title: 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.affiliation: Ordway Research Institute, Albany, New York 12208, USA.
authors.forename: Nelson
authors.initials: N
authors.lastname: Jumbe
authors.forename: Arnold
authors.initials: A
authors.lastname: Louie
authors.forename: Robert
authors.initials: R
authors.lastname: Leary
authors.forename: Weiguo
authors.initials: W
authors.lastname: Liu
authors.forename: Mark R
authors.initials: MR
authors.lastname: Deziel
authors.forename: Vincent H
authors.initials: VH
authors.lastname: Tam
authors.forename: Reetu
authors.initials: R
authors.lastname: Bachhawat
authors.forename: Christopher
authors.initials: C
authors.lastname: Freeman
authors.forename: James B
authors.initials: JB
authors.lastname: Kahn
authors.forename: Karen
authors.initials: K
authors.lastname: Bush
authors.forename: Michael N
authors.initials: MN
authors.lastname: Dudley
authors.forename: Michael H
authors.initials: MH
authors.lastname: Miller
authors.forename: George L
authors.initials: GL
authors.lastname: Drusano
mesh_terms: D000818:Animals; D000900:Anti-Bacterial Agents / Q000494:pharmacology*; D004305:Dose-Response Relationship, Drug; D004352:Drug Resistance, Microbial; D018432:Drug Resistance, Multiple; D005260:Female; D007700:Kinetics; D051379:Mice; D008813:Mice, Inbred ICR; D008962:Models, Theoretical; D009010:Monte Carlo Method; D009154:Mutation; D015242:Ofloxacin / Q000494:pharmacology; D011552:Pseudomonas Infections / Q000188:drug therapy*; D011550:Pseudomonas aeruginosa / Q000378:metabolism / Q000472:pathogenicity; D015363:Quinolones / Q000494:pharmacology; D013997:Time Factors
chemical_list: D000900:Anti-Bacterial Agents; D015363:Quinolones; D015242:Ofloxacin