chemical Society.",

year = "2021",

month = aug,

day = "13",

doi = "10.1021/acsinfecdis.1c00265",

language = "English",

volume = "7",

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journal = "ACS infectious diseases",

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Patel, JS, Norambuena, J, Al-Tameemi, H, Ahn, YM, Perryman, AL, Wang, X, Daher, SS, Occi, J, Russo, R, Park, S , Zimmerman, M, Ho, HP, Perlin, DS , Dartois, V, Ekins, S, Kumar, P, Connell, N, Boyd, JM & Freundlich, JS 2021, 'Bayesian Modeling and Intrabacterial Drug Metabolism Applied to Drug-Resistant Staphylococcus aureus', ACS infectious diseases, vol. 7, no. 8, pp. 2508-2521. https://doi.org/10.1021/acsinfecdis.1c00265

TY - JOUR

T1 - Bayesian Modeling and Intrabacterial Drug Metabolism Applied to Drug-Resistant Staphylococcus aureus

AU - Patel, Jimmy S.

AU - Norambuena, Javiera

AU - Al-Tameemi, Hassan

AU - Ahn, Yong Mo

AU - Perryman, Alexander L.

AU - Wang, Xin

AU - Daher, Samer S.

AU - Occi, James

AU - Russo, Riccardo

AU - Park, Steven

AU - Zimmerman, Matthew

AU - Ho, Hsin Pin

AU - Perlin, David S.

AU - Dartois, Véronique

AU - Ekins, Sean

AU - Kumar, Pradeep

AU - Connell, Nancy

AU - Boyd, Jeffrey M.

AU - Freundlich, Joel S.

PY - 2021/8/13

Y1 - 2021/8/13

N2 - 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.

AB - 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.

KW - AzoR

KW - Bayesian modeling

KW - Staphylococcus aureus

KW - YceI

KW - intrabacterial drug metabolism

KW - quinoline

UR - https://www.scopus.com/pages/publications/85113612370

U2 - 10.1021/acsinfecdis.1c00265

DO - 10.1021/acsinfecdis.1c00265

M3 - Article

C2 - 34342426

SN - 2373-8227

VL - 7

SP - 2508

EP - 2521

JO - ACS infectious diseases

JF - ACS infectious diseases

IS - 8

ER -

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

Abstract

UN SDGs

ASJC Scopus subject areas

Keywords

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