Yersinia pestis, the agent of plague, has been weaponized and is, therefore, a potential agent of bioterror and biowarfare. The mortality of pneumonic plague approaches 60% when treatment is delayed by as little as 24 hrs after the onset of symptoms. Streptomycin (Strep) and doxycycline (Doxy) are the only FDA antibiotics approved for treating plague. These indications are based on scant clinical data and few animal studies. Unknown is whether the current dosing regimens for these drugs are optimized for efficacy. Also, the efficacy of Strep and Doxy relative to other antibiotics is poorly defined. Our long term goal is to use in-vitro and in-vivo infection systems, together with mathematical methods, to design treatment strategies that optimize outcome for infections due to a variety of pathogens, especially those that can be used as bioweapons. The objective of this project is to use an in-vitro hollow fiber model of Y. pestis infection, in which the human pharmacokinetics of antibiotics are simulated, to define the relative efficacies of various aminoglycosides, quinolones, beta-lactams, and tetracyclines to the """"""""gold standards"""""""" Strep and Doxy. Then, using pharmacodynamic principles and mathematical models, we will develop antibiotic regimens that will optimize therapeutic outcome. The results will be validated by our coinvestigators in the Inhalational Murine Model for B Anthracis &Y Pestis section in a murine model of plague pneumonia using purely murine pharmacokinetics of the drug and a dosing regimen that simulates the pharmacokinetics of the drug that is reported in man. Our central hypothesis is that our novel in-vitro hollow fiber infection model can be used to identify antimicrobial agents and to design dose-optimized antibiotic regimens for the treatment of Yersinia pestis infection in humans and can serve as a robust tool for designing animal studies directed toward the validation of human dosing regimens. We further hypothesize that pharmacodynamically-driven dosing of antibiotic agents will optimize outcome by maximizing the kill of drug-susceptible bacteria while preventing the amplification of drug-resistant subpopulations.
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