Author(s): Drusano GL, Louie A, Deziel M, Gumbo T
Abstract Share this page
Abstract Antibiotic resistance is seen in both the hospital and community settings. Approaches are required to minimize the increase in resistant strains, such as good antibiotic stewardship and the limiting of antibiotic use to appropriate circumstances. There are instances when drug dose and/or schedule can be used to minimize the probability that mutants will take over the bacterial population. Over the past several years, significant advances have been made in understanding the relationship between drug concentrations and amplification of resistant mutant subpopulations. In this review, we examine the use of preclinical models for facilitating this understanding. We also use mathematical techniques, including Monte Carlo simulation, to bridge between the identification of exposures to minimize resistance and the examination of candidate drug doses to achieve this end. Examples are provided for Pseudomonas aeruginosa, Streptococcus pneumoniae, Staphylococcus aureus, and Mycobacterium tuberculosis. In each instance, quinolone antimicrobials were examined. More investigations with other pathogens and drug classes are required.
This article was published in Clin Infect Dis
and referenced in Journal of Antimicrobial Agents