Application of a mathematical model to prevent in vivo amplification of antibiotic-resistant bacterial populations during therapy
Nelson Jumbe, … , Michael H. Miller, George L. Drusano
Nelson Jumbe, … , Michael H. Miller, George L. Drusano
Published July 15, 2003
Citation Information: J Clin Invest. 2003;112(2):275-285. https://doi.org/10.1172/JCI16814.
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Article Infectious disease

Application of a mathematical model to prevent in vivo amplification of antibiotic-resistant bacterial populations during therapy

Abstract

The worldwide increase in the prevalence of multi-antibiotic–resistant bacteria has threatened the physician’s ability to provide appropriate therapy for infections. The relationship between antimicrobial drug concentration and infecting pathogen population reduction is of primary interest. Using data derived from mice infected with the bacterium Pseudomonas aeruginosa and treated with a fluoroquinolone antibiotic, a mathematical model was developed that described relationships between antimicrobial drug exposures and changes in drug-susceptible and -resistant bacterial subpopulations at an infection site. Dosing regimens and consequent drug exposures that amplify or suppress the emergence of resistant bacterial subpopulations were identified and prospectively validated. Resistant clones selected in vivo by suboptimal regimens were characterized. No mutations were identified in the quinolone resistance–determining regions of gyrA/B or parC/E. However, all resistant clones demonstrated efflux pump overexpression. At base line, MexAB-OprM, MexCD-OprJ, and MexEF-OprN were represented in the drug-resistant population. After 28 hours of therapy, MexCD-OprJ became the predominant pump expressed in the resistant clones. The likelihood of achieving resistance-suppression exposure in humans with a clinically prescribed antibiotic dose was determined. The methods developed in this study provide insight regarding how mathematical models can be used to identify rational dosing regimens that suppress the amplification of the resistant mutant population.

Authors

Nelson Jumbe, Arnold Louie, Robert Leary, Weiguo Liu, Mark R. Deziel, Vincent H. Tam, Reetu Bachhawat, Christopher Freeman, James B. Kahn, Karen Bush, Michael N. Dudley, Michael H. Miller, George L. Drusano

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Model validation. The emergence-of-resistance model developed in this st...
Model validation. The emergence-of-resistance model developed in this study was prospectively evaluated and validated by generation of response predictions for doses not previously studied that would encourage selection of resistance (a) or suppress emergence of resistance (b). An exposure of an AUC/MIC ratio of 157:1 was calculated to prevent emergence of resistance. Experiments were performed to 48 hours, not 24 hours as in the studies performed to generate parameter estimates; conditions predicted by the model were used. Levofloxacin dosing occurred at time 0 and at 24 hours. The lines are model predictions (not best-fit curves). Squares represent experimental measurements of the total population. Circles represent experimental measurements of the resistant subpopulation. The model predicted changes in the resistant mutant population well at both exposures.