Although pathogens with inherited resistance are an increasingly important problem, resistance is not the only reason antibiotic treatment fails and for some bacterial infections, like pneumococcal pneumonia, resistance is not a significant reason for treatment failure, at least not yet. One goal of this study is to develop and evaluate antibiotic treatment regimes that simultaneously maximize the rate of microbiological cure of infections with susceptible bacteria and minimize the likelihood of resistance emerging during the course of therapy. Another goal is to explore the potential efficacy of different antibiotics and pairs of antibiotics to treat infectons caused by widespread pathogenic bacteria that are designated non-susceptible (resistant) to the drugs currently employed for treatment. Towards these ends we will use a combination of mathematical and computer simulation models, parameter estimation, and pharmaco- population- and evolutionary- dynamic experiments to develop a framework for the design and interpretation of the results of different antibiotic choice and dosing regimens. These experiments will be done in vitro with antibiotic susceptible and resistant pathogenic strains of methicillin sensitive and resistant Staphylococcus aureus (MSSA and MRSA), Streptococcus pneumoniae, and Pseudomonas aeruginosa (including those from CF patients) each with antibiotics of four or more classes and pairs of these drugs. Particular consideration will be given to evaluating the (i) population dynamic and evolutionary consequences of exposure to low doses of antibiotics by these bacteria, (ii) the absolute and relative efficacy of different antibiotics and antibiotic pairs for treating infections of these bacteria within polysaccharide matrices known as biofilms or as colonies on the surfaces of tissues. Currently the criteria for susceptibility (resistance) and the rational design of antibiotic treatment are based on one and two parameters, respectively the Minimum Inhibitory Concentration (MIC) of the antibiotic estimated under conditions that are optimal for the action of the drug, and the MIC and one of three measures of the changes in the concentrations of the antibiotic in the plasma of patients, the peak concentration, the amount of time the concentration exceeds the MIC, or the area under the concentration time curve. As consequence of this parametric reductionism, are we not using antibiotics that could be effective? Are current antibiotic treatment regimes optimal for maximizing the rate of cure and minimizing the likelihood of resistance emerging and spreading during the course of therapy? This study is intended to answer these questions and identify measures to improve estimates of antibiotic susceptibility and the efficacy of antibiotic treatment.
The proposed studies will evaluate the limitations of currently employed measures of antibiotic susceptibility and resistance and protocols for the rational design and antibiotic treatment and explore alternatives. Mathematical and computer simulation models, in vitro parameter estimation, and pharmco- population and evolutionary dynamic experiments will be used explore comprehensive measures of antibiotic susceptibility and protocols for antibiotic treatment. These experiments will be done with antibiotic susceptible and resistant pathogenic strains of Staphylococcus aureus (MRSA as well as MSSA), Streptococcus pneumoniae (pneumococcus), and Pseudomonas aeruginosa (including isolates from CF patients) each with antibiotics of at least four different classes and pairs of these drugs.
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